Weighted Sum Energy Efficiency Maximization Research Articles

User scheduling and power allocation for downlink multi-cell multi-carrier NOMA systems

In Non-Orthogonal Multiple Access (NOMA), the best way to fully exploit the benefits of the system is the efficient resource allocation. For the NOMA power domain, the allocation of power and spectrum require solving the mixed-integer nonlinear programming NP-hard problem. In this paper, we investigate user scheduling and power allocation in Multi-Cell Multi-Carrier NOMA (MCMC-NOMA) networks. To achieve that, we consider Weighted Sum Rate Maximization (WSRM) and Weighted Sum Energy Efficiency Maximization (WSEEM) problems. First, we tackle the problem of user scheduling for fixed power using Fractional Programming (FP), the Lagrange dual method, and the decomposition method. Then, we consider Successive Pseudo-Convex Approximation (SPCA) to deal with the WSRM problem. Finally, for the WSEEM problem, SPCA is utilized to convert the problem into separable scalar problems, which can be parallelly solved. Thus, the Dinkelbach algorithm and constraints relaxation are used to characterize the closed-form solution for power allocation. Extensive simulations have been implemented to show the efficiency of the proposed framework and its superiority over other existing schemes.

WSEE Maximization of mmWave NOMA Systems

We maximize the weighted sum energy efficiency (WSEE) of millimeter-wave hybrid massive multiple-input multiple-output systems, employing non-orthogonal multiple access. The WSEE is a non-convex metric, and we maximize it by approximating it either as a generalized convex program (GCP) or as a second-order conic program, which requires lesser computation time than the GCP. We further reduce the computation time by proposing a low-complexity iterative algorithm that yields a Karush–Kuhn–Tucker point of the WSEE maximization problem. We show that the proposed algorithms yield equal WSEE and outperform two other baseline schemes.

Weighted Sum Energy Efficiency Maximization of Device-to-Device groups underlaying NOMA cellular network

The problem of Weighted Sum Energy Efficiency (WSEE) Maximization of Device-to-Device (D2D) groups with underlaid Non-Orthogonal Multiple Access (NOMA) cellular network is investigated in this paper. In the novel network model, transmitters in each of the D2D groups will be able to communicate with multiple D2D receivers in the same spectral sub-band via NOMA protocol. Similarly, Base Station (BS) also communicates with multiple Cellular Users (CUs) via NOMA strategy. Power allocation for this hybrid network is presented in this paper. Optimization problem is formulated as WSEE Maximization of the D2D groups. This framework is suitable for energy-crunch devices, where the nodes can have different energy efficiency requirements. Further, to meet Quality of Service (QoS), rate constraints are imposed on all the receivers in the network. The optimization problem is challenging because of its non-convex sum-of-ratios form. So, it is first converted into an equivalent convex form. Subsequently, a successive convex approximation based algorithm is used to obtain optimal power distribution. The proposed power allocation algorithm is shown to be convergent and the simulation results closely match with the optimal values at all Signal-to-Noise Ratio (SNR) values.

A Framework for Weighted-Sum Energy Efficiency Maximization in Wireless Networks

Weighted-sum energy efficiency (WSEE) is a key performance metric in heterogeneous networks, where the nodes may have different energy efficiency (EE) requirements. Nevertheless, WSEE maximization is a challenging problem due to its nonconvex sum-of-ratios form. Unlike previous work, this paper presents a systematic approach to WSEE maximization under not only power constraints, but also data rate constraints, using a general SINR expression. In particular, the original problem is transformed into an equivalent form, and then a sequential convex optimization (SCO) algorithm is proposed. This algorithm is theoretically guaranteed to converge for any initial feasible point, and, under suitable constraint qualifications, achieves a Karush-Kuhn-Tucker (KKT) solution. Furthermore, we provide remarkable extensions to the proposed methodology, including systems with multiple resource blocks as well as a more general power consumption model which is not necessarily a convex function of the transmit powers. Finally, numerical analysis reveals that the proposed algorithm exhibits fast convergence, low complexity, and robustness (insensitivity to initial points).

Energy-Efficient Beam Coordination Strategies With Rate-Dependent Processing Power

This paper proposes energy-efficient coordinated beamforming strategies for multi-cell multi-user multiple-input single-output system. We consider a practical power consumption model, where part of the consumed power depends on the base station or user specific data rates due to coding, decoding and backhaul. This is different from the existing approaches where the base station power consumption has been assumed to be a convex or linear function of the transmit powers. Two optimization criteria are considered, namely network energy efficiency maximization and weighted sum energy efficiency maximization. We develop successive convex approximation based algorithms to tackle these difficult nonconvex problems. We further propose decentralized implementations for the considered problems, in which base stations perform parallel and distributed computation based on local channel state information and limited backhaul information exchange. The decentralized approaches admit closed-form solutions and can be implemented without invoking a generic external convex solver. We also show an example of the pilot contamination effect on the energy efficiency using a heuristic pilot allocation strategy. The numerical results are provided to demonstrate that the rate dependent power consumption has a large impact on the system energy efficiency, and, thus, has to be taken into account when devising energy-efficient transmission strategies. The significant gains of the proposed algorithms over the conventional low-complexity beamforming algorithms are also illustrated.

Energy-Efficient D2D Overlaying Communications With Spectrum-Power Trading

In this paper, we investigate device-to-device (D2D) overlaying communications with spectrum-power trading, where D2D users (DUs) consume transmit power to relay cell-edge cellular users (CUs) for uplink transmission in exchange for bandwidth from CUs for D2D communications. The proposed spectrum-power trading aims at exploiting individual disparities from both the spectrum and the power perspectives. Recently, energy efficiency (EE) defined by the ratio of the date rate to the power consumption has become increasingly important for devices due to their limited capacity batteries. As such, our goal is to maximize the weighted sum EE (WSEE) of DUs via a joint D2D relay selection, bandwidth allocation, and power allocation while guaranteeing the quality of service of each CU. Specifically, we study WSEE maximization problems for two different cases, i.e., public-interest DUs and self-interest DUs, depending on whether the DUs are willing to share their obtained bandwidth with each other or not. For the case of public-interest DUs, we show that for a given D2D relay selection, the objective function of the WSEE maximization problem in a fractional form can be transformed into a subtractive form that is more tractable based on the fractional programming theory. To perform D2D relay selection, we first reveal a fundamental relationship between the WSEE and two other EE metrics, i.e., system-centric EE and fairness-centric EE , which, to the best of our knowledge, has never been found in the existing works. Based on this insight, the D2D relay selection problem can be cast as a minimum weighted bipartite matching problem. For the case of self-interest DUs, we show that the corresponding problem can also be solved with optimality by the algorithm proposed for the previous case. Simulation results demonstrate the effectiveness of the proposed algorithm.

Weighted Sum Energy Efficiency Maximization in Ad Hoc Networks

In this letter, we propose a distributed adaptive-pricing algorithm aimed at solving the weighted sum energy efficiency (EE) maximization problem in ad hoc networks. It is theoretically proven that the proposed distributed algorithm strictly converges to the Karush-Kuhn-Tucker (KKT) point of the problem. Significant performance enhancement is observed by numerical results with fast convergence. Moreover, it is shown that the proposed algorithm degrades gracefully when decreasing overhead of information exchange.