Amiotosh Ghosh

Cross-Layer Antenna Selection and Channel Allocation for MIMO Cognitive Radios

We propose algorithms to address the spectrum efficiency and fairness issues of multi band multiuser Multiple-Input and Multiple-Output (MIMO) cognitive ad-hoc networks. To improve the transmission efficiency of the MIMO system, a cross layer antenna selection algorithm is proposed. Using the transmission efficiency results, user data rate of the cognitive ad-hoc network is determined. Objective function for the average data rate of the multi band multiuser cognitive MIMO ad-hoc network is also defined. For the average data rate objective function, primary users interference is considered as performance constraint. Furthermore, using the user data rate results, a learning-based channel allocation algorithm is proposed. Finally, numerical results are presented for performance evaluation of the proposed antenna selection and channel allocation algorithms.

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

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.

Multiple-input multiple-output cross-layer antenna selection and beamforming for cognitive networks

Beamforming techniques can be used to suppress co-channel interference in radio devices. In a cognitive setting, beamforming can be beneficial as it can be applied to cancel interference among co-located primary users and cognitive users. In this study, the authors propose an antenna selection algorithm combined with zero-forcing beamforming to improve the throughput of cognitive multiple-input multiple-output (MIMO) radios. The algorithm consists of two phases. First, cognitive nodes apply antenna selection approach to achieve high transmission efficiency among communicating pairs. Cognitive nodes then exploit the spatial opportunities of MIMO systems and employ beamforming to cancel interference between cognitive and primary users. In that, the authors maximise an objective function for the system throughput where precoding is applied on the transmitted spatial multiplexed signals. Numerical results show the advantages offered by the proposed algorithm under different system scenarios.

Blind primary user identification in MIMO cognitive networks

Early detection of primary users presence is one of the most important tasks for cognitive communication. Also, in cognitive settings cognitive nodes may receive signals from primary users and from other cognitive users simultaneously. For such scenario, we propose primary user signal detection using modulation class identification method. We consider multiple transmit and multiple receive antennas for cognitive nodes. We employ Artificial Neural Network (ANN) for the modulation identification purpose. The proposed algorithm works as higher order moments and cumulants are calculated from the received signal samples at each of the receiving branches of cognitive nodes. After this step, these features are fed to the ANN to determine the presence of primary users. Final identification decision is drawn using the decision from all receiving branches. We also present numerical results of our algorithm and compare these results with the theoretical results of the energy detection algorithm.

Learning-based relay selection for cooperative networks

We investigate a cross-layer relay selection scheme based on Q-learning algorithm. For the study, we consider multi-relay adaptive decode and forward (DF) cooperative-diversity networks over multipath time-varying Rayleigh fading channels. The proposed scheme selects relay subsets that maximizes the link layer transmission efficiency without having knowledge of channel state information (CSI). Results show that the proposed scheme outperforms the capacity based cooperative transmission with the same number of reliable relays in terms of transmission efficiency gain. Furthermore, the proposed scheme is shown to offer high bandwidth efficiency from low to high signal-to-noise ratios (SNRs).

Channel selection for heterogeneous nodes in cognitive networks

We propose algorithms 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 provided. Furthermore, analytical throughput for such system is determined. Finally, numerical results are presented for performance evaluation of the proposed channel allocation algorithms.

Game theory for channel assignment of cognitive radios

We propose channel selection algorithm for cognitive ad-hoc radios using noncooperative game theory and present simulation results that show, the proposed algorithm distributes cognitive nodes according to the bandwidth ratio of the channels.

Combined antenna selection and beamforming in cross-layer design for cognitive networks

We investigate the performance of an antenna selection algorithm applied with precoding to improve the throughput performance of cognitive multiple-input multiple-output (MIMO) radios. We assume cognitive MIMO radios coexist in the same frequency band with the primary users. In such event, for concurrent spectrum access, cognitive nodes use beamforming techniques to cancel the mutual information between cognitive and primary users. In that, cognitive nodes exploit the degrees of freedom offered by MIMO systems to beamform the transmitted signal in an attempt to cancel interference at primary users. In the cross-layer design, cognitive nodes maximize an objective function for the link layer throughput where precoding is applied at the physical layer on the transmitted spatial multiplexed signals. We present a closed form solution for the overall throughput in-terms of the physical layer parameters. Numerical results are presented to show the efficacy of the proposed scheme for different network settings.

Cross-Layer Design for Cognitive MIMO Ad-Hoc Networks

We propose algorithms to address the spectrum efficiency and fairness issues of multi band multiuser Multiple-Input and Multiple-Output (MIMO) cognitive ad-hoc networks. To improve the transmission efficiency of the MIMO system, a cross layer antenna selection algorithm is proposed. Using the transmission efficiency results, user data rate of the cognitive ad-hoc network is determined. Objective function for the average data rate of the multi band multiuser cognitive MIMO ad-hoc network is also defined. For the average data rate objective function, primary users interference is considered as performance constraint. Finally, numerical results are presented to show the efficiency of the proposed antenna selection and channel allocation algorithms.

Performance of resource allocation schemes in adaptively modulated TDMA network

In this paper we investigate performance of resource allocation schemes in WLANs of metropolitan area using HiperLAN type 2 standard. Inside the WLANs user moves from one place to another and user rates are dynamically adjusted based on its distance from the Access Point. 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 users 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. For performance comparison we evaluate average and variance of number of packets in the user buffer, user buffer overflow and overhead in the uplink phase. Our results show that the second adaptive resource allocation technique i.e., WMORR outperforms the other two.