Abdulrahman Alabbasi

Energy Efficient Resource Allocation for Cognitive Radios: A Generalized Sensing Analysis

In this paper, two resource allocation schemes for energy efficient cognitive radio systems are proposed. Our design considers resource allocation approaches that adopt spectrum sharing combined with soft-sensing information, adaptive sensing thresholds, and adaptive power to achieve an energy efficient system. An energy per good-bit metric is considered as an energy efficient objective function. A multi-carrier system, such as, orthogonal frequency division multiplexing, is considered in the framework. The proposed resource allocation schemes, using different approaches, are designated as sub-optimal and optimal. The sub-optimal approach is attained by optimizing over a channel inversion power policy. The optimal approach utilizes the calculus of variation theory to optimize a problem of instantaneous objective function subject to average and instantaneous constraints with respect to functional optimization variables. In addition to the analytical results, selected numerical results are provided to quantify the impact of soft-sensing information and the optimal adaptive sensing threshold on the system performance.

An energy efficient cognitive radio system with quantized soft sensing and duration analysis

In this paper, an energy efficient cognitive radio system is proposed. The proposed design optimizes the secondary user transmission power and the sensing duration combined with soft-sensing information to minimize the energy per goodbit. Due to the non-convex nature of the problem we prove its pseudo-convexity to guarantee the optimal solution. Furthermore, a quantization scheme, that discretize the soft-sensing information, is proposed and analyzed to reduce the overload of the continuously adapted power. Numerical results show that the energy per goodbit performance of the proposed system outperforms the benchmark systems. The impact of the quantization levels and other system parameters is evaluated in the numerical results.

Energy efficient scheme for cognitive radios utilizing soft sensing

In this paper we propose an energy efficient cognitive radio system. Our design considers an underlaying resource allocation combined with soft sensing information to achieve a sub-optimum energy efficient system. The sub-optimality is achieved by optimizing over a channel inversion power policy instead of considering a water-filling power policy. We consider an Energy per Goodbit (EPG) metric to express the energy efficient objective function of the system and as an evaluation metric to our system performance. Since our optimization problem is not a known convex problem, we prove its convexity to guarantee its feasibility. We evaluate the proposed scheme comparing to a benchmark system through both analytical and numerical results.

Energy Efficiency and SINR Maximization Beamformers for Spectrum Sharing With Sensing Information

In this paper, we consider a cognitive radio multi-input-multi-output environment, in which we adapt our beamformer to maximize both energy efficiency (EE) and signal-to-interference-plus-noise ratio (SINR) metrics. Our design considers an underlaying communication using adaptive beamforming schemes combined with sensing information to achieve optimal energy-efficient systems. The proposed schemes maximize EE and SINR metrics subject to cognitive radio and quality-of-service constraints. The analysis of the proposed schemes is classified into two categories based on knowledge of the secondary-transmitter-to-primary-receiver channel. Since the optimizations of EE and SINR problems are not convex problems, we transform them into a standard semidefinite programming (SDP) form to guarantee that the optimal solutions are global. An analytical solution is provided for one scheme, while the second scheme is left in a standard SDP form. Selected numerical results are used to quantify the impact of the sensing information on the proposed schemes compared to the benchmark ones.

Energy efficiency and SINR maximization beamformers for cognitive radio utilizing sensing information

In this paper we consider a cognitive radio multi-input multi-output environment in which we adapt our beamformer to maximize both energy efficiency and signal to interference plus noise ratio (SINR) metrics. Our design considers an underlaying communication using adaptive beamforming schemes combined with the sensing information to achieve an optimal energy efficient system. The proposed schemes maximize the energy efficiency and SINR metrics subject to cognitive radio and quality of service constraints. Since the optimization of energy efficiency problem is not a convex problem, we transform it into a standard semi-definite programming (SDP) form to guarantee a global optimal solution. Analytical solution is provided for one scheme, while the other scheme is left in a standard SDP form. Selected numerical results are used to quantify the impact of the sensing information on the proposed schemes compared to the benchmark ones.

Interplay of energy and bandwidth consumption in CRAN with optimal function split

Cloud radio access network (CRAN) has been proposed as a potential energy saving architecture and a scalable solution to increase the capacity and performance of radio networks. The original CRAN decouples the digital unit (DU) from radio unit (RU) and centralizes the DUs. However, stringent delay and bandwidth constraints are incurred by fronthaul in CRAN, i.e. the network segment connecting RUs and DUs. In this study, we propose a modified CRAN architecture, namely hybrid cloud RAN (H-CRAN), where a DUs functionalities can be virtualized and split at several conceivable points. Each split option results in two-level deployment of the processing functions, i.e., central cloud level and edge cloud level, connected by a transport layer called “midhaul”. We study the interplay of energy efficiency and midhaul bandwidth consumption when baseband functions are centralized at the edge cloud vs central cloud. We jointly minimize the power and midhaul bandwidth consumption in H-CRAN, while satisfying the network constraints. The addressed problem with the associated constrains are modeled as a mixed integer constraint optimization problem. Numerical results show the compromise between energy and bandwidth consumption, with the optimal placement of baseband processing functions in H-CRAN architecture.

Optimal Cross-Layer Design for Energy Efficient D2D Sharing Systems

In this paper, we propose a cross-layer design, which optimizes the energy efficiency of a potential future 5G spectrum-sharing environment, in two sharing scenarios. In the first scenario, underlying sharing is considered. We propose and minimize a modified energy per good bit (MEPG) metric, with respect to the spectrum sharing user’s transmission power and media access frame length. The cellular users, legacy users, are protected by an outage probability constraint. To optimize the non-convex targeted problem, we utilize the generalized convexity theory and verify the problem’s strictly pseudoconvex structure. We also derive analytical expressions of the optimal resources. In the second scenario, we minimize a generalized MEPG function while considering a probabilistic activity of cellular users and its impact on the MEPG performance of the spectrum sharing users. Finally, we derive the associated optimal resource allocation of this problem. Selected numerical results show the improvement of the proposed system compared with other systems.

Outage Analysis of Spectrum Sharing Over

Future wireless technologies, such as fifth-generation (5G), are expected to support real-time applications with high data throughput, e.g., holographic meetings. From a bandwidth perspective, cognitive radio (CR) is a promising technology to enhance the systems throughput via sharing the licensed spectrum. From a delay perspective, it is well known that increasing the number of decoding blocks will improve system robustness against errors while increasing delay. Therefore, optimally allocating the resources to determine the tradeoff of tuning the length of the decoding blocks while sharing the spectrum is a critical challenge for future wireless systems. In this paper, we minimize the targeted outage probability over the block-fading channels while utilizing the spectrum-sharing concept. The secondary users outage region and the corresponding optimal power are derived, over two-block and

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-Block Fading With Sensing Information

-block fading channels. We propose two suboptimal power strategies and derive the associated asymptotic lower and upper bounds on the outage probability with tractable expressions. These bounds allow us to derive the exact diversity order of the secondary users outage probability. To further enhance the systems performance, we also investigate the impact of including the sensing information on the outage problem. The outage problem is then solved via proposing an alternating optimization algorithm, which utilizes the verified strict quasi-convex structure of the problem. Selected numerical results are presented to characterize the systems behavior and show the improvements of several sharing concepts.