  This paper uses a spatial Aloha model to describe a distributed autonomous
wireless network in which a group of transmit-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 non-overlapping 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 R 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 R less
than or equal to 2 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 R=2 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.
