Energy and Cost Efficient Mobile Communication using Multi-Cell MIMO and Relaying Peter Rost, Member, IEEE, Gerhard Fettweis, IEEE Fellow, and J. Nicholas Laneman, Senior Member, IEEE

Abstract In this paper, relaying and multi-cell MIMO transmission are investigated as possibilities to improve the resource reuse and to organize cellular networks more flexibly. Our analysis focuses on approaches for future cellular systems, which jointly exploit relaying and multi-cell MIMO transmission. We evaluate these approaches using the system level model of the European research project WINNER and analyze the achievable throughput under practical constraints using three different normalization approaches: cost-normalization, energy-normalization, and joint cost-energy-normalization. It turns out to be a reasonable choice for the uplink if multiple base stations cooperatively serve relays and relays coordinate their resources to serve users. If a relay-based deployment is subject to a cost and energynormalization, its downlink performance is dominated by multi-cell MIMO.

Index Terms Relaying, multiple antenna systems, cellular networks, half-duplex constraint, cost-benefit tradeoff, wide-area coverage scenario

Manuscript submitted 30 March 2010, revised July 27, 2010. G. Fettweis and P. Rost are with the Vodafone Chair for Mobile Communications Systems, Technische Universit¨at Dresden (TUD), Dresden, Germany (Email: {fettweis, rost}@ifn.et.tudresden.de). J. N. Laneman is with the University of Notre Dame (IN), USA (Email: [email protected]). Part of this work has been performed in the framework of the IST project IST-4-027756 WINNER II, which has been partly funded by the European Union, and the Celtic project CP5-026 WINNER+. The authors would like to acknowledge the contributions of their colleagues in WINNER II and WINNER+, although the views expressed are those of the authors and do not necessarily represent the project.

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R EVIEWER C OMMENTS We highly appreciate the very helpful and detailed comments by the reviewers of this article. Almost all comments and recommendations were considered in the revised manuscript and each individual recommendation is commented in the following: Reviewer 1 1) In Section II, authors discussed the achievable rate region of multi-cell MIMO cooperation, it is not very clear what are the differences between a cooperative BS systems and single BS with multiple antennas. I think that one major difference is that for cooperative BS system, the individual BS power constraint should be applied, but for single BS system, only the total power constraint is required. Authors’ comment: You are right that one main difference between both systems is the per-antenna-group power constraint, which, however, is not of particular interest in multicarrier OFDMA as for each assigned resource (frequency and antenna) an antenna-group might transmit at too low or too high power. On average (or the expectation) the perantenna-group fulfills the constraints. In addition, a per-antenna-group power constraint mainly affects the maximum per-user rate but not the maximum common-rate, which is of particular interest in our mobile communication system. We added a comment to the manuscript. 2) Since the paper consider a multi-cell system, it is also not clear how the inter-cell interference is taken into account in the achievable rate equations for multi-cell MIMO, coordinated relaying and integrated approaches. This has to be clearly clarified, otherwise, it is just a single cell system. Authors’ comment: The rate region in Fig. 2 shows results for the channel model illustrated in Fig. 1. The main results in Section V consider inter-cell interference. We added a comment to the manuscript. 3) For the coordinatetd relaying scheme, what relaying protocols are used? The achievable rate region really depends on the relaying protocols. For different protocols, the rates are much different. Authors’ comment: The scheme is explained in Section II-B.

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4) In Fig. 2, what the reason for choosing ρ′ in that way as shown at the end of page. 4. Authors’ comment: As mentioned in the manuscript, this particular value results from half-way position relay nodes as well as the specific value of the path loss exponent. 5) Also in Page 4, Eq. (9), what are the definition of two rates RT xW F and RET W , how these rates can be calcualted in multi-cell networks. Authors’ comment: Rate region RT xW F is derived in Section II-A and rate region RET W has been derived in [1], [2], which is not repeated in this manuscript in order to focus on the manuscript’s contributions (and to keep the page limit :-) ). 6) What will be the typical values of CRelay /Csite . Crelay should be much smaller than Csite as BS performs much complex functions than relays. Authors’ comment: A typical is αRN ≈ 0.2 and has been derived in [3]. We added a comment to the manuscript. 7) What is the definition of dref ? Is it the distance from the centre of one site to the centre of adjacent site? Please also check Eq. (11). It seems that A should be (nx ∗ dref )(ny ∗ √ √ √ 3/3 ∗ dref ), where 3/2 should be 3/3? Please check this. Maybe I miss some steps in calcualtion. Authors’ comment: Yes, dref is the inter-site distance, i. e. the distance between two √ adjacent sites. We checked again the referred equation and indeed it must be 3/2, which is the height of a equilateral triangle. 8) In Eq. (14), what is the difference between dis (α) and dref . It is quite confusing. Authors’ comment: dis (αRN ) solves the equation ρref = ρRN while dref is the reference inter-site distance. We preferred not using the same identifier for both, which would be even more confusing;) 9) It is also not clear the differences between Psum (Ptx,site ) and Psum (PT x,RN ). Authors’ comment: Thanks for this comment – the definitions have been revised and are hopefully more intuitive now. 10) Are the throughput results in Figs. 4-6 obtained from equations in Section II or they are obtained by actual simulations? Please clarify. Authors’ comment: As mentioned at the beginning of Section V, these results are obtained using a snapshot-based system level simulator in which the equations of Section II are applied. July 27, 2010

