This paper investigates joint design and optimization of both low density parity check (LDPC) codes and M-algorithm based detectors including iterative tree search (ITS) and soft-output M-algorithm (SOMA) in multiple-input multiple-output (MIMO) systems via the tool of extrinsic information transfer (EXIT) charts. First, we present EXIT analysis for ITS and SOMA. We indicate that the extrinsic information transfer curves of ITS obtained by Monte Carlo simulations based on output log-likelihood rations are not true EXIT curves, and the explanation for such a phenomenon is given, while for SOMA, the true EXIT curves can be computed, enabling the code design. Then, we propose a new design rule and method for LDPC code degree profile optimization in MIMO systems. The algorithm can make the EXIT curves of the inner decoder and outer decoder match each other properly, and can easily attain the desired code with the target rate. Also, it can transform the optimization problem into a linear one, which is computationally simple. The significance of the proposed optimization approach is validated by the simulation results that the optimized codes perform much better than standard non-optimized ones when used together with SOMA detector.
YOU MingHou1,2, TAO XiaoFeng1,2, CUI QiMei1,2 & ZHANG Ping1,2 1 Wireless Technology Innovation Institute, Beijing University of Posts and Telecommunications, Beijing 100876, China
With the irreversible trend of the convergence and cooperation among heterogeneous networks, there emerge some important issues for network evolution. One of them is to reconfigure network elements such as cellular base stations (BSs) or access points (APs) of wireless local area networks (WLANs) according to the real-time network environment, in order to maximize the cooperation gain of different networks. In this paper, we consider cognitive pilot channel (CPC) as an assistant to enable cooperation among heterogeneous networks. Based on the widely used reinforcement learning algorithm, this paper has proposed the heterogeneous network self-optimization algorithm (HNSA) to solve the adaptation problem in reconfigurable systems. In the algorithm, distributed agents perform reinforcement learning, and make decisions cooperatively with the help of CPC in order to reduce the system blocking rate and improve network revenue. Finally our simulation proves the anticipated goal is achieved.
