Cloud Radio Access Downlink with Backhaul Constrained Oblivious - PowerPoint PPT Presentation

Page 1 of 66 Cloud Radio Access Downlink with Backhaul Constrained Oblivious Processing Shlomo Shamai Department of Electrical Engineering Technion − Israel Institute of Technology Joint work with S.-H. Park, O. Simeone and O. Sahin HyNeT Colloquium, University of Maryland October 2013

Page 2 of 66 Outline I. Backgrounds and motivations II. Basic setting III. State of the art IV. Joint precoding and multivariate compression V. Special cases and extensions VI. Numerical results Wyner model and general MIMO fading VII. Concluding remarks

Page 3 of 66 Outline I. Backgrounds and motivations II. Basic setting III. State of the art IV. Joint precoding and multivariate compression V. Special cases and extensions VI. Numerical results Wyner model and general MIMO fading VII. Concluding remarks

Page 4 of 66 Backgrounds Cloud radio access networks • – Promoted by Huawei [Liu et al] , Intel [Intel] , Alcatel-Lucent [Segel-Weldon] , China Mobile [China] , Texas Inst. [Flanagan] , Ericsson [Ericsson] – Base stations (BSs) (e.g., macro-BS and pico-BS) operate as soft relays. An Illustration of the downlink of cloud radio access networks

Page 5 of 66 Backgrounds Cloud radio access networks (ctd’) • – Low-cost deployment of BSs • Encoding/decoding functionalities migrated to the central unit • No need to consider cell association – Effective interference mitigation • Joint encoding/decoding at the central unit – But, the backhaul links have limited capacity • Macro BSs: increasingly fiber cables [Segel-Weldon] • Dedicated relays: wireless [Maric et al][Su-Chang] • Home BSs: last-mile connections The distribution of backhaul connections for macro BSs (green: fiber, orange: copper, blue: air) [Segel-Weldon] .

Page 6 of 66 Outline I. Backgrounds and motivations II. Basic setting III. State of the art IV. Joint precoding and multivariate compression V. Special cases and extensions VI. Numerical results Wyner model and general MIMO fading VII. Concluding remarks

Page 7 of 66 Basic Setting We focus on the downlink • 1 MS H ˆ 1 focus M y 1 DEC 1 BS 1 x C 1 1 1 n antennas ,1 M n M 1 , , M antennas ,1 B Central N M ENC H C N M N N B MS M BS x ˆ N B M N y N DEC M B N M N M n antennas , B N B n antennas , M N M   Notation: N N – { 1, , }, { 1, , } N N B M B M

Page 8 of 66 Basic Setting Assuming flat-fading channel, the received signal at MS is given • k by    N , y H x z k M k k k where     H H , , H ,  k k ,1 k N , B H     CN H H , , , ~ ( , ) x x x z 0 I  1 N k B Per-BS power constraints •  2   N , x P i B i i The results of this work can be extended to more general power constraints: –       x Θ x H , 1, , . l L   l i

Page 9 of 66 Basic Setting Backhaul constraints • – Each BS is connected to the central encoder via a backhaul link of i capacity bits per channel use (c.u.). C i Oblivious BSs • – The codebooks of the MSs are not known to the BSs. As assumed in cloud radio access networks, e.g., [Liu et al]-[Ericsson] . • – Systems with informed BSs treated in [Ng et al][Sohn et al][Zakhour-Gesbert][Simeone et al: 12] .

Page 10 of 66 Outline I. Backgrounds and motivations II. Basic setting III. State of the art IV. Joint precoding and multivariate compression V. Special cases and extensions VI. Numerical results Wyner model and general MIMO fading VII. Concluding remarks

Page 11 of 66 Previous Work: Uplink Distributed compression • – Received signals at different BSs are statistically correlated. – This correlation can be utilized to improve the achievable rates [Sanderovich et al][dCoso-Simoens][Park et al:TVT][Zhou et al] . : Side information Conventional compression Distributed compression

Page 12 of 66 Previous Work: Uplink Joint decompression and decoding [Sanderovich et al][Yassaee-Aref][Lim et al] • – Potentially larger rates can be achieved with joint decompression and decoding (JDD) at the central unit [Sanderovich et al] . – Optimization of the Gaussian test channels with JDD [Park et al:SPL] . Joint decompression and decoding Numerical results in 3-cell uplink [Park et al:SPL] (SDD: separate decompression and decoding)

