  Information exchange is a crucial component of many real-world multi-agent
systems. However, the communication between the agents involves two major
challenges: the limited bandwidth, and the shared communication medium between
the agents, which restricts the number of agents that can simultaneously
exchange information. While both of these issues need to be addressed in
practice, the impact of the latter problem on the performance of the
multi-agent systems has often been neglected. This becomes even more important
when the agents' information or observations have different importance, in
which case the agents require different priorities for accessing the medium and
sharing their information. Representing the agents' priorities by fairness
weights and normalizing each agent's share by the assigned fairness weight, the
goal can be expressed as equalizing the agents' normalized shares of the
communication medium. To achieve this goal, we adopt a queueing theoretic
approach and propose a distributed fair scheduling algorithm for providing
weighted fairness in single-hop networks. Our proposed algorithm guarantees an
upper-bound on the normalized share disparity among any pair of agents. This
can particularly improve the short-term fairness, which is important in
real-time applications. Moreover, our scheduling algorithm adjusts itself
dynamically to achieve a high throughput at the same time. The simulation
results validate our claims and comparisons with the existing methods show our
algorithm's superiority in providing short-term fairness, while achieving a
high throughput.