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Reviewer 2 1) p3,29: Please provide a short justification why AWGN is considered here although you consider mobile com. in the paper (where a fading channel is the natural choice). Authors’ comment: We added a comment to the manuscript. 2) p3,35: explain what the superscript ”cross” symbol stands for (transpose, I assume though) This brings me to the general comment that readability of the paper would be greatly improved if all symbols, notations and abbreviations would be properly introduced and (shortly) explained; I would prefer if I dont have to consider referenced papers to properly read the paper. Authors’ comment: We added a paragraph to explain all notations. 3) all equations: explain all notations! Authors’ comment: See above. 4) Fig. 2: why is the capacity region of HK shown whereas the text says that ETW is considered? Please consider adding ETW region to the graph. Authors’ comment: The text has been updated. 5) Fig. 3: please consider providing some details on cloverleaf vs. hexagonal setup to provide some insight why this setup is chosen Authors’ comment: [3] already showed that there are only marginal differences between both setups. We would like to suggest to not add a discussion on cloverleaf and hexagonal setups as this might move the readers’ focus away from the actual contribution of this manuscript. 6) p19, Ref [16]: please consider correct spelling of the German title (upper/lower case) Authors’ comment: The text has been updated. 7) p20. Ref [22]: lte should be LTE Authors’ comment: The text has been updated. 8) p19&20: please consider adding a reference to P. Rost’s PhD thesis from which large parts of the paper are taken from to help better understanding of the background and framework of the investigations presented in the paper. Authors’ comment: The text has been updated and the reference has been added. However, the major part of this manuscript is different to [3], which only considered a cost-benefit

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tradeoff analysis and LQ-precoding instead of TxWF. Reviewer 3 ... In my opinion, such results could be (1) Propose methods to do the evaluation and comparison between different technologies/deployment strategies (2) Analyze how we can get qualitatively different optimal solutions, as a function of the different model parameters and other assumptions. (3) Propose models that are simple enough to enable such analysis and still be of practical relevance (4) ... The current manuscript certainly contains some results along these lines, but I’m still not fully convinced. For item (1), there the different proposals for normalization. However, I have my doubts about the practical relevance of this strategy, since the resulting operating point probably is not what an operator would use. In order to make such a normalization correspond to a practical deployment strategy, you would also need to include a business model, i.e. a model for the revenue as a function of the technical QoS. Also for item (2) you do something along these lines, but only for the parameter alpha. The numerical results depend on a large number of assumptions, chosen more or less adhoc. As a reader, I’m not convinced about the relevance about these assumptions and how they influence the results. Note that I’m not only talking about the choice of parameter values but at least as much about the choices of system architecture and algorithms. In order to make an interesting contribution, I would propose to extend the study within at least one of items (1)-(3) listed above and to focus more on generic results like methodology, models and qualitative conclusions rather than on the specific simulation study with all its specific assumptions. Authors’ comment: We agree that the business model is important such as whether the operator tries to improve the cell throughput, per-user throughput, worst-user throughput, ... These criteria are well covered by this analysis using the average or 5%-ile throughput. Our proposed approach is able to allow the operator drawing conclusions whether relaying provides benefits for a given normalization constraint (such as costs) with respect to the chosen criteria. Hence, we are convinced that the manuscript is full in line with your first comment (1). Regarding comments (2) and (3), we capture with α the most important July 27, 2010

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charateristics, i.e. costs of the system. All details on the actual costs can be abstracted by α (no matter whether they are OPEX or CAPEX). Once results according to our normalization are obtained, they can be used as lookup-curves by the operator, who obtains different αvalues depending on system and scenario characteristics. We chose all paramters in this manuscript according to the European project WINNER+, which serves as baseline for the IMT-A calibration process and provided significant inputs as IEG. Hence, choosing the same algorithms and paramters as WINNER+ appears to be the best option to address your comment. Since the paper provides significant input according to your comments (1)-(3), we would like to propose to not further complicate the manuscript with new proposals, algorithms, ... However, we highly appreciate your input for further research. 1) Do you consider single antenna or multiple antenna BSs and MSs? This should be clarified already in the Abstract. Authors’ comment: Table I lists the antenna configuration. A comment for the example in Section II-B has been added. 2) I cannot imagine that this is the fist paper that combines CoMP (network MIMO) with relaying. Please include some relevant references. Authors’ comment: We added reference [12], which is the only work that we found being related to our work 3) Clarify that you consider coherent BS cooperation. Note that this puts very high requirements on the system. As far as I can understand, you need RF over fiber techniques to get sufficient phase coherence between the different BSs. Authors’ comment: We added a comment on this. However, please note that at the Vodafone Chair, TU Dresden, recent demonstrations showed that multi-cell MIMO with 3 BSs using CSI-exchange over Ethernet is already working (without remote radio heads). 4) Eq. (1): Clarify the dimensions of the vectors and matrices. Authors’ comment: We added corresponding notes. 5) The notation x† is here used to denote Hermitian transpose, as far as I can understand, but is often used as a notation for matrix pseudo-inverse. Please define the notation carefully, especially since you choose not to use the most common choice (xH or x∗ is more common, in my experience). July 27, 2010

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Authors’ comment: The notation has been changed accordingly and explained in the manuscript’s introduction. 6) Fig. 1 and the first paragraphs of Sect. II talks about sources and destinations, but then you quickly change terminology and talk about BS, MS, uplink and downlink, instead. Clarify! Authors’ comment: The text has been appropriately updated. 7) Top of Page 3: Here you talk about the ”rate region”. Clarify under what assumptions this actually is the rate region. I’m sure that most information theorists wouldn’t call this a rate region. Authors’ comment: We use the term ’achievable rate region’, which is the set of all rates achievable under the given constraints and an accepted term even in the information theory community. 8) Between Eqs. (5) and (6): Provide a reference on this SIC scheme, for completeness. Also, clarify how the detection order was determined, i.e. if you used any pivoting. Authors’ comment: No detection reordering has been applied. 9) Section II.B: Provide some example to the reader what the common message would be used for. Authors’ comment: The common message has no semantic function but is only decoded by both decoders in order to reduce the interference on the private message. 10) Eq. (7) and following text: In practice, H would be complex valued, whereas you only discuss the case of real valued a and b. Authors’ comment: Values a and b need not to be real valued - (9) has been changed accordingly. 11) Bottom of Page 4: It’s not clear to me why these values of rho and rho’ would correspond to the specified path loss exponent. Authors’ comment: This channel results from choosing the specific path loss exponent and half-way positioned relay nodes. 12) Below (10): You propose to use On/Off power control. There are a number of papers in the literature on On/Off power control and how it compares to using ideal power control. Even if these papers do not cover exactly the scenario you consider, you should still be able to quote interesting results. July 27, 2010

