Impairments in Cooperative Mobile Networks: Models ... - IEEE Xplore

European Wireless 2014

Impairments in Cooperative Mobile Networks: Models, Impact on Performance and Mitigation invited paper Konstantinos Manolakis , Volker Jungnickel , Christian Oberli‡ , Thorsten Wild , Volker Braun

Technische Universität Berlin, Department of Telecommunication Systems, Berlin, Germany Pontificia Universidad Católica de Chile, Department of Electrical Engineering, Santiago, Chile Alcatel-Lucent Bell Laboratories, Wireless Access Domain, Stuttgart, Germany Email: [email protected] Inter-BS link (X2)

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Abstract—Base station cooperation is a powerful technique for eliminating inter-cell interference and enhancing spectral efficiency in cellular networks. However, impairment effects due to channel estimation, feedback quantization, channel time variance as well as imperfect carrier and sampling frequencies among base stations are limiting the potential gains. This paper provides a unifying framework for modeling these impairments in cooperative multi-user multi-cellular systems, for analyzing the performance degradation due to the resulting inter-user interference and finally for mitigating impairments. Evaluation of the performance degradation reveals how critical impairments are. Multi-cell channel estimation, clustering and feedback compression are investigated as mitigation tools, while it is shown that advanced channel prediction and synchronization of distributed base stations can also compensate the performance degradation. In this way, limitations in mobility, feedback delay and number of supported users can be relaxed, which makes base station cooperation a practical and highly attractive technique.

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Fig. 1. Distributed JT CoMP with two base stations and two terminals. Using zero-forcing precoding with perfect CSI at the transmitters and ideal synchronization among base stations allows for interference-free data reception.

I. I NTRODUCTION During recent years, base station cooperation has received a lot of attention as an advanced technique for cellular networks. Also known as joint transmission coordinated multi-point (JT CoMP) in the downlink and joint detection (JD) CoMP in the uplink, this technique eliminates inter-cell interference and enhances spectral efficiency. Field trials in [1] and [2] have shown the potential of JT CoMP, which is considered as a promising candidate for 5th generation cellular systems [3]. JT CoMP can be understood as a generalization of multipleinput multiple-output (MIMO), where antennas of multiple distributed base stations are considered as inputs and antennas of terminals in those cells are considered as outputs of a distributed MIMO system. Joint signal processing at the base stations, known as precoding, allows for eliminating the intercell interference ( [4], [5]). The concept of a JT CoMP system with two base stations (BSs) and two users is shown in Fig. 1. In real-world systems, there are impairments that cause a mismatch between the precoder applied to the data and the channel over which the downlink transmission is realized. As shown in this paper, this mismatch causes inter-user interference (IUI), which limits the potential gains of JT CoMP. Note this kind of degradation effects also apply to JD CoMP.

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In frequency division duplex (FDD) systems, terminals estimate the channel and provide quantized channel state information (CSI) to the BSs, as illustrated in Fig. 1, with noise, out-of-cluster interference and quantization contributing to inaccuracy. In addition, there is a delay, equal to the time between when the channel is observed and when the resulting estimate is used for precoding. This delay is mainly generated by the computational time, the transmission over the air and (for distributed architectures) also by the backhaul network, which is used for CSI exchange between base stations. In time division duplex (TDD) systems, CSI for downlink precoding is obtained by channel estimation at the BSs. Considering the channel time variance due to mobility, the provided channel state information at the transmitter (CSIT) is outdated when used for the precoder calculation; this effect is also known as channel aging. Finally, base stations are driven by local oscillators with individual carrier and sampling frequency offsets, which also cause a mismatch between channel and precoder. Sources of imperfect CSIT are highlighted in Fig. 2 for an FDD system. Modeling of impairments and impact on performance: A first modeling framework for JT CoMP with channel and synchronization impairments has been provided by the authors

