Low-complexity optimal energy-efficient resource allocation in downlink OFDMA networks

An expansion of services and unprecedented traffic growth is anticipated in future networks, aligned with the adoption of the long-awaited Fifth Generation (5G) of mobile communications. To support this demand, without exposing mobile operators to the pressure of CAPEX and OPEX, 5G uses new frequency bands, and adopts promising trends, including: densification, softwarization, and autonomous management. While the first technology is proposed to handle the traffic growth requirements, the softwarization and autonomous management are expected to play, in synergy, to ensure the desired trade-off between reducing the CAPEX and OPEX, while guaranteeing the quality of service (QoS). Softwarization is expected to transform the network design, from one size fits all, to more demand oriented adaptive resource allocation. In this work, we focus on this point, by discussing how these technologies act in synergy towards enabling RAN sharing. Particularly, we focus on how they fit into the issue of energy efficient Multi-Operator Resource Allocation (MO-RA). After a survey and classification of schemes leveraging this synergy for distinct resource allocation (RA) objectives, we present a detailed survey and qualitative classification of RA schemes with respect to energy efficiency. This work presents an innovative survey, since it concentrates on multiple operators, and the enabling of Mobile Virtual Network Operators (MVNOs), which will come into play with the complete virtualization of mobile networks. Based on the deep literature analysis of the different operations that can bring energy savings to MO-RA, we conclude the work with listing open challenges and future research directions.

This thesis presents novel resource allocation algorithms for energy and power efficient transmissions in future mobile wireless networks. Algorithms presented in thesis incorporate a combination of practical conditions: mixed services, rate fairness, the density of deployment of femtocells and non-linear power consumption. The resource allocation problem of sparse and dense deployment of Orthogonal Frequency Division Multiple Access based heterogeneous femtocell networks is solved by optimising spectral efficiency, energy efficiency and spectral and energy efficiency trade-off respectively. In general, this research proposes several resource allocation algorithms that are practical, efficient and lower in computational complexity.

Energy efficiency (EE) is one fundamental performance metric for design of orthogonal frequency division multiple access (OFDMA) networks besides spectral efficiency (SE). The conventional resource allocation is to maximize data throughput without consideration of EE. In this paper, we propose a new low-complexity resource allocation algorithm to maximize EE, which first meet the quality-of-services (QoS) demand of every user and then assign the rest of subcarriers with criterion of energy efficiency. We also compare this new algorithm with the near-optimal EE algorithm, maximum-power-decrease-first (MPDF), which is proposed in [9]. The proposed algorithm differs from the MPDF algorithm in terms of the number of waterfilling for subcarrier allocation. The waterfilling operation in our proposed algorithm is not related to the number of users so that it reduces the computational complexity significantly. Finally, the simulation results show that the proposed algorithm approach the EE performance of the MPDF algorithm with lower complexity.

In 3GPP Release-12 specification, the adoption of small cells in long term evolution (LTE) networks is considered as a promising solution to meet the growing traffic demand for mobile data usage. However, energy consumption becomes a crucial challenge for network operators to deploy more small cells in heterogeneous networks in order to fulfill the requirement for achieving high data rates. In this paper, an advanced signaling mechanism has been proposed to practically acquire global channel state information (CSI) between network components. Based on the signaling scheme, an energy-efficient cell selection and resource allocation (ECR) algorithm is proposed to increase system energy efficiency by handovering UEs from light-loaded base station to nearby cells. Some active cells can therefore be switched off to reduce total energy consumption. Simulation results show that better energy efficiency can be achieved by adopting the proposed signaling and ECR schemes.

