In this paper, we investigate the antenna and node selection issues for amplify-and-forward (AF) and decode-and-forward (DF) multi-antenna relay networks in correlated channels. Based on the channel statistics, optimal selection criteria for antenna and relay node are derived jointly, aiming to maximize the ergodic capacity. Instantaneous channel knowledge-based selection schemes, motivated by traditional antenna selection algorithms, are investigated as well. It is shown that the proposed node selection schemes derived from antenna selection on relay nodes are feasible and effective in relay systems. Statistical selection shows considerable capacity gain compared to full complexity scheme and random selection strategy in AF mode, while instantaneous selection performs better in DF relaying. Furthermore, the proposed schemes are shown to be robust to channel estimation errors due to their correlation-oriented nature.

A semi-distributed resource allocation scheme based on multihop equilibrium is proposed for OFDM-relay networks. This method aims to reduce the amount of feedback information from the relay nodes (RNs). Moreover, it utilizes radio resource by striking an efficient balance between the capacities of the BS-RN link and RN-MS link. Simulation results show that the proposed semi-distributed scheme achieves good performances in terms of throughputs and fraction of satisfied users.

This letter proposes an intra-cell partial spectrum reuse (PSR) scheme for cellular OFDM-relay networks. The proposed method aims to increase the system throughput, while the SINR of the cell edge users can be also promoted by utilizing the PSR scheme. The novel pre-allocation factor γ not only indicates the flexibility of PSR, but also decreases the complexity of the reuse mechanism. Through simulations, the proposed scheme is shown to offer superior performances in terms of system throughput and SINR of last 5% users.

This letter addresses linear processing issues for the downlink of closed-loop multi-user non-regenerative MIMO-relay systems with arbitrary number of antennas at each node. First three precoding design schemes at BS are presented. Then given the fixed BS linear processing matrix, we propose a joint iterative linear processing scheme for the relay station and mobile stations, aiming to minimize the total mean squared error (MSE). Finally Simulation results are provided to show the performance gain of joint linear processing at the multi-antenna nodes.

Multi-agent based simulation has been widely used in behavior finance, and several single-processed simulation platforms with Agent-Based Modeling (ABM) have been proposed. However, traditional simulations of stock markets on single processed computers are limited by the computing capability since financial researchers need larger and larger number of agents and more and more rounds to evolve agents' intelligence and get more efficient data. This paper introduces a distributed multi-agent simulation platform, named PSSPAM, for stock market simulation focusing on large scale of parallel agents, communication system and simulation scheduling. A logical architecture for distributed artificial stock market simulation is proposed, containing four loosely coupled modules: agent module, market module, communication system and user interface. With the customizable trading strategies inside, agents are deployed to multiple computing nodes. Agents exchange messages with each other and with the market based on a customizable network topology through a uniform communication system. With a large number of agent threads, the round scheduling strategy is used during the simulation, and a worker pool is applied in the market module. Financial researchers can design their own financial models and run the simulation through the user interface, without caring about the complexity of parallelization and related problems. Two groups of experiments are conducted, one with internal communication between agents and the other without communication between agents, to verify PSSPAM to be compatible with the data from Euronext-NYSE. And the platform shows fair scalability and performance under different parallelism configurations.