46 research outputs found
Edge-Caching Wireless Networks: Performance Analysis and Optimization
Edge-caching has received much attention as an efficient technique to reduce
delivery latency and network congestion during peak-traffic times by bringing
data closer to end users. Existing works usually design caching algorithms
separately from physical layer design. In this paper, we analyse edge-caching
wireless networks by taking into account the caching capability when designing
the signal transmission. Particularly, we investigate multi-layer caching where
both base station (BS) and users are capable of storing content data in their
local cache and analyse the performance of edge-caching wireless networks under
two notable uncoded and coded caching strategies. Firstly, we propose a coded
caching strategy that is applied to arbitrary values of cache size. The
required backhaul and access rates are derived as a function of the BS and user
cache size. Secondly, closed-form expressions for the system energy efficiency
(EE) corresponding to the two caching methods are derived. Based on the derived
formulas, the system EE is maximized via precoding vectors design and
optimization while satisfying a predefined user request rate. Thirdly, two
optimization problems are proposed to minimize the content delivery time for
the two caching strategies. Finally, numerical results are presented to verify
the effectiveness of the two caching methods.Comment: to appear in IEEE Trans. Wireless Commu
Task-Based Information Compression for Multi-Agent Communication Problems with Channel Rate Constraints
A collaborative task is assigned to a multiagent system (MAS) in which agents
are allowed to communicate. The MAS runs over an underlying Markov decision
process and its task is to maximize the averaged sum of discounted one-stage
rewards. Although knowing the global state of the environment is necessary for
the optimal action selection of the MAS, agents are limited to individual
observations. The inter-agent communication can tackle the issue of local
observability, however, the limited rate of the inter-agent communication
prevents the agent from acquiring the precise global state information. To
overcome this challenge, agents need to communicate their observations in a
compact way such that the MAS compromises the minimum possible sum of rewards.
We show that this problem is equivalent to a form of rate-distortion problem
which we call the task-based information compression. We introduce a scheme for
task-based information compression titled State aggregation for information
compression (SAIC), for which a state aggregation algorithm is analytically
designed. The SAIC is shown to be capable of achieving near-optimal performance
in terms of the achieved sum of discounted rewards. The proposed algorithm is
applied to a rendezvous problem and its performance is compared with several
benchmarks. Numerical experiments confirm the superiority of the proposed
algorithm.Comment: 13 pages, 9 figure
Empirical Risk-aware Machine Learning on Trojan-Horse Detection for Trusted Quantum Key Distribution Networks
Quantum key distribution (QKD) is a cryptographic technique that leverages
principles of quantum mechanics to offer extremely high levels of data security
during transmission. It is well acknowledged for its capacity to accomplish
provable security. However, the existence of a gap between theoretical concepts
and practical implementation has raised concerns about the trustworthiness of
QKD networks. In order to mitigate this disparity, we propose the
implementation of risk-aware machine learning techniques that present risk
analysis for Trojan-horse attacks over the time-variant quantum channel. The
trust condition presented in this study aims to evaluate the offline assessment
of safety assurance by comparing the risk levels between the recommended safety
borderline. This assessment is based on the risk analysis conducted.
Furthermore, the proposed trustworthy QKD scenario demonstrates its numerical
findings with the assistance of a state-of-the-art point-to-point QKD device,
which operates over optical quantum channels spanning distances of 1m, 1km, and
30km. Based on the results from the experimental evaluation of a 30km optical
connection, it can be concluded that the QKD device provided prior information
to the proposed learner during the non-existence of Eve's attack. According to
the optimal classifier, the defensive gate offered by our learner possesses the
capability to identify any latent Eve attacks, hence effectively mitigating the
risk of potential vulnerabilities. The Eve detection probability is provably
bound for our trustworthy QKD scenario.Comment: 20 Pages, 14 figure, Journa
UAV Relay-Assisted Emergency Communications in IoT Networks: Resource Allocation and Trajectory Optimization
In this paper, a UAV is deployed as a flying base station to collect data
from time-constrained IoT devices and then transfer the data to a ground
gateway (GW). In general, the latency constraint at IoT users and the limited
storage capacity of UAV highly hinder practical applications of UAV-assisted
IoT networks. In this paper, full-duplex (FD) technique is adopted at the UAV
to overcome these challenges. In addition, half-duplex (HD) scheme for
UAV-based relaying is also considered to provide a comparative study between
two modes. In this context, we aim at maximizing the number of served IoT
devices by jointly optimizing bandwidth and power allocation, as well as the
UAV trajectory, while satisfying the requested timeout (RT) requirement of each
device and the UAV's limited storage capacity. The formulated optimization
problem is troublesome to solve due to its non-convexity and combinatorial
nature. Toward appealing applications, we first relax binary variables into
continuous values and transform the original problem into a more
computationally tractable form. By leveraging inner approximation framework, we
derive newly approximated functions for non-convex parts and then develop a
simple yet efficient iterative algorithm for its solutions. Next, we attempt to
maximize the total throughput subject to the number of served IoT devices.
Finally, numerical results show that the proposed algorithms significantly
outperform benchmark approaches in terms of the number of served IoT devices
and the amount of collected data.Comment: 30 pages, 11 figure
Machine Learning-Enabled Joint Antenna Selection and Precoding Design: From Offline Complexity to Online Performance
We investigate the performance of multi-user multiple-antenna downlink
systems in which a BS serves multiple users via a shared wireless medium. In
order to fully exploit the spatial diversity while minimizing the passive
energy consumed by radio frequency (RF) components, the BS is equipped with M
RF chains and N antennas, where M < N. Upon receiving pilot sequences to obtain
the channel state information, the BS determines the best subset of M antennas
for serving the users. We propose a joint antenna selection and precoding
design (JASPD) algorithm to maximize the system sum rate subject to a transmit
power constraint and QoS requirements. The JASPD overcomes the non-convexity of
the formulated problem via a doubly iterative algorithm, in which an inner loop
successively optimizes the precoding vectors, followed by an outer loop that
tries all valid antenna subsets. Although approaching the (near) global
optimality, the JASPD suffers from a combinatorial complexity, which may limit
its application in real-time network operations. To overcome this limitation,
we propose a learning-based antenna selection and precoding design algorithm
(L-ASPA), which employs a DNN to establish underlaying relations between the
key system parameters and the selected antennas. The proposed L-ASPD is robust
against the number of users and their locations, BS's transmit power, as well
as the small-scale channel fading. With a well-trained learning model, it is
shown that the L-ASPD significantly outperforms baseline schemes based on the
block diagonalization and a learning-assisted solution for broadcasting systems
and achieves higher effective sum rate than that of the JASPA under limited
processing time. In addition, we observed that the proposed L-ASPD can reduce
the computation complexity by 95% while retaining more than 95% of the optimal
performance.Comment: accepted to the IEEE Transactions on Wireless Communication