Energy-Efficient Resource Allocation for Heterogeneous Services in OFDMA Downlink Networks: Systematic Perspective

Xu, Quansheng; Li, Xi; Ji, Hong; Du, Xiaojiang

doi:10.1109/tvt.2014.2312288

Cited by 70 publications

(41 citation statements)

References 31 publications

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“…Contributions in this direction are given in [6]- [8]. In [6], [7], multi-carrier networks are considered and the global energy efficiency (GEE) of the system (defined as the ratio between the achievable sum rate and the total power consumption) is optimized using orthogonal or semiorthogonal subcarrier allocation schemes. In [8], the authors consider a multi-antenna system and aim at maximizing the GEE when non-linear interference cancellation techniques are used.…”

Section: A State-of-the-artmentioning

confidence: 99%

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Globally Optimal Energy-Efficient Power Control and Receiver Design in Wireless Networks

Zappone

Björnson

Sanguinetti

et al. 2017

IEEE Trans. Signal Process.

237

228

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Abstract-The characterization of the global maximum of energy efficiency (EE) problems in wireless networks is a challenging problem due to the non-convex nature of investigated problems in interference channels. The aim of this work is to develop a new and general framework to achieve globally optimal solutions. First, the hidden monotonic structure of the most common EE maximization problems is exploited jointly with fractional programming theory to obtain globally optimal solutions with exponential complexity in the number of network links. To overcome this issue, we also propose a framework to compute suboptimal power control strategies characterized by affordable complexity. This is achieved by merging fractional programming and sequential optimization. The proposed monotonic framework is used to shed light on the ultimate performance of wireless networks in terms of EE and also to benchmark the performance of the lower-complexity framework based on sequential programming. Numerical evidence is provided to show that the sequential fractional programming framework achieves global optimality in several practical communication scenarios.

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Section: A State-of-the-artmentioning

confidence: 99%

“…by c k ∈ C N R the linear detector that BS a(k) applies to its received signal to detect the signal from UE k, a lower bound 7 on the uplink SINR of UE k takes the following form [50]:…”

mentioning

confidence: 99%

Globally Optimal Energy-Efficient Power Control and Receiver Design in Wireless Networks

Zappone

Björnson

Sanguinetti

et al. 2017

IEEE Trans. Signal Process.

237

228

View full text Add to dashboard Cite

show abstract

“…Jointly considering the interruption delay constraint and data rate constraint of SUs, we can obtain that the nth subchannel can be selected as the handoff target subchannel of the mth SU of which the current subchannel is n m , only if it meets the conditions specified in both (16) and (21). Denoting as the set of all the subchannels and m,n m as the set of candidate handoff subchannel of the mth SU with the current subchannel being n m , we obtain:…”

Section: Candidate Handoff Spectrum Setmentioning

confidence: 99%

“…In this paper, we formulate the joint performance of interrupted SUs as the total energy efficiency, which is defined as the ratio of the sum rate and total power consumption of the interrupted SUs [20,21], i.e.,…”

Section: Total Energy Efficiency Of Interrupted Susmentioning

confidence: 99%

Energy efficiency-based joint spectrum handoff and resource allocation algorithm for heterogeneous CRNs

Chai

Qin

Chen

et al. 2016

J Wireless Com Network

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Cognitive radio networks (CRNs) are expected to improve spectrum utilization significantly by allowing secondary users (SUs) to opportunistically access the licensed spectrum of primary users (PUs). In an integrated network consisting of multiple heterogeneous CRNs, SUs with multiple interfaces may have to conduct inter-system or intra-system spectrum handoff due to the arrival of PUs or performance degradation on serving spectrum. In this case, designing an optimal spectrum handoff scheme which offers quality of service (QoS) guarantee and performance enhancement of the SUs is of particular importance. On the other hand, resource allocation strategy on target channel also plays an important role in affecting the transmission performance of handoff SUs. In this paper, we jointly design spectrum handoff and resource allocation strategy for handoff SUs in heterogeneously integrated CRNs. To achieve joint resource management among various CRNs, we propose a joint radio resource management architecture, based on which the proposed spectrum handoff and resource allocation scheme can be conducted. Jointly considering the transmission performance of the handoff SUs, we formulate the total energy efficiency of the SUs and design an optimization problem which maximizes the energy efficiency subject to spectrum handoff, QoS, and power constraints of the SUs. An iterative algorithm is proposed to solve the formulated nonlinear fractional optimization problem. Within each iteration, the optimization problem is transformed equivalently into two subproblems, i.e., power allocation subproblem of each SU-spectrum pair and spectrum handoff subproblem for all the SUs. The two subproblems are solved, respectively, through applying Lagrange dual method and the Kuhn-Munkres (K-M) algorithm. Numerical results demonstrate the efficiency of the proposed algorithm.

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“…In state-of-the-art studies, the EE is usually optimized under the interference-free assumption by orthogonal transmission scheme [6] or successive interference cancellation [7] and the sum rate function in the numerator is thus concave. However, orthogonal transmission schemes may decrease the spectral efficiency, especially in dense networks.…”

Section: Introductionmentioning

confidence: 99%

A Parallel Algorithm for Energy Efficiency Maximization in Massive MIMO Networks

Yang

Pesavento

2016

2016 IEEE Global Communications Conference (GLOBECOM)

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Abstract-In this paper, we propose a novel iterative algorithm based on successive convex approximation for the nonconvex energy efficiency optimization problem in massive MIMO networks. The stationary points of the original problem are found by solving a sequence of successively refined approximate problems, and the proposed algorithm has the following advantages: 1) fast convergence as the structure of the original energy efficiency function is preserved as much as possible in the approximate problem, and 2) easy implementation as each approximate problem is natural for parallel computation and all variable updates have a closed-form expression. The proposed algorithm is guaranteed to converge and its advantages are also illustrated numerically.

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Energy-Efficient Resource Allocation for Heterogeneous Services in OFDMA Downlink Networks: Systematic Perspective

Cited by 70 publications

References 31 publications

Globally Optimal Energy-Efficient Power Control and Receiver Design in Wireless Networks

Globally Optimal Energy-Efficient Power Control and Receiver Design in Wireless Networks

Energy efficiency-based joint spectrum handoff and resource allocation algorithm for heterogeneous CRNs

A Parallel Algorithm for Energy Efficiency Maximization in Massive MIMO Networks

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