Lina S. Mohjazi

RF-powered cognitive radio networks: technical challenges and limitations

The increasing demand for spectral- and energy-efficient communication networks has spurred great interest in energy harvesting cognitive radio networks. Such a revolutionary technology represents a paradigm shift in the development of wireless networks, as it can simultaneously enable the efficient use of the available spectrum and the exploitation of RF energy in order to reduce reliance on traditional energy sources. This is mainly triggered by the recent advancements in microelectronics that puts forward RF energy harvesting as a plausible technique in the near future. On the other hand, it has been suggested that the operation of a network relying on harvested energy needs to be redesigned to allow the network to reliably function in the long term. To this end, the aim of this survey article is to provide a comprehensive overview of recent development and the challenges regarding the operation of CRNs powered by RF energy. In addition, the potential open issues that might be considered for future research are also discussed in this article.

Self-Optimization of Pilot Power in Enterprise Femtocells Using Multi objective Heuristic

Deployment of a large number of femtocells to jointly provide coverage in an enterprise environment raises critical challenges especially in future self-organizing networks which rely on plug-and-play techniques for configuration. This paper proposes a multi-objective heuristic based on a genetic algorithm for a centralized self-optimizing network containing a group of UMTS femtocells. In order to optimize the network coverage in terms of handled load, coverage gaps, and overlaps, the algorithm provides a dynamic update of the downlink pilot powers of the deployed femtocells. The results demonstrate that the algorithm can effectively optimize the coverage based on the current statistics of the global traffic distribution and the levels of interference between neighboring femtocells. The algorithm was also compared with the fixed pilot power scheme. The results show over fifty percent reduction in pilot power pollution and a significant enhancement in network performance. Finally, for a given traffic distribution, the solution quality and the efficiency of the described algorithm were evaluated by comparing the results generated by an exhaustive search with the same pilot power configuration.

Deployment challenges of femtocells in future indoor wireless networks

Femtocell technology is expected to improve both coverage and capacity, especially indoors. Providers of cellular services are increasingly implementing this technology in their networks. However, the decentralized manner of femtocells operation introduces several challenges. This paper presents an overview of the femtocells. It highlights the network requirements for deploying femtocells and discusses the major issues that affect their operation and performance. The paper also describes the key challenges of deploying femtocells and some of the proposed solutions. It then presents the future direction on applying optimization to enhance femtocell performance.

Downlink Beamforming for SWIPT Multi-User MISO Underlay Cognitive Radio Networks

This letter investigates downlink beamforming for simultaneous wireless information and power transfer in multi-user multi-input single-output underlay cognitive radio networks. Under the assumption of the integrated power splitting (PS) receiver architecture at the secondary receivers, we aim to minimize the total transmission power from the secondary base station by jointly optimizing the beamforming vectors and the PS ratios. We first exploit the structure of the relaxed problem to provide a closed form expression for the optimal solution. Since the underlying optimization problem is non-convex, we resort to semidefinite programming relaxation and we analytically prove that the relaxation is in fact tight and yields optimal beamforming solution.

Energy Detection of Unknown Signals Over Cascaded Fading Channels

Energy detection is a favorable mechanism in several applications relating to the identification of deterministic unknown signals such as in radar systems and cognitive radio communications. The present work quantifies the detrimental effects of cascaded multipath fading on energy detection and investigates the corresponding performance capability. A novel analytic solution is first derived for a generic integral that involves a product of the Meijer G-function, the Marcum Q-function, and arbitrary power terms. This solution is subsequently employed in the derivation of an exact closed-form expression for the average probability of detection of unknown signals over N*Rayleigh channels. The offered results are also extended to the case of square-law selection, which is a relatively simple and effective diversity method. It is shown that the detection performance is considerably degraded by the number of cascaded channels and that these effects can be effectively mitigated by a nonsubstantial increase of diversity branches.

Unified analysis of cooperative spectrum sensing over generalized multipath fading channels

The present work is devoted to the analytic performance evaluation of cooperative spectrum sensing (CSS) over generalized fading channels. The proposed analysis is based on the efficient Gaussian-Finite-Mixture (GFM) that allows the derivation of a simple and accurate closed-form expression for the average probability of energy detection (ED) under different fading environments. Capitalizing on this, we derive generalized closed-form expressions for the global probabilities of detection for the CSS with two main hard centralized fusion rules, namely, the AND and the OR rules. The efficiency and usefulness of the proposed expressions is justified by comparing the corresponding complementary receiver operating characteristic (ROC) curves for both multipath and composite multipath/shadowing fading channels, which are otherwise particularly difficult to obtain. The offered analytic results are corroborated by respective results from computer simulations and it is shown that the corresponding performance depends significantly on both the severity of fading and the involved number of users in the collaborative network.

Performance Analysis of Differential Modulation in SWIPT Cooperative Networks

In this letter, the performance of differential modulation in simultaneous wireless information and power transfer (SWIPT) cooperative amplify-and-forward (AF) networks is investigated. In particular, we derive novel closed-form expressions for the probability density function (pdf) of the end-to-end signal-to-noise ratio (SNR) and the average bit error rate (ABER) of the considered SWIPT cooperative scenario. Based on the derived results, we analyze the impact of the underlying system parameters on the system performance. Numerical results show that the optimum location of the relay terminal is closer to the source than to the destination. Moreover, it is demonstrated that the value of the power splitting (PS) ratio at the relay significantly impacts the system performance. The results of Monte Carlo simulations are provided to corroborate the analysis.

Performance evaluation of heuristic techniques for coverage optimization in femtocells

Self-optimization of coverage is an essential element for successful deployment of enterprise femtocells. This paper evaluates the performance of genetic algorithm, particle swarm and simulated annealing heuristic techniques to solve a multi-objective coverage optimization problem when a number of femtocells are deployed to jointly provide indoor coverage. This paper demonstrates the different behaviors of the proposed algorithms. The results show that genetic algorithm and particle swarm have a higher potential of solving the problem compared to simulated annealing. This is due to their faster convergence time which is an important parameter for dynamic update of femtocells.

Clustering based self-optimization of pilot power in dense femtocell deployments using genetic algorithms

Femtocells are small base stations used to enhance cellular coverage in an indoor environment. However, dense femtocell deployments can lead to severe performance degradation. This paper adopts a new strategy to self-optimize the pilot power of femtocells by creating disjoint femtocell clusters which are managed by the chosen cluster heads (CHs). Each CH optimizes the coverage of its connected members by applying a multi-objective heuristic based on genetic algorithm. The simulation results show that the proposed approach can significantly reduce both the computational time and the data overhead compared with the centralized power optimization.