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This paper uses a spatial Aloha model to describe a distributed autonomous wireless network in which a group of transmit&#x2013;receive pairs (users) shares a common collision channel via slotted-Aloha-like random access. The objective of this study is to develop an intelligent algorithm to be embedded into the transceivers so that all users know how to self-tune their medium access probability (MAP) to achieve overall Pareto optimality in terms of network throughput under spatial reuse while maintaining network stability. While the optimal solution requires each user to have complete information about the network, our proposed algorithm only requires users to have local information. The fundamental of our algorithm is that the users will first self-organize into a number of nonoverlapping neighborhoods, and the user with the maximum node degree in each neighborhood is elected as the local leader (LL). Each LL then adjusts its MAP according to a parameter <inline-formula> <tex-math notation="LaTeX">$R$</tex-math></inline-formula>, which indicates the radio intensity level in its neighboring region, whereas the remaining users in the neighborhood simply follow the same MAP value. We show that by ensuring <inline-formula> <tex-math notation="LaTeX">$R \le \text{2}$</tex-math></inline-formula> at the LLs, the stability of the entire network can be assured, even when each user only has partial network information. For practical implementation, we propose each LL to use <inline-formula> <tex-math notation="LaTeX">$R=\text{2}$</tex-math></inline-formula> as the constant reference signal to its built-in proportional and integral controller. The settings of the control parameters are discussed, and we validate through simulations that the proposed method is able to achieve close-to-Pareto-front throughput.

Efficient and Scalable Distributed Autonomous Spatial Aloha Networks via Local Leader Election