QuaRo: A Queue-Aware Robust Coordinated Transmission Strategy for Downlink C-RANs

A queue-aware robust (QuaRo) coordinated transmission strategy is proposed for Cloud Radio Access Networks (C-RANs) with a central BaseBand processing Unit (BBU) connected to multiple Remote Radio Heads (RRHs). Such QuaRo strategy is adaptive to both user-traffic urgency via Queue State Information (QSI) and wireless channel opportunity via the observed (yet imperfect) Channel State Information (CSI). This involves clustering the RRHs into virtual user-centric clusters and performing Coordinated Beamforming (CB) from each virtual cluster to the target user in the downlink. The underlying control policy is formulated via Lyapunov optimization to minimize the average total transmit power at the RRHs while ensuring the stability of the system. In particular, the designed control policy does not require a-priori knowledge of the probability distribution of data-traffic arrival and channel states, and is robust against the instantaneous channel estimation error in each time slot. Extensive simulation results are presented to illustrate performance gains and robustness of the proposed solutions.

[1]  N.D. Sidiropoulos,et al.  On downlink beamforming with greedy user selection: performance analysis and a simple new algorithm , 2005, IEEE Transactions on Signal Processing.

[2]  Vincent K. N. Lau,et al.  Low Complexity Delay-Constrained Beamforming for Multi-User MIMO Systems With Imperfect CSIT , 2013, IEEE Transactions on Signal Processing.

[3]  I. Bechar,et al.  A Bernstein-type inequality for stochastic processes of quadratic forms of Gaussian variables , 2009, 0909.3595.

[4]  Satoshi Nagata,et al.  Coordinated multipoint transmission and reception in LTE-advanced: deployment scenarios and operational challenges , 2012, IEEE Communications Magazine.

[5]  Yuanming Shi,et al.  Group Sparse Beamforming for Green Cloud-RAN , 2013, IEEE Transactions on Wireless Communications.

[6]  Malolan Chetlur,et al.  Quantifying multiplexing gains in a Wireless Network Cloud , 2012, 2012 IEEE International Conference on Communications (ICC).

[7]  Vikram Srinivasan,et al.  CloudIQ: a framework for processing base stations in a data center , 2012, Mobicom '12.

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

[9]  Gustavo de Veciana,et al.  “Wireless networks without edges”: Dynamic radio resource clustering and user scheduling , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[10]  Vincent K. N. Lau,et al.  Queue-Aware Dynamic Clustering and Power Allocation for Network MIMO Systems via Distributed Stochastic Learning , 2010, IEEE Transactions on Signal Processing.

[11]  Michael J. Neely Energy Optimal Control for Time-Varying Wireless Networks , 2006, IEEE Trans. Inf. Theory.

[12]  Jiandong Li,et al.  Dynamic Joint Resource Optimization for LTE-Advanced Relay Networks , 2013, IEEE Transactions on Wireless Communications.

[13]  Wei Yu,et al.  Sparse Beamforming and User-Centric Clustering for Downlink Cloud Radio Access Network , 2014, IEEE Access.

[14]  Dario Pompili,et al.  Elastic resource utilization framework for high capacity and energy efficiency in cloud RAN , 2016, IEEE Communications Magazine.

[15]  Stefan Parkvall,et al.  LTE: the evolution of mobile broadband , 2009, IEEE Communications Magazine.

[16]  Leandros Tassiulas,et al.  Stability properties of constrained queueing systems and scheduling policies for maximum throughput in multihop radio networks , 1990, 29th IEEE Conference on Decision and Control.

[17]  Donald C. Cox,et al.  Channel and capacity estimation errors , 2002, IEEE Communications Letters.

[18]  Vincent K. N. Lau,et al.  A Survey on Delay-Aware Resource Control for Wireless Systems—Large Deviation Theory, Stochastic Lyapunov Drift, and Distributed Stochastic Learning , 2011, IEEE Transactions on Information Theory.

[19]  Zhisheng Niu,et al.  Joint Scheduling and Dynamic Clustering in Downlink Cellular Networks , 2011, 2011 IEEE Global Telecommunications Conference - GLOBECOM 2011.

[20]  Zhi-Quan Luo,et al.  Semidefinite Relaxation of Quadratic Optimization Problems , 2010, IEEE Signal Processing Magazine.

[21]  Dario Pompili,et al.  Dynamic Radio Cooperation for Downlink Cloud-RANs with Computing Resource Sharing , 2015, 2015 IEEE 12th International Conference on Mobile Ad Hoc and Sensor Systems.

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

[23]  Stephen P. Boyd,et al.  Stochastic Subgradient Methods , 2007 .

[24]  Leandros Tassiulas,et al.  Resource Allocation and Cross-Layer Control in Wireless Networks , 2006, Found. Trends Netw..

[25]  Wei-Chiang Li,et al.  A convex approximation approach to weighted sum rate maximization of multiuser MISO interference channel under outage constraints , 2010, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[26]  Federico Boccardi,et al.  A dynamic joint clustering scheduling algorithm for downlink CoMP systems with limited CSI , 2012, 2012 International Symposium on Wireless Communication Systems (ISWCS).

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