The widespread application of multimedia wireless services and requirements of ubiquitous access have triggered rapidly booming energy consumption at both the base station side and the user equipment (UE) side. Hence, energy-efficient design in wireless networks is very important and is becoming an inevitable trend. In this paper, we study the energy-efficient resource allocation in both downlink and uplink cellular networks with orthogonal frequency division multiple access (OFDMA). For the downlink transmission, the generalized energy efficiency (EE) is maximized while for the uplink case the minimum individual EE is maximized, both under certain prescribed per-UE quality-of-service (QoS) requirements. For both transmission scenarios, we first provide the optimal solution and then develop a suboptimal but low-complexity approach by exploring the inherent structure and property of the energy-efficient design. For the downlink case, by modifying the original problem, we also find a computationally efficient and numerically tractable upper bound on the EE, which indicates the performance limit and is demonstrated to be quite tight if the number of subcarriers is larger than that of UEs and motivates us to find a near-optimal approach relying on the quasiconcave relation between the modified EE and transmit power. Simulation results show that the energy-efficient design greatly improves EE compared with the conventional spectral-efficient design and the low-complexity suboptimal approaches can achieve a promising tradeoff between performance and complexity.

In cognitive radio networks, wireless nodes adapt to the surrounding radio environment and utilize the spectrum of licensed users. The cognitive radio environment is dynamic, and wireless channels are accessible by both legitimate and illegitimate users. Therefore, maintaining the security of cognitive radio networks is a challenging task, which must be addressed thoroughly. Further, with the recent exponential surge in wireless nodes and associated high data rate requirements, energy consumption is also growing at an unprecedented rate. Hence, energy efficiency becomes an important metric that must be considered in the design of future wireless networks. Accordingly, by considering the great ecological and economic benefits of green wireless networks, this work focus on energy-efficient resource allocation in secure cognitive radio networks. Since physical-layer security is an emerging technique that improves the security of communication devices, in this paper, an ergodic secure energy efficiency problem for a cognitive radio network is formulated with a primary user, a secondary user, and an eavesdropper. As the formulated problem is non-convex, a concave lower bound is applied to transform the original non-convex problem into a convex one. Further, by adopting the fractional programming and dual decomposition techniques, optimal power allocation strategies are obtained with the aim of maximizing the ergodic secure energy efficiency of the secondary user with constraints on the average interference power and average transmit power. Numerical examples are used to demonstrate the effectiveness of the proposed algorithm.

Energy-efficient resource allocation for D2D communication underlaying cellular networks with incomplete CSI

Next generation high data rate applications such as High-Definition (HD) video transmission place a heavy load on wireless network resources. To achieve the strict quality of service requirements of HD video in a multi-user network requires efficient resource allocation. Rate-distortion theory provides an appropriate basis for this. The Nash Bargaining Solution (NBS) is proven to generate an optimal solution to a rate-distortion based convex resource allocation problem. Particle Swarm Optimization (PSO) is a meta-heuristic optimizer capable of generating near-optimal solutions. PSO, however, is not constrained to convex optimization problems. The motivation of this work is to demonstrate a PSO implementation capable of achieving an optimal solution to the resource allocation problem. The capability of the PSO approach to reach an optimal solution suggests its potential for resource allocation in a network of heterogeneous applications where convex optimization methods do not apply.

Recent advances in self-interference cancelation techniques enable full-duplex relaying (FDR) systems, which transmit and receive simultaneously in the same frequency band with high spectrum efficiency. Unlike most existing works, we study the problem of energy-efficient resource allocation in FDR networks. We consider a shared FDR deployment scenario, where an FDR relay is deployed at the intersection of several adjacent cell sectors. First, a simple but practical transmission strategy is proposed to deal with the involved interference, i.e., multiaccess interference, multiuser interference, and self-interference. Then, the problem of joint power and subcarrier allocation is formulated to maximize the network-level energy efficiency while taking the residual self-interference into account. Since the formulated problem is a mixed combinatorial and nonconvex optimization problem with high computation complexity, we use Dinkelbach and discrete stochastic optimization methods to solve the energy-efficient resource-allocation problem efficiently. Simulation results are presented to show the effectiveness of the proposed scheme.

