BOOST: Base station ON-OFF switching strategy for energy efficient massive MIMO HetNets

Feng, Mingjie; Mao, Shiwen; Jiang, Tao

doi:10.1109/infocom.2016.7524485

Cited by 56 publications

(56 citation statements)

References 31 publications

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“…Instead there is an implicit connection via the zero maximum transmit power for inactive APs. This is different from previous approaches (e.g., [9]) where αm appears in the SINRs and therefore would break the SOC structure.…”

Section: Globally Optimal Solutioncontrasting

confidence: 74%

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Optimal Design of Energy-Efficient Cell-Free Massive Mimo: Joint Power Allocation and Load Balancing

Chien

Björnson

Larsson

2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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A large-scale distributed antenna system that serves the users by coherent joint transmission is called Cell-free Massive MIMO (multiple input multiple output). For a given user set, only a subset of the access points (APs) is likely needed to satisfy the users' performance demands. To find a flexible and energy-efficient implementation, we minimize the total power consumption at the APs in the downlink, considering both the hardware and transmit powers, where APs can be turned off. Even though this is a non-convex optimization problem, a globally optimal solution is obtained by solving a mixed-integer second-order cone program. We also propose a low-complexity algorithm that exploits group-sparsity in the problem formulation. Numerical results manifest that our optimization framework can greatly reduce the power consumption compared to keeping all APs turned on and only minimizing the transmit powers.Index Terms-Cell-free Massive MIMO, total power minimization, sparse optimization, energy efficiency.

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Section: Globally Optimal Solutioncontrasting

confidence: 74%

“…The problem of AP activation in distributed antenna systems has previously been analyzed for cloud radio access networks [9][10][11]. How-This paper was supported by ELLIIT and CENIIT.…”

Section: Relation To Prior Workmentioning

confidence: 99%

Optimal Design of Energy-Efficient Cell-Free Massive Mimo: Joint Power Allocation and Load Balancing

Chien

Björnson

Larsson

2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…Subject to: (14)- (21). In this paper, we also analyze the aggregate delay of the proposed energy saving scheme.…”

Section: Of 28mentioning

confidence: 99%

“…In device-assisted-networking for cellular greening (DANCE) schemes, small cell BSs are activated or deactivated for varying traffic loads to optimize the number of activated/deactivated small cell BSs [20]. In [21], the authors proposed a joint BS on/off switching and user association algorithm for energy efficiency maximization. In [22], the on/off states of small cell BSs are controlled based on two uniform and non-uniform distributions of UEs.…”

mentioning

confidence: 99%

An Efficient Energy Saving Scheme for Base Stations in 5G Networks with Separated Data and Control Planes Using Particle Swarm Optimization

Kang

Chung

2017

Energies

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Abstract:Reducing energy consumption of mobile communication networks has gained significant attentions since it takes a major part of the total energy consumption of information and communication technology (ICT). In this paper, we consider 5G networks with heterogeneous macro cells and small cells, where data and control planes are separated. We consider two types of data traffic, i.e., low rate data traffic and high rate data traffic. In basic separation architecture, a macro cell base station (MBS) manages control signals, while a small cell base station (SBS) manages both low rate data traffic and high rate data traffic. In the considered modified separation architecture, an MBS manages control signals and low rate data traffic, while an SBS manages high rate data traffic. Then, an efficient energy saving scheme for base stations (BSs) is proposed, where the state of a BS is determined depending on the number of user equipments (UEs) that request high rate data traffic and the number of UEs that exist under the overlapping areas commonly covered by the considered BS and the neighbor BSs. We formulate an optimization problem for the proposed energy saving scheme and obtain the solution using particle swarm optimization (PSO). Numerical results show that the proposed energy saving scheme in the modified separated network architecture has better energy efficiency compared to the conventional energy saving schemes in both basic and modified separated network architectures. Also, the proposed energy saving scheme has lower aggregate delay.

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“…In device-assisted-networking for cellular greening (DANCE) scheme, a macro cell BS manages the control signal of UEs and a small cell controller activates or deactivates the small cell BSs when the traffic load varies to optimize the number of activated/deactivated small cell BSs [17]. In [18], the authors propose a joint BS on/off switching and user association algorithm in order to maximize the energy efficiency, based on the time-and space-dependent traffic load in massive MIMO heterogeneous networks. In [19], the authors change the on/off states of small cell BSs dynamically based on UE distribution.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Performance Evaluation of a Context Information-Based Optimized Handover Scheme in 5G Networks

Seo

Chung

2017

Entropy

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Abstract:Recently, green networks are considered as one of the hottest topics in Information and Communication Technology (ICT), especially in mobile communication networks. In a green network, energy saving of network nodes such as base stations (BSs), switches, and servers should be achieved efficiently. In this paper, we consider a heterogeneous network architecture in 5G networks with separated data and control planes, where basically a macro cell manages control signals and a small cell manages data traffic. Then, we propose an optimized handover scheme based on context information such as reference signal received power, speed of user equipment (UE), traffic load, call admission control level, and data type. In this paper, the main objective of the proposed optimal handover is either to reduce the number of handovers or the total energy consumption of BSs. To this end, we develop optimization problems with either the minimization of the number of total handovers or the minimization of energy consumption of BSs as the objective function of the optimization problem. The solution of the optimization problem is obtained by particle swarm optimization, since the developed optimization problem is an NP hard problem. Performance analysis results via simulation based on various probability distributions of the characteristics of UE and BS show that the proposed optimized handover based on context information performs better than the previous call admission control based handover scheme, from the perspective of the number of handovers and total energy consumption. We also show that the proposed handover scheme can efficiently reduce either the number of handovers or the total energy consumption by applying either handover minimization or energy minimization depending on the objective of the application.

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BOOST: Base station ON-OFF switching strategy for energy efficient massive MIMO HetNets

Cited by 56 publications

References 31 publications

Optimal Design of Energy-Efficient Cell-Free Massive Mimo: Joint Power Allocation and Load Balancing

Optimal Design of Energy-Efficient Cell-Free Massive Mimo: Joint Power Allocation and Load Balancing

An Efficient Energy Saving Scheme for Base Stations in 5G Networks with Separated Data and Control Planes Using Particle Swarm Optimization

Modeling and Performance Evaluation of a Context Information-Based Optimized Handover Scheme in 5G Networks

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