Estructura de la Población

choose from multiple beam sets. Here, the system may profit from an improved channel quantization, yielding a capacity increase of α = 2.11 forMIMO 2× 2 with two beam sets. However, it has to be considered that then also thePMI feedback overhead doubles from 1 bit to 2 bit.

Interference prediction: Note that considering independent adaptation of beam sets for all BSs does not influence the received interference covariance matrix Z_k,u, since the Wishart product Wm_(Wm₎H _{equals the scaled identity}

matrix if we assume Wm_{to be unitary. However, changing the power allocation}

for differentMIMOtransmission modes results in a multi-cell system where Z_k,u cannot be predicted at the receiver side. In order to support cell-edge terminals, we suggest to arrange e.g. SS with full base station power in an agreed access scheme known to the users.

52 CoMPSchemes Based on Interf.-Aware Transceivers or Interf. Coord. 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 −15 −10 −5 0 5 10 15

SINRest/ SINRavail[dB]

CDF MRC, fs. i.i.d., µ=0.1 MRC, ff. i.i.d., µ=0.1 MRC, fs. cov., µ=0.1 MMSE, fs. cov., µ=0.1 MMSE, fs. corr. N=3 MMSE, fs. corr. N=12

Figure 5.6 SINRestimation errors[TSWJ09]. c
2009IEEE.

5.1.8

Multi-Cell Performance under Imperfect

CSI

In the following, we take channel estimation errors into account, i.e. using (5.9)- (5.13). Fig.5.6indicates the estimation error of theSS SINRat the terminal. We compare the ratio of the estimated SINRest to the achievable SINRavail under perfect CSIR and estimated equalization weights. Employing either MRC in an asynchronous network or IRC in a synchronized one leads to significantly different estimation errors. ForMRC based on (5.10), the estimation suffers in two ways: There is a median shift of −1.9 dB, i.e. SINRest is systematically too low. In addition, the estimation error has a considerable variance. With overestimatedSINRconditions, the channel may be overloaded, i.e. the reported channel quality indicator (CQI) and the supported MCS do not match, which results in substantial performance degradation and increased block error rate (BLER). Assuming that strong channel codes as well as hybrid automatic repeat request (HARQ) mechanisms are able to correct errors if 10% of the resources are overloaded, we have to ensure that the 90th percentile of SINRest/SINRavail is below 0 dB. This can be achieved by introducing a safety factor S < 1, shifting all SINRestcorrespondingly.

ForMRCbased on(5.10), we can estimate S to be 2.3 dB from Fig.5.6. Focus- ing on the median value, there is an overall penalty (offset) of approx. SINRpen= 4.2 dB at the multiple access channel(MAC) compared to SINRavail. Averaging the interference power σ_IF2 over the entire frequency band, i.e. using(5.9), reduces the penalty to SINRpen= 3.7 dB. Covariance estimation, i.e. (5.11), leads to unbiased SINRest, but the S-factor is higher due to the larger variance, resulting in SINRpen= 6.3 dB. Concentrating on asynchronous downlink transmission, we conclude that an interference estimation scheme assuming a frequency-flati.i.d.

σ2

5.1 DL Multi-User Beamforming withIRC 53 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1 2 3 4 5 6 7 8

spectral efficiency [bit/s/Hz/cell]

CDF

1.76 SISO, µ=0.1 SU, µ=0.1, i.i.d. σ_IF SS, µ=0.1, i.i.d. σ_IF SU, MMSE corr. N=12 SS, MMSE corr. N=12 MMSE corr. N=12 MMSE perfect CSIR

Figure 5.7 MIMO2× 2 system performance under channel estimation

errors[TSWJ09]. c
2009IEEE.

The penalties can be reduced further if the interference is estimated more precisely, e.g. in a synchronous system using anMMSEreceiver and the correla- tion approach as given in (5.12) and (5.13). For a correlation window spanning

N = 3 pilot symbols, we assume to be able to distinguish between the chan-

nels belonging to 3 out of 57 sectors or cells. Hence, interference cannot be separated sufficiently, and thus SINRis systematically overestimated. However, already with a correlation window spanning N = 12 pilot symbols, 12 sectors and thus more interferers can be identified, and the SINR is determined more precisely [TSWJ08]. The safety factor is then S = 0.9 dB, and the median shift becomes negligible.

Fig. 5.7 shows the achievable sum-rates in the multi-cell system including SINRpen. As a lower bound, we use the performance in the SISOcase including the effects of estimation errors for the desired channel ,h_k,u. The upper bound is given by the adaptive transmission system assuming perfectCSIR.Assuming theUEis able to estimate its dedicated channel with µ = 0.1 and Z_u according to(5.10) and the system is forced toSU-MIMOmode only, results in an inferior performance compared to theSS transmission using MRC. The reason is that the estimation error leads to inter-stream interference in the SU-MIMO case, which is not present with SStransmission.

The next threeCDFcurves are all based on the estimates (5.12) and(5.13). Although the MMSEreceiver can exploit the knowledge of interference, the SS mode using theMMSEreceiver outperformsSU-MIMOtransmission. Fully adap- tive transmission yields a significant system throughput gain, which is mostly related toMU-MIMOscheduling. Note that the gap to the adaptive system with perfect CSIR amounts to 8% only, indicating the robustness of the proposed

54 CoMPSchemes Based on Interf.-Aware Transceivers or Interf. Coord.

scheme. Finally, we come to the following conclusion: Synchronized downlink transmission from all BSs in combination with MMSE receivers based on esti- mates (5.12) and (5.13) outperforms the asynchronous case. However, if the system design would be constrained to non-synchronized BSs, SS transmission in combination with theMRCreceiver would be a suitable choice. The difference in the average throughput between both cases is significant and amounts to 76% in our results. Thus, the overall throughput gain achievable with synchronized BSs is still significant even under practical considerations.

5.1.9

Summary

In this section, we have evaluated the gains from using interference-aware, frequency-selective MU-MIMO scheduling in a cellular network with synchro- nized base stations. Terminals were assumed to be able to estimate their ded- icated and a certain number of interfering channel coefficients. Two important observations were made: Efficient MU-MIMO transmission can be achieved by using fixed unitary precoding, i.e. without the requirement of full channel knowl- edge. Further, proper application of theMU-MIMOmode enables to conveniently serve even users with multiple streams who experience relatively poorSNRcon- ditions. Thus, theMU-MIMOmode establishes a win/win situation for both, low and high rate users. In addition, it was shown that knowledge on the interference channels yields a more precise estimation of the achievableSINRcompared to the traditional approach, where interference is assumed white. Thus,CQIfeedback and supported modulation and coding scheme can be matched more accurately.

Acknowledgements

The authors are grateful for financial support from the German Ministry of Education and Research (BMBF) in the national collaborative project EASY-C under contract No. 01BU0631.

5.2

Uplink Joint Scheduling and Cooperative Interference

Prediction