Bassem Khalfi

Distributed Learning-Based Cross-Layer Technique for Energy-Efficient Multicarrier Dynamic Spectrum Access With Adaptive Power Allocation

This paper proposes energy and cross-layer aware resource allocation techniques that allow dynamic spectrum access (DSA) users, by means of learning algorithms, to locate and exploit unused spectrum opportunities effectively. Specifically, we design private objective functions for DSA users with multiple channel access and adaptive power allocation capabilities. We also propose simple two-phase heuristics for allocating spectrum and power resources among users. The proposed heuristics split the spectrum and power allocation problem into two subproblems, and solve each of them separately. The spectrum allocation problem is solved, during the first phase using learning. Two procedures to learn the channel selection are proposed and compared in terms of optimality, scalability, and robustness. The power allocation, on the other hand, is formulated as a real optimization problem and solved, during the second phase, by traditional optimization solvers. Simulation results show that energy and cross-layer awareness and multiple channel access capability improve the performance of the system in terms of the per-user average rewards received from accessing the dynamic spectrum access system. In addition, the two proposed methods for channel selection via learning represent a tradeoff between optimality, scalability, and robustness.

Power allocation analysis for dynamic power utility in cognitive radio systems

The focus of this paper is to investigate the fundamental limits of power allocation when taking into account a dynamic power pricing scheme. This paper proposes an optimal power allocation analysis for wireless systems when real time power pricing is available. We propose to minimize the total power consumption cost while ensuring minimum individual and total throughput limits. We consider different models for the power pricing function. Analytic solutions for the power allocation are derived for each model. The derived solutions are shown to be modified versions of the water-filling solution. Low-complexity algorithms are proposed for the resource allocation with each pricing model. Performance comparison and pricing effect are shown through simulations.

Efficient Usage of Renewable Energy in Communication Systems Using Dynamic Spectrum Allocation and Collaborative Hybrid Powering

In this paper, we introduce a new green resource allocation problem using hybrid powering of communication systems from renewable and nonrenewable sources. The objective is to efficiently allocate the power delivered from the different micro-grids to satisfy the network requirements. Minimizing a defined power cost function instead of the net power consumption aims to encourage the use of the available renewable power through collaboration between the base stations within and outside the different micro-grids. The different degrees of freedom in the system, ranging from assignment of users to base stations, possibility of switching the unnecessary base stations to the sleep mode, dynamic power allocation, and dynamic allocation of the available bandwidth, allow us to achieve important power cost savings. Since the formulated optimization problem is a mixed integer-real problem with a nonlinear objective function, we propose to solve the problem using the branch and bound (B&B) approach, which allows to obtain the optimal or a suboptimal solution with a known distance to the optimal. The relaxed problem is shown to be a convex optimization which allows to obtain the lower bound. For practical applications with large number of users, we propose a heuristic solution based on decomposing the problem into two subproblems. The users-to-base stations assignment is solved using an algorithm inspired from the bin-packing approach while the bandwidth allocation is performed through the bulb-search approach. Simulation results confirm the important savings in the nonrenewable power consumption when using the proposed approach and the efficiency of the proposed disjointed algorithms.

Distributed Fair Spectrum Assignment for Large-Scale Wireless DSA Networks

This paper proposes a distributed and fair resource allocation scheme for large-scale wireless dynamic spectrum access networks based on particle filtering theory. We introduce a proportionally fair global objective function to maximize the total network throughput while ensuring fairness among users. We rely on particle filtering theory to enable distributed access and allocation of spectrum resources without compromising the overall achievable throughput. Through intensive simulation, we show that our proposed approach performs well by achieving high overall throughput while also improving fairness between users.

Implementation and Analysis of Reward Functions Under Different Traffic Models for Distributed DSA Systems

In this paper, we implement and analyze a resource allocation protocol for distributed dynamic spectrum allocation (DSA) systems. The DSA protocol is a learning-based protocol that allows secondary users (SU) to exploit the spectrum bands efficiently in a distributed manner without the need of information exchange. The implementation and test of the proposed protocol is done using ns3 assuming that the SUs selecting the same band share it in accordance with a carrier sense multiple access (CSMA) scheme. The evaluation of the proposed protocol is done under various traffic models. We show the importance of the objective functions choice; used as a utility to be maximized in the learning. We also show the impact of various practical aspects taken into consideration while implementing the protocol on the systems achieved performance.

