Source compression in two-way two-relay network using compute-and-forward relaying

Abstract In this paper, the performance of relaying network that employs vector quantization with Compute-and-Forward (CF) relaying technique in AWGN and Rayleigh fading channel is analyzed. Radio relaying is needed when the source and destination cannot directly communicate with each other. Source compression techniques of optimum vector quantization and lattice quantization is employed at source nodes. Physical layer network coding (PNC) with compute and forward relaying is an ingenious relaying scheme that exploits the broadcast nature of wireless channel and forwards the linear combination of signals received from multiple nodes. On the reception of linear combination of messages from the relay nodes, source nodes are capable of extracting the data intended for them. The end-to-end error performance at the source nodes is analyzed. As the performance of CF relaying is sensitive to channel estimation error, we estimate the channel using Least Square Estimation (LSE) and Minimum Mean Square Estimation (MMSE) channel estimation algorithms. The sum-rate of nearly 7 bits/s/Hz for AWGN channel at 20 dB SNR in compute-and-forward relaying is achieved which is only 3.75 bits/s/Hz for decode-and-forward relaying (DF). Thus source compression techniques with efficient CF relaying in PNC is a promising technique to improve the efficiency in wireless relaying networks.

[1]  Toshiaki Koike-Akino,et al.  Optimized constellations for two-way wireless relaying with physical network coding , 2009, IEEE Journal on Selected Areas in Communications.

[2]  Il-Min Kim,et al.  Error Performance Analysis of BPSK Modulation in Physical-Layer Network-Coded Bidirectional Relay Networks , 2010, IEEE Transactions on Communications.

[3]  Michael Gastpar,et al.  Compute-and-Forward: Harnessing Interference Through Structured Codes , 2009, IEEE Transactions on Information Theory.

[4]  Imran Khan,et al.  Efficient compressive sensing based sparse channel estimation for 5G massive MIMO systems , 2018 .

[5]  Patrick Mitran,et al.  Performance Bounds for Bidirectional Coded Cooperation Protocols , 2008, IEEE Trans. Inf. Theory.

[6]  Tao Jiang,et al.  Norm-adaption penalized least mean square/fourth algorithm for sparse channel estimation , 2016, Signal Process..

[7]  Alister G. Burr,et al.  Layered Design of Hierarchical Exclusive Codebook and Its Capacity Regions for HDF Strategy in Parametric Wireless 2-WRC , 2011, IEEE Transactions on Vehicular Technology.

[8]  Ying-Chang Liang,et al.  Optimal channel estimation and training design for two-way relay networks , 2009, IEEE Transactions on Communications.

[9]  Kin K. Leung,et al.  Physical Layer Network Coding and Precoding for the Two-Way Relay Channel in Cellular Systems , 2011, IEEE Transactions on Signal Processing.

[10]  Tao Yang,et al.  Asymptotically Optimal Error-Rate Performance of Linear Physical-Layer Network Coding in Rayleigh Fading Two-Way Relay Channels , 2012, IEEE Communications Letters.

[11]  Shijun Lin,et al.  Cooperative mechanism for PNC two-hop slotted ALOHA network ☆ , 2016 .

[12]  Masanori Hamamura,et al.  Zero‐attracting variable‐step‐size least mean square algorithms for adaptive sparse channel estimation , 2015 .

[13]  Alister G. Burr,et al.  Linear Physical-Layer Network Coding Over Hybrid Finite Ring for Rayleigh Fading Two-Way Relay Channels , 2014, IEEE Transactions on Communications.

[14]  Soung Chang Liew,et al.  Asynchronous Physical-Layer Network Coding , 2012, IEEE Transactions on Wireless Communications.