Resource Allocation in Downlink Large-Scale MIMO Systems

Hamdi, Rami; Driouch, Elmahdi; Ajib, Wessam

doi:10.1109/access.2016.2630999

Cited by 26 publications

(12 citation statements)

References 28 publications

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“…We can decompose the right side of equation ( 7) into a sum of four terms: the desired signal, the fluctuation caused by the uncertain channel gains, the interference from other UEs, and the noise, as shown in (8). According to [9], the achievable downlink capacity for user k, C k is given by the use-and-thenforget (UatF) bound:…”

Section: Achievable Downlink Data Capacitymentioning

confidence: 99%

“…Since in massive MIMO, and in particular CF massive MIMO, different UEs are served simultaneously in the same time-frequency domain, controlling the inter-user interference is important. The power allocation plays a crucial role in controlling this interference and in optimizing the performance [8], [9]. In this paper we address this problem for CF massive MIMO.…”

Section: Introductionmentioning

confidence: 99%

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Power Allocation in Cell-Free Massive MIMO: A Deep Learning Method

2020

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Massive multiple-input multiple-output (MIMO) is a key technology in 5G. It enables multiple users to be served in the same time-frequency block through precoding or beamforming techniques, thus increasing capacity, reliability and energy efficiency. A key issue in massive MIMO is the allocation of power to the individual antennas, in order to achieve a specific objective, e.g., the maximization of the minimum capacity guaranteed to each user. This is a nondeterministic polynomial (NP)-hard problem that needs to be solved in a timely manner since the state of the channels evolves in time and the power allocation should stay in tune with this state. Although several heuristics have been proposed to solve this problem, these entail a considerable time-complexity. As a result, with the present methods, it cannot be guaranteed that power allocation happens in time. To solve this problem, we propose a deep neural network (DNN). A DNN has a low time complexity, but requires an extensive, offline, training process before it becomes operational. The DNN we propose is the combination of two convolutional layers and four fully connected layers. It takes as input the long-term fading information and it outputs the power for each antenna element to each user. We limit ourselves to the case of time-division duplex (TDD) based sub-6GHz networks. Numerical results show that, our DNN-based method approximates very closely the results of a commonly used heuristic based on the bisection algorithm.INDEX TERMS Cell-free massive MIMO, deep learning, power allocation.

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Section: Achievable Downlink Data Capacitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Power Allocation in Cell-Free Massive MIMO: A Deep Learning Method

2020

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“…Of course, a trade-off between these benefits exists [6] and different objectives and performance measures are used, from spectral efficiency and constructive interference [7] to fairness measures [8]. Other factors are also taken into account, such as the circuit power consumption [9]. Another aspect investigated is the difference between FDD (frequency division duplex) and TDD (time division duplex) in terms of CSI (channel state information) collection, which is essential for antenna selection [10].…”

Section: Introductionmentioning

confidence: 99%

Distributing Complexity: A New Approach to Antenna Selection for Distributed Massive MIMO

Šiljak

Macaluso

Marchetti

2018

IEEE Wireless Commun. Lett.

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Antenna selection in Massive MIMO (Multiple Input Multiple Output) communication systems enables reduction of complexity, cost and power while keeping the channel capacity high and retaining the diversity, interference reduction, spatial multiplexity and array gains of Massive MIMO. We investigate the possibility of decentralised antenna selection both to parallelise the optimisation process and put the environment awareness to use. Results of experiments with two different power control rules and varying number of users show that a simple and computationally inexpensive algorithm can be used in real time. The algorithm we propose draws its foundations from selforganisation, environment awareness and randomness.

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“…As millions of base stations and billions of user terminals are expected to operate simultaneously and consume huge amounts of energy, energy efficiency is becoming an increasingly important issue in wireless communications in order to reduce operational expenditure and carbon dioxide emissions [1,2]. In [3,4], power allocation strategies were investigated for both single-cell and multicell systems under the assumption of perfect channel state information (CSI). In [5], a resource allocation scheme was proposed for improving energy efficiency under imperfect CSI conditions.…”

Section: Introductionmentioning

confidence: 99%

Lagrangian Dual Decomposition for Joint Resource Allocation Optimization Problem in OFDMA Downlink Networks

Jia

Chen

et al. 2018

Mathematical Problems in Engineering

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This paper proposes an efficient method for joint power and subcarrier allocation in a multicell multiuser OFDMA downlink network. The joint optimization problem is formulated with the objective of maximizing the energy efficiency subject to the constraints on the quality of service in sum transmission rates for each cell and the total transmit power for the network. Due to intercell cochannel interferences and multiple variable coupling, the problem is intractable in its original form. To relax the difficulties in coordinating cochannel interferences, we introduce the tolerable interferences constraints for interference channels. To cope with the multiple variable coupling, we decompose the joint optimization problem into two iterative processes of user scheduling and a parametric convex optimization problem, where the energy efficiency is treated as the parameter and approached by bisection search. Then, by double dual decomposition, the parametric convex problem is transformed into Lagrangian dual problems at two levels of cells and subcarriers, and a decentralized solution is obtained in closed form. Based on the reformulations, an iterative subgradient algorithm is presented for approaching the joint optimization problem with acceptable complexity. Computer simulations are conducted to validate the proposed algorithm and examine the effects of various system parameters.

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Resource Allocation in Downlink Large-Scale MIMO Systems

Cited by 26 publications

References 28 publications

Power Allocation in Cell-Free Massive MIMO: A Deep Learning Method

Power Allocation in Cell-Free Massive MIMO: A Deep Learning Method

Distributing Complexity: A New Approach to Antenna Selection for Distributed Massive MIMO

Lagrangian Dual Decomposition for Joint Resource Allocation Optimization Problem in OFDMA Downlink Networks

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