209 research outputs found
Optimizing Average-Maximum TTR Trade-off for Cognitive Radio Rendezvous
In cognitive radio (CR) networks, "TTR", a.k.a. time-to-rendezvous, is one of
the most important metrics for evaluating the performance of a channel hopping
(CH) rendezvous protocol, and it characterizes the rendezvous delay when two
CRs perform channel hopping. There exists a trade-off of optimizing the average
or maximum TTR in the CH rendezvous protocol design. On one hand, the random CH
protocol leads to the best "average" TTR without ensuring a finite "maximum"
TTR (two CRs may never rendezvous in the worst case), or a high rendezvous
diversity (multiple rendezvous channels). On the other hand, many
sequence-based CH protocols ensure a finite maximum TTR (upper bound of TTR)
and a high rendezvous diversity, while they inevitably yield a larger average
TTR. In this paper, we strike a balance in the average-maximum TTR trade-off
for CR rendezvous by leveraging the advantages of both random and
sequence-based CH protocols. Inspired by the neighbor discovery problem, we
establish a design framework of creating a wake-up schedule whereby every CR
follows the sequence-based (or random) CH protocol in the awake (or asleep)
mode. Analytical and simulation results show that the hybrid CH protocols under
this framework are able to achieve a greatly improved average TTR as well as a
low upper-bound of TTR, without sacrificing the rendezvous diversity.Comment: Accepted by IEEE International Conference on Communications (ICC
2015, http://icc2015.ieee-icc.org/
Computational Intelligence Inspired Data Delivery for Vehicle-to-Roadside Communications
We propose a vehicle-to-roadside communication protocol based on distributed clustering where a coalitional game approach is used to stimulate the vehicles to join a cluster, and a fuzzy logic algorithm is employed to generate stable clusters by considering multiple metrics of vehicle velocity, moving pattern, and signal qualities between vehicles. A reinforcement learning algorithm with game theory based reward allocation is employed to guide each vehicle to select the route that can maximize the whole network performance. The protocol is integrated with a multi-hop data delivery virtualization scheme that works on the top of the transport layer and provides high performance for multi-hop end-to-end data transmissions. We conduct realistic computer simulations to show the performance advantage of the protocol over other approaches
Energy savings using an adaptive base station-to-relay station switching paradigm
Applying a Base Station (BS) sleep approach during low traffic periods has recently been advocated as a strategy for reducing energy consumption in cellular networks. The complete switching off of certain BS however, can lead to coverage holes and severe performance degradation in terms of off-cell user throughput, greater transmit power dissipation in both the up and downlinks, and more complex interference management. This paper presents a novel cellular network energy saving model in which certain BS rather being turned off are switched to Relay Station (RS) mode during low traffic periods. The switched RS and other shared RS deployed at the cross border of each cell are responsible for upholding the same quality of service (QoS) provision as when all BS are active. A centralised adaptive switching threshold algorithm is also introduced to undertake the switching decision, instead of using a fixed threshold. Simulation results confirm the new BS-RS Switching model using an adaptive threshold can reduce network energy consumption by more than half, as well as improving off-cell users’ throughput
TinyQMIX: Distributed Access Control for mMTC via Multi-agent Reinforcement Learning
Distributed access control is a crucial component for massive machine type
communication (mMTC). In this communication scenario, centralized resource
allocation is not scalable because resource configurations have to be sent
frequently from the base station to a massive number of devices. We investigate
distributed reinforcement learning for resource selection without relying on
centralized control. Another important feature of mMTC is the sporadic and
dynamic change of traffic. Existing studies on distributed access control
assume that traffic load is static or they are able to gradually adapt to the
dynamic traffic. We minimize the adaptation period by training TinyQMIX, which
is a lightweight multi-agent deep reinforcement learning model, to learn a
distributed wireless resource selection policy under various traffic patterns
before deployment. Therefore, the trained agents are able to quickly adapt to
dynamic traffic and provide low access delay. Numerical results are presented
to support our claims.Comment: 6 pages, 4 figures, presented at VTC Fall 202
Annual Report of the Commission of the Department of Public Utilities for the Year Ending November 30, 1937
Millimeter wave (mmWave) communications provide great potential for
next-generation cellular networks to meet the demands of fast-growing mobile
data traffic with plentiful spectrum available. However, in a mmWave cellular
system, the shadowing and blockage effects lead to the intermittent
connectivity, and the handovers are more frequent. This paper investigates an
``all-mmWave'' cloud radio access network (cloud-RAN), in which both the
fronthaul and the radio access links operate at mmWave. To address the
intermittent transmissions, we allow the mobile users (MUs) to establish
multiple connections to the central unit over the remote radio heads (RRHs).
Specifically, we propose a multipath transmission framework by leveraging the
``all-mmWave'' cloud-RAN architecture, which makes decisions of the RRH
association and the packet transmission scheduling according to the
time-varying network statistics, such that a MU experiences the minimum
queueing delay and packet drops. The joint RRH association and transmission
scheduling problem is formulated as a Markov decision process (MDP). Due to the
problem size, a low-complexity online learning scheme is put forward, which
requires no a priori statistic information of network dynamics. Simulations
show that our proposed scheme outperforms the state-of-art baselines, in terms
of average queue length and average packet dropping rate
Computation Offloading in Beyond 5G Networks: A Distributed Learning Framework and Applications
Facing the trend of merging wireless communications and multi-access edge
computing (MEC), this article studies computation offloading in the beyond
fifth-generation networks. To address the technical challenges originating from
the uncertainties and the sharing of limited resource in an MEC system, we
formulate the computation offloading problem as a multi-agent Markov decision
process, for which a distributed learning framework is proposed. We present a
case study on resource orchestration in computation offloading to showcase the
potentials of an online distributed reinforcement learning algorithm developed
under the proposed framework. Experimental results demonstrate that our
learning algorithm outperforms the benchmark resource orchestration algorithms.
Furthermore, we outline the research directions worth in-depth investigation to
minimize the time cost, which is one of the main practical issues that prevent
the implementation of the proposed distributed learning framework
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