Approaching Throughput-optimality in Distributed CSMA Scheduling Algorithms with Collisions

It was shown recently that CSMA (Carrier Sense Multiple Access)-like distributed algorithms can achieve the maximal throughput in wireless networks (and task processing networks) under certain assumptions. One important, but idealized assumption is that the sensing time is negligible, so that there is no collision. In this paper, we study more practical CSMA-based scheduling algorithms with collisions. First, we provide a Markov chain model and give an explicit throughput formula which takes into account the cost of collisions and overhead. The formula has a simple form since the Markov chain is "almost" time-reversible. Second, we propose transmission-length control algorithms to approach throughput optimality in this case. Sufficient conditions are given to ensure the convergence and stability of the proposed algorithms. Finally, we characterize the relationship between the CSMA parameters (such as the maximum packet lengths) and the achievable capacity region.Comment: To appear in IEEE/ACM Transactions on Networking. This is the longer versio

Techniques for Decentralized and Dynamic Resource Allocation

abstract: This thesis investigates three different resource allocation problems, aiming to achieve two common goals: i) adaptivity to a fast-changing environment, ii) distribution of the computation tasks to achieve a favorable solution. The motivation for this work relies on the modern-era proliferation of sensors and devices, in the Data Acquisition Systems (DAS) layer of the Internet of Things (IoT) architecture. To avoid congestion and enable low-latency services, limits have to be imposed on the amount of decisions that can be centralized (i.e. solved in the ``cloud") and/or amount of control information that devices can exchange. This has been the motivation to develop i) a lightweight PHY Layer protocol for time synchronization and scheduling in Wireless Sensor Networks (WSNs), ii) an adaptive receiver that enables Sub-Nyquist sampling, for efficient spectrum sensing at high frequencies, and iii) an SDN-scheme for resource-sharing across different technologies and operators, to harmoniously and holistically respond to fluctuations in demands at the eNodeB' s layer. The proposed solution for time synchronization and scheduling is a new protocol, called PulseSS, which is completely event-driven and is inspired by biological networks. The results on convergence and accuracy for locally connected networks, presented in this thesis, constitute the theoretical foundation for the protocol in terms of performance guarantee. The derived limits provided guidelines for ad-hoc solutions in the actual implementation of the protocol. The proposed receiver for Compressive Spectrum Sensing (CSS) aims at tackling the noise folding phenomenon, e.g., the accumulation of noise from different sub-bands that are folded, prior to sampling and baseband processing, when an analog front-end aliasing mixer is utilized. The sensing phase design has been conducted via a utility maximization approach, thus the scheme derived has been called Cognitive Utility Maximization Multiple Access (CUMMA). The framework described in the last part of the thesis is inspired by stochastic network optimization tools and dynamics. While convergence of the proposed approach remains an open problem, the numerical results here presented suggest the capability of the algorithm to handle traffic fluctuations across operators, while respecting different time and economic constraints. The scheme has been named Decomposition of Infrastructure-based Dynamic Resource Allocation (DIDRA).Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Throughput Analysis of Wireless Ad-Hoc Cognitive Radio Networks

In this dissertation we consider the throughput performance of cognitive radio networks and derive the optimal sensing and access schemes for secondary users that maximizes their sum-throughput while guaranteeing certain quality of service to primary networks. First, we consider a cognitive radio network where secondary users have access to N licensed primary frequency bands with their usage statistics and are subject to certain inter-network interference constraint. In particular, to limit the interference to the primary network, secondary users are equipped with spectrum sensors and are capable of sensing and accessing a limited number of channels at the same time. We consider both the error-free and erroneous spectrum sensing scenarios, and establish the jointly optimal random sensing and access scheme, which maximizes the secondary network expected sum throughput while honoring the primary interference constraint. We show that under certain conditions the optimal sensing and access scheme is independent of the primary frequency bandwidths and usage statistics; otherwise, they follow water-filling-like strategies. Next, we study the asymptotic performance of two multi-hop overlaid ad-hoc networks that utilize the same temporal, spectral, and spatial resources based on random access schemes. The primary network consists of Poisson distributed legacy users with density λ^(p) and the secondary network consists of Poisson distributed cognitive radio users with density λ^(s) = (λ^(p))^(β) that utilize the spectrum opportunistically. Both networks employ ALOHA medium access protocols where the secondary nodes are additionally equipped with range-limited perfect spectrum sensors to monitor and protect primary transmissions. We study the problem in two distinct regimes, namely β > 1 and 0 < β < 1. We show that in both cases, the two networks can achieve their corresponding stand-alone throughput scaling even without secondary spectrum sensing ; this implies the need for a more comprehensive performance metric than just throughput scaling to evaluate the influence of the overlaid interactions. We thus introduce a new criterion, termed the asymptotic multiplexing gain, which captures the effect of inter-network interference . With this metric, we clearly demonstrate that spectrum sensing can substantially improve the overlaid cognitive networks performance when β > 1. On the contrary, spectrum sensing turns out to be redundant when β < 1 and employing spectrum sensors cannot improve the networks performance. Finally, we present a methodology employing statistical analysis and stochastic geometry to study geometric routing schemes in wireless ad-hoc networks. The techniques developed in this section enable us to establish the asymptotic connectivity and the convergence results for the mean and variance of the routing path lengths generated by geometric routing schemes in random wireless networks