Mahdi Ben Ghorbel

Distributed dynamic spectrum access with adaptive power allocation: Energy efficiency and cross-layer awareness

This paper proposes energy and cross-layer aware resource allocation techniques that allow dynamic spectrum access users, by means of learning algorithms, to locate and exploit unused spectrum opportunities effectively. Specifically, we design private objective functions for spectrum users with multiple channel access and adaptive power allocation capabilities. We also propose a simple, two-phase heuristic for allocating spectrum and power resources among users. The proposed heuristic splits the spectrum and power allocation problem into two sub-optimal problems, and solve each of them separately. The spectrum allocation problem is solved, during the first phase, using learning whereas, the power allocation is formulated as an 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.

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.

Resources allocation for large-scale dynamic spectrum access system using particle filtering

This paper proposes an efficient spectrum and power allocation solution for a large scale dynamic spectrum access (DSA) systems. Unlike conventional methods relying on optimization techniques which need huge computational capabilities and full information exchange, in this paper we rely on particle filtering to allocate the available bands among users in a distributed manner. Particle filter is based on the representation of the searched state, bands allocation per user in our case, by a set of particles. The Particle filter has the advantage, with comparison to Kalman-based filters, of its adaptivity to general scenarios (non-linear models, non-Gaussian noise, multi-modal distributions). Like Kalman-based filters, two model equations are needed for particle filter, (i) A state evolution equation to characterize the time evolution of the state. For our case, we derive a prediction equation of the channel allocation from the previous allocation from the channel fading temporal correlation, (ii) An observation equation which relates the observation, the Quality of Service in our case, to the channel allocation (state). This equation will be useful in the weighting and re-sampling phases of the filtering algorithm. The performances are analyzed in terms of the per user achieved throughput. In addition, comparison with performance when Q-learning is employed to show the efficiency of our approach.

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.

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.

Optimal power allocation for smart-grid powered point-to-point cognitive radio system

This paper proposes an optimal power allocation analysis for a point-to-point wireless system when powered by a smart grid. We propose to minimize the total power consumption cost while ensuring individual and total throughput constraints. The power cost is computed based on different dynamic pricing models of the power consumption. Analytical solutions are derived for each pricing model. The derived solutions are shown to be modified versions of the water-filling solution. Water-filling based algorithms are proposed for the resource allocation with each pricing model. Performance comparison and pricing effect are shown through simulations.