Energy efficient resource allocation for 5G Heterogeneous Networks

Saeed, Arsalan; Katranaras, Efstathios; Zoha, Ahmed; Imran, Ali; Imran, Muhammad Ali; Dianati, Mehrdad

doi:10.1109/camad.2015.7390492

Cited by 14 publications

(14 citation statements)

References 11 publications

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“…We assume a predefined threshold for the system load to balance energy efficiency and QoS tradeoff, while the data rate threshold varies according to the type of BS that the user is associated with. We compare the performance of our proposed traffic offloading scheme with the schemes proposed in [38], [39] and [40] in addition to the standard, where there is no offloading mechanism implemented. The work proposed in [38] exploits linear programing to develop a heuristic based offloading algorithm.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…We compare the performance of our proposed traffic offloading scheme with the schemes proposed in [38], [39] and [40] in addition to the standard, where there is no offloading mechanism implemented. The work proposed in [38] exploits linear programing to develop a heuristic based offloading algorithm. The scheme proposed in [39] called (JPUA) proposed a mechanism with joint macro BS transmit power control and load-aware user association to reduce the interference impact in a two-tier heterogeneous network.…”

Section: Numerical Resultsmentioning

confidence: 99%

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Energy Efficient Traffic Offloading in Multi-Tier Heterogeneous 5G Networks Using Intuitive Online Reinforcement Learning

AlQerm

Shihada

2019

IEEE Trans. on Green Commun. Netw.

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The energy efficiency in multi-tier heterogeneous 5G networks is a critical issue due to the fact that the macro base stations (BSs) power consumption is considerably high and proportional to their traffic load. Traffic offloading from macrocells to small cells is envisioned as a potential solution to improve energy efficiency in 5G heterogeneous networks. However, traffic offloading causes traffic congestion at small cells, interference as a result of small cells transmissions, and aggregate power consumption of small cells. These factors make the traffic offloading procedure more challenging. In this paper, we propose a conditional traffic offloading scheme, which relies on macrocells and small cells system load information to determine the most energy efficient traffic offloading strategy, select the proper operation mode of small cells, and fulfill macro users applications' quality of service (QoS) requirements. The proposed scheme is developed using a novel intuitive online reinforcement learning methodology to perform the conditional traffic offloading in which each macro BS conjectures the offloading strategies of other macrocells. The convergence of the proposed scheme is proved and numerical results demonstrate its capability to achieve fast convergence with QoS guarantee and significant energy efficiency.

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Section: Numerical Resultsmentioning

confidence: 99%

Section: Numerical Resultsmentioning

confidence: 99%

Energy Efficient Traffic Offloading in Multi-Tier Heterogeneous 5G Networks Using Intuitive Online Reinforcement Learning

AlQerm

Shihada

2019

IEEE Trans. on Green Commun. Netw.

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“…6(a) that the performace of all considered schemes increase when K increases since the number of elements in the summation of E WS (p) increases. However, the average 25 EE of individual cell, i.e. E WS (p)/K, of the EE schemes decrease with K. This is because more femtocells results in more interference signals.…”

Section: Fig 5 Plots the Ee Performances Of The Proposed Ee Schemes mentioning

confidence: 98%

“…Resouce allocation for OFDM Hetnets was studied in [8], [23]- [25]. In particular, [8], [23] proposed spectral efficiency (SE) frameworks which maximize throughput under constraints of users' power.…”

Section: A Related Workmentioning

confidence: 99%

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Energy-Efficient Resource Allocation for OFDMA Heterogeneous Networks

Tran

et al. 2019

IEEE Trans. Commun.

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We proposed several energy-efficient resource allocation algorithms for the downlink of an orthogonal frequencydivision-multiple-access (OFDMA) based femtocell heterogeneous networks (HetNets). Heterogeneous QoS and fairness in rate are investigated in the proposed resource allocation problem. A dense deployment of femtocells in the coverage area of a central macrocell is considered and energy usage of both femtocell and macrocell users are optimized simultaneously. We aim to maximize the weighted sum of the individual energy efficiencies (WSEEMax) and the network energy efficiency (NEEMax) while satisfying the following: (1) minimum throughput for delaysensitive (DS) users, (2) fairness constraint for delay-tolerant (DT) users, (3) required constraints of OFDMA systems. The problem is formulated in three different forms: mixed 0-1 integer programming formulation, time-sharing formulation and sparsity-inducing formulation. The proposed resource block (RB) and power optimization problems are combinatorial and highly non-convex due to the fractional form of the objective function, the integer constraint of OFDMA RBs and non-affine fairness. We adopt the successive convex approximation (SCA) approach and transform the problems into a sequence of convex subproblems. With the proposed algorithms, we show that the overall joint RB and power allocation schemes converge to suboptimal solutions. Numerical examples confirm the merits of the proposed algorithms.

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