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Authors’ comment: This paper does not consider On/Off power control but chooses to use either only common messages on a particular resource or to use only private messages. In this sense, either the common or private message is ’turned off’ but the counterpart then receives the full power such that on each resource still the maximum transmission power is used. 13) Page 6, line 2: Can you provide any examples of what ”current systems” that use the described frequency planning strategy? Authors’ comment: ’Current’ has been changed to ’Future’ – FFR is an active discussion item in 3GPP and IEEE 802.16. 14) Sect. IV.A: Most cost models separate between fixed costs and running costs. Here it seems that you make the assumptions that both these terms scale in the same way when going from one deployment to the other. At least worth a comment. Authors’ comment: The cost model captures both fixed and flexible costs as both must be considered for a specific period of time. Hence, we obtain a specific value for the relative costs. We added a comment. 15) Sect. V.A: What do you mean by ”resources” in ”number of resources”? Clarify if you used the proportional fair scheduler or some scheduler with absolute fairness. Authors’ comment: A comment has been added clarifying ”resources”. As mentioned in the manuscript a ”fair scheduler” is applied and no proportional fair. We added the word ”absolute”. 16) Page 11, last paragraph: What do you mean by the ”non-normalized scenario”? Do you mean the reference scenario? Authors’ comment: The expression has been clarified. 17) Sect. VI: Here you relate to results from [22]. Are you sure that the results are comparable to a sufficient extent that such conclusions can be drawn? Authors’ comment: We added another reference that the theoretical WINNER+ model allows for this conclusions (in addition to the reference on our testbed results, which showed that indoor relays indeed significantly improve the indoor data rates).

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Energy and Cost Efficient Mobile Communication using Multi-Cell MIMO and Relaying I. I NTRODUCTION Among the most serious challenges in current and future mobile communication systems are the mitigation and avoidance of inter-cell interference. Future mobile communication systems are likely to use multi-cell MIMO approaches in which multiple base stations (BSs) cooperatively serve user terminals (UTs) using techniques introduced in the context of the MIMO broadcast channel [4], [5] and the MIMO multicast channel [6], [7]. Furthermore, additional relay nodes will help to provide high data rates in otherwise shadowed areas as well as to improve the channel conditions at cell borders [8]. We explore system architectures based upon the transmission schemes introduced in [9], where we conducted a link-level analysis of the relay-assisted interference channel. These schemes are able to combine the benefits of multi-cell MIMO transmission [10] and relaying [8], [11] in one integrated system architecture. To our knowledge, only [12] considered a similar setup where base stations cooperatively transmit and receive, while user terminals interact with each other and relay messages using a limited-capacity link. Using a system level analysis of a next-generation mobile communication network we compare the performance of a conventional system, a system using multi-cell MIMO only, a system using relaying only, and a system in which both multi-cell MIMO and relaying are integrated. A relay-based deployment improves the performance of an existing system at the expense of additional costs and energy. In [13], Werner et al. compared relay-based deployments with conventional systems on the basis of a performance normalization. An indifference curve compares the required deployment density of base stations and relay nodes to provide the same performance as a reference system without relays. Such a tool provides means to identify the potential cost-savings of relay-enhanced cells. By contrast, this work normalizes the deployment costs and the energy to demonstrate the potential performance gains offered by relaying and multi-cell MIMO. July 27, 2010

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Throughout this paper, x ∼ CN (0, σ 2 ) denotes a circularly symmetric i.i.d. Gaussian random

process with each element having zero mean and variance σ 2 . We will use non-italic lowercase letters x to denote random variables and italic letters (N and n) to denote scalar values. Matrices are denoted by bold uppercase letter, e. g., H denotes the compound channel matrix with dimension NRx × NTx where NRx is the sum of receive antennas and NTx is the sum of transmit antennas. Vectors are denoted by x, the transpose of a vector or matrix is denoted by xT , the Hermitian transpose is denoted by xH , and the trace of matrix X is denoted by tr(X). We further use the capacity function C(x) = log(1 + x) with logarithms taking to the base of 2. Section II introduces the protocols, which are applied to the next generation mobile communication system level model presented in Section III. In Section IV, we explain the three different methods to normalize the comparison of a relay-based system and a conventional system. We compare the performance results in Section V and conclude the paper in Section VI. II. S YSTEM AND P ROTOCOL D ESIGN The relay-assisted interference channel [9] is illustrated in Fig. 1 and models a network in which two communication pairs are supported by two relay nodes. Conference links C1,2 and C2,1 between both BSs allow for multi-cell MIMO cooperation, where both BSs form one virtual antenna array. A detailed link-level analysis in [9] compared possible candidates for the relayinterference channel. It showed that two of the compared protocols provide the most significant performance gains. Using the exemplary channel model illustrated in Fig. 1 (our analysis in Section V uses a multi-cell multi-user OFDMA system model), both approaches are introduced in the following as well as possible simplifications to reduce their complexity. A. Multi-cell MIMO approach Consider Fig. 1 and assume that both relays remain silent, i. e. X3 = X4 = 0. Then, the channel output at each UT in an additive white Gaussian noise (AWGN) system model is given by yUT = HBS VBS wBS + nUT ,

(1)

with the two-dimensional Gaussian distributed source signal wBS = (w1 w2 )T ∼ CN (0, Rww ), the Gaussian distributed channel output signal yUT ∼ CN (0, Ryy ) with dimension NRx × 1, July 27, 2010

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the compound channel matrix HBS with dimension NRx × NTx , the precoding matrix VBS with dimension NRx × 2, which is applied across both source nodes and results in a channel input

2 with covariance PBS , and the receiver noise nUT with covariance Rnn = σnn I.

Our analysis uses a multi-cell MIMO approach based on the transmit Wiener filter derived in [14], which is given by VBS = β



HH BS HBS

tr(Rnn ) I + tr(PBS )

−1

HH BS

and uses the power normalization factor v u tr(PBS ) u . β = u  −2 t tr(Rnn ) H H HBS Rww HBS tr HBS HBS + tr(PBS ) I

(2)

(3)

The achievable rate region RTxWF (HBS ) for the transmit Wiener filter is the set of all rate pairs

(R1 , R2 ) ∈ RTxWF (HBS ) that satisfy



2



[HBS VBS ]i,i [Rww ]i,i   ∀i ∈ [1; 2] : Ri < C 

2

.

