Dynamic Resource Allocation for Smart-Grid Powered MIMO Downlink Transmissions

• Published in: IEEE journal on selected areas in communications Vol. 34; no. 12; pp. 3354 - 3365
• Main Authors: Wang, Xin, Chen, Tianyi, Chen, Xiaojing, Zhou, Xiaolin, Giannakis, Georgios B.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Batteries; Communications systems; Control algorithms; Control theory; Decoupling method; Downlink; Downlinking; Electric power systems; Electricity pricing; Energy costs; Energy efficiency; MIMO; MIMO broadcast channels; MIMO communication; Random processes; Randomness; Renewable energy sources; Resource allocation; Resource management; Smart grid; Smart grids; stochastic optimization; Stochastic processes; Sustainable development
• ISSN: 0733-8716; 1558-0008
• DOI: 10.1109/JSAC.2016.2600543

Benefiting from technological advances in the smart grid era, next-generation multi-input multi-output (MIMO) communication systems are expected to be powered by renewable energy sources (RES) integrated in the distribution grid, thus realizing the vision of "green communications." However, penetration of renewables introduces variabilities in the traditional power system, making RES benefits achievable only after appropriately mitigating their inherently high variability, which challenges existing resource allocation strategies. Aligned with this goal, an infinite time-horizon resource allocation problem is formulated to maximize the time-average MIMO downlink throughput, subject to a time-average energy cost budget. By using the advanced time decoupling technique, a novel stochastic subgradient-based online control approach is developed for the resultant smart-grid powered communication system. It is established analytically that even without a priori knowledge of the independently and identically distributed (i.i.d.) processes involved such as channel coefficients, renewables, and electricity prices, the proposed online control algorithm is still able to yield a feasible and asymptotically optimal solution. Numerical results further demonstrate that the proposed algorithm also works well in non-i.i.d. scenarios, where the underlying randomness is highly correlated over time.