163 research outputs found
Andrew’s white cross, Hussein’s red blood:Being Scottish Shia in Brexit’s no-man’s-land
Brexit was a project shaped at the fringes of official politics. Unusually, however, it maintained its fringe-like qualities, including its lack of clarity and ambivalence, even as it took center stage in the political affairs of the country for more than three years. In such a transitional period, powerless segments of society, including vulnerable nonwhite communities, face a much larger and more multifaceted crisis than other sectors of the population. The question of border controls and forms of identification for migrants and those of hyphenated nationality is an alarming sign of a homeland turning into a hostile environment. By relying on data gathered in a three-year-long ethnographic study of the Shia Muslim communities in Scotland, I elaborate on how Scottish citizens marked by their religious culture and darker skin are handling the uncertainty created by Brexit
On feedback-based rateless codes for data collection in vehicular networks
The ability to transfer data reliably and with low delay over an unreliable service is intrinsic to a number of emerging technologies, including digital video broadcasting, over-the-air software updates, public/private cloud storage, and, recently, wireless vehicular networks. In particular, modern vehicles incorporate tens of sensors to provide vital sensor information to electronic control units (ECUs). In the current architecture, vehicle sensors are connected to ECUs via physical wires, which increase the cost, weight and maintenance effort of the car, especially as the number of electronic components keeps increasing. To mitigate the issues with physical wires, wireless sensor networks (WSN) have been contemplated for replacing the current wires with wireless links, making modern cars cheaper, lighter, and more efficient. However, the ability to reliably communicate with the ECUs is complicated by the dynamic channel properties that the car experiences as it travels through areas with different radio interference patterns, such as urban versus highway driving, or even different road quality, which may physically perturb the wireless sensors.
This thesis develops a suite of reliable and efficient communication schemes built upon feedback-based rateless codes, and with a target application of vehicular networks. In particular, we first investigate the feasibility of multi-hop networking for intra-car WSN, and illustrate the potential gains of using the Collection Tree Protocol (CTP), the current state of the art in multi-hop data aggregation. Our results demonstrate, for example, that the packet delivery rate of a node using a single-hop topology protocol can be below 80% in practical scenarios, whereas CTP improves reliability performance beyond 95% across all nodes while simultaneously reducing radio energy consumption. Next, in order to migrate from a wired intra-car network to a wireless system, we consider an intermediate step to deploy a hybrid communication structure, wherein wired and wireless networks coexist. Towards this goal, we design a hybrid link scheduling algorithm that guarantees reliability and robustness under harsh vehicular environments. We further enhance the hybrid link scheduler with the rateless codes such that information leakage to an eavesdropper is almost zero for finite block lengths.
In addition to reliability, one key requirement for coded communication schemes is to achieve a fast decoding rate. This feature is vital in a wide spectrum of communication systems, including multimedia and streaming applications (possibly inside vehicles) with real-time playback requirements, and delay-sensitive services, where the receiver needs to recover some data symbols before the recovery of entire frame. To address this issue, we develop feedback-based rateless codes with dynamically-adjusted nonuniform symbol selection distributions. Our simulation results, backed by analysis, show that feedback information paired with a nonuniform distribution significantly improves the decoding rate compared with the state of the art algorithms. We further demonstrate that amount of feedback sent can be tuned to the specific transmission properties of a given feedback channel
Efficient Cluster Selection for Personalized Federated Learning: A Multi-Armed Bandit Approach
Federated learning (FL) offers a decentralized training approach for machine
learning models, prioritizing data privacy. However, the inherent heterogeneity
in FL networks, arising from variations in data distribution, size, and device
capabilities, poses challenges in user federation. Recognizing this,
Personalized Federated Learning (PFL) emphasizes tailoring learning processes
to individual data profiles. In this paper, we address the complexity of
clustering users in PFL, especially in dynamic networks, by introducing a
dynamic Upper Confidence Bound (dUCB) algorithm inspired by the multi-armed
bandit (MAB) approach. The dUCB algorithm ensures that new users can
effectively find the best cluster for their data distribution by balancing
exploration and exploitation. The performance of our algorithm is evaluated in
various cases, showing its effectiveness in handling dynamic federated learning
scenarios
An Efficient Distributed Multi-Agent Reinforcement Learning for EV Charging Network Control
The increasing trend in adopting electric vehicles (EVs) will significantly
impact the residential electricity demand, which results in an increased risk
of transformer overload in the distribution grid. To mitigate such risks, there
are urgent needs to develop effective EV charging controllers. Currently, the
majority of the EV charge controllers are based on a centralized approach for
managing individual EVs or a group of EVs. In this paper, we introduce a
decentralized Multi-agent Reinforcement Learning (MARL) charging framework that
prioritizes the preservation of privacy for EV owners. We employ the
Centralized Training Decentralized Execution-Deep Deterministic Policy Gradient
(CTDE-DDPG) scheme, which provides valuable information to users during
training while maintaining privacy during execution. Our results demonstrate
that the CTDE framework improves the performance of the charging network by
reducing the network costs. Moreover, we show that the Peak-to-Average Ratio
(PAR) of the total demand is reduced, which, in turn, reduces the risk of
transformer overload during the peak hours.Comment: 8 pages, 4 figures, accepted at Allerton 202
Efficient Beam Alignment in Millimeter Wave Systems Using Contextual Bandits
In this paper, we investigate the problem of beam alignment in millimeter
wave (mmWave) systems, and design an optimal algorithm to reduce the overhead.
Specifically, due to directional communications, the transmitter and receiver
beams need to be aligned, which incurs high delay overhead since without a
priori knowledge of the transmitter/receiver location, the search space spans
the entire angular domain. This is further exacerbated under dynamic conditions
(e.g., moving vehicles) where the access to the base station (access point) is
highly dynamic with intermittent on-off periods, requiring more frequent beam
alignment and signal training. To mitigate this issue, we consider an online
stochastic optimization formulation where the goal is to maximize the
directivity gain (i.e., received energy) of the beam alignment policy within a
time period. We exploit the inherent correlation and unimodality properties of
the model, and demonstrate that contextual information improves the
performance. To this end, we propose an equivalent structured Multi-Armed
Bandit model to optimally exploit the exploration-exploitation tradeoff. In
contrast to the classical MAB models, the contextual information makes the
lower bound on regret (i.e., performance loss compared with an oracle policy)
independent of the number of beams. This is a crucial property since the number
of all combinations of beam patterns can be large in transceiver antenna
arrays, especially in massive MIMO systems. We further provide an
asymptotically optimal beam alignment algorithm, and investigate its
performance via simulations.Comment: To Appear in IEEE INFOCOM 2018. arXiv admin note: text overlap with
arXiv:1611.05724 by other author
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