Exploiting MIMO antennas in cooperative cognitive radio networks

Abstract—Recently, a new paradigm for cognitive radio net-works has been advocated, where primary users (PUs) recruit some secondary users (SUs) to cooperatively relay the primary traffic. However, all existing work on such cooperative cognitive radio networks (CCRNs) operate in the temporal domain. The PU needs to give out a dedicated portion of channel access time to the SUs for transmitting the secondary data in exchange for the SUs ’ cooperation, which limits the performance of both PUs and SUs. On the other hand, Multiple Input Multiple Output (MIMO) enables transmission of multiple independent data streams and suppression of interference via beam-forming in the spatial domain over MIMO antenna elements to provide significant performance gains. Researches have not yet explored how to take advantage of the MIMO technique in CCRNs. In this paper, we propose a novel MIMO-CCRN framework, which enables the SUs to utilize the capability provided by the MIMO to cooperatively relay the traffic for the PUs while concurrently accessing the same channel to transmit their own traffic. We design the MIMO-CCRN architecture by considering both the temporal and spatial domains to improve spectrum efficiency. Further we provide theoretical analysis for the primary and secondary transmission rate under MIMO cooperation and then formulate an optimization model based on a Stackelberg game to maximize the utilities of PUs and SUs. Evaluation results show that both primary and secondary users achieve higher utility by leveraging MIMO spatial cooperation in MIMO-CCRN than with conventional schemes. I

Adaptive Scheduling in MIMO-based Heterogeneous Ad hoc Networks

Abstract-The demands for data rate and transmission reliability constantly increase with the explosive use of wireless devices and the advancement of mobile computing techniques. Multiple-input and multiple-output (MIMO) technique is considered as one of the most promising wireless technologies that can significantly improve transmission capacity and reliability. Many emerging mobile wireless applications require peer-to-peer transmissions over an ad hoc network, where the nodes often have different number of antennas, and the channel condition and network topology vary over time. It is important and challenging to develop efficient schemes to coordinate transmission resource sharing among a heterogeneous group of nodes over an infrastructure-free mobile ad hoc network. In this work, we propose a holistic scheduling algorithm that can adaptively select different transmission strategies based on the node types and channel conditions to effectively relieve the bottleneck effect caused by nodes with smaller antenna arrays, and avoid the transmission failure due to the violation of lower degree of freedom constraint resulted from the channel dependency. The algorithm also takes advantage of channel information to opportunistically schedule cooperative spatial multiplexed transmissions between nodes and provide special transmission support for higher priority nodes with weak channels, so that the data rate of the network can be maximized while user transmission quality requirement is supported. The performance of our algorithm is studied through extensive simulations and the results demonstrate that our algorithm is very effective in handling node heterogeneity and channel constraint, and can significantly increase the throughput while reducing the transmission delay

Cross-layer Design for Wireless Mesh Networks with Advanced Physical and Network Layer Techniques

Cross-layer optimization is an essential tool for designing wireless network protocols. We present a cross-layer optimization framework for wireless networks where at each node, various smart antenna techniques such as beam-forming, spatial division multiple access and spatial division multiplexing are employed. These techniques provide interference suppression, capability for simultaneous communication with several nodes and transmission with higher data rates, respectively. By integrating different combinations of these multi-antenna techniques in physical layer with various constraints from MAC and network layers, three Mixed Integer Linear Programming models are presented to minimize the scheduling period. Since these optimization problems are combinatorially complex, the optimal solution is approached by a Column Generation (CG) decomposition method. Our numerical results show that the resulted directive, multiple access and multiplexing gains combined with scheduling, effectively increase both the spatial reuse and the capacity of the links and therefore enhance the achievable system throughput. The introduced cross-layer approach is also extended to consider heterogeneous networks where we present a multi-criteria optimization framework to model the design problem with an objective of jointly minimizing the cost of deployment and the scheduling period. Our results reveal the significant benefits of this joint design method. We also investigate the achievable performance gain that network coding (with opportunistic listening) when combined with Successive Interference Cancellation (SIC) brings to a multi-hop wireless network. We develop a cross-layer formulation in which SIC enables concurrent receptions from multiple transmitters and network coding reduces the transmission time-slot for minimizing the scheduling time. To solve this combinatorially complex non-linear problem, we decompose it to two linear sub-problems; namely opportunistic network coding aware routing, and scheduling sub-problems. Our results affirm our expectation for a remarkable performance improvement when both techniques are jointly used. Further, we develop an optimization model for combining SIC with power control (PC). Our model optimally adjusts the transmission power of nodes to avoid interference on unintended receivers and properly embraces undesired interference through SIC. Therefore, it provides a balance between usage of PC and SIC at the transmitting and receiving sides, respectively. Our results show considerable throughput improvement in dense and heavily loaded networks