All points on the Pareto boundary can be obtained by solving the weighted sum rate maximization problem for some weighted coefficients. Unfortunately, the problem is non-convex and difficult to solve without performing an exhaustive search. In this paper, we propose an optimal distributed beamforming strategy for the two-user multiple-input single-output (MISO) interference channel (IC). Through minimizing the interference signal power leaked to the other receiver for fixed useful signal power received at the intended receiver, the original non-convex optimization problem can be converted into a family of convex optimization problems, each which can be solved in distributed manner with only local channel state information at each transmitter. After some conversion, we derive the closed-form solutions to all Pareto optimal points based on a game-theoretic viewpoint which indicates that linear combinations of the maximum-ratio transmit (MRT) and zero-forcing (ZF) beamforming strategies can achieve any point on the Pareto boundary of the rate region for the two-user MISO interference channel, and the only computation involved is to solve a basic quadratic equation. Finally, the result is validated via numerical simulations.

In this paper, a distributed cooperative multicell beamforming algorithm is proposed, and a detail analysis and solving method for instantaneous and statistical channel state information (CSI) are presented. Firstly, an improved distributed iterative beamforming algorithm is proposed for the multiple-input single-output interference channel (MISO IC) scenario which chooses virtual signal-to-interference-and-noise (SINR) as decision criterion to initialize and then iteratively solves the constrained optimization problem of maximizing the virtual SINR for a given level of generated interference to other users. Then, the algorithm is generalized to the multicell date sharing scenario with a heuristics power allocation scheme based on a viewpoint of the layered channel. Finally, the performance is illustrated through numerical simulations.

This letter presents a novel weighted reliability-based (WRB) algorithm for decoding low-density parity-check (LDPC) codes. Viewing the well-known normalized min sum (NMS) algorithm as reliability-based, the WRB algorithm can be seen as a simplified version of the NMS algorithm. Unlike the NMS algorithm, the WRB algorithm does not update the soft information sent between the variable nodes and check nodes, which greatly reduces the decoding complexity. For finite geometry LDPC codes with larger row redundancy and column weights, simulation results show that the WRB algorithm almost matches the error performance of the NMS algorithm.

The application of millimeter wave (mmWave) directional transmission technology in high-speed railway (HSR) scenarios helps to achieve the goal of multiple gigabit data rates with low latency. However, due to the high mobility of trains, the traditional initial access (IA) scheme with high time consumption is difficult to guarantee the effectiveness of the beam alignment. In addition, the high path loss at the coverage edge of the millimeter wave remote radio unit (mmW-RRU) will also bring great challenges to the stability of IA performance. Fortunately, the train trajectory in HSR scenarios is periodic and regular. Moreover, the cell-free network helps to improve the system coverage performance. Based on these observations, this paper proposes an efficient IA scheme based on location and history information in cell-free networks, where the train can flexibly select a set of mmW-RRUs according to the received signal quality. We specifically analyze the collaborative IA process based on the exhaustive search and based on location and history information, derive expressions for IA success probability and delay, and perform the numerical analysis. The results show that the proposed scheme can significantly reduce the IA delay and effectively improve the stability of IA success probability.