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in [6]. As the effect of synchronization impairments can also be described as a channel error, imperfect CSIT was captured by the impairments’ equivalent channel mean square error (MSE). In [7] it was found that channel aging is the major impairment, while in [8] it was shown that the signal to interference ratio (SIR) is inverse to the MSE and that it increases with the number of BSs and decreases with the number of terminals. Performance limitations of cooperative systems with imperfect CSI due to channel estimation and aging effects have been recently analyzed in [9], while in [10] the importance of CSIT has been also pointed out. The spectral efficiency of precoded transmission with imperfect CSI has been investigated in [11]. In [12], an adaptive transmission scheme using delayed and quantized CSI has been further proposed. Downlink scheduling in single-cell multi-user orthogonal frequency division multiplexing (OFDM) systems in the presence of channel aging has been addressed in [13], while in [14] the authors investigated scheduling methods dealing with imperfect CSI. A JT CoMP model with imperfect channel state information at the receiver (CSIR) and minimum mean square error (MMSE) channel estimation can be found in [15]. In [16] and [17], signal models for JT CoMP with multiple carrier frequency offsets (CFOs) and sampling frequency offsets (SFOs) were provided. Performance evaluation showed that accurate synchronization among BSs is essential. Mitigation of impairments and performance compensation: Regarding mitigation of impairments and enhancement of CSIT accuracy, multi-cell channel estimation methods have been proposed in [18] and [19]. Results showed that channel estimation in coordinated multi-point (CoMP) faces additional challenges compared to conventional (non-CoMP) cellular systems. In [20], an overview of so-called limited feedback schemes can be found. Channel prediction has been investigated as a technique for compensating channel aging effects and enhancing JT CoMP performance. During recent years, several techniques have been developed, see [21]–[26]. Synchronization in multi-cellular networks is significantly more challenging compared to point-to-point transmission. Estimation of multiple CFOs in the downlink is analyzed in [27] and [28]. However, IUI is not compensated, as it is caused by imperfect synchronization among BSs. Practical Global Positioning System (GPS) frequency synchronization of distributed base stations and clock distribution have been investigated in [2] and [29]. A round-trip synchronization protocol has been presented for distributed networks in [30]. In this paper, we arrange our existing related work into a unifying framework and also integrate new results in it. The aim is to show how impairments can be handled and that JT CoMP is a practical and beneficial technique. Our framework includes modeling, evaluation and mitigation of channel and synchronization impairments for CoMP. Analytical MSE expressions are provided for each impairment, while SIR evaluation indicates limitations in terms of mobility, feedback delay, synchronization accuracy and number of users. Multicell channel estimation, clustering, CSI feedback compression

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as well as frequency synchronization are investigated, while it is demonstrated how channel prediction can compensate degradation effects and relax requirements. Finally, new waveforms with higher robustness to synchronization impairments are presented. The paper is organized as follows. In Section II, impairments are introduced and a modeling framework based on the MSE is presented. The impact of impairments onto the SIR and spectral efficiency is analyzed in Section III. In Section IV, impairments’ mitigation and compensation methods against performance degradations are presented. Conclusions are summarized in Section V. II. C AUSING FACTORS AND M ODELING OF I MPAIRMENTS A. General model for JT CoMP with impairments Here, a general JT CoMP signal model with imperfect CSIT is presented and it is shown how impairments cause IUI. We consider a multi-cellular network with Nb BSs serving simultaneously Nb ≥ Nu ≥ 2 single-antenna terminals. The narrowband channel matrix H has size Nu ×Nb . Considering OFDM, H denotes the frequency response on a single subcarrier. The precoder mismatch is modeled by a channel error Δ, which can be due to any of the impairments. In our model, the zero-forcing (ZF) precoder W is calculated as the right-hand Moore-Penrose pseudo-inverse of channel matrix H, which we assume exists. Transmitting the Nu × 1 data vector s over = H + Δ and considering additive (erroneous) channel H white Gaussian noise (AWGN) n at the receivers, we obtain y = s + ΔWs + n.

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Expression (1) reveals that channel error matrix Δ, which describes the imperfect CSIT in our model, breaks the inverse relationship between channel and precoder and causes IUI. In what follows, MSE expressions that refer to elements of matrix Δ are presented for each impairment, providing a first measure of the misalignment between channel and precoder. The MSE is defined as the mean power of the channel error, normalized to the mean power of (perfect) channel H. The MSE expressions are directly plugged into SIR expressions provided in Section III. Any simplified expressions used tolerate approximation errors below 10 % with respect to the exact values. B. Channel impairments The impairments leading to imperfect CSIT include channel estimation, CSI feedback quantization and channel aging during feedback delay, and are highlighted in Fig. 2 for an FDD point-to-point precoded system. Channel estimation and quantization errors: In [15], a simple model including CSIR impairments has been introduced. The model is able to emulate an MMSE-based channel estimator in a very simple and efficient manner, preserving the importance of accurate estimates in conjunction with different multi-antenna receive combining algorithms. The model also includes imperfect CSIT due to quantization and channel aging.