Page 13 of 66 Previous Work: Downlink Compressed dirty-paper coding (CDPC) [Simeone et al:09] • – Joint dirty-paper coding [Costa] for all MSs A simpler scheme based on zero-forcing DPC [Caire-Shamai] was studied in [Mohiuddin et al:13] . • – Followed by independent compression DPC output signals for different BSs are compressed independently. • independent compression Central encoder C M s x Compression 1 1  Channel 1 x 1  BS 1  1 ENC 1 encoder 1 Joint Dirty-Paper Coding s x M C N N Channel N N M B Compression  B x M  BS N N  encoder B ENC N B B

Page 14 of 66 Previous Work: Downlink Compressed dirty-paper coding [Simeone et al:09] (ctd’) • - With constrained backhaul links, we obtain a modified BC with the added quantization noises. - Per-cell sum-rate             2 2 2 2 2 1 (1 ) 1 2(1 ) (1 ) P P P    log R   per-cell 2   System model P where is the effective SNR at the MSs decreased from to P Quantization is performed at the central P unit using the forward test channel  . P        2 C 1 (1 ) / (2 1) 1 P   , X X Q m m m where : DPC precoding output, X m CN C Q : quantization noise with Q ~ (0, P / 2 ), m m : cell-index, thus is independent over the index . m Q m m

Page 15 of 66 Previous Work: Downlink Reverse compute-and-forward (RCoF) [Hong-Caire] • – Downlink counterpart of the compute-and-forward (CoF) scheme proposed for the uplink in [Nazer et al] . • Exchange the role of BSs and MSs and use CoF in reverse direction. – System model      • L , for all { 1, , }. N N L C C i L B M i z h 1 1 C MS 1 BS 1 Central encoder z L C h BS L MS L L

Page 16 of 66 Previous Work: Downlink Reverse compute-and-forward (RCoF) [Hong-Caire] (ctd’) • z Point-to-point channels h 1 1 w     Λ 1 t z ( h a , , ) mod   Central encoder 1 eff 1 1 1 C BS 1 MS 1 Precoding over finite field     w w 1 1       1 Q     z L     C  w   w  L L     Λ eff ( , , ) mod t z h a   w L L L L L h MS L BS L L – The same lattice code is used by each BS.   – Each MS estimates a function by decoding on the lattice L ˆ k w j a w k  k j , j 1 code. – Achievable rate per MS is given by           SNR       min ,min h a , ,SNR R C R where   R h a , ,SNR max log ,0   per-MS   l l  1  L     l H 1 H SNR a I hh a    

Page 17 of 66 Outline I. Backgrounds and motivations II. Basic setting III. State of the art IV. Joint precoding and multivariate compression V. Special cases and extensions VI. Numerical results Wyner model and general MIMO fading VII. Concluding remarks

Page 18 of 66 Central Encoder • Structure of the central encoder – Precoding: interference mitigation – Compression: backhaul communication – Achievable rate for MS (single-user detection) k    ; s y R I k k k

Page 19 of 66 Channel Encoding • Channel encoding for MS k – Assume Gaussian codewords  CN ENC ( ) ~ ( , ) s M 0 I k k k where  nR {1, ,2 }, M k k : rate for MS , R k k : coding block length. n

Page 20 of 66 Precoding • Linear precoding     H x E As 1 1        x As         H x E As     N N B B where H       H H , , , , , , A A A s s s     1 1 N N M M   0     n n , 1 , B i B i N j   B      , , E I n n n n , , , i n B j B l B B l   B i ,   1 1 l l   0     ( n n ) n , , B B i B i – Remark: Non-linear dirty-paper coding [Costa] can be also considered. All kind of pre-processing can be accommodated as long as the message to compress is • treated as a Gaussian vector.

Page 21 of 66 Conventional Compression • Conventional Compression Central encoder 1 BS COMP 1 C x x 1 1 1 x DECOMP 1 1 Precoding N BS COMP B N B C x x N N N B B B x DECOMP N N B B

Page 22 of 66 Multivariate Compression • Multivariate Compression Central encoder BS 1 jointCOMP C x x 1 1 1 x DECOMP 1 1 Precoding BS N B C x x N N N B B B x DECOMP N N B B