Multi-Cell MIMO Cooperative Networks: A New Look at Interference

Gesbert, David; Hanly, Stephen V.; Huang, Hong; Shamai, Shlomo; Simeone, Osvaldo; Yu, Wei

doi:10.1109/jsac.2010.101202

Cited by 1,586 publications

(1,230 citation statements)

References 162 publications

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“…There have been plenty of schemes conceiving such issues in wireless and cellular networks with the help of MIMO diversity and beamforming transmission; Even the beamforming with MIMO spatial and multiplex gain is useful to improve throughput and the number of users in emerging wireless networks. Intensive work was carried out to enhance the performance of overall wireless network [18]. One of them is load balancing.…”

Section: Mimo With Beamforming For Load Balancingmentioning

confidence: 99%

QoS-aware User Load Management with Beamforming for Energy Efficiency in Wireless Networks

Khani¹,

Chen²,

Yin³

2017

IJCTE

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Abstract-To overcome the congestion by load management of cell network, beam breathing based load management scheme is proposed to meet the issue. The proposed energy efficient load management scheme is based on beamforming layer breathing and multiple users handling of single beamforming transmission. The beam breathing is first handled by downlink beamforming transmission power control (breathe in or out) to manage the congestion. Subsequently, a single beam multi-users method for load balancing is discussed. Lastly, the linear equalizers Zero Forcing (ZF) and Minimum Mean Square Error (MMSE) with successive interference cancellation are exploited to recover information from multiplexed transmissions. The proposed scheme is effective to decrease the power consumption, congestion control, load balancing and is also constructive to increase the data rate in wireless communication. Simulation results reveal that the proposed work is quite better than the existing solutions in terms of throughput and energy consumption.Index Terms-Beamforming breathing, load balancing, congestion control, inter-cell interference, energy-efficient network.

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Section: Mimo With Beamforming For Load Balancingmentioning

confidence: 99%

QoS-aware User Load Management with Beamforming for Energy Efficiency in Wireless Networks

Khani¹,

Chen²,

Yin³

2017

IJCTE

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“…In the full coordination mode, user data and channel state information (CSI) are exchanged between the coordinated BSs such that the multiple BSs are simultaneously transmitting data signals to the MSs within the coordinated areas. Apparently, full coordination is the most complex CoMP mode as it demands a significant amount of signaling to be exchanged among the BSs via an ideal backhaul [2]. Thus, this highly complex mode may not be suitable to a large scale network.…”

Section: Copyright Cmentioning

confidence: 99%

Sum-rate maximization in the multicell MIMO broadcast channel with interference coordination

Nguyen

Le‐Ngoc

2013

2013 IEEE International Conference on Communications (ICC)

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Abstract-This paper studies the precoding designs to maximize the weighted sum-rate (WSR) in a multicell multiple-input multiple-output (MIMO) broadcast channel (BC). We consider a multicell network under universal frequency reuse with multiple mobile stations (MS) per cell. With interference coordination (IC) between the multiple cells, the base-station (BS) at each cell only transmits information signals to the MSs within its cell using the dirty paper coding (DPC) technique, while coordinating the inter-cell interference (ICI) induced to other cells. The main focus of this work is to jointly optimize the encoding covariance matrices across the BSs in order to maximize the network-wide WSR. Since this optimization problem is shown to be nonconvex, obtaining its globally optimal solution is highly complicated. To address this problem, we consider two low-complexity solution approaches with distributed implementation to obtain at least locally optimal solutions. In the first approach, by applying a successive convex approximation technique, the original nonconvex problem is decomposed into a sequence of simpler problems, which can be solved optimally and separately at each BS. In the second approach, the WSR problem is solved via an equivalent problem of weighted sum mean squared error minimization. Both solution approaches will unfold the control signaling among the coordinated BSs to allow their distributed implementation. Simulation results confirm the convergence of the proposed algorithms, as well as their superior performances over schemes with linear precoding or no interference coordination among the BSs.

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“…The optimal solution of resource scheduling is considered difficult to obtain given that it is a nonconvex problem [1,2,3,4]. Previous research work has investigated nearoptimal algorithms for scheduling channels or subcarriers under the conditions of fairness and power control [5,6,7,8].…”

Section: Introductionmentioning

confidence: 99%

Near-Optimal Resource Allocation in Cooperative Cellular Networks Using Genetic Algorithms

Luo

Armour

McGeehan

2015

Multiple Access Communications

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Abstract. This paper shows how a genetic algorithm can be used as a method of obtaining the near-optimal solution of the resource block scheduling problem in a cooperative cellular network. An exhaustive search is initially implemented to guarantee that the optimal result, in terms of maximizing the bandwidth efficiency of the overall network, is found, and then the genetic algorithm with the properly selected termination conditions is used in the same network. The simulation results show that the genetic algorithm can approximately achieve the optimum bandwidth efficiency whilst requiring only 24% of the computation effort of the exhaustive search in the investigated network.

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Multi-Cell MIMO Cooperative Networks: A New Look at Interference

Cited by 1,586 publications

References 162 publications

QoS-aware User Load Management with Beamforming for Energy Efficiency in Wireless Networks

QoS-aware User Load Management with Beamforming for Energy Efficiency in Wireless Networks

Sum-rate maximization in the multicell MIMO broadcast channel with interference coordination

Near-Optimal Resource Allocation in Cooperative Cellular Networks Using Genetic Algorithms

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