Energy-Efficient Resource Allocation in OFDMA Networks

With the explosive growth of wireless data traffic, the energy-efficient design in wireless networks is becoming an inevitable trend. In this paper, we study the low-complexity design issue of energy-efficient radio resource allocation in downlink OFDMA networks. Distinguished from the existing works, we focus on the tradeoff between the energy efficiency and the total transmit power. The closed-form optimal solution is obtained by solving the first-order optimality condition of the energy efficiency maximization problem. Furthermore, based on the analytical solutions, we develop a low-complexity optimal algorithm for the energy-efficient resource allocation. Simulation results show that the proposed algorithm achieves the optimal energy efficiency with significantly lower computational complexity compared with the conventional iterative methods.

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 this study, the physical layer resource allocation in orthogonal frequency‐division multiple access (OFDMA) systems is studied by considering the non‐linear effects of power amplifier (PA). The non‐linearity produces cross‐correlation among the subcarriers and results in deteriorating the OFDMA subcarriers orthogonality. Hence, the signal‐to‐interference ratio (SIR) of the output signal of PA for OFDMA signal with different power scaling factors for subcarriers is derived. Then, the theoretical SIR is verified by simulation result. Next, the optimisation problem is designed to maximise the total achievable downlink rate of all users by assigning subcarriers and power scaling factors. This non‐convex problem is solved by comprehensive learning particle swarm optimisation. The simulation results show that when PA is non‐linear, better total rate is achieved by considering non‐linearity in resource allocation compared with the conventional resource allocation techniques.

Conventional design of wireless networks mainly focuses on system capacity and spectral efficiency (SE). As green radio (GR) becomes an inevitable trend, energy-efficient design is becoming more and more important. In this paper, the fundamental tradeoff between energy efficiency (EE) and SE in downlink orthogonal frequency division multiple access (OFDMA) networks is addressed. We first set up a general EE-SE tradeoff framework, where the overall EE, SE and per-user quality-of-service (QoS) are all considered, and prove that under this framework, EE is strictly quasiconcave in SE. We then discuss some basic properties, such as the impact of channel power gain and circuit power on the EE-SE relation. We also find a tight upper bound and a tight lower bound on the EE-SE curve for general scenarios, which reflect the actual EE-SE relation. We then focus on a special case that priority and fairness are considered and suggest an alternative upper bound, which is proved to be achievable for flat fading channels. We also develop a low-complexity but near-optimal resource allocation algorithm for practical application of the EE-SE tradeoff. Numerical results confirm the theoretical findings and demonstrate the effectiveness of the proposed resource allocation scheme for achieving a flexible and desirable tradeoff between EE and SE.

In Orthogonal frequency division multiple access (OFDMA) cellular system, the user at the different location of the cell requires different number of subcarriers to satisfy the user’s data rate requirement. In this paper, based on the Interference to Signal Ratio (ISR), we analyze the average number of downlink subcarriers required at inner and outer region for the two-hop OFDMA cellular system. Firstly we propose a two-hop OFDMA cellular system model. Then based on this model, we calculate the Cumulative Distribution Function of ISR at inner and outer region respectively, and obtain the theoretical analysis results for the average number of subcarriers required at two regions of the reference cell. Finally, with the help of the numerical calculation and Monte-Carlo simulations, we analyze the impact of the various parameters on the average number of subcarriers required of the two-hop cellular system. And the results are compared with the average number of subcarriers required of the one-hop cellular system.

In this paper, we propose a joint resource allocation, routing, and connection admission control (CAC) scheme for uplink transmission in orthogonal frequency division multiple access (OFDMA) relay networks with cooperative relaying. For cooperative relaying, relay station can relay uplink data from mobile station (MS) to base station with cooperation of the MS using transmit diversity. Transmit diversity can be achieved by virtual MISO via distributed space---time coding. The proposed scheme jointly allocates OFDMA resources and selects path for each user with CAC to maximize the upink throughput of cooperative OFDMA relay networks. The basic OFDMA resource unit is considered as a resource element which is one subcarrier over one OFDMA symbol. An efficient multi-choice multi-dimensional knapsack (MMKP) algorithm is presented for the proposed scheme. The proposed MMKP algorithm provides a unified framework which is applicable to OFDMA networks with and without cooperative relaying. We evaluate the performance of the proposed scheme with and without cooperative relaying in a hilly terrain with heavy tree density by using OPNET-based simulation. We show that the cooperative relaying improve the uplink system throughput compared with non-cooperative relaying, and the proposed scheme outperforms the conventional link quality-based scheme in both cooperative and non-cooperative relay networks.