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Channel aging during the feedback delay: When observing the mobile radio channel for time intervals equal to the feedback delay, we can assume that large-scale parameters such as path loss and shadow fading remain constant. It has been shown in [7] that for a Rayleigh fading channel with a Jake’s U-shaped power spectrum and a maximum Doppler frequency fD , the (normalized) MSE is given by 2π 2 (fD t)2 , fD t < 0.1 MSEt ≈ 2π 2 (fD t)2 + 3π 4 (fD t)4 , 0.1 ≤ fD t < 0.2 (2) This MSE describes the effect of channel aging over time. It should be understood as a worst case scenario, if no compensation technique e.g. channel prediction is used. C. Synchronization impairments The most important synchronization impairments are the CFO and the SFO, as shown in Fig. 3. OFDM systems are sensitive to synchronization misalignments, which cause intercarrier interference (ICI) [31], which is mainly caused by the CFO. As shown in [16], the IUI suffered by JT CoMP systems due to the CFO is typically higher than the ICI. Considering these observations, the equivalent channel MSE, which is responsible for the IUI, due to Gaussian distributed CFOs with zero mean and variance σf2 is modeled in [7] as MSEφ ≈ 4π 2 σf2 t2 ,

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In Fig. 4, the MSE due to both channel aging and CFO are evaluated over the feedback delay time, according to expressions (2) and (3). As observed, every time the feedback delay doubles with either impairment, the MSE grows by 6 dB. Doubling the user’s velocity v also increases the MSE by 6 dB. Reducing the oscillator’s accuracy Oc by one order of magnitude increases the MSE by 20 dB (Oc = σf /fc , with fc the carrier frequency). These results reveal that channel aging has a larger impact on the MSE: even for a pedestrian velocity of 3 km/h, the MSE is around 5 dB higher than the one due to

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Fig. 4. MSE due to channel aging (2): second order (solid black) and fourth order (dashed red) approximations for user’s velocities v. Equivalent channel MSE (3) for various oscillator’s accuracies Oc is shown with blue and purple.

an average-quality oscillator with an accuracy of Oc = 10−9 . III. I MPACT OF I MPAIRMENTS ON P ERFORMANCE A major challenge in evaluating the impact of impairments on the performance is to capture correctly the role of the channel and the precoder. In [8], the mean SIR (power ratio of mean self-signal to mean IUI), which a single-antenna user observes in JT CoMP with ZF precoding, was analyzed and found equal to SIRj =

1 1 2 (N − 1) · E {λ−1 } + N − 1 . σδ,j u u

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Notation E {·} is used for the expectation operator, while λ denotes the eigenvalues of matrix HHH ((·)H denotes the Hermitian operator). The j th user’s mean channel error power, i.e. mean power ofelements in j th row of Δ is given by 2 σδ,j = E Δj ΔH j . Expression (4) is a general formula, applicable to any channel matrix for which the eigenvalue statistics are known or can be calculated. As mentioned in Section II, perfect data detection is assumed, i.e. (4) considers only IUI caused by imperfect CSIT. For a channel matrix with independent, identical distributed (i.i.d.) complex Gaussian random variables with zero mean and variance σh2 , (4) simplifies to SIRj =

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2 /σh2 describes the imperfect CSIT of The ratio MSEj = σδ,j the channel to user j due to any of the impairments under study. Equation (5) is valid for Nb ≥ Nu ≥ 2 and shows that for ZF precoding, the mean SIR of a single-antenna terminal is inversely proportional to the MSE, that it grows with the number of base stations and and drops with the number of terminals. For the uplink, following similar mathematical steps