[Ryy ]i,i − [HBS VBS ]i,i [Rww ]i,i 

(4)

The rate region given in (4) coincides with the broadcast channel (BC) rate region of a multipleinput multiple-output (MIMO) system where all base stations form one virtual antenna array without per-antenna-group power constraint [15]. We choose this simplified approach as our analysis applies a multi-carrier OFDMA system where the power-constraints must be considered over all subcarriers and antennas. Each resource assignment might result in too high or too low power at a particular antenna and subcarrier. This averages out over all subcarriers and antennas such that the per-antenna-group constraints are fulfilled with high probability. Our analysis and discussion in Section V assume that at most three BSs are cooperating and the conference links do not constrain the achievable rates. We further assume in Section V that all BSs are able to coherently transmit in order to achieve the previously derived rate region. The uplink of the considered system can be expressed by the following equation: yBS = HH BS xUT + nBS

(5)

with channel input xUT = (x5 x6 )T . Again we consider an approach, which is of interest for practical implementations. The receiver uses the QR decomposition HH BS = QR into the unitary matrix Q and upper triangular matrix R. After multiplying the channel output yBS with QH , July 27, 2010

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the effective channel is given by R and therefore user i is only interfered by users j > i. The remaining interference is removed by a successive interference cancelation (SIC) such that the rate for each terminal i is given by  2   [R]i,i Pi  ∀i ∈ [1; 2] : Ri < C   2 σnn

(6)

2 for power allocation Pi at the i-th UT and receiver noise σnn at each BS.

B. Coordinated Relaying This paper compares multi-cell MIMO with a relaying protocol, which uses multi-cell MIMO in a first phase between BSs and relay nodes (RNs), and in a second phase it employs HanKobayashi (HK) coding [16] to coordinate the resources between RNs and UTs. HK coding is a superposition coding scheme, where the first transmitter divides the message in two parts w(1,1) and w(1,D) with power assignment P(1,1) and P(1,D) (similarly the second transmitter uses w(2,2) and w(2,D) ). While the first part w(1,1) is only decoded by the first receiver, w(1,D) is decoded by both receivers in order to reduce the interference-level for w(1,1) and is called common message. Both common messages and the own private message are jointly decoded by the respective receiver. Let P(i,i) + P(i,D) = Pi , then the channel input at terminal i is given by xi =

p p P(i,i) w(i,i) + P(i,D) w(i,D) .

The corresponding rate region RHK (HRN ) has been derived in [16]. Consider the downlink transmission illustrated in Fig. 1 and divide it into two phases: in phase 1 both BSs transmit using 0 < t1 < 1 fraction of all resources, and in phase 2 both RNs transmit on the remaining t2 = 1 − t1 fraction of all resources. Using this setup, the achievable rate region is the convex hull Co

[

t1 ,t2 :t1 +t2 =1

!

t1 RTxWF (HBS ) ∩ t2 RHK (HRN ) ,

(7)

where t1 RTxWF (HBS ) denotes a scaling of all rates in RTxWF (HBS ) by t1 (and similarly for RHK ).

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A typical downlink rate region is illustrated in Fig. 2 for the single-antenna AWGN channel model illustrated in Fig. 1 and for the channel matrices     4 ρ′ 1 ρ   and HRN =  HBS =  ′ −j π/4 −j π/4 ρ ·e 4 ρ·e 1

where HRN is the channel between BSs and RNs as well as between RNs and UTs. The cross-

coupling is given by ρ = 0.7,

ρ′ =

1 ρ

1 . − 43

This particular channel results from half-way placed relays and a path loss exponent α = 4. We further use a symmetric scenario where the transmission power at each node is given by Pi = 10 and the receiver noise is of unit variance. This simple example does not capture the effects of inter-cell interference and fading in mobile communication networks, which are in the focus of this work. However, Fig. 2 already demonstrates the potential benefits of relaying compared to multi-cell MIMO and non-cooperative transmission. These benefits will be further discussed and analyzed using a more realistic channel model in Section V. HK coding requires a complex optimization of the power assignment at the transmitter and a complex joint decoder at the receiver. Etkin et al. derived in [1], [2] an approach, which has been proven to be within 1 bpcu of channel capacity while being much simpler than the HK coding approach. Their approach aligns the interference caused by each transmitter with the noise power at the non-intended receiver. If the channel between RNs and UTs is given by   1 b  (8) HRN =  a 1 both terminals choose the private message power such that

2 2 kak2 · P(1,1) ≤ σnn,2 and kbk2 · P(2,2) ≤ σnn,1

(9)

2 holds (σnn,i denotes the receiver noise covariance at terminal i). Compared to the full HK

approach, each terminal at first decodes both common messages jointly and then each one decodes its own private message. Coefficients a and b reflect the cross-coupling of both communication pairs, which in a mobile communication system usually are constrained by 0 < kak2 , kbk2 < 1, as terminals are assigned based on the lowest path loss to the serving base station. The achievable

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rate region RETW (HRN ) of the ETW approach has been derived in [1], [2] and is omitted here for brevity. Signaling the individual power assignments and joint decoding are complex and signalingintensive operations. Empirical observations in [3] show that a floating power assignment 0 < P(i,i) /Pi < 1 is used in very few cases where the interference is not weak enough to be ignored but also not strong enough to dominate the transmission. Hence, we further simplify the EtkinTse-Wang (ETW) approach by applying the power assignment   2 P1 σnn,2 /a2 ≥ P1/2 P(1,1) =  0 otherwise,

