Improved Energy Detector for Wideband Spectrum Sensing in Cognitive Radio Networks

In this paper, an improved energy detector for a wideband spectrum sensing is proposed. For a better detection of the spectrum holes the overall band is divided into equal non-overlapping sub-bands. The main objective is to determine the detection thresholds for each of these subbands jointly. By defining the problem as an optimization problem, we aim to find the maximum aggregated opportunistic throughput of cognitive radio networks. Introducing practical constraints to this optimization problem will change the problem into a convex and solvable one. The results of this paper show that the proposed improved energy detector will increase the aggregated throughput considerably

DYNAMIC SMART GRID COMMUNICATION PARAMETERS BASED COGNITIVE RADIO NETWORK

The demand for more spectrums in a smart grid communication network is a significant challenge in originally scarce spectrum resources. Cognitive radio (CR) is a powerful technique for solving the spectrum scarcity problem by adapting the transmission parameters according to predefined objectives in an active wireless communication network. This paper presents a cognitive radio decision engine that dynamically selects optimal radio transmission parameters for wireless home area networks (HAN) of smart grid applications via the multi-objective differential evolution (MODE) optimization method. The proposed system helps to drive optimal communication parameters to realize power saving, maximum throughput and minimum bit error rate communication modes. A differential evolution algorithm is used to select the optimal transmission parameters for given communication modes based on a fitness function that combines multiple objectives based on appropriate weights. Simulation results highlight the superiority of the proposed system in terms of accuracy and convergence as compared with other evolution algorithms (genetic optimization, particle swarm optimization, and ant colony optimization) for different communication modes (power saving mode, high throughput mode, emergency communication mode, and balanced mode)

Energy Efficiency and Throughput Optimization of Cognitive Relay Networks

In this paper, we investigate the energy efficiency and throughput optimization problem of cognitive relay networks. We propose to design sensing time and signal to noise ratio (SNR) to maximize the energy efficiency and throughput, since analytical and empirical studies have shown that sensing time and SNR are key factors for energy efficiency and throughput. We design a method that simultaneously considers the parameters of spectrum sensing time and SNR to optimize the energy efficiency of cognitive radio networks. Furthermore, we conduct deep experiments which show that there exists the optimal sensing time to maximize energy efficiency and throughput. In addition, optimal sensing time and optimal SNR can be jointly designed to maximize energy efficiency. Finally, we provide simulation results to show that energy efficiency of cognitive relay transmission scheme can be significantly improved compared with that of direct transmission scheme in cognitive radio networks

Optimization of the overall success probability of the energy harvesting cognitive wireless sensor networks

Wireless energy harvesting can improve the performance of cognitive wireless sensor networks (WSNs). This paper considers radio frequency (RF) energy harvesting from transmissions in the primary spectrum for cognitive WSNs. The overall success probability of the energy harvesting cognitive WSN depends on the transmission success probability and energy success probability. Using the tools from stochastic geometry, we show that the overall success probability can be optimized with respect to: 1) transmit power of the sensors; 2) transmit power of the primary transmitters; and 3) spatial density of the primary transmitters. In this context, an optimization algorithm is proposed to maximize the overall success probability of the WSNs. Simulation results show that the overall success probability and the throughput of the WSN can be significantly improved by optimizing the aforementioned three parameters. As RF energy harvesting can also be performed indoors, hence, our solution can be directly applied to the cognitive WSNs that are installed in smart buildings

Joint Cooperative Spectrum Sensing and MAC Protocol Design for Multi-channel Cognitive Radio Networks

In this paper, we propose a semi-distributed cooperative spectrum sen sing (SDCSS) and channel access framework for multi-channel cognitive radio networks (CRNs). In particular, we c onsider a SDCSS scheme where secondary users (SUs) perform sensing and exchange sensing outcomes with ea ch other to locate spectrum holes. In addition, we devise the p -persistent CSMA-based cognitive MAC protocol integrating the SDCSS to enable efficient spectrum sharing among SUs. We then perform throughput analysis and develop an algorithm to determine the spectrum sensing and access parameters to maximize the throughput for a given allocation of channel sensing sets. Moreover, we consider the spectrum sensing set optimization problem for SUs to maxim ize the overall system throughput. We present both exhaustive search and low-complexity greedy algorithms to determine the sensing sets for SUs and analyze their complexity. We also show how our design and analysis can be extended to consider reporting errors. Finally, extensive numerical results are presented to demonstrate the sig nificant performance gain of our optimized design framework with respect to non-optimized designs as well as the imp acts of different protocol parameters on the throughput performance.Comment: accepted for publication EURASIP Journal on Wireless Communications and Networking, 201