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as the ones taken in [8] for the downlink, it can be shown that the mean SIR in JD CoMP using ZF is also given by (5). For practical purposes, the spectral efficiency (bits/s/Hz) can be estimated by the following expression found in [32]: 0, SIR < −7.6 dB C= 0.9 · log2 (1 + 0.85 · SIR) − 0.18, SIR ≥ −7.6 dB (6) IV. M ITIGATION OF I MPAIRMENTS IN JT C O MP In this section, we propose and evaluate methods for mitigating impairments and compensating performance degradation in CoMP. These are multi-cell channel estimation, clustering, feedback compression, channel prediction and base station synchronization. A. Multi-cell channel estimation In JT CoMP, the CSI values acquired at the terminals or at the base stations are sources of imperfect CSIT, depending on whether an FDD or TDD system is considered. Mitigation of channel impairments via channel estimation in CoMP faces additional challenges compared to channel estimation in conventional cellular (non-CoMP) systems. When considering the JT CoMP (downlink), radio channels to multiple base stations have to be estimated by mobile users. In the uplink, radio channels from multiple mobile users possibly located in neighboring cells have to be estimated as well. In [33], cell-specific reference sequences have been proposed, consisting of a comb cyclically shifted in the frequency domain for identifying the cells. Orthogonal sequences in time domain are further used to identify the antennas within a cell. In this way, the frequency-selective multi-cell channel can be identified with high precision also at the cell edge. A further concept for multi-cell downlink channel estimation, using socalled virtual pilots from block-orthogonal sequences can be found [34]. In the uplink, receive power levels between different superimposed users may differ by several orders of magnitude, which can be challenging for the channel estimator. In [35] we observe that conventional least squares (LS)-based methods, as used in typical non-CoMP channel estimation, are inadequate for such receive power imbalances. While the research community sees 2-D Wiener filtering [18] as a viable approach to this problem, an often neglected fact is the difficulty of obtaining the statistics (i.e. the receive covariances) of the time-varying frequency-selective channels [35]. Conventional sample covariance estimation will result in huge "warm-up times", requiring terminals to be active for over 1000 ms in order to achieve their full performance in channel estimation. Novel covariance estimation techniques presented in [35], called "shrinkage", help to speed up this process. A further way for estimating with high accuracy the channel covariance is provided in [36]. In a multi-user scenario, the pilot sequence assignment plays an important role. In Long Term Evolution – Advanced (LTE-A) [37], the Zadoff-Chu sequences specified by the standard across multiple cells are pseudo-random and not fully

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orthogonal. In [19] these effects are discussed in light of different channel estimators. While low complexity channel estimators clearly benefit from an introduction of orthogonality by proper sequence assignment, a full-blown multi-user matrix Wiener filter (MWF) is able to perform almost as well with the pseudo-random sequences as with orthogonal ones. Again, such a MWF has to rely on statistical knowledge of the channels. In [38] a multi-stage channel estimation technique for a multi-user multi-cell situation is introduced, supported by subspace noise estimation techniques. The presented practical estimator has a brief warm-up time. Even in large CoMP clusters with strong path-loss imbalances (e.g. 7 cells), the average losses of the channel estimation method in terms of signal to interference and noise ratio (SINR) degradation at the output of the receive combiner, are below 1 dB with respect to perfect CSIR. Fig. 5 depicts the performance of uplink multi-user channel estimation for JD CoMP in Winner II C2 spatial channel model with a velocity of 3 km/h. A CoMP cluster of 8 single antenna cells is considered, receiving the signals of 8 users that share the same time-frequency resources. LTE-A signals are used with Zadoff-Chu pilot sequences [37]. The channel estimation is linear and pilot-based. The y-axis represents the SINR at the output of an MMSE-based interference rejection combiner (IRC), while the x-axis measures the interference over thermal noise levels (IoT). With perfect CSI the SINR increases with increasing IoT thus with relatively decreasing noise level. Conventional single user LS estimation already saturates early in its performance and is far from ideal CSI. It fails in dealing with the strong path loss differences of CoMP and due to its lack of interference rejection capability. MMSE estimation per user has a higher saturation point than LS. A MWF that estimates the channel jointly for all users ("MMSE joint") almost attains perfect CSI. As this 2-D MWF is complex and requires extensive parameter knowledge, a sliding window in the frequency domain can be used to simplify computational complexity and reduce the number of required statistical parameters ("MMSE windowed"). While the curve of Fig. 5 was obtained with perfect statistical parameter knowledge, the multi-stage approach in [38] may be used to harvest statistical knowledge quickly when the parameters are unknown. B. Clustering and feedback compression When applying CoMP in the downlink of FDD systems, BSs require accurate CSI for precoding. While the precoding matrix indicator (PMI) feedback mechanism, provisioned by the LTE-A standard [37] is acceptable for coordinated beamforming and scheduling, for JT CoMP this is not enough, as the phase information between different base stations is not carried. Moreover, PMI assumes that the BSs use the same power, which is typically not given, due to path loss variations. It is intuitive that providing complex-valued CSI for multiple base stations not only increases the amount of information that mobile users need to report, but also increases the overall