In heterogeneous networks, interference management is very important since the interference due to spectrum-sharing can significantly decrease the overall performance. In the existing work, various resource allocation methods are proposed to either improve energy efficiency or alleviate interference. Very little research has addressed their joint interaction. Furthermore, most existing work assumes that all of the channel state information is known, which might not be realistic. In this paper, we propose a game-theoretical scheme using energy-efficient resource allocation and interference pricing for an interference-limited environment in two-tier heterogeneous networks without complete channel state information. We formulate the problems of resource allocation and interference management as a two-stage Stackelberg game with incomplete information. A backward induction method is used to analyze the proposed game, since it can capture the sequential dependencies of the decisions in the stages of the game.

This paper is devoted to studying the processes of resource allocation in dynamic resource networks. In such networks, the capacities of the arcs depend on time. Resource allocation in the network occurs in discrete time. The resource of each vertex is distributed only between adjacent vertices according to some rules. The study of the processes of resource redistribution in such networks is carried out. The main goal is to develop methods for finding the limit state (distribution) of a resource in a dynamic resource network. It is shown that the approach based on the construction of an auxiliary network is also applicable to reduce the problem of resource allocation in a dynamic network to a similar problem in an auxiliary network. Theorems on the existence of a limit state on an auxiliary graph are proved for strongly regular periodic dynamical networks. To find the limit states, one can use the approaches which are developed for the shortest path problem in dynamic networks.

Resource allocation and admission control algorithm based on non-cooperation game in wireless mesh networks

In this paper, an energy efficient resource allocation is investigated to enhance the network performance of cellular networks. Specifically, the small cells (SCs) using both green and on-grid energy and the power splitter-enabled wireless charging for user equipments (UEs) are considered. A quality-of-service (QoS)-constrained problem is then designed to maximize energy efficiency (EE) through the joint strategies of power allocation, resource block (RB) assignment, and power splitting ratio adjustment. By exploiting some mathematical transformations, the non-convex optimization problem can be solved by adopting the proposed joint wireless charging and hybrid power based resource allocation (JWHRA) algorithm. The efficiency of proposed algorithm is validated via simulation. Numerical results show that the proposed JWHRA scheme can provide higher EE with its joint design on wireless charging and hybrid energy source.

With the rapid increasing of maritime activities, maritime wireless networks (MWNs) with high reliability, high energy efficiency, and low delay are required. However, the centralized networking with fixed resource scheduling is not suitable for MWNs due to the special environment. In this paper, we introduce the collaborative relay communication in distributed MWNs to improve the link reliability, and propose an orthogonal time-frequency resource block reservation based multiple access (RRMA) scheme for both one-hop direct link and two-hop collaborative relay link to reduce the interference. To further improve the network performance, we formulate an energy efficiency (EE) maximization resource allocation problem and solve it by an iterative algorithm based on the Dinkelbach method. Finally, numerical results are provided to investigate the proposed RRMA scheme and resource allocation algorithm, showing that the low outage probability and transmission delay can be attained by the proposed RRMA scheme. Moreover, the proposed resource allocation algorithm is capable of achieving high EE in distributed MWNs.

In this paper, we present and evaluate the performance of a resource allocation algorithm to enhance the Quality of Service (QoS) provision and energy efficiency of uplink Long Term Evolution (LTE) systems. The proposed algorithm considers the main constraints in uplink LTE resource allocation, i.e., the allocation of contiguous sets of resource blocks of the localized Single Carrier—Frequency Division Multiple Access (SC-FDMA) physical layer to each user, and the imperfect knowledge of the users' uplink buffer status and packet waiting time. The optimal resource allocation is formulated as a discrete connected cake-cutting problem, where different agents are allocated consecutive subsequences of a sequence of indivisible items. This problem is NP-hard, therefore a suboptimal algorithm is introduced, which performs resource allocation using information on the estimated uplink packet delay, the average delay and data rate of past allocations, as well as the required uplink power per resource block. Based on simulation results, the proposed algorithm achieves significant performance improvement in terms of packet timeout rate, goodput, and fairness. Moreover, the effect of poor QoS provision on energy efficiency is demonstrated through the evaluation of the performance in terms of energy consumption per successfully received bit.