(10)

which implies that we only use either common or private messages. This reduces the signaling overhead for the power level to 1 Bit. As a fast-fading link adaptation using ETW coding appears unrealistic due to the immense signaling overhead, we utilize only the long-term statistics of HRN at RNs and UTs instead. This simplified ETW approach based on the long-term statistics of HRN has been applied to obtain the results in Section V. C. An Integrated Approach In mobile communication systems with both multi-cell MIMO and relaying, it is preferable if the BS serves those UTs in its main lobe direction or with LOS towards the BS. In these cases, the maximum rate is more likely to be limited by the available modulation and coding schemes (MCS) than the achievable signal-to-interference-and-noise ratio (SINR). Since relaying suffers from a half-duplex loss, a direct transmission from BSs to UTs is likely to outperform relaying. On the other hand, UTs at the cell edge between two sites or with a very good link towards the next RN should be served using relaying. Since it is very complex and unreliable to predict whether a UT should prefer using a BS or a RN, we show in Section V results for an integrated approach where UTs are assigned to the closest RN or BS based on the path loss. Such an approach allows for implementing one interface at both RN and BS such that the UT needs not implement any additional, relay-supporting functionality. D. Conventional approach Conventional system architectures without relay nodes (x3 = x4 = 0) do not implement any kind of BS cooperation but coordinate their resources using time division multiple access July 27, 2010

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(TDMA) and frequency division multiple access (FDMA). In order to improve the spatial resource reuse factor, future systems divide the available bandwidth into an edge band and center band [17]. Edge band resources are used to serve users at the cell edge with high intercell interference. Furthermore, this protocol reserves a center band, which is used by all cells to serve users with high SINR. The actual assignment of users to edge and center band uses the expected long-term SINR, which is assumed to be perfectly known. In our analysis, a user is served using edge band resources if the expected SINR drops below 10 dB, based upon the empirical studies in [18]. III. R EFERENCE S CENARIO This paper discusses system level results for the wide-area reference scenario defined by the European research project WINNER [19], [20]1 . Wide-area corresponds to a hexagonal cellstructure, which is illustrated in Fig. 3 (the actual cell layout varies as UTs are assigned based on their path loss). Each site is equipped with three base station antenna arrays (indicated with “BS”) serving three adjacent sectors (with main lobe directions indicated by arrows) and two stationary relays per sector (indicated by triangles). Sites are uniformly placed, such that the distance between two adjacent sites is given by dref = 1000 m. Our analysis considers results for one central site surrounded by two tiers with a total of 18 interfering sites. We apply Orthogonal Frequency Division Multiplexing (OFDM) [21], [22] at a carrier frequency fc = 3.95 GHz with bandwidth Bw = 100 MHz and Nc = 2048 subcarriers. We apply time division duplex (TDD) between uplink (UL) and downlink (DL). All further air interface parameters are listed in Table I and described in further detail in [20]. The WINNER system offers a variety of channel models suitable for each scenario. Each consists of a model for the probability that a strong line-of-sight (LOS) link exists, the path-loss model, and the power delay profile, which models the small scale fading assuming each tap is Gaussian distributed. We further assume block fading in which each channel realization is independently generated. Table I lists all channel models, which are used in our analysis. We further assume Gaussian alphabets, perfect rate adaptation, and infinite blocklengths in order to use the relevant capacity expressions [9] and to have an upper bound on the achievable 1

All referred WINNER documents are publicly available on http://www.ist-winner.org

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rates. Throughout the following discussion we assume perfect channel estimation and that the system is perfectly synchronized. This paper considers neither automatic repeat-request (ARQ), which is not necessary due to the perfect link adaptation, nor quality-of-service (QoS), since all users are assumed to have equal priority. The considered scheduler assumes full queues for each user, which implies that all available resources are fully exploited. We address the problem of an in-band feederlink between BSs and RNs, which might result in empty relay-buffers due to an unexpected high throughput on the links between a relay and its assigned users. IV. N ORMALIZATION OF R ESULTS A. Cost-Normalization In order to normalize the achieved results we have to either keep the costs for the system deployment or the system performance constant. In [13], the authors chose the latter normalization and compared the minimum required deployment costs, which are necessary to satisfy a predefined downlink performance measure. By contrast, we choose the former way of normalization and compare the performance of a system using only macro-BSs with a system using additionally deployed RNs. In order to normalize the costs for both deployments, the relay-based deployment must use a lower BS density, which affects both uplink and downlink performance. A cost-normalized performance comparison is of particular interest for service providers, who have a predefined budget available. We use CSite to denote the costs of one site and CRelay to denote the costs of one relay node, which includes hardware costs, rental costs, average costs for power supply as well as backhaul costs (in case of a site). In order to present results independent of actual budget figures, we conduct our analysis solely based on the relative relay costs αRN = CRelay /CSite for which [3] derived a typical value, i. e. αRN ≈ 0.2. This relative relay costs can capture both fixed and running costs as the former requires the cost-benefit-tradeoff analysis to be applied to a specific period of operation. Assume a regular base station distribution with an inter-site distance dref as shown in Fig. 3. The area covered by nx BSs in x-direction and ny in y-direction is given by  √ A = (nx dref ) ny 3/2dref .

(11)

The overall costs for this deployment are nx ny CSite , hence the costs normalized over the covered

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area are given by ρref =

nx ny CSite C  = √ Site2 , √ 3/2d 3 (nx dref ) ny /2dref ref

(12)

which is used as reference value for the relay based deployment. With NRelay relays deployed at each site, the costs normalized over the covered area by the relay based deployment are given by ρRN =

CSite + NRelay CRelay √ . 3/2d2 ref

(13)

In order to normalize the costs for both deployments we need to find an inter-site distance dis (αRN ) such that ρref = ρRN , which is solved by p dis (α) = dref 1 + αRN NRelay .

(14)

B. Energy-Normalization The normalization of the system’s deployment costs not necessarily implies normalized energy consumption. While the cost-normalization approach increases the BS density to achieve normalized deployments, the energy-normalization appropriately adjusts the RN and BS power such that the overall energy consumption is the same (but the costs increase depending on the energy costs). The overall energy or power consumption of a radio access point (RAP) can be modeled as the sum of the linearly weighted transmit power PTx and a constant part Pconst , which is independent of the transmit power [23]: Psite (PTx ) = ǫTx,site PTx + Pconst,site

(15)

PRN (PTx ) = ǫTx,RN PTx + Pconst,RN ,

(16)

where Psite denotes the sum-power consumption at one site and PRN denotes the sum-power consumption at one RN. All quantities depend on the number of BSs per site, the number of power amplifiers, the power amplifier efficiency, the cooling power, and the battery backup. Using the reference values given in [23], we can apply the following values: ǫTx,site = 44

Pconst,site = 59 dBm

(17)

ǫTx,RN = 4

Pconst,RN = 47 dBm.

