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Cooperative-relaying networks have great potential for deployment in next-generation wireless communication networks. However, a cooperative-relaying network using two-dimensional multiple-input-multiple-output technology can further enhance the network performance. In this article, we propose a game theoretical framework for mobile two-dimensional multiple-input-multiple-output communication networks to achieve optimal relay selection and cooperative control. A source node, relay, and destination node can make service-selection decisions dynamically in a two-level network, based on the mobile-channel satisfaction parameters (e.g. spatial-temporal correlation and relay survivability). To model this dynamic interactive decision problem, we propose a hierarchical dynamic game framework. At the outer level, we formulate an evolutionary game to model and analyze the process of adaptive selection of relays for maximizing the multiple-input-multiple-output capacity by relay selection and power allocation. At the inner level, by aligning relays, we formulate an evolutionary game to model a self-organizing network structure for relays, to increase the capacity. A closed-loop evolutionary game equilibrium is considered to solve the dynamic game. Numerical results show that the proposed algorithm can effectively improve the quality of service for mobile two-dimensional multiple-input-multiple-output communication networks.

The limited radio spectrum has become a bottleneck for various wireless communications. To better utilize the scare radio spectrum, cognitive radios have recently attracted increasing attention, which makes spectrum sharing more viable. Sharing radio spectrum from primary users to secondary users is of great importance. A licensed primary user (PU) can lease its spectrum to secondary users (SUs) for wireless communications. This paper studies the problem of social welfare maximization of distributed spectrum sharing among a PU and SUs. We first formulate the problem of social welfare maximization which takes into account both the cost of the PU and the utility gained by each SU. The social welfare maximization is a convex optimization problem and thus can be solved by a centralized algorithm. However, the utility function of each SU may contain the private information. To avoid privacy leakage of SUs, we propose an iterative distributed algorithm based on a pricing-based decomposition framework. It is theoretically proved that our algorithm converges to the optimal solution. Simulation results are presented to show that our algorithm achieves the optimal social welfare and converges quickly in a practical setting.

Location awareness plays an indispensable role in a wide variety of application domains such as environment monitoring and vehicle tracking. In this paper we focus on the localization of mobile users in sparse mobile networks which exist in many practical scenarios where users are distributed over a vast area. The unique characteristics of sparse mobile networks present several challenges for accurate localization, such as constant movement and little information from anchors. By analyzing five large datasets of real users traces with entropy analysis from five sites, we make an important observation that there are strong patterns with user mobility. Motivated by this observation, we propose a localization approach called EMP by exploiting mobility patterns of users for localization in sparse mobile networks. EMP implements a range-free distributed algorithm, with which each user collaboratively estimates its current location by fusing two localization sources, that is, network connectivity with other nodes and mobility patterns . With trace driven simulations, we demonstrate that EMP significantly improves the localization accuracy, comparing with other existing localization approaches.