In this paper, we develop an energy efficient resource allocation scheme for orthogonal frequency division multiple access (OFDMA) networks with in-band full-duplex (IBFD) communication between the base station and user equipments (UEs) considering a realistic self-interference (SI) model. Our primary aim is to maximize the system energy efficiency (EE) through a joint power control and sub-carrier assignment in both the downlink (DL) and uplink (UL), where the quality of service requirements of the UEs in DL and UL are guaranteed. The formulated problem is non-convex due to the non-linear fractional objective function and the non-convex feasible set which is generally intractable. In order to handle this difficulty, we first use fractional programming to transform the fractional objective function to the subtractive form. Then, by employing Dinkelbach method, we propose an iterative algorithm in which an inner problem is solved in each iteration. Applying majorization-minimization approximation, we make the inner problem convex. Also, by introducing a penalty function to handle integer sub-carrier assignment variables, we propose an iterative algorithm for addressing the inner problem. We show that our proposed algorithm converges to the locally optimal solution which is also demonstrated by our simulation results. In addition, simulation results show that by applying the IBFD capability in OFDMA networks with efficient SI cancellation techniques, our proposed resource allocation algorithm attains a 75% increase in the EE as compared to the half-duplex system.

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.

SummaryIn this paper, we present a QoS‐driven energy efficient power and subcarrier allocation in green orthogonal frequency division multiple access networks. Our proposed scheme aims at maximizing the effective energy efficiency (EEE), defined as overall effective capacity per total power consumption, subject to given user delay‐QoS requirements and an average sum transmit power constraints. We analytically obtain the uniquely global optimal power and subcarrier using fractional programming, which transforms the fractional program into an equivalent parametric convex program that has a tractable solution. Analytical results show that the optimal solution has the same structure as effective capacity maximization and overall power minimization solutions. Simulation results show that there exists a trade‐off between EEE and delay. When the delay is more stringent, the EEE decreases. Moreover, when the number of subcarriers increase, EEE increases, where a fixed optimal EEE can be achieved regardless of stringency of delay‐QoS requirements. Furthermore, our proposed resource allocation provides significant EEE gains over both the independent resource allocation and overall power minimization schemes. Copyright © 2015 John Wiley & Sons, Ltd.

We address the problem of uplink and downlink resource allocation in heterogeneous networks where device-to-device (D2D) communication is allowed. We consider a realistic, large-scale LTE network in which users can download/upload data using different paradigms, namely, downlink/uplink transmissions from/to macro or micro base stations, and D2D communication in the uplink LTE bands. We propose an approximate dynamic programming algorithm to perform resource allocation scheduling for both upload and download data traffic, while taking into account the interference caused by resource sharing between the different data transfer paradigms. Through simulation, we compare the performance of our approach to solutions employed in today's networks, such as eICIC techniques and proportional fairness scheduling. Results show that our approach significantly improves the system performance in terms of both overall throughput and energy efficiency.

We propose a predictive energy-efficient scheduling scheme that optimizes the user equipment (UE)'s bits/joule metric subject to quality-of-service (QoS) constraints in downlink orthogonal frequency-division multiple access (OFDMA) systems. This is achieved by minimizing the number of wake-up transmission time intervals (TTIs), where the UE receiver circuit is on , in a longer time horizon than studied before. The proposed predictive scheduler is supported by a ray-tracing (RT) engine that increases the scheduler's knowledge by long-term information about the users' propagation characteristics. A convex multiobjective binary-integer-programming formulation for the problem is presented to optimize both the UE's energy efficiency (EE) and QoS. The multiobjective formulation is then used for benchmarking of a low-complexity and computationally efficient heuristic scheduler. The results show that the proposed schedulers have significantly improved the UE's EE and the overall capacity of the system, compared with a recently published EE scheduling scheme while maintaining target QoS.