(18)

The significant difference between a RN and a site results from overall 12 antennas at one site and only one antenna per RN. This work considers only a downlink-based energy normalization, July 27, 2010

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where the transmit power related energy dominates compared to an uplink-based energy normalization, where only signal processing power needs to be considered. Again we assume a regular distribution of BSs with inter-site distance dref where in the site-only deployment a BS antenna transmits with average power PTx,ref . Similarly to (12) we can express the power-density over the covered area by ̺ref =

Psite (PTx,ref ) √ , 3/2d2 ref

(19)

and similarly to (13) for the relay-based deployment by ̺RN =

Psite (PTx,site ) + Nsum PRN (PTx,RN ) √ . 3/2d2 ref

(20)

In order to fulfill the energy-normalization constraint ̺ref = ̺RN , we express the overall power consumption at one site as a function of the RN transmit power: Psite (PTx,site ) = Psite (PTx,ref ) − Nsum PRN (PTx,RN ) .

(21)

Using the definition of the overall power consumption in (15), we can express the average transmit power of a BS antenna as function of the RN transmit power: PTx,site = PTx,ref −

Nsum PRN (PTx,RN ) . ǫTx,site

(22)

C. Joint Energy and Cost-Normalization Our third measure to analyze the performance of a relay-based deployment is based on a joint energy-cost-normalization where the density of the relay-based deployment is increased in order to normalize the costs, and the energy per RAP is appropriately adjusted to normalize the energy consumption. Due to the decreased BS density, the energy-consumption per area-element is given by ̺RN (α) =

Psite (PTx,site ) + Nsum PRN (PTx,RN ) . √ 3/2 (d (α))2 is

(23)

We again normalize the energy-consumption by solving the equation ̺RN (α) = ̺ref , which results in PTx,site =



dis (α) dref

2

Psite (PTx,ref ) − Pconst,site − Nsum PRN (PTx,RN ) , ǫTx,site

(24)

with dis (α) p = 1 + αRN NRelay ≥ 1. dref July 27, 2010

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This implies that for increasing deployment costs the density of radio access points decreases and the power per node increases in order to normalize the energy. If the operational expenditures consider the costs for energy supply (αRN as a function of PTx,site ), we have to solve an iterative problem to find such a energy-normalization which still satisfies the given cost-normalization. The solution to this problem mainly depends on how dominantly the energy-costs affect the operational expenditures. V. R ESULTS A. Simulation Methodology In order to evaluate the different protocols, we use a snapshot based system-level simulation, which does not explicitly model user mobility but models the effects of mobility on the user’s channel instead. Users are randomly placed, and for each snapshot 16 frames, each consisting of 15 OFDM-symbols, are simulated before the next snapshot is drawn. For each frame an independent instance of the small scale fading process is drawn and the user scheduling is performed. The overall area is partitioned into rectangles of 30 m × 30 m. For each user the corresponding spatial block (x, y) is determined and the average throughput θ(x, y) = Et {θ(x, y, t)} over all users, which have been placed in this block, is used to determine the number of resources (one resource element consists of 15 OFDM-symbols and 8 subcarriers). Using an absolute fair scheduler, the number of resources assigned to a user is proportional to the inverse of the average −1

throughput, i. e. θ (x, y). Alternatively, we could use the 5% quantile of throughput θ5% (x, y) with Pr {θ(x, y, t) ≤ θ5% } ≤ 5%, which could improve the system’s fairness. On the other hand, a user with a high throughput variance gets even more resources in order to improve the 5% quantile of throughput at the expense of other users’ performance. B. Cost-Normalization At first we explore the system performance if the relay-based deployment is subject to a cost-normalization constraint [3]. Consider Fig. 4, which shows the average throughput θ and 5% quantile throughput θ5% in uplink and downlink. Examine at first the downlink results in Fig. 4(a), which show for most cost-factors αRN a performance advantage of multi-cell MIMO over both relaying protocols and a quite significant performance gain over a conventional system July 27, 2010

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approach. Fig. 4(a) further shows the performance results for limited cooperation (solid lines) where the BS cooperation is limited to the same site and excludes inter-site cooperation. The average throughput of multi-cell remains almost unaffected by this limitation but the 5% quantile throughput decreases by almost 40%, which proves that particularly users at the cell edge between two sites benefit from an inter-site BS cooperation. We also apply this limitation to the link between BS and RN of both relay-based protocols, which is indicated by solid lines in Fig. 4. In contrast to multi-cell MIMO, this limitation has no effect on the protocol performance, which points out that users at the cell edge between two sites are served by relay nodes. However, only the integrated approach (indicated with ”mixed” in Fig. 4) can slightly improve the θ5% for αRN < 0.05, which implies that in the downlink multi-cell is preferable over relaying. This conclusion changes for the uplink where the integrated approach significantly improves the average and 5% quantile throughput for almost all cost factors. However, again the θ5% of the relay-only strategy severely drops, which is caused by those users located close to the BS. The increased RAP density implies a lower average path loss of mobile terminals to their assigned RAP, which increases the average throughput performance if relay nodes are deployed. We can further see from Fig. 4(b) that the average throughput of all direct communication approaches do not differ but the 5% quantile throughput is significantly improved by multi-cell MIMO compared to a conventional system, which reflects the insufficient performance figures of users located close to the cell edge. Again, a limited cooperation has almost no effect on multi-cell MIMO and relaying, which suggests that the system could easily waive an inter-site cooperation in order to reduce the deployment and infrastructure costs. C. Energy-Normalization After the cost-normalized performance analysis, we examine now the results for the energynormalized results in Fig. 5, which only shows the downlink performance as the uplink performance remains unaffected by an energy-normalization. While for cost-normalized deployments relaying was partly able to meet the performance figures of multi-cell MIMO, it is now outperformed for all relay transmit powers by at least 50%. The maximum average throughput and 5% quantile throughput drop by about 40% and 50% compared to the scenario without energy-normalization. The best performance is achieved for relay transmit power 30 dBm and BS transmit power 19.1 dBm (per antenna) as most users are assigned to relays and those assigned July 27, 2010

