ﻻ يوجد ملخص باللغة العربية
In this work, we propose a new learning framework for optimising transmission strategies when irregular repetition slotted ALOHA (IRSA) MAC protocol is considered. We cast the online optimisation of the MAC protocol design as a multi-arm bandit problem that exploits the IRSA structure in the learning framework. Our learning algorithm quickly learns the optimal transmission strategy, leading to higher rate of successfully received packets with respect to baseline transmission optimizations.
We consider online convex optimization (OCO) over a heterogeneous network with communication delay, where multiple workers together with a master execute a sequence of decisions to minimize the accumulation of time-varying global costs. The local dat
We present novel convex-optimization-based solutions to the problem of blind beamforming of constant modulus signals, and to the related problem of linearly constrained blind beamforming of constant modulus signals. These solutions ensure global opti
Motivated by the analogy between successive interference cancellation and iterative belief-propagation on erasure channels, irregular repetition slotted ALOHA (IRSA) strategies have received a lot of attention in the design of medium access control p
We study the problem of reconstructing a block-sparse signal from compressively sampled measurements. In certain applications, in addition to the inherent block-sparse structure of the signal, some prior information about the block support, i.e. bloc
Besides mimicking bio-chemical and multi-scale communication mechanisms, molecular communication forms a theoretical framework for virus infection processes. Towards this goal, aerosol and droplet transmission has recently been modeled as a multiuser