Optimized Uplink Transmission in Multi-Antenna C-RAN With Spatial Compression and Forward

MIMO and cloud radio access network (C-RAN) are promising techniques for implementing future wireless communication systems, where a large number of antennas are deployed either being co-located at the base station or totally distributed at separate sites called remote radio heads (RRHs), both to achieve enormous spectrum efficiency and energy efficiency gains. Here, we consider a general antenna deployment method for wireless networks, termed multi-antenna C-RAN, where a flexible number of antennas can be equipped at each RRH to more effectively balance the performance and fronthaul complexity tradeoff beyond the conventional massive MIMO and single-antenna C-RAN. To coordinate and control the fronthaul traffic over multi-antenna RRHs, under the uplink communication setup, we propose a new “spatial-compression-and-forward (SCF)” scheme, where each RRH first performs a linear spatial filtering to denoise and maximally compress its received signals from multiple users to a reduced number of dimensions, then conducts uniform scalar quantization over each of the resulting dimensions in parallel, and finally sends the total quantized bits via a finite-rate fronthaul link to the baseband unit (BBU) for joint information decoding. Under this scheme, we maximize the minimum SINR of all users at the BBU by a joint resource allocation over the wireless transmission and fronthaul links. Specifically, each RRH determines its own spatial filtering solution in a distributed manner to reduce the signaling overhead with the BBU, while the BBU jointly optimizes the users' transmit power, the RRHs' fronthaul bits allocation, and the BBU's receive beamforming with fixed spatial filters at individual RRHs. Numerical results show that, given a total number of antennas to be deployed, multi-antenna C-RAN with the proposed SCF and joint optimization significantly outperforms both massive MIMO and single-antenna C-RAN under practical fronthaul capacity constraints.

[1]  C-ran the Road towards Green Ran , 2022 .

[2]  Ying-Chang Liang,et al.  Joint Beamforming and Power Allocation for Multiple Access Channels in Cognitive Radio Networks , 2008, IEEE Journal on Selected Areas in Communications.

[3]  Michael Gastpar,et al.  The distributed Karhunen-Loeve transform , 2002, 2002 IEEE Workshop on Multimedia Signal Processing..

[4]  Erik G. Larsson,et al.  Energy and Spectral Efficiency of Very Large Multiuser MIMO Systems , 2011, IEEE Transactions on Communications.

[5]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[6]  Suhas N. Diggavi,et al.  Wireless Network Information Flow: A Deterministic Approach , 2009, IEEE Transactions on Information Theory.

[7]  Geoffrey Ye Li,et al.  An Overview of Massive MIMO: Benefits and Challenges , 2014, IEEE Journal of Selected Topics in Signal Processing.

[8]  Shlomo Shamai,et al.  Distributed MIMO Receiver—Achievable Rates and Upper Bounds , 2007, IEEE Transactions on Information Theory.

[9]  Liang Liu,et al.  Joint Power Control and Fronthaul Rate Allocation for Throughput Maximization in OFDMA-Based Cloud Radio Access Network , 2014, IEEE Transactions on Communications.

[10]  Sae-Young Chung,et al.  Noisy Network Coding , 2010, IEEE Transactions on Information Theory.

[11]  Aitor del Coso,et al.  Distributed compression for MIMO coordinated networks with a backhaul constraint , 2009, IEEE Transactions on Wireless Communications.

[12]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[13]  Shlomo Shamai,et al.  Robust distributed compression for cloud radio access networks , 2012, 2012 IEEE Information Theory Workshop.

[14]  Wei Yu,et al.  Optimized Backhaul Compression for Uplink Cloud Radio Access Network , 2013, IEEE Journal on Selected Areas in Communications.

[15]  Shlomo Shamai,et al.  Uplink Macro Diversity of Limited Backhaul Cellular Network , 2008, IEEE Transactions on Information Theory.

[16]  Holger Boche,et al.  Solution of the multiuser downlink beamforming problem with individual SINR constraints , 2004, IEEE Transactions on Vehicular Technology.

[17]  Shlomo Shamai,et al.  Joint Decompression and Decoding for Cloud Radio Access Networks , 2013, IEEE Signal Processing Letters.

[18]  Thomas L. Marzetta,et al.  Noncooperative Cellular Wireless with Unlimited Numbers of Base Station Antennas , 2010, IEEE Transactions on Wireless Communications.

[19]  Shlomo Shamai,et al.  Communication Via Decentralized Processing , 2005, IEEE Transactions on Information Theory.

[20]  Wei Yu,et al.  Uplink Multicell Processing with Limited Backhaul via Per-Base-Station Successive Interference Cancellation , 2012, IEEE Journal on Selected Areas in Communications.

[21]  Zhi-Quan Luo,et al.  Distributed Estimation Using Reduced-Dimensionality Sensor Observations , 2005, IEEE Transactions on Signal Processing.

[22]  Shlomo Shamai,et al.  Robust and Efficient Distributed Compression for Cloud Radio Access Networks , 2012, IEEE Transactions on Vehicular Technology.

[23]  Rui Zhang,et al.  Downlink and Uplink Energy Minimization Through User Association and Beamforming in C-RAN , 2014, IEEE Transactions on Wireless Communications.

[24]  Roy D. Yates,et al.  A Framework for Uplink Power Control in Cellular Radio Systems , 1995, IEEE J. Sel. Areas Commun..