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to BSs have very good channel conditions. This result will certainly change if the constant energy consumption Pconst,RN at a RN is reduced and the BS transmit power is increased. D. Joint Cost-Energy-Normalization Finally, Fig. 6 shows the results if both overall costs and the energy consumption are normalized as described in Section IV-C. For each relative cost-factor αRN , Fig. 6 only plots the maximum achievable performance (maximized over all possible PTx,RN and PTx,site ). In all cases the maximum is again achieved by PTx,RN = 30 dBm. We can observe a significant performance loss of relaying compared to multi-cell MIMO due to the normalized energy. Compared to the cost-normalized case, the performance drops by almost 50%. The integrated approach and the relaying-only approach achieve comparable results as a result of the low transmission power at the BS. VI. C ONCLUSIONS In this paper we compared multi-cell MIMO and relaying on the basis of three different normalization criteria. Relaying provides significant performance gains for the uplink transmission even if simplifications such as limited BS cooperation and a binary power assignment are used. On the other hand, the downlink is dominated by multi-cell MIMO, which advises the usage of relaying in the uplink where UTs suffer from high path loss and low transmission power, and multi-cell MIMO in the downlink. However, this conclusion will change in case of micro-cellular deployments where relay nodes provide a serious alternative to improve indoor coverage and data rates [24], [25]. In this case, a provider is forced to deploy additional indoor base stations to offer an acceptable service quality and therefore relays become more beneficial.

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X1 1

Y3 : X3 3

Y5 5

2 X2

4 Y4 : X4

6 Y6

C1,2 /C2,1

Fig. 1.

Relay-assisted interference channel (applied to the downlink of a mobile communication system) with two BSs

S = {1, 2}, two relays R = {3, 4}, and two UTs D = {5, 6}. Both BSs are connected by conference links with capacities C1,2 and C2,1 , respectively. Channel inputs are denoted by Xt and channel outputs are denoted by Yt .

4

+

c Relaying b Multi-cell MIMO rs HK c b

c b rs

1

rs

c b c b

rs

0

Fig. 2.

0

1

2 R1 [bpcu]

rs

+

+

R1 = R2

c b

+

rs

+

rs

2

c b

+

rs

c b

+

c b

+

+ R2 [bpcu]

rs

+

bc

3

3

c b

4

Achievable downlink rate regions for the relay-assisted interference channel and ρ = 0.7.

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u u

u

u BS BSBS u

u

u

u

u BS BSBS u

u

u

u u u u

u

BS BSBS

u

u

u

u

BS BSBS

u

u

u

u

BS BSBS

u

22

u

u

u

u BS BSBS u

u

u

u

u

u BS BSBS u

u

u

u

u

u

Fig. 3. Reference scenario considered in this paper with one tier of interfering sites. Triangles indicate relay nodes and arrows the main lobe direction in each cell.

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90 per km2 in Wide-area,

User density

250 per km2 in Manhattan-area

Average no. users/cell

26 in Wide/Manhattan-area

Channel models

as defined in [26]

Number of antennas BS/RN/UT

4/1/1

BS transmit power

46 dBm

RN transmit power

37 dBm

UT transmit power

24 dBm

Noise figure

7 dB

Noise power spectral density

−174 dBm/Hz

FFT-Size

2048

Carrier frequency

3.95 GHz

System bandwidth

100 MHz

OFDM-Symbol duration

20.48 µs

Superframe duration

5.89 ms

Guard interval

2.00 µs

Used subcarriers

[−920; 920] \ {0}

Channel state information

assumed to be perfectly known at transmitter and receiver BS to RN: B5a

Channel models according to [27]

BS to outdoor UT: C2 RN to outdoor UT: C2 TABLE I

PARAMETERS OF THE WINNER SYSTEM

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+ +

+ +

rs

rs

rs

-1

10

bc θ5%

10-2

0

bc

u t

c b Multi-Cell MIMO c sr Conventional b c 2 Relays, relay-only b t 2 Relays, mixed u

0.05

++

sru t c b

sr t u c b

+ +

+ +

rs

t u

++

++

+ +

t u

u t rs c b

+ +

u t t u

u t rsb c

+

Throughput in MBit/s

100

u t bc rs

++

θ u t bc rs

++

101

24

++

SUBMITTED TO IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS

u t rs

u t rs

b c

c b

0.10 0.15 0.20 RN to BS Cost Ratio αRN

0.25

(a) Downlink 1

10

10-4

0

0.05

++

u t c b rs

t u

u t

++

b c rs rs rs Multi-Cell MIMO rs Conventional c 2 Relays, relay-only b t 2 Relays, mixed u

θ

++

++

++

b c

u t

b c rs

b c rs

+

10-3

t u

u t c b rs

++

rs

bc

rs

u t c b rs

++

10-2

u t

u t c b

++

bc

++

10-1

rs

++

sr t u

++

Throughput in MBit/s

100

u t bc

++

u t bc

0.10 0.15 0.20 RN to BS Cost Ratio αRN

θ5%

0.25

(b) Uplink Fig. 4. Cost-Benefit tradeoff with normalized deployment costs. Dashed lines indicate full cooperation and solid lines indicate limited cooperation.

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101

θ

++

++

++

+ +

+ +

+ +

++ Throughput in MBit/s

u t c b rs

u t c b rs

u t bcrs

u t c b rs

+ +

100

-1

u t rs

u t c b rs

t u

u t rs

sr c b

10

θ5%

+

Multi-Cell MIMO rs Conventional c b bc 2 Relays, relay-only t 2 Relays, mixed u

bc -2

10

24

26 28 Relay Node Power in dBm

30

Fig. 5. Energy-Benefit downlink tradeoff with normalized energy consumption. Dashed lines indicate full cooperation and solid lines indicate limited cooperation.

101

10

10-2

Fig. 6.

