Performance of MIMO Cognitive Ad-hoc Networks

Cognitive ad-hoc networks are able to share primary user frequency bands following certain interference preconditions. For considered cognitive network, cognitive communication is limited by the interference imposed on the primary user. Probability of channel availability for cognitive nodes for such opportunistic access is determined. Furthermore, this probability of channel availability is used for the performance analysis purpose. A Carrier Sense Multiple Access (CSMA) Media Access Control (MAC) protocol for the cognitive network is considered and for that the embedded Markov model of cognitive nodes is determined. This Markov model is used to determine the average channel access delay, throughput and service rate of cognitive nodes. This network is further extended to consider multiple frequency bands for cognitive access. For this propose algorithms are proposed to address the channel allocation and fairness issues of multi-band multiuser cognitive ad-hoc networks. Nodes in the network have unequal channel access probability and have no prior information about the offered bandwidth or number of users in the multiple access system. In that, nodes use reinforcement learning algorithm to predict future channel selection probability from the past experience and reach an equilibrium state. Proof of convergence of this multi party stochastic game is established. Nevertheless, cognitive nodes can reduce the convergence time by exchanging channel selection information and thus further improve the network performance. To further improve the spectrum utilization, this study is extended to include Multiple-input Multiple-output (MIMO) techniques. To improve the transmission efficiency of the MIMO system, a cross-layer antenna selection algorithm is proposed. The proposed cross-layer antenna selection and beamforming algorithm works as the data link layer efficiency information is used for antenna selection purpose to achieve high efficiency at the data link layer. Having analyzed the cognitive network, to consider more realistic scenario primary users identification method is proposed. An artificial intelligent method has been adopted for this purpose. Numerical results are presented for the algorithm and compare these results with the theoretical ones

Resource assignment for adaptively modulated interconnected WLANs

Deployment of large wireless local area network (WLAN) in outdoor environment to provide broadband wireless access (BWA) is gaining more attention. Although several standards have been developed for this purpose numerous research and development is going on to improve the performance of the standards. Management of the wireless resource is one of the key ingredients for providing BWA to the users and total number of serviceable users in a certain area. In this thesis we investigate WLANs performance in a metropolitan area using HiperLAN type 2 standard. Inside the LANs user can move from one place to another and user rates are dynamically adjusted based on their distance from the Access Points. To manage the wireless resources of the network we propose resource allocation schemes and evaluate their performance. We develop a generic simulation software for the network and use it for three resource allocation policies namely Minimum Overhead Round Robin (MORR), which does not depend on user's buffer condition, Weighted Minimum Overhead Round Robin (WMORR) which is a function of user buffer as well as the waiting time for transmission opportunity and Weighted Round Robin (WRR) which is a function of user buffer only. We evaluate average buffer occupancy, packet delivery time, buffer packet drop probability, overhead in downlink and uplink phases for performance comparison of the new allocation schemes. Our results show that the second adaptive resource allocation technique i.e. WMORR outperforms the other tw

On the Performance of Interference-Aware Cognitive Ad-Hoc Networks

Abstract-In this paper we analyze the effects of channel availability on channel access delay and service probability of cognitive networks using a modified IEEE 802.11 Media Access Control Protocol (MAC). For the designed cognitive network, cognitive communication is limited by the interference imposed on primary users. We determine the probability of accessing the channel under Rayleigh fading condition for this opportunistic network. We then use this probability to determine the embedded Markov model of the cognitive nodes. We use this Markov model to determine the average channel access delay, and service rate of cognitive nodes. Both simulation and analytical results are presented to access the system performance. Index Terms-Cognitive networks, IEEE 802.11, access delay, service rate