Exploiting wideband spectrum occupancy heterogeneity for weighted compressive spectrum sensing

Compressive sampling has shown great potential for making wideband spectrum sensing possible at sub-Nyquist sampling rates. As a result, there have recently been research efforts that aimed to develop techniques that leverage compressive sampling to enable compressed wideband spectrum sensing. These techniques consider homogeneous wideband spectrum, where all bands are assumed to have similar PU traffic characteristics. In practice, however, wideband spectrum is not homogeneous, in that different spectrum bands could have different PU occupancy patterns. In fact, the nature of spectrum assignment, in which applications of similar types are often assigned bands within the same block, dictates that wideband spectrum is indeed heterogeneous, as different application types exhibit different behaviors. In this paper, we consider heterogeneous wideband spectrum, where we exploit this inherent, block-like structure of wideband spectrum to design efficient compressive spectrum sensing techniques that are well suited for heterogeneous wideband spectrum. We propose a weighted ίι —minimization sensing information recovery algorithm that achieves more stable recovery than that achieved by existing approaches while accounting for the variations of spectrum occupancy across both the time and frequency dimensions. Through intensive numerical simulations, we show that our approach achieves better performance when compared to the state-of-the-art approaches.

Joint data and power transfer optimization for energy harvesting wireless networks.

Energy harvesting techniques emerge as a potential solution for prolonging the lifetime of the energy-constrained mobile wireless devices. In this paper, we focus on radio frequency (RF) energy harvesting for multiuser multicarrier mobile wireless networks. The mobile users are capable of harvesting energy from the dedicated subcarriers over which they are communicating with the base station in addition to harvesting ambient RF signals. We propose a joint data and energy transfer optimization framework for powering mobile wireless devices through RF energy harvesting while minimizing the overall power consumption. The proposed framework determines the optimal power resources that need to be allocated to meet data rate requirements of downlink and uplink communications. Simulations show that substantial power savings are achieved by allowing the ambient RF energy harvesting as well as by exploiting the different system parameters.

Cooperative joint power splitting and allocation approach for simultaneous energy delivery and data transfer

In this paper, we propose to minimize the total energy consumption cost of a simultaneous data transmission and power delivery from different sources. The receiver is designed to simultaneously process information and harvest energy from the received signal through a power splitter. We derive an optimal power allocation and splitting ratios for each source node that minimizes the total power cost while ensuring the required data rates for each link. The solution profits from the variability between the channel gains and data requirements. Numerical simulations allow to analyze the performance of the proposed solution.

Extracting and Exploiting Inherent Sparsity for Efficient IoT Support in 5G: Challenges and Potential Solutions

Besides enabling an enhanced mobile broadband, the next generation of mobile networks (5G) are envisioned for the support of massive connectivity for heterogeneous Internets of Things. These IoTs are envisioned for a large number of use cases including smart cities, environment monitoring, smart vehicles, and so on. Unfortunately, most IoTs have very limited computing and storage capabilities and need cloud services. Hence, connecting these devices through 5G systems requires huge spectrum resources in addition to handling massive connectivity and improved security. This article discusses the challenges facing the support of IoTs through 5G systems. The focus is devoted to discussing physical layer limitations in terms of spectrum resources and radio access channel connectivity. We show how sparsity can be exploited for addressing these challenges, especially in terms of enabling wideband spectrum management and handling the connectivity by exploiting device-to-device communications and edge cloud. Moreover, we identify major open problems and research directions that need to be explored toward enabling the support of massive heterogeneous IoTs through 5G systems.

Aggregate Hardware Impairments Over Mixed RF/FSO Relaying Systems With Outdated CSI

In this paper, we propose a dual-hop radio-frequency (RF)/free-space optical system with multiple relays employing the decode-and-forward and amplify-and-forward with a fixed gain relaying scheme. The RF channels are subject to a Rayleigh distribution while the optical links experience a unified fading model emcopassing the atmospheric turbulence that follows the Málaga distribution (or also called the