θ5%

0

++

++ rs

u t c b

sr rs t u t u c b Multi-Cell MIMO c b rs Conventional bc 2 Relays, relay-only t 2 Relays, mixed u

u t c b

u t c b

+ +

u t rs bc

+ +

u t bcrs

rs

rs

rs

+

-1

+ +

+ +

100

rs

+ +

sr t u c b

++

++ u t rsbc

+ +

Throughput in MBit/s

++

u t bc rs

++

θ

u t c b

0.05

0.10 0.15 0.20 RN to BS Cost Ratio αRN

u t c b 0.25

Cost-Benefit downlink tradeoff with normalized energy and costs.

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R EFERENCES [1] R. Etkin, D. Tse, and H. Wang, “Gaussian interference channel capacity to within one bit: the symmetric case,” in IEEE Information Theory Workshop, Chengdu, China, October 2006, pp. 601–605. [2] ——, “Gaussian interference channel capacity to within one bit,” IEEE Transactions on Information Theory, vol. 54, no. 12, pp. 5534–5562, December 2008. [3] P. Rost, “Opportunities, benefits, and constraints of relaying in mobile communication systems,” Ph.D. dissertation, Technische Universit¨at Dresden, Dresden, Germany, 2009. [4] S. Vishwanath, N. Jindal, and A. Goldsmith, “Duality, achievable rates, and sum-rate capacity of Gaussian MIMO broadcast channels,” IEEE Transactions on Information Theory, vol. 49, no. 10, pp. 2658–2668, October 2003. [5] H. Weingarten, Y. Steinberg, and S. Shamai, “The capacity region of the Gaussian MIMO broadcast channel,” in IEEE International Symposium on Information Theory, Chicago (IL), USA, June 2004, p. 174. [6] R. Ahlswede, “Multi-way communication channels,” in International Symposium on Information Theory, Tsakkadsor, Armenian SSR, 1971, pp. 23–52. [7] H. Liao, “Multiple access channels,” Ph.D. dissertation, Department of Electrical Engineering, University of Hawaii, Honolulu, 1972. [8] R. Pabst, B. Walke, D. Schultz, P. Herhold, H. Yanikomeroglu, S. Mukherjee, H. Viswanath, M. Lott, W. Zirwas, M. Dohler, H. Aghvami, D. Falconer, and G. Fettweis, “Relay-based deployment concepts for wireless and mobile broadband radio,” IEEE Communications Magazine, vol. 42, no. 9, pp. 80–89, September 2004. [9] P. Rost, G. Fettweis, and J. Laneman, “Opportunities, constraints, and benefits of relaying in the presence of interference,” in IEEE International Conference on Communications, Dresden, Germany, June 2009. [10] S. Shamai and B. Zaidel, “Enhancing the cellular downlink capacity via co-processing at the transmitting end,” in IEEE Vehicular Technology Conference (VTC), vol. 3, Rhodes, Greece, May 2001, pp. 1745–1749. [11] T. Cover and A. E. Gamal, “Capacity theorems for the relay channel,” IEEE Transactions on Information Theory, vol. 25, no. 5, pp. 572–584, September 1979. [12] S. Shamai, O. Somekh, O. Simeone, A. Sanderovich, B. Zaidel, and H. Poor, “Cooperative multi-cell networks: Impact of limited-capacity backhaul and inter-users links,” in Joint Workshop on Coding and Communications, Durnstein, Austria, October 2007. [13] M. Werner, P. Moberg, and P. Skillermark, “Cost assessment of radio access network deployments with relay nodes,” in IST Mobile & Wireless Communications Summit, Stockholm, Sweden, June 2008. [14] M. Joham, W. Utschik, and J. Nossek, “Linear transmit processing in MIMO communications systems,” IEEE Transactions on Signal Processing, no. 8, pp. 2700–2712, August 2005. [15] W. Yu and T. Lan, “Transmitter optimization for the multi-antenna downlink with per-antenna power constraints,” IEEE Transactions on Signal Processing, vol. 55, no. 6, pp. 2646–2660, June 2007. [16] T. Han and K. Kobayashi, “A new achievable rate region for the interference channel,” IEEE Transactions on Information Theory, vol. IT-27, no. 1, pp. 49–60, January 1981. [17] T. Nielsen, J. Wigard, and P. Mogensen, “On the capacity of a GSM frequency hopping network with intelligent underlayoverlay,” in IEEE Vehicular Technology Conference (VTC), vol. 3, Phoenix (AZ), USA, May 1997, pp. 1867–1871. [18] F. Boye, “Analyse und Bewertung der Implikationen von Relaying auf die Systemeigenschaften eines zellularen 4GMobilfunknetzes mit Mehrantennen¨ubertragung,” Master’s thesis, Technische Universit¨at Dresden, Dresden, Germany, 2007. [19] WINNER, “IST-Winner,” http://www.ist-winner.org, January 2009.

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[20] IST-4-027756 WINNER II, “D6.13.7 Test scenarios and calibration issue 2,” December 2006. [21] S. Weinstein and P. Ebert, “Data transmission by frequency-division multiplexing using the discrete fourier transform,” IEEE Transactions on Communications, vol. 19, no. 5, pp. 628–634, October 1971. [22] L. Cimini, “Analysis and simulation of a digital mobile channel using orthogonal frequency division multiplexing,” IEEE Transactions on Information Theory, vol. 33, no. 7, pp. 665–675, July 1985. [23] F. Richter, A. Fehske, and G. Fettweis, “Traffic demand and energy efficiency in heterogeneous cellular mobile radio networks,” in IEEE Vehicular Technology Conference (VTC), Taipeh, Taiwan, May 2010. [24] CELTIC / CP5-026 WINNER+, “Enabling Techniques for LTE-A and beyond,” July 2010. [25] G. Fettweis, J. Holfeld, V. Kotzsch, P. Marsch, E. Ohlmer, Z. Rong, and P. Rost, “Field trial results for LTE-advanced concepts,” in IEEE Intnl. Conf. on Acoustics, Speech and Signal Processing (ICASSP), Dallas (TX), USA, March 2010. [26] IST-4-027756 WINNER II, “D1.1.1 WINNER II Interim channel models,” November 2006. [27] ——, “D1.1.2 WINNER II channel models,” September 2007.

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