15 research outputs found
Coping with spectrum and energy scarcity in Wireless Networks: a Stochastic Optimization approach to Cognitive Radio and Energy Harvesting
In the last decades, we have witnessed an explosion of wireless communications and networking, spurring a great interest in the research community. The design of wireless networks is challenged by the scarcity of resources, especially spectrum and energy. In this thesis, we explore the potential offered by two novel technologies to cope with spectrum and energy scarcity: Cognitive Radio (CR) and Energy Harvesting (EH). CR is a novel paradigm for improving the spectral efficiency in wireless networks, by enabling the coexistence of an incumbent legacy system and an opportunistic system with CR capability. We investigate a technique where the CR system exploits the temporal redundancy introduced by the Hybrid Automatic Retransmission reQuest (HARQ) protocol implemented by the legacy system to perform interference cancellation, thus enhancing its own throughput.
Recently, EH has been proposed to cope with energy scarcity in Wireless Sensor Networks (WSNs). Devices with EH capability harvest energy from the environment, e.g., solar, wind, heat or piezo-electric, to power their circuitry and to perform data sensing, processing and communication tasks. Due to the random energy supply, how to best manage the available energy is an open research issue. In the second part of this thesis, we design control policies for EH devices, and investigate the impact of factors such as the finite battery storage, time-correlation in the EH process and battery degradation phenomena on the performance of such systems.
We cast both paradigms in a stochastic optimization framework, and investigate techniques to cope with spectrum and energy scarcity by opportunistically leveraging interference and ambient energy, respectively, whose benefits are demonstrated both by theoretical analysis and numerically.
As an additional topic, we investigate the issue of channel estimation in UltraWide-Band (UWB) systems. Due to the large transmission bandwidth, the channel has been typically modeled as sparse. However, some propagation phenomena, e.g., scattering from rough surfaces and frequency distortion, are better modeled by a diffuse channel. We propose a novel Hybrid Sparse/Diffuse (HSD) channel model which captures both components, and design channel estimators based on it
Adaptive Scheduling and Trajectory Design for Power-Constrained Wireless UAV Relays
This paper investigates the adaptive trajectory and communication scheduling
design for an unmanned aerial vehicle (UAV) relaying random data traffic
generated by ground nodes to a base station. The goal is to minimize the
expected average communication delay to serve requests, subject to an average
UAV mobility power constraint. It is shown that the problem can be cast as a
semi-Markov decision process with a two-scale structure, which is optimized
efficiently: in the outer decision, the UAV radial velocity for waiting phases
and end radius for communication phases optimize the average long-term
delay-power trade-off; given outer decisions, inner decisions greedily minimize
the instantaneous delay-power cost, yielding the optimal angular velocity in
waiting states, and the optimal relay strategy and UAV trajectory in
communication states. A constrained particle swarm optimization algorithm is
designed to optimize these trajectory problems, demonstrating 100x faster
computational speeds than successive convex approximation methods. Simulations
demonstrate that an intelligent adaptive design exploiting realistic UAV
mobility features, such as helicopter translational lift, reduces the average
communication delay and UAV mobility power consumption by 44% and 7%,
respectively, with respect to an optimal hovering strategy and by 2% and 13%,
respectively, with respect to a greedy delay minimization scheme.Comment: Submitted to IEEE Transactions on Wireless Communication
Cognitive Access Policies under a Primary ARQ process via Forward-Backward Interference Cancellation
This paper introduces a novel technique for access by a cognitive Secondary
User (SU) using best-effort transmission to a spectrum with an incumbent
Primary User (PU), which uses Type-I Hybrid ARQ. The technique leverages the
primary ARQ protocol to perform Interference Cancellation (IC) at the SU
receiver (SUrx). Two IC mechanisms that work in concert are introduced: Forward
IC, where SUrx, after decoding the PU message, cancels its interference in the
(possible) following PU retransmissions of the same message, to improve the SU
throughput; Backward IC, where SUrx performs IC on previous SU transmissions,
whose decoding failed due to severe PU interference. Secondary access policies
are designed that determine the secondary access probability in each state of
the network so as to maximize the average long-term SU throughput by
opportunistically leveraging IC, while causing bounded average long-term PU
throughput degradation and SU power expenditure. It is proved that the optimal
policy prescribes that the SU prioritizes its access in the states where SUrx
knows the PU message, thus enabling IC. An algorithm is provided to optimally
allocate additional secondary access opportunities in the states where the PU
message is unknown. Numerical results are shown to assess the throughput gain
provided by the proposed techniques.Comment: 16 pages, 11 figures, 2 table
Federated Learning with Communication Delay in Edge Networks
Federated learning has received significant attention as a potential solution
for distributing machine learning (ML) model training through edge networks.
This work addresses an important consideration of federated learning at the
network edge: communication delays between the edge nodes and the aggregator. A
technique called FedDelAvg (federated delayed averaging) is developed, which
generalizes the standard federated averaging algorithm to incorporate a
weighting between the current local model and the delayed global model received
at each device during the synchronization step. Through theoretical analysis,
an upper bound is derived on the global model loss achieved by FedDelAvg, which
reveals a strong dependency of learning performance on the values of the
weighting and learning rate. Experimental results on a popular ML task indicate
significant improvements in terms of convergence speed when optimizing the
weighting scheme to account for delays.Comment: Accepted for publication at IEEE Global Communications Conference
(Globecom 2020