23,550 research outputs found
Interference Management for Over-the-Air Federated Learning in Multi-Cell Wireless Networks
Federated learning (FL) over resource-constrained wireless networks has
recently attracted much attention. However, most existing studies consider one
FL task in single-cell wireless networks and ignore the impact of
downlink/uplink inter-cell interference on the learning performance. In this
paper, we investigate FL over a multi-cell wireless network, where each cell
performs a different FL task and over-the-air computation (AirComp) is adopted
to enable fast uplink gradient aggregation. We conduct convergence analysis of
AirComp-assisted FL systems, taking into account the inter-cell interference in
both the downlink and uplink model/gradient transmissions, which reveals that
the distorted model/gradient exchanges induce a gap to hinder the convergence
of FL. We characterize the Pareto boundary of the error-induced gap region to
quantify the learning performance trade-off among different FL tasks, based on
which we formulate an optimization problem to minimize the sum of error-induced
gaps in all cells. To tackle the coupling between the downlink and uplink
transmissions as well as the coupling among multiple cells, we propose a
cooperative multi-cell FL optimization framework to achieve efficient
interference management for downlink and uplink transmission design. Results
demonstrate that our proposed algorithm achieves much better average learning
performance over multiple cells than non-cooperative baseline schemes.Comment: This work has been accepted by IEEE Journal on Selected Areas in
Communication
Improving Macrocell - Small Cell Coexistence through Adaptive Interference Draining
The deployment of underlay small base stations (SBSs) is expected to
significantly boost the spectrum efficiency and the coverage of next-generation
cellular networks. However, the coexistence of SBSs underlaid to an existing
macro-cellular network faces important challenges, notably in terms of spectrum
sharing and interference management. In this paper, we propose a novel
game-theoretic model that enables the SBSs to optimize their transmission rates
by making decisions on the resource occupation jointly in the frequency and
spatial domains. This procedure, known as interference draining, is performed
among cooperative SBSs and allows to drastically reduce the interference
experienced by both macro- and small cell users. At the macrocell side, we
consider a modified water-filling policy for the power allocation that allows
each macrocell user (MUE) to focus the transmissions on the degrees of freedom
over which the MUE experiences the best channel and interference conditions.
This approach not only represents an effective way to decrease the received
interference at the MUEs but also grants the SBSs tier additional transmission
opportunities and allows for a more agile interference management. Simulation
results show that the proposed approach yields significant gains at both
macrocell and small cell tiers, in terms of average achievable rate per user,
reaching up to 37%, relative to the non-cooperative case, for a network with
150 MUEs and 200 SBSs
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