On Linearly Precoded Rate Splitting for Gaussian MIMO Broadcast Channels

In this paper, we consider a general $K$ -user Gaussian multiple-input multiple-output (MIMO) broadcast channel (BC). We assume that the channel state is deterministic and known to all the nodes. While the private-message capacity region is well known to be achievable with dirty paper coding (DPC), we are interested in the simpler linearly precoded transmission schemes. In particular, we focus on linear precoding schemes combined with rate-splitting (RS). First, we derive an achievable rate region with minimum mean square error (MMSE) precoding at the transmitter and joint decoding of the sub-messages at the receivers. Then, we study the achievable sum rate of this scheme and obtain two findings: 1) an analytically tractable upper bound on the sum rate that is shown numerically to be a close approximation, and 2) how to reduce the number of active streams – crucial to the overall complexity – while preserving the sum rate to within a constant loss. The latter results in two practical algorithms: a stream elimination algorithm and a stream ordering algorithm. Finally, we investigate the constant-gap optimality of linearly precoded RS with respect to the capacity. Our result reveals that, while the achievable rate of linear precoding alone can be arbitrarily far from the capacity, the introduction of RS can help achieve the capacity region to within a constant gap in the two-user case. Nevertheless, we prove that the RS scheme’s constant-gap optimality does not extend to the three-user case. Specifically, we show, through a pathological example, that the gap between the sum rate and the sum capacity can be unbounded.

[1]  David Tse,et al.  Multiaccess Fading Channels-Part I: Polymatroid Structure, Optimal Resource Allocation and Throughput Capacities , 1998, IEEE Trans. Inf. Theory.

[2]  Te Sun Han,et al.  A new achievable rate region for the interference channel , 1981, IEEE Trans. Inf. Theory.

[3]  Giuseppe Caire,et al.  Joint Spatial Division and Multiplexing—The Large-Scale Array Regime , 2013, IEEE Transactions on Information Theory.

[4]  Hua Wang,et al.  Gaussian Interference Channel Capacity to Within One Bit , 2007, IEEE Transactions on Information Theory.

[5]  Bruno Clerckx,et al.  Robust Transmission in Downlink Multiuser MISO Systems: A Rate-Splitting Approach , 2016, IEEE Transactions on Signal Processing.

[6]  Shlomo Shamai,et al.  Nested linear/Lattice codes for structured multiterminal binning , 2002, IEEE Trans. Inf. Theory.

[7]  Syed Ali Jafar,et al.  Interference Alignment and Degrees of Freedom of the $K$-User Interference Channel , 2008, IEEE Transactions on Information Theory.

[8]  Syed Ali Jafar,et al.  Transmitter Cooperation under Finite Precision CSIT: A GDoF Perspective , 2014, 2015 IEEE Global Communications Conference (GLOBECOM).

[9]  Bruno Clerckx,et al.  Optimal DoF Region of the $K$ -User MISO BC With Partial CSIT , 2017, IEEE Communications Letters.

[10]  Bruno Clerckx,et al.  Rate-splitting multiple access for downlink communication systems: bridging, generalizing, and outperforming SDMA and NOMA , 2017, EURASIP Journal on Wireless Communications and Networking.

[11]  Syed Ali Jafar,et al.  GDoF Region of the MISO BC: Bridging the Gap Between Finite Precision and Perfect CSIT , 2018, IEEE Transactions on Information Theory.

[12]  Bruno Clerckx,et al.  Sum-Rate Maximization for Linearly Precoded Downlink Multiuser MISO Systems With Partial CSIT: A Rate-Splitting Approach , 2016, IEEE Transactions on Communications.

[13]  Lihua Li,et al.  MMSE-Based Precoding for Rate Splitting Systems With Finite Feedback , 2018, IEEE Communications Letters.

[14]  Bruno Clerckx,et al.  Tomlinson-Harashima Precoded Rate-Splitting for Multiuser Multiple-Antenna Systems , 2018, 2018 15th International Symposium on Wireless Communication Systems (ISWCS).

[15]  Chandra Nair,et al.  The Capacity Region of the Two-Receiver Gaussian Vector Broadcast Channel With Private and Common Messages , 2014, IEEE Transactions on Information Theory.

[16]  Daniela Tuninetti,et al.  Interference as Noise: Friend or Foe? , 2015, IEEE Transactions on Information Theory.

[17]  Aydano B. Carleial,et al.  Interference channels , 1978, IEEE Trans. Inf. Theory.

[18]  Mahesh K. Varanasi,et al.  Rate Splitting, Superposition Coding and Binning for Groupcasting over the Broadcast Channel: A General Framework , 2020, ArXiv.

[19]  David Gesbert,et al.  Degrees of Freedom of Time Correlated MISO Broadcast Channel With Delayed CSIT , 2012, IEEE Transactions on Information Theory.

[20]  Shlomo Shamai,et al.  On the achievable throughput of a multiantenna Gaussian broadcast channel , 2003, IEEE Transactions on Information Theory.

[21]  N. Jindal,et al.  High SNR Analysis for MIMO Broadcast Channels: Dirty Paper Coding Versus Linear Precoding , 2006, IEEE Transactions on Information Theory.

[22]  Amir K. Khandani,et al.  Real Interference Alignment: Exploiting the Potential of Single Antenna Systems , 2009, IEEE Transactions on Information Theory.

[23]  David Tse,et al.  Sum capacity of the vector Gaussian broadcast channel and uplink-downlink duality , 2003, IEEE Trans. Inf. Theory.

[24]  Shlomo Shamai,et al.  Rate Splitting for Multi-Antenna Downlink: Precoder Design and Practical Implementation , 2020, IEEE Journal on Selected Areas in Communications.

[25]  Shlomo Shamai,et al.  The Capacity Region of the Gaussian Multiple-Input Multiple-Output Broadcast Channel , 2006, IEEE Transactions on Information Theory.

[26]  Andrea J. Goldsmith,et al.  On the optimality of multiantenna broadcast scheduling using zero-forcing beamforming , 2006, IEEE Journal on Selected Areas in Communications.

[27]  Andrea J. Goldsmith,et al.  On the duality of Gaussian multiple-access and broadcast channels , 2002, IEEE Transactions on Information Theory.

[28]  Giuseppe Caire,et al.  A Rate Splitting Strategy for Massive MIMO With Imperfect CSIT , 2015, IEEE Transactions on Wireless Communications.