Abstract
Distributed wireless networks with smart users (independent and rational) are becoming popular, and researchers are studying distributed equilibrium solutions like Nash Equilibrium (NE) to analyze and predict the convergence of such networks. Our goal is to drive the distributed wireless network to NE with high total throughput. Study of the distribution of network metrics at NE with high total throughput shows that communication links still have significant amount of interference. Adding an interference-received term with an optimal weight (αopt∗) to the link’s payoff can push the distributed network to converge to NE with high total throughput. The channel allocation trend at NE with high total throughput is as follows: each of theC−1links occupies its own channel, and the remainingN−C+1links share the remaining one channel, whereNis the number of links andCis the number of channels in the network. The links (transmitters and receivers) are randomly located andC<N(limited resources). The transmitter of a link has a direct connection with the receiver of the link; hence, several links overlap. This leads to a dense network with considerable amount of interference especially for links sharing channels. A practical application of our work is when smart devices in a room, hall, or concert arena have a direct communication with other smart devices in the area using limited bandwidth. Using best response technique and definitions of NE, we derive and propose an approximate way to mathematically expressαopt∗(referred to asα^opt) along with its probability density function (PDF) for a specific scenario. Then, a generic equation forα^optis inferred for varying network sizes (links) and available resources (channels). Implementing such a policy enhances the total throughput of the distributed wireless network by up to 15%. In a more general setting, our distributed policy can achieve up to 75% of the maximum total throughput (benchmark value reached by centralized solution via exhaustive search) at a fraction of the time and computation resources.
Highlights
Wireless communication and networking is expanding exponentially [1]
We are going to approximately derive some bounds and probability density function (PDF) of α∗opt that need to be in the individual link’s utility to converge to a Nash Equilibrium (NE) solution with high Ttot for a random “10-link 4-channel” scenario for scenarios with a varying number of links and channels. bαopt is quite close to α∗opt, but the main advantage of bαopt is that it can be computed in a fraction of the time and computational resources required to compute α∗opt
The value of Ttot obtained by implementing the policy U j = T j + α∗opt × Ifrom is illustrated by the red line. α∗opt is the optimal value of α, which is obtained by varying the value of α for a wide range and choosing the value of α that results in maximum Ttot
Summary
Wireless communication and networking is expanding exponentially [1]. Scientists are exploring new techniques to keep up with the escalated traffic. Various technologies in mobile communication (3G, 4G, and 5G) [2], wireless LAN (WiFi) [3], and personal area networking (Bluetooth) [4] are catering to the need of the present wireless communications, but new innovations and techniques are essential to meet up with the future trends and ever growing demand [5]. By dynamically allocating the resources, the smart radios can enhance the efficiency of the network by several folds in comparison to FRA [7]. Intelligent radios are capable to sense the environment, reason based on the observations made [8], and adopt their transmission parameters to get higher efficiency and performance [9]. We focus on dynamically allocating the channels (spectrum) to the intelligent wireless users [10]
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