Tanumay Manna

Collaborative spectrum sensing in real cognitive radio network

Collaborative spectrum sensing through coalition games among secondary users (SU) in cognitive environment is implemented to provide better performance than traditional fusion center based centralized cooperative spectrum sensing. In this paper, collaborative spectrum sensing have been designed and implemented on Wireless Open-Access Research Platform (WARP) to study its performance on real wireless cognitive environment. The WARP has been used for energy detection based spectrum sensing of primary user (PU), and a separate reporting channel has been designed to transfer the local sensing bits to a cluster head (CH) selected through distributed coalition games. In addition, through WARP implementation the inherent tradeoff has been shown between the gains in terms of probability of detection of PU and the costs in terms of false alarm probability.

Joint optimization of detection thresholds and power allocation in OFDM based cognitive femtocell networks

Spectrum sensing in cognitive femtocells play an important role in detecting spectrum holes and to opportunistically use under-utilized frequency bands without causing harmful interference to macrocell users. In case of mass deployment of femtocells, interference management is essential to maximize the capacity and achievable data rates. In this paper, a novel algorithm is proposed, based on Lagrangian dual decomposition technique, for jointly optimizing the detection threshold and power allocation to maximize the total aggregated opportunistic throughput per unit bandwidth of a co-channel OFDM based femtocell network, densely deployed over a macrocell architecture, under a constraint of the total interference generated towards macrocell users. Simulation results show that the proposed algorithm is stable, attains convergence and achieves considerable rate gain.

Game theoretic spectrum sharing for cognitive radios

This paper explains the use of game theory to be applied for spectrum sharing scenario in unlicensed bands. A multi variable payoff function [2] is used to illustrate the process of achieving Nash-Equilibrium by varying the various modulation schemes and power for two non-cooperative systems sharing the same spectrum. This non-cooperative spectrum sharing often leads to a situation where one system is at a disadvantageous position. The situation can be improved if a global utility function is considered that represents a fairness objective. A modified global utility function is proposed that improves the Nash-Equilibrium condition for the two systems scenario than the already existing ones.

A Framework for Implementation of Wireless Body Area Network over Software Defined Radios

Wireless Body Area Network over Software Defined Radio (SDR) Platform is the solution towards providing ubiquitous wireless health monitoring of patients over Internet. In this paper, a system is designed to enable ECG signals to be compressed and communicated to remote server at regular intervals over SDR platform comprised of Wireless Open Access Research Platform boards. To enable this, a TCP/IP based client-server model is proposed which allows data to be sent whenever the SDR channel is free. Test-bed analysis has shown that signal compression through Discrete Wavelet Transform by choosing the correct filter can provide significant 83.8% energy efficiency, without much data loss. This work provides the framework and highlights the open issues to develop the SDR platform into a Cognitive Radio module.

A Fast Hardware Based Hidden Markov Model Predictor for Cognitive Radio

Traditionally Cognitive Radio Networks (CRNs) have been designed to support real-time secondary users (SUs) like VoIP. Hidden Markov Model (HMM) based prediction may provide non-real time access of channels (e.g: Wireless Body Area Network) by the cognitive users in presence of high primary users activity (PUs) that uses sensing information of PU arrival prior to SU transmission. But, HMM model is in general computationally intensive causing high prediction time. In this paper, an advanced model of HMM predictor is designed using reconfigurable FPGA platform that takes the advantage of parallel processing and pipelining architecture. The strength of this work is the design architecture for HMM predictor named as H2M2 engine which is a Hardware Co-simulation (HW-CoSim) based design taking a novel edge trigger based input. This H2M2 engine runs autonomously with minimum interaction with the embedded processor of the cognitive radios without any setup and hold time violations. Thus this predictor is 75% time efficient in comparison with normal HMM predictor making the whole system of cognitive radio transmission energy efficient.

Designing an analytical model for IEEE 802.15.6 scheduled access mode in non-saturation regime

This paper presents an analytical model with closed form expressions for estimating the average delay, throughput and energy consumption of a sensor node for scheduled access mechanism in IEEE 802.15.6 based Wireless Body Area Network (WBAN) operating in non-saturation regime under error prone conditions. Forming WBAN over an OPNET simulation platform, results are generated for the validation of the analytical model. The analysis also determines the optimal allocation interval in Managed Access Phase (MAP1) to ensure high Quality of Service (low delay, minimal packet loss) for different applications having distinct traffic loads.