A Fair and Scalable Power Control Scheme in Multi-cell Massive MIMO

In this paper, we study joint power control and scheduling in uplink massive multiple-input multiple-output (MIMO) systems with randomly arriving data traffic. We consider both co-located and Cell-Free (CF) Massive MIMO, where the difference lies in whether the antennas are co-located at the base station or spread over a wide network area. The data is generated at each user according to an individual stochastic process. Using Lyapunov optimization techniques, we develop a dynamic scheduling algorithm (DSA), which decides at each time slot the amount of data to admit to the transmission queues and the transmission rates over the wireless channel. The proposed algorithm optimizes the long-term user throughput under various fairness policies while keeping the transmission queues stable. Simulation results show that the state-of-the-art power control schemes developed for Massive MIMO with infinite backlogs can fail to stabilize the system even when the data arrival rates are within the network capacity region. Our proposed DSA shows advantage in providing finite delay with performance optimization whenever the network can be stabilized.Index Terms-Massive MIMO, dynamic resource allocation, cross-layer control, Lyapunov optimization, drift-plus-penalty. has been studied in [21], where the optimal control strategy is decoupled into subproblems of flow control, routing and resource allocation. The proposed algorithms based on the driftplus-penalty (DPP) technique are shown to provide stability and achieve time-average throughput arbitrarily close to the optimal fairness operating point. Similar types of DPP-based algorithms can be found in [22], [23]. A general presentation of cross-layer control (CLC) and resource allocation strategies can be found in [24], with special focus on flow control algorithms that achieve optimal network fairness with stability guarantees. Recently, Lyapunov optimization has been used to study power control and scheduling in delay-aware device-todevice communication [25] and packet-based communication with deadlines [26]. Although the use of Lyapunov optimization in communication systems is not a new topic, very few works have

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“…Using the techniques in [33], the original problem can be transformed into the following equivalent problem, for the case with MRC:…”

Section: Baseline Heuristic Schemesmentioning

confidence: 99%

Dynamic Resource Allocation in Co-Located and Cell-Free Massive MIMO

Chen

Björnson

Larsson

2020

IEEE Trans. on Green Commun. Netw.

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“…Note that in [1], which is the conference version of the current paper, we proposed the novel GM per-cell MMF power control for the case of uncorrelated Rayleigh fading channel and assumed that the pilots are reused in every cell. In this paper, we consider correlated Rayleigh fading and arbitrary pilot reuse sets, and we also demonstrate that the optimization framework can be used in many other scenarios.…”

Section: Nw-mmf Nw-pf Gm Per-cell Mmfmentioning

confidence: 99%

Enhanced Fairness and Scalability of Power Control Schemes in Multi-Cell Massive MIMO

Ghazanfari

Cheng

Björnson

et al. 2020

IEEE Trans. Commun.

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This paper studies the transmit power optimization in multi-cell massive multiple-input multiple-output (MIMO) systems. Network-wide max-min fairness (NW-MMF) and networkwide proportional fairness (NW-PF) are two well-known power control schemes in the literature. The NW-MMF focus on maximizing the fairness among users at the cost of penalizing users with good channel conditions. On the other hand, the NW-PF focuses on maximizing the sum SE, thereby ignoring fairness, but gives some extra attention to the weakest users. However, both of these schemes suffer from a scalability issue which means that for large networks, it is highly probable that one user has a very poor channel condition, pushing the spectral efficiency (SE) of all users towards zero. To overcome the scalability issue of NW-MMF and NW-PF, we propose a novel power control scheme that is provably scalable. This scheme maximizes the geometric mean (GM) of the per-cell max-min SE. To solve this new optimization problem, we prove that it can be rewritten in a convex optimization form and then solved using standard tools. The simulation results highlight the benefits of our model which is balancing between NW-PF and NW-MMF.

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“…Therefore, we can obtain the globally optimal solution to (74) in tractable time by using interior-point methods [60]. The computational cost is higher than the linear or SOCP problems since the cost of evaluating the first and second derivatives of the objective and constraint functions is complicated in many applications [60,65,66]. This optimization class will be utilized in Paper B when we work with joint pilot design and UL power control for multi-cell Massive MIMO.…”

Section: Geometric Programmingmentioning

confidence: 99%

Spatial Resource Allocation in Massive MIMO Communications : From Cellular to Cell-Free

Chien

2020

Linköping Studies in Science and Technology. Dissertations

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Printed in Sweden by LiU-Tryck, Linköping 2020 investigates the use of deep learning for power control optimization in Massive MIMO. We formulate the joint data and pilot power optimization for maximum sum SE in multi-cell Massive MIMO systems, which is a non-convex problem. We propose a new optimization algorithm, inspired by the weighted MMSE approach, to obtain a stationary point in polynomial time. We then use this algorithm together with deep learning to train a convolutional neural network to perform the joint data and pilot power control in sub-millisecond runtime. The solution is suitable for online optimization.iv Finally, the fifth part of this thesis considers a large-scale distributed antenna system that serves the users by coherent joint transmission called Cell-free Massive MIMO. 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 DL, considering both the hardwareconsumed and transmit powers, where APs can be turned off to reduce the former part. Even though this is a non-convex optimization problem, a globally optimal solution is obtained by solving a mixed-integer second-order cone program (SOCP). We also propose low-complexity algorithms that exploit group-sparsity or received power strength in the problem formulation.

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A Fair and Scalable Power Control Scheme in Multi-cell Massive MIMO

Cited by 10 publications

References 19 publications

Dynamic Resource Allocation in Co-Located and Cell-Free Massive MIMO

Dynamic Resource Allocation in Co-Located and Cell-Free Massive MIMO

Enhanced Fairness and Scalability of Power Control Schemes in Multi-Cell Massive MIMO

Spatial Resource Allocation in Massive MIMO Communications : From Cellular to Cell-Free

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