We consider the allocation of spectral and power resources every subframe (1~ms) on the uplink of the Long Term Evolution (LTE) wideband orthogonal frequency division multiple access (OFDMA) cellular network. System bandwidth is divided into multiple sub-bands. The fractional power control to manage interference is allowed to be a function of the sub-band. Moreover, the channel gains from the base-station to the mobiles (user equipment (UE) in LTE) can vary with the sub-band. The power and spectral resources are to be allocated among different UEs in a cell based on channel conditions, desired fairness/QoS, and constraints on power due to interference and power limitation at a UE. For $N$ UEs and $M$ sub-bands, we exploit the structure of the underlying optimization problem to obtain an $O(NM)$ algorithm for optimal resource allocation when a UE can be allocated resources in a non-contiguous manner in frequency. To implement this in an OFDMA system, we delineate a simple control signalling structure where the UE computes in $O(M)$ time a waterfilling solution every subframe it is assigned resources. When the allocation is restricted to contiguous allocation in frequency due to single carrier constraint in LTE, we convert the solution computed above in $O(NM)$ time into a feasible solution. For typical network scenarios, the solution thus computed is numerically shown to be very close to the optimal solution with no restrictions.

This paper considers the primary user activity or the subchannel availability in optimally distributing the available resources for an orthogonal frequency-division multiple-access (OFDMA) cognitive radio multicast network. For this purpose, a risk-return model is presented, and a general rate-loss function, which gives a reduction in the attainable throughput whenever primary users reoccupy the temporarily accessible subchannels, is introduced. Taking the maximization of the expected sum rate of secondary multicast groups as the design objective, an efficient joint subcarrier and power-allocation scheme is proposed. Specifically, the design problem is solved via a dual optimization method under constraints on the tolerable interference thresholds at individual primary user's frequency bands. It is shown that as the number of subcarriers gets large (which is often the case in practice), the dual-domain solution becomes globally optimum with regard to the primal problem. More attractively, the ?practically optimal? performance of this approach is achieved with a substantially lower complexity, which is only linear in the total number of subcarriers as opposed to exponential complexity typically required by a direct search method. Our proposed design is valid for unicast and multicast transmissions and is applicable for a wide range of rate-loss functions, among which, the linear function is a special case. The superiority of the dual scheme is thoroughly verified by numerical examples.

In LTE/LTE-A orthogonal frequency-division multiple-access (OFDMA) systems, the number of subcarriers is usually very high, upto 2048, and the number of simultaneously supported users may also be very high. These characteristics of OFDMA systems not only result in the severe peak-to-average power ratio (PAPR) problem, but also prevent schedulers from employing the optimum or near-optimum subcarrier-allocation algorithms, due to their relatively high-complexity when the number of subcarriers and the number of users are high. Multicarrier direct-sequence code-division multiple-access (MC DS-CDMA) is one of high-flexibility communication schemes. Owing to the DS spreading, the number of subcarriers in MC DS-CDMA can be significantly lower than that in OFDMA, and can be configurated according to the frequency-selectivity of wireless channels, so that each subcarrier experiences independent fading. Therefore, the relatively high-complexity optimum or near-optimum subcarrier-allocation algorithms may be employed by MC DS-CDMA for achieving the best possible performance. In this contribution, we study the subcarrier-allocation issue in MC DS-CDMA. Specifically, three representative subcarrier algorithms, namely the greedy algorithm, worst subcarrier avoiding (WSA) algorithm and the optimum Hungarian algorithm, are introduced to and studied associated with the MC DS-CDMA. Furthermore, according to the characteristics of the MC DS-CDMA, a subcarrier-allocation algorithm, named as the parallel Hungarian algorithm, is proposed. The achievable performance of these subcarrier-allocation algorithms are compared in terms of complexity and bit error rate (BER).

Traditional handover algorithms are based on received signal strength indicator (RSSI) and traffic load intensity in orthogonal frequency division multiple access (OFDMA) systems, in order to guarantee all UEs' Quality of Service (QoS) and handover success ratio, a transceiver is activated even if there is only one low date rate activated user in its coverage area. In this paper, we propose an energy efficient handover algorithm combined coordinated multiple-points (CoMP) with OFDMA in a cellular systems, energy efficiency is introduced into the handover algorithm as an important parameter. The idea of the design is to enable adjacent cells to shutdown partial transceivers in some timeslots in low traffic conditions by concentrating the activated edge UEs to less cells. All activated UEs are classified into edge users and center users according to their distance to base stations, the analysis indicates that there exist an optimal edge distance corresponding to the maximum energy efficiency under different load for the proposed algorithm. Furthermore, a practical and low-complexity scheme that only considers the edge users is put forward. The edge users are identified by the reference signal received power (RSRP). Simulation results show that it can obtain up to 60% energy reduction at low traffic scenario.