Dynamic Resource Allocation for Smart-Grid Powered MIMO Downlink Transmissions

Wang, Xin; Chen, Tianyi; Chen, Xiaojing; Zhou, Xiaolin; Giannakis, Georgios B.

doi:10.1109/jsac.2016.2600543

Cited by 46 publications

(32 citation statements)

References 26 publications

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“…Defining the performance as in (6) in the constraint in (3) reflects a maximization with respect to the long term capacity experienced by the users. A constraint of the form u(r) ≥ 0 enforces a minimum average capacity for all users if u(r) := r − c min .…”

Section: A Examplesmentioning

confidence: 99%

Optimal Wireless Resource Allocation With Random Edge Graph Neural Networks

Eisen

Ribeiro

2020

IEEE Trans. Signal Process.

258

205

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We consider the problem of optimally allocating resources across a set of transmitters and receivers in a wireless network. The resulting optimization problem takes the form of constrained statistical learning, in which solutions can be found in a model-free manner by parameterizing the resource allocation policy. Convolutional neural networks architectures are an attractive option for parameterization, as their dimensionality is small and does not scale with network size. We introduce the random edge graph neural network (REGNN), which performs convolutions over random graphs formed by the fading interference patterns in the wireless network. The REGNN-based allocation policies are shown to retain an important permutation equivariance property that makes them amenable to transference to different networks. We further present an unsupervised model-free primal-dual learning algorithm to train the weights of the REGNN. Through numerical simulations, we demonstrate the strong performance REGNNs obtain relative to heuristic benchmarks and their transference capabilities.

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Section: A Examplesmentioning

confidence: 99%

Optimal Wireless Resource Allocation With Random Edge Graph Neural Networks

Eisen

Ribeiro

2020

IEEE Trans. Signal Process.

258

205

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“…To solve the problem in [93], [94] over an infinite scheduling period by an online algorithm, Wang et al [97] rely on the stochastic subgradient method to obtain resource schedules "on-the-fly" by suppressing (decoupling) the time-coupling between the variables and constraints. The random variables are supposed to be i.i.d..…”

Section: B Distributed Schedulingmentioning

confidence: 99%

“…Using stochastically estimated Lagrange multipliers, this method updates the subgradients with their online approximations based on the instantaneous decision variables per time slot. It is proven in [97] that asymptotically optimal and feasible solutions can be achieved in no need of any prior knowledge of underlying randomnesses. The optimality gap diminishes when the iteration stepsize approaches zero.…”

Section: B Distributed Schedulingmentioning

confidence: 99%

See 1 more Smart Citation

Modeling and Analysis of Energy Harvesting and Smart Grid-Powered Wireless Communication Networks: A Contemporary Survey

Chen

et al. 2020

IEEE Trans. on Green Commun. Netw.

Self Cite

110

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The advancements in smart power grid and the advocation of "green communications" have inspired the wireless communication networks to harness energy from ambient environments and operate in an energy-efficient manner for economic and ecological benefits. This article presents a contemporary review of recent breakthroughs on the utilization, redistribution, trading and planning of energy harvested in future wireless networks interoperating with smart grids. This article starts with classical models of renewable energy harvesting technologies. We embark on constrained operation and optimization of different energy harvesting wireless systems, such as point-to-point, multipoint-to-point, multipoint-to-multipoint, multi-hop, and multi-cell systems. We also review wireless power and information transfer technologies which provide a special implementation of energy harvesting wireless communications. A significant part of the article is devoted to the redistribution of redundant (unused) energy harvested within cellular networks, the energy planning under dynamic pricing when smart grids are in place, and two-way energy trading between cellular networks and smart grids. Applications of different optimization tools, such as convex optimization, Lagrangian dual-based method, subgradient method, and Lyapunov-based online optimization, are compared. This article also collates the potential applications of energy harvesting techniques in emerging (or upcoming) 5G/B5G communication systems. It is revealed that an effective redistribution and two-way trading of energy can significantly reduce the electricity bills of wireless service providers and decrease the consumption of brown energy. A list of interesting research directions are provided, requiring further investigation.Index Terms-5G/B5G communication networks, energy har-). The first two authors contributed equally to this work. vesting, smart grid, energy redistribution and trading, optimization techniques. arXiv:1912.13203v1 [eess.SY] 31 Dec 2019 EH and smart gridpowered wireless networks Types and models for RES (Sec. II) Smart grid-powered wireless networks (Sec. VIII) Wireless power and information transfer (Sec. VII) 5G/B5G applications of EH and smart gridpowered wireless networks (Sec. XI) Energy redistribution Energy planning under dynamic pricing (Sec. IX) Two-way energy trading and cooperation (Sec. X) EH-based wireless networks Point-to-point link (Sec. III) Multipoint-to-point system (Sec. IV) Multipoint-to-multipoint system (Sec. V) Multi-hop link (Sec. VI) Lessons learnt and future directions (Sec. XII) Fig. 1. The main structure of this article. from the aspects of network deployment, energy/spectrum efficiency, and delay/bandwidth versus power. Featuring energyharvesting wireless networks, beamforming techniques are reviewed in [22]. Energy scheduling, optimization, and application are presented in [23]. Circuit design, hardware implementation, EH techniques, and communication protocols are reviewed in [24]-[27] with specific emphases on radio frequen...

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“…In the aforementioned three cases, we are able to derive the closed-form expressions of the auxiliary variables ν k (t) that solve (16). For other concave non-decreasing utility functions, the closed-form solutions might not be available, standard convex solvers can be used to find the optimal solutions.…”

Section: ) Max-min Fairness (Mmf)mentioning

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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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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Dynamic Resource Allocation for Smart-Grid Powered MIMO Downlink Transmissions

Cited by 46 publications

References 26 publications

Optimal Wireless Resource Allocation With Random Edge Graph Neural Networks

Optimal Wireless Resource Allocation With Random Edge Graph Neural Networks

Modeling and Analysis of Energy Harvesting and Smart Grid-Powered Wireless Communication Networks: A Contemporary Survey

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

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