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feedback delay. This is because channel information has to be processed and organized into packets; the computational complexity and time of this process also grows with the number of base stations to which CSI is reported. Afterwards, CSI has to be transmitted over the air and eventually over the backhaul network. The traffic and resulting delays of the overall delay have been studied in [39] for distributed architectures. It is evident that compressing efficiently the feedback information with a minimum cost in CSI accuracy is a essential goal for JT CoMP systems. A first step towards feedback reduction in practical CoMP systems is limiting the cluster size, a process also called clustering. In [40], it has been demonstrated that BSs, for which the receive power at the terminal is below some threshold with respect to the strongest BS power, can be excluded from clustering and the terminals do not need to report CSI to them. Evaluation on real-world measured channels in [40] showed that for a 12 dB threshold, the average cluster size is 2.6. However, close to the cluster edges, inter-cluster interference problems occur, replacing the former inter-cell interference problem. In [41] it was shown that proper signal to leakage and noise ratio (SLNR)-based precoding techniques are able to deal with this cluster edge problems at the price of a small channel knowledge exchange between neighbor clusters. For dealing with this problem, a greedy dynamic clustering approach was introduced in [42], and it was found that it outperfors static clustering. A further tool for reducing the amount of feedback (and the corresponding feedback delay) is to compress efficiently the CSI. While the research community has mainly focused on codebooks for narrowband channels [20], the proposal in [43] is Karhunen-Loéve transformations (KLT) in order to remove the redundancy in frequency domain OFDM channel transfer functions. It was shown in [43] that time-domain transformations perform almost as well as KLT and are also more practical. In [22], a time-domain based feedback scheme has been proposed, which is in line with the information-theoretic results in [43]. The method focuses on the dominant multi-

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Fig. 6. CDF of feedback (kbits) per reporting interval for different clustering power thresholds. Solid: measured multi-cell channels for 0.33 inter-site distance antenna down-tilt, dashed: 7-cell SCME, users in the middle cell.

path propagation components, while it splits the feedback in long-term and short-term components to enhance redundancy removal. This approach also exploits the sparse nature of the multi-path propagation channel. In [38], further algorithms for the time-domain compression have been provided, refining the results of [22] with algorithms for multi-path detection, waterfilling-based bit loading across multiple channel taps and methods for combating the channel aging effects of the feedback with prediction techniques. A further time-domain feedback scheme has been investigated in [40]. There, the most significant channel paths are selected after AWGN estimation and removal, which at the same time increases the signal-to-noise ratio (SNR) by around 6 dB. Afterwards, adaptive quantization according to the achieved SNR is applied. Allowing imperfect CSI causes some IUI, which is of minor importance, as long as significantly lower than the residual AWGN plus out-of-cluster interference level. Controlling thus the feedback quantization and tolerating a channel MSE such that IUI remains relatively small (but not too small), allows for further feedback reduction. Both methods have been implemented on JT CoMP demonstration environments and have also been verified on realworld channel measurements, as described in [44] and [40], respectively. It has been found in both cases that typical 20 MHz macro channels can be represented with a maximum of about 23 channel taps. Considering OFDM systems with a typically much higher number of pilots, this already consists a feedback reduction compared to frequency-domain CSI. It has been also shown in [44] that for 20 MHz bandwidth and with a short-term reporting interval of 10 ms and a longterm reporting interval of 100 ms, fewer than 20 kbit/s per mobile user are required in a pico cell scenario to report the frequency selective channels of a CoMP cluster of 4 base stations with 2 antennas each. From the feedback rate of 20 kbit/s, around 0,5 kbit/s corresponds to the long-term part. With this feedback rate, the maximum single-stream-

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per-user CoMP throughput is achieved at an SINR of 20 dB, when 4 users are simultaneously scheduled on the same timefrequency resource, each of them with a single data stream. Similar results have been obtained in [40]. Here, evaluation on measured channels showed that for a power threshold of 12 dB (clustered cells 2.6 in average), the average feedback traffic can be reduced to 3.5 kbit per reporting interval and per mobile user if adaptive AWGN-based quantization is applied, and to even 2.5 kbit if out-of-cluster interference is also considered. Fig. 6 illustrates the statistics of the feedback from one user to multiple BSs for different power thresholds and for an antenna down-tilt of 0.33 times the inter-site distance (ISD), which is used for keeping the inter-cell interference low. Next to the results for measured channels, simulations using the SCME with 7 cells are shown with dashed lines.

