Ankit Gupta

A General Approach Toward Green Resource Allocation in Relay-Assisted Multiuser Communication Networks

The rapid growth of energy consumption due to the strong demands of wireless multimedia services, has become a major concern from the environmental perspective. In this paper, we investigate a novel energy-efficient resource allocation scheme for relay-assisted multiuser networks to maximize the energy efficiency (EE) of the network by jointly optimizing the subcarrier pairing permutation formed in one-to-many/many-to-one manner, subcarrier allocation, as well as the power allocation altogether. By analyzing the properties of the complex mixed-integer nonlinear programming problem, which is generally very difficult to solve in its original form, we transform the problem into an equivalent convex problem by relaxing the integer variables using the concept of subcarrier time sharing, and by applying a successive convex approximation approach. Based on the dual decomposition method, we derive an optimal solution to the joint optimization problem. The impact of different network parameters, namely number of subcarriers and number of users, on the attainable EE and spectral efficiency (SE) performance of the proposed design framework is also investigated. The numerical results are provided to validate the theoretical findings and to demonstrate the effectiveness of the proposed algorithm for achieving higher EE and SE than the existing schemes.

QoS-Driven Resource Allocation and EE-Balancing for Multiuser Two-Way Amplify-and-Forward Relay Networks

In this paper, we study the problem of energy-efficient resource allocation in multiuser two-way amplify-and-forward (AF) relay networks with the aim of maximizing the energy efficiency (EE), while ensuring the quality-of-service (QoS) requirements and balancing the EE of the user links. We formulate an EE-balancing optimization problem that maximizes the ratio of the spectral efficiency (SE) over the total power dissipation subject to QoS and a limited transmit power constraints. The problem which maximizes the EE by jointly optimizing the subcarrier pairing, power allocation, and subcarrier allocation, turns out to be a non-convex fractional mixed-integer nonlinear programming problem, which has an intractable complexity in general. We apply a concave lower bound on the achievable sum rate and a series of convex transformations to make the problem convex one and propose an iterative algorithm for iteratively tightening the lower bound and finding the optimal solution through dual decomposition approach. In addition, a low-complexity suboptimal algorithm is investigated. We then characterize the impact of various network parameters on the attainable EE and SE of the network employing both EE maximization and SE maximization algorithms when the network is designed from the EE perspective. Simulation results demonstrate the effectiveness of the proposed algorithms.

Energy Efficient Resource Allocation for Multiuser Relay Networks

In this paper, a novel resource allocation algorithm is investigated to maximize the energy efficiency (EE) in multiuser decode-and-forward (DF) relay interference networks. The EE optimization problem is formulated as the ratio of the spectrum efficiency (SE) over the entire power consumption of the network subject to total transmit power, subcarrier pairing, and allocation constraints. The formulated problem is a nonconvex fractional mixed binary integer programming problem, i.e., NP-hard to solve. Furthermore, we resolve the convexity of the problem by a series of convex transformations and propose an iterative EE maximization algorithm to jointly determine the optimal subcarrier pairing at the relay, subcarrier allocation to each user pair and power allocation to all source and the relay nodes. Additionally, we derive an asymptotically optimal solution by using the dual decomposition method. To gain more insights into the obtained solutions, we further analyze the resource allocation algorithm in a two-user case with interference-dominated and noise-dominated regimes. In addition, a suboptimal algorithm is investigated with reduced complexity at the cost of acceptable performance degradation. Simulation results are used to evaluate the performance of the proposed algorithms and demonstrate the impacts of various network parameters on the attainable EE and SE.

A Utility-Based Joint Subcarrier and Power Allocation for Green Communications in Multi-User Two-Way Regenerative Relay Networks

In this paper, we investigate utility-based joint subcarrier and power allocation algorithms for improving the energy efficiency (EE) in multi-user two-way regenerative relay networks. With the objective of determining the best subcarrier allocation for each user pair, subcarrier pairing permutation, and power allocation to all the nodes, a network price is introduced to the power consumption as a penalty for the achievable sum rate, followed by the examination of its impact on the tradeoff between the EE and spectral efficiency. The formulated optimization problem is a non-convex mixed-integer nonlinear programming problem, and thus a concave lower bound on the objective function and a series of convex transformations are applied to transform the problem into a convex one. Through dual decomposition, we propose a utility-based resource allocation algorithm for iteratively tightening the lower bound and finding the optimal solution of the primal problem. By exploring the structure of the obtained optimal solution, an optimal price that enables green resource allocation is found from the perspective of maximizing EE. Additionally, a suboptimal algorithm is investigated to strike a balance between computational complexity and optimality. Simulation results evince the effectiveness of the proposed algorithms.

