A classical problem that has captured the attention of scientists and engineers from the networking community for decades is to design efficient techniques for the allocation of scarce resources (like, spectrum, power, computing resources and so on) among different contenders. In particular, the use of game theory in addressing various resource allocation issues in wireless network (like, congestion control, power control, medium access control, routing, edge computing, and so on) has increased many-folds over the past few years. In this talk, we will concentrate upon the design and analysis of distributed algorithms for three key resource allocation problems in the next-Generation wireless network using Minority Game (MG) theoretic framework. The problems include (i) lightweight energy-efficient Medium Access Control (MAC) protocol design, (ii) mobile Data Offloading via IEEE 802.11 WiFi network, and (iii) content distribution in Device-to-Device (D2D) communication network.
The performance of our proposed MG-MAC protocol for channel supporting Multi-packet Reception (MPR) is analyzed by studying several important metrics (like, mean of Attendance, Volatility, System Throughput and Energy Expenditure). Besides analytically deriving the system throughput, the protocol's behavior is also analyzed from the perspective of Crowd-Anticrowd Theory, a popular tool from the econophysics literature. Mobile data offloading - the next problem of our interest - is built upon exponential learning-based MG framework. The effectiveness of our proposed scheme is studied w.r.t several key metrics (like, pricing parameter of cellular service, cellular offered throughput, and temperature coefficient of the MG). Results of our investigation on several related issues, like designing an effective model for tuning cellular pricing parameter w.r.t the offered load, introducing a provision for applying the algorithm in the multi-AP environment, and studying the behavior of different classes of nodes in heterogeneous population - will also be covered in the discussion. Last, we'll look into the performance of a novel D2D content sharing scheme using the frameworks of Evolutionary Minority Game (EMG) and Multiple Choice Minority Game (MCMG). We'll present the results of the algorithm's performance in terms of its convergence, content downloading efficiency, as well as the effects of different parameters (such as pricing parameters, cellular data rates, and cluster size).