Channel Matrix Sparsity With Imperfect Channel State Information in Cloud Radio Access Networks

Channel matrix sparsification is considered as a promising approach to reduce the progressing complexity in large-scale cloud radio access networks based on ideal channel condition assumption. In this paper, the research of channel sparsification is extend to practical scenarios in which the perfect channel state information (CSI) is not available. First, a tractable lower bound of signal-to-interference-plus-noise ratio (SINR) fidelity, which is defined as a ratio of SINRs with and without channel sparsification, is derived to evaluate the impact of channel estimation error. Based on the theoretical results, a Dinkelbach-based algorithm is proposed to achieve the global optimal performance of channel matrix sparsification based on the criterion of distance. Finally, all these results are extended to a more challenging scenario with pilot contamination. Finally, simulation results are shown to evaluate the performance of channel matrix sparsification with imperfect CSIs and verify our analytical results.

[1]  Yong Li,et al.  System architecture and key technologies for 5G heterogeneous cloud radio access networks , 2015, IEEE Netw..

[2]  Supeng Leng,et al.  Joint Scheduling and Beamforming Coordination in Cloud Radio Access Networks With QoS Guarantees , 2016, IEEE Transactions on Vehicular Technology.

[3]  Xiaojun Yuan,et al.  Dynamic Nested Clustering for Parallel PHY-Layer Processing in Cloud-RANs , 2016, IEEE Transactions on Wireless Communications.

[4]  Dmitri Moltchanov,et al.  Distance distributions in random networks , 2012, Ad Hoc Networks.

[5]  H. Vincent Poor,et al.  Energy-Efficient Resource Allocation Optimization for Multimedia Heterogeneous Cloud Radio Access Networks , 2016, IEEE Transactions on Multimedia.

[6]  K. C. Ho,et al.  A simple and efficient estimator for hyperbolic location , 1994, IEEE Trans. Signal Process..

[7]  H. Vincent Poor,et al.  Cluster Content Caching: An Energy-Efficient Approach to Improve Quality of Service in Cloud Radio Access Networks , 2016, IEEE Journal on Selected Areas in Communications.

[8]  Alex B. Gershman,et al.  Training-based MIMO channel estimation: a study of estimator tradeoffs and optimal training signals , 2006, IEEE Transactions on Signal Processing.

[9]  Chintha Tellambura,et al.  Ambient Backscatter Communication Systems: Detection and Performance Analysis , 2016, IEEE Transactions on Communications.

[10]  Mérouane Debbah,et al.  Large System Analysis of Base Station Cooperation for Power Minimization , 2015, IEEE Transactions on Wireless Communications.

[11]  Wenbo Wang,et al.  Superimposed Training Based Channel Estimation for Uplink Multiple Access Relay Networks , 2015, IEEE Transactions on Wireless Communications.

[12]  H. Vincent Poor,et al.  Fronthaul-constrained cloud radio access networks: insights and challenges , 2015, IEEE Wireless Communications.

[13]  Xuelong Li,et al.  Recent Advances in Cloud Radio Access Networks: System Architectures, Key Techniques, and Open Issues , 2016, IEEE Communications Surveys & Tutorials.

[14]  Vincent K. N. Lau,et al.  Recent Advances in Underlay Heterogeneous Networks: Interference Control, Resource Allocation, and Self-Organization , 2015, IEEE Communications Surveys & Tutorials.

[15]  Robert W. Heath,et al.  Interference Coordination: Random Clustering and Adaptive Limited Feedback , 2012, IEEE Transactions on Signal Processing.

[16]  H. Vincent Poor,et al.  Inter-Tier Interference Suppression in Heterogeneous Cloud Radio Access Networks , 2015, IEEE Access.

[17]  H. Vincent Poor,et al.  Training Design for Channel Estimation in Uplink Cloud Radio Access Networks , 2016, IEEE Transactions on Signal Processing.

[18]  H. Vincent Poor,et al.  Training Design and Channel Estimation in Uplink Cloud Radio Access Networks , 2014, IEEE Signal Processing Letters.

[19]  Yuan Li,et al.  Heterogeneous cloud radio access networks: a new perspective for enhancing spectral and energy efficiencies , 2014, IEEE Wireless Communications.

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

[21]  Yuanming Shi,et al.  Large-Scale Convex Optimization for Dense Wireless Cooperative Networks , 2015, IEEE Transactions on Signal Processing.

[22]  H. Vincent Poor,et al.  Channel Estimation for Two-Way Relay Networks in the Presence of Synchronization Errors , 2014, IEEE Transactions on Signal Processing.

[23]  Yuanming Shi,et al.  Robust Group Sparse Beamforming for Multicast Green Cloud-RAN With Imperfect CSI , 2015, IEEE Transactions on Signal Processing.

[24]  H. Vincent Poor,et al.  Ergodic Capacity Analysis of Remote Radio Head Associations in Cloud Radio Access Networks , 2014, IEEE Wireless Communications Letters.

[25]  Qiang Liu,et al.  Acquisition of channel state information in heterogeneous cloud radio access networks: challenges and research directions , 2015, IEEE Wireless Communications.

[26]  Khaled Ben Letaief,et al.  Downlink User Capacity of Massive MIMO Under Pilot Contamination , 2015, IEEE Transactions on Wireless Communications.