Optimal Energy-Efficient Resource Allocation in Energy Harvesting Cognitive Radio Networks with Spectrum Sensing

In this paper, we investigate an energy-efficient power allocation scheme in the cognitive radio (CR) networks that utilizes the energy harvested from ambient sources and spectrum sensing information. The optimization problem is formulated as a ratio of the spectral efficiency (SE) to the total energy consumption under the energy and battery causality constraints, which is solved by an iterative algorithm to obtain the optimal solutions. Moreover, the expressions for the optimal solutions are obtained through the dual-decomposition method. Finally, with the help of exhaustive simulation results we show that the proposed power allocation scheme immoderately improves the the average energy efficiency (EE) and SE performance of the network.

Efficient joint subcarrier and power allocation for achieving green multiuser full-duplex decode-and-forward relay networks

In this paper, we investigate resource allocation algorithm for multiuser full-duplex (FD) decode-and-forward (DF) relay network for improving the energy efficiency (EE). The problem of energy-efficient joint subcarrier and power allocation is formulated as a ratio of the spectral efficiency (SE) of the network to the total energy consumption in the network. The formulated problem is a non-convex fractional mixed binary-integer non-linear programming problem. Further, we resolve this problem by a series of convex transformations and introduce a network penalty factor, which works as EE parameter, to convert the fractional mixed-integer form into a parametric subtractive form, and then design an effective energy-efficient iterative resource allocation algorithm to find the optimal solution. In addition, we also propose a low-complexity suboptimal algorithm. The effectiveness of the proposed iterative and suboptimal algorithms are evinced by simulation results.

QoS-Driven Energy-Efficient Resource Allocation in Multiuser Amplify-and-Forward Relay Networks

In this paper, we investigate energy-efficient joint subcarrier pairing, subcarrier allocation, and power allocation algorithms for improving the network energy efficiency (EE) in multiuser amplify-and-forward (AF) relay networks while ensuring the desired quality-of-service (QoS) requirement for the users through the concept of “network price.” Further, we introduce a network price paid for the consumed power as a penalty for the achievable sum rate and formulate a resource allocation problem subject to limited transmit power budget and QoS constraints. The formulated problem is a nonconvex binary mixed-integer nonlinear programming (MINLP) problem and it is hard to solve the problem. We then apply a concave lower bound on the pricing-based network utility to transform the problem into a convex one. The dual decomposition method is adopted to propose a

Joint power allocation and beamforming design for full-duplex MIMO cellular systems with spectrum sharing radar

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Joint Subcarrier Pairing and Power Allocation for Two-Way Energy-Efficient Relay Networks

-price resource allocation algorithm to find the near-optimal solution. Next, we discuss the optimal utility-price from an EE perspective. Moreover, we rigorously analyze the behavior of the network pricing-based resource allocation in two-user case under different noise operating regimes, and discuss the corresponding strategies for achieving energy-efficient transmission, generating water-filling and channel-reversal approaches. To strike a balance between the computational complexity and the optimality, we propose a low-complexity suboptimal algorithm. Furthermore, we extend the proposed algorithm to maximize the EE of multiuser multirelay full-duplex (FD) relay networks and the relay networks with an eavesdropper. The performance gain of the proposed algorithms is validated through computer simulations.

Green resource allocation and EE-balancing in multiuser two-way amplify-and-forward relay networks

The new networking paradigm of opportunistic spectrum sharing is a promising technology for mitigating the scarcity of spectrum, which has resulted from the exponential increase in the number of wireless devices and ubiquitous services. In light of the new concept of Authorized/Licensed Shared Access (ASA/LSA), in this work, we consider the coexistence and spectrum sharing between a collocated multiple-input-multiple-output (MIMO) radar and a full-duplex (FD) multi-user MIMO cellular system consisting of a FD base station (BS) serving multiple downlink and uplink users simultaneously, and accordingly focus on maximizing the detection probability of the radar. The main objective of this paper is to develop an optimization technique for jointly optimizing the beamforming weights at the BS and transmit power for uplink users that can maximize the detection probability of radar while guaranteeing the quality-of-service requirements of each user and power budget for the uplink users and the BS. The joint beamforming design is a non-convex problem which, we convert into a second-order cone programming (SOCP) problem and propose an iterative algorithm for finding the optimal solution. Numerical results demonstrate the feasibility of the spectral coexistence and show a scalable trade-off in performance of both systems.