Flow Allocation for Maximum Throughput and Bounded Delay on Multiple Disjoint Paths for Random Access Wireless Multihop Networks

In this paper, we consider random access, wireless, multi-hop networks, with multi-packet reception capabilities, where multiple flows are forwarded to the gateways through node disjoint paths. We explore the issue of allocating flow on multiple paths, exhibiting both intra- and inter-path interference, in order to maximize average aggregate flow throughput (AAT) and also provide bounded packet delay. A distributed flow allocation scheme is proposed where allocation of flow on paths is formulated as an optimization problem. Through an illustrative topology it is shown that the corresponding problem is non-convex. Furthermore, a simple, but accurate model is employed for the average aggregate throughput achieved by all flows, that captures both intra- and inter-path interference through the SINR model. The proposed scheme is evaluated through Ns2 simulations of several random wireless scenarios. Simulation results reveal that, the model employed, accurately captures the AAT observed in the simulated scenarios, even when the assumption of saturated queues is removed. Simulation results also show that the proposed scheme achieves significantly higher AAT, for the vast majority of the wireless scenarios explored, than the following flow allocation schemes: one that assigns flows on paths on a round-robin fashion, one that optimally utilizes the best path only, and another one that assigns the maximum possible flow on each path. Finally, a variant of the proposed scheme is explored, where interference for each link is approximated by considering its dominant interfering nodes only.Comment: IEEE Transactions on Vehicular Technolog

Throughput Optimal Flow Allocation on Multiple Paths for Random Access Wireless Multi-hop Networks

In this paper we consider random access wireless multi-hop mesh networks with multi-packet reception capabilities where multiple flows are forwarded to the gateways through node disjoint paths. We address the issue of aggregate throughput-optimal flow rate allocation with bounded delay guarantees. We propose a distributed flow rate allocation scheme that formulates flow rate allocation as an optimization problem and derive the conditions for non-convexity for an illustrative topology. We also employ a simple model for the average aggregate throughput achieved by all flows that captures both intra- and inter-path interference. The proposed scheme is evaluated through NS-2 simulations. Our preliminary results are derived from a grid topology and show that the proposed flow allocation scheme slightly underestimates the average aggregate throughput observed in two simulated scenarios with two and three flows respectively. Moreover it achieves significantly higher average aggregate throughput than single path utilization in two different traffic scenarios examined.Comment: Accepted for publication at the 9th IEEE BROADBAND WIRELESS ACCESS WORKSHOP (BWA2013), IEEE Globecom 2013 Workshop

Distributed Game Theoretic Optimization and Management of Multichannel ALOHA Networks

The problem of distributed rate maximization in multi-channel ALOHA networks is considered. First, we study the problem of constrained distributed rate maximization, where user rates are subject to total transmission probability constraints. We propose a best-response algorithm, where each user updates its strategy to increase its rate according to the channel state information and the current channel utilization. We prove the convergence of the algorithm to a Nash equilibrium in both homogeneous and heterogeneous networks using the theory of potential games. The performance of the best-response dynamic is analyzed and compared to a simple transmission scheme, where users transmit over the channel with the highest collision-free utility. Then, we consider the case where users are not restricted by transmission probability constraints. Distributed rate maximization under uncertainty is considered to achieve both efficiency and fairness among users. We propose a distributed scheme where users adjust their transmission probability to maximize their rates according to the current network state, while maintaining the desired load on the channels. We show that our approach plays an important role in achieving the Nash bargaining solution among users. Sequential and parallel algorithms are proposed to achieve the target solution in a distributed manner. The efficiencies of the algorithms are demonstrated through both theoretical and simulation results.Comment: 34 pages, 6 figures, accepted for publication in the IEEE/ACM Transactions on Networking, part of this work was presented at IEEE CAMSAP 201

Spectrum Sharing in Wireless Networks via QoS-Aware Secondary Multicast Beamforming

Secondary spectrum usage has the potential to considerably increase spectrum utilization. In this paper, quality-of-service (QoS)-aware spectrum underlay of a secondary multicast network is considered. A multiantenna secondary access point (AP) is used for multicast (common information) transmission to a number of secondary single-antenna receivers. The idea is that beamforming can be used to steer power towards the secondary receivers while limiting sidelobes that cause interference to primary receivers. Various optimal formulations of beamforming are proposed, motivated by different ldquocohabitationrdquo scenarios, including robust designs that are applicable with inaccurate or limited channel state information at the secondary AP. These formulations are NP-hard computational problems; yet it is shown how convex approximation-based multicast beamforming tools (originally developed without regard to primary interference constraints) can be adapted to work in a spectrum underlay context. Extensive simulation results demonstrate the effectiveness of the proposed approaches and provide insights on the tradeoffs between different design criteria

Network Management, Optimization and Security with Machine Learning Applications in Wireless Networks

Wireless communication networks are emerging fast with a lot of challenges and ambitions. Requirements that are expected to be delivered by modern wireless networks are complex, multi-dimensional, and sometimes contradicting. In this thesis, we investigate several types of emerging wireless networks and tackle some challenges of these various networks. We focus on three main challenges. Those are Resource Optimization, Network Management, and Cyber Security. We present multiple views of these three aspects and propose solutions to probable scenarios. The first challenge (Resource Optimization) is studied in Wireless Powered Communication Networks (WPCNs). WPCNs are considered a very promising approach towards sustainable, self-sufficient wireless sensor networks. We consider a WPCN with Non-Orthogonal Multiple Access (NOMA) and study two decoding schemes aiming for optimizing the performance with and without interference cancellation. This leads to solving convex and non-convex optimization problems. The second challenge (Network Management) is studied for cellular networks and handled using Machine Learning (ML). Two scenarios are considered. First, we target energy conservation. We propose an ML-based approach to turn Multiple Input Multiple Output (MIMO) technology on/off depending on certain criteria. Turning off MIMO can save considerable energy of the total site consumption. To control enabling and disabling MIMO, a Neural Network (NN) based approach is used. It learns some network features and decides whether the site can achieve satisfactory performance with MIMO off or not. In the second scenario, we take a deeper look into the cellular network aiming for more control over the network features. We propose a Reinforcement Learning-based approach to control three features of the network (relative CIOs, transmission power, and MIMO feature). The proposed approach delivers a stable state of the cellular network and enables the network to self-heal after any change or disturbance in the surroundings. In the third challenge (Cyber Security), we propose an NN-based approach with the target of detecting False Data Injection (FDI) in industrial data. FDI attacks corrupt sensor measurements to deceive the industrial platform. The proposed approach uses an Autoencoder (AE) for FDI detection. In addition, a Denoising AE (DAE) is used to clean the corrupted data for further processing