A lot of research has been conducted on channel prediction of mobile radio channels during the last years. It has been shown that JT CoMP systems become more robust, when linear channel prediction techniques such as Kalman and Wiener filtering are used. For achieving robust operation at higher mobilities, it is also important to adapt the precoder to different reliabilities of the predicted channels [45]. Kalman filters provide such information intrinsically, which can be reported semi-statically from the terminals [23]. Doppler-delay based channel prediction is a non-linear approach [26]. For each link between a transmit and receive antenna, each channel path component reaches the receiver with an individual delay and is thus modeled by a superposition of discrete complex-valued sub-paths, each with its own Doppler frequency (depends on velocity and angle of arrival). From a short channel history, these parameters are estimated for each path. Assuming that multi-path parameters remain static over short periods of time, the channel is predicted by inserting the estimated parameters into the channel model. The above approach has been studied using the SCME. Fig. 7 and Fig. 8 illustrate the improvements in terms of channel MSE and (normalized) channel square errors statistics, respectively, achieved for a velocity of 30 km/h at 2.65 GHz. It can be also observed, that CSI quantization with more than 6 bits per real and imaginary part practically does not improve the MSE further. In practice, gains in CSIT accuracy translate into performance gains, because the achievable SIR, given the residual IUI, is inversely proportional to the MSE, as shown in (4). Channel prediction allows for higher mobility, relaxes the feedback delay requirements and ultimately reduces the synchronization requirements likewise, as the CFO has an effect which is equivalent to a Doppler frequency. D. Carrier synchronization of distributed base stations In the downlink, multiple CFOs from base stations using OFDM can be estimated by the methods proposed in [27] and [28], and ICI can be reduced. However, in order to avoid IUI,

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which is a more serious limitation in JT CoMP, BSs need to be synchronized with each other. In [29], it was proposed to use an external time reference provided by GPS for synchronizing the carrier frequencies of outdoor distributed base stations (using GPS for time/frame synchronization has been already used in 2G systems). According to our scheme, GPS satellites transmit specific sequences with a 1 ms period, from which the phase information and a one pulse per second (PPS) reference signal are recovered at the receiver. The clock driver at the base station then uses an internal oscillator, typically oven-controlled crystal oscillator (OCXO) or Rubidium, which is phase-locked to the PPS signal. Note that a high Q-factor and low phase noise are needed to stabilize the clock between the pulses’ time instants. For optional indoor base station cooperation (e.g. small

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cells embedded into macro cells), synchronization over the network using standard protocols such as IEEE1588v2 or solutions like synchronous Ethernet [46] have been proposed. In [1], these techniques were successfully integrated into our LTE-A testbed. In [2], a scheme for clock distribution was presented. It was shown that all local clock signals needed to synchronize the carrier frequencies of the distributed radio front-ends, as well as the LTE frame, OFDM symbols and sample frequencies, can be derived from a global reference. OFDM requires very accurate time and frequency synchronization and shows clear signs of degradation when this is not fulfilled. In [47], a novel filtered multi-carrier waveform approach was introduced, called Universal Filtered MultiCarrier (UFMC). With UFMC, due to reduced subcarrier-sidelobe-levels compared to OFDM, additional robustness against ICI can be introduced. This ICI may come from impairments like CFO. The emerging discussing for 5th generation wireless systems include the introduction of new waveforms replacing OFDM. Here in the CoMP scenario (as well as for other use cases), new waveforms like UFMC can add increased robustness against impairments as well as higher tolerance to synchronization misalignments. V. C ONCLUSION A signal model for cooperative multi-cell multi-user systems using zero-forcing precoding was presented, including channel estimation, feedback quantization, channel aging and imperfect synchronization among base stations. The channel MSE and the resulting SIR were analyzed for all impairments. Results indicate that coordinated transmission needs short feedback times, low mobility and accurate carrier frequency synchronization among base stations. It was also shown that multi-cell channel estimation, dynamic clustering and feedback compression are essential for impairment mitigation. Channel prediction acts as a tool for reducing the precoder mismatch and relaxes mobility and feedback delay requirements. Synchronization techniques for distributed base stations were also addressed. Overall, coordinated transmission is a highly promising and also practical technique. Using mitigation tools, encompassing algorithms and hardware, is vital for serving efficiently a high number of users on the same time and frequency resource. ACKNOWLEDGMENTS This work was funded on the German side by the Deutsche Forschungsgemeinschaft (DFG) under project CoMP impairments (JU 2793/3-1) and CoMP mobil (JU 2793/4-1). On the Chilean side, it was funded by projects from CONICYT, Departamento de Relaciones Internacionales “Programa de Cooperación Científica Internacional” CONICYT/DFG-622 and FONDECYT 1110370. The Alcatel-Lucent Bell Laboratories in Germany are acknowledged by the authors for funding part of this work and supporting the research activities. We would also like to thank our colleagues Stephan Jaeckel and Lars Schulz from the Fraunhofer Heinrich Hertz Institute

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