Distributed Coordinated Transmission with Forward-Backward Training for 5G Radio Access

CoMP transmission and reception have been considered in cellular networks for enabling larger coverage, improved rates, and interference mitigation. To harness the gains of coordinated beamforming, fast information exchange over a backhaul connecting the cooperating BSs is required. In practice, the bandwidth and delay limitations of the backhaul may not be able to meet such stringent demands. These impairments motivate the study of cooperative approaches based only on local CSI that require minimal or no information exchange between the BSs. To this end, several distributed approaches are introduced for CB-CoMP. The proposed methods rely on the channel reciprocity and iterative spatially precoded over-the-air pilot signaling. We elaborate how F-B training facilitates distributed CB by allowing BSs and UEs to iteratively optimize their respective transmitters/receivers based on only locally measured CSI. The trade-off due to the overhead from the F-B iterations is discussed. We also consider the challenge of dynamic TDD where the UE-UE channel knowledge cannot be acquired at the BSs by exploiting channel reciprocity. Finally, standardization activities and practical requirements for enabling the proposed F-B training schemes in 5G radio access are discussed.

[1]  Preben E. Mogensen,et al.  Achieving low latency and energy consumption by 5G TDD mode optimization , 2014, 2014 IEEE International Conference on Communications Workshops (ICC).

[2]  Antti Tölli,et al.  Effective CSI Signaling and Decentralized Beam Coordination in TDD Multi-Cell MIMO Systems , 2013, IEEE Transactions on Signal Processing.

[3]  Antti Tölli,et al.  Decentralized Sum Rate Maximization With QoS Constraints for Interfering Broadcast Channel Via Successive Convex Approximation , 2016, IEEE Transactions on Signal Processing.

[4]  Jarkko Kaleva,et al.  Decentralized Joint Precoding With Pilot-Aided Beamformer Estimation , 2018, IEEE Transactions on Signal Processing.

[5]  Matti Latva-aho,et al.  Bi-directional signaling strategies for dynamic TDD networks , 2015, 2015 IEEE 16th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).

[6]  Mats Bengtsson,et al.  Distributed CSI Acquisition and Coordinated Precoding for TDD Multicell MIMO Systems , 2016, IEEE Transactions on Vehicular Technology.

[7]  Antti Tölli,et al.  Decentralized Coherent Coordinated Multi-Point Transmission for Weighted Sum Rate Maximization , 2014, 2015 IEEE Global Communications Conference (GLOBECOM).

[8]  Michael L. Honig,et al.  Bi-Directional Training for Adaptive Beamforming and Power Control in Interference Networks , 2014, IEEE Transactions on Signal Processing.

[9]  Thomas L. Marzetta,et al.  Pilot Contamination and Precoding in Multi-Cell TDD Systems , 2009, IEEE Transactions on Wireless Communications.

[10]  Zhi-Quan Luo,et al.  An iteratively weighted MMSE approach to distributed sum-utility maximization for a MIMO interfering broadcast channel , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[11]  Mikael Skoglund,et al.  Sum-Rate Maximization in Sub-28-GHz Millimeter-Wave MIMO Interfering Networks , 2016, IEEE Journal on Selected Areas in Communications.