Traffic engineering (TE) problem is a central component of the next generation cloud-based wireless networks. In this paper, we study a new resource allocation scheme for effective traffic engineering under practical constraints such as the finite buffer size at each node. To reduce the computational effort required in the existing single-slot TE approaches and to deal with practical hardware limitations on link flows and buffers, we propose a two time-scale, low-complexity TE algorithm which incorporates a novel link scheduling component. The algorithm can be distributedly implemented. Simulation results demonstrate the effectiveness of the proposed algorithm.

Alternating direction method of multipliers (ADMM) has been recognized as an efficient approach for solving many large-scale learning problems over a computer cluster. However, traditional synchronized computation does not scale well with the problem size, as the speed of the algorithm is limited by the slowest workers. In this paper, we propose an asynchronous distributed ADMM (AD- ADMM) which can effectively improve the time efficiency of distributed optimization. Our main interest lies in characterizing the convergence conditions of the AD-ADMM, under the popular partially asynchronous model which is defined based on a maximum tolerable delay in the network. Specifically, by considering general and possibly non-convex cost functions, we show that the AD-ADMM converges to the set of Karush-Kuhn-Tucker (KKT) points as long as the algorithm parameters are chosen appropriately according to the network delay. We also show that the asynchrony of ADMM has to be handled with care, as a slightly different implementation can significantly jeopardize the algorithm convergence.

We consider the distributed network routing problem in a large-scale hierarchical network whereby the nodes are partitioned into subnetworks, each managed by a network controller (NC), and there is a central NC to coordinate the operation of the distributed NCs. We propose a semi-asynchronous routing algorithm for such a network, whereby the computation is distributed across the NCs and is parallel within each NC. A key feature of the algorithm is its ability to handle a certain degree of asynchronism: the distributed NCs can perform their local computation asynchronously at different processing speed. The efficiency of the proposed algorithm is validated through numerical experiments.

In this paper, we propose a beamforming algorithm for binaural hearing aids with enhanced noise suppression capability. The enhancement is based on incorporating a priori spatial information into the conventional multichannel Wiener filtering (MWF) approach for noise suppression. We develop a low complexity algorithm for the resulting quadratically constrained beamforming problem. Through numerical experiments, we demonstrate that the new algorithm can achieve better noise suppression performance than the existing beamforming algorithms under fairly realistic conditions. In addition, we propose two techniques to further reduce the algorithm's computational complexity and the communication overhead between two hearing aids without sacrificing the noise suppression performance.

In wireless sensor networks, random deployment of nodes may cause serious coverage overlapping among the nodes and the original network may suffer severe coverage problems due to death of the nodes after deployment. In this paper, efficient distributed coverage hole repair algorithms are proposed taking density of the nodes in the post deployment scenario. The proposed algorithms consider limited mobility of the nodes and can select the mobile nodes based on their degree of coverage overlapping. In order to repair coverage holes of the network, nodes with higher degree of density are moved to maintain uniform network density without increasing the coverage degree of the neighbors of a mobile node. Simulation results show that the energy consumption due to mobility of nodes is least as compared to other similar protocols of the Wireless Sensor Networks. Besides, it is observed that substantial amount of coverage overlapping can be minimized and percentage of coverage of the holes can be maximized.

We consider a heterogenous network (HetNet) consisting of a number of base stations (BSs) and network routers connected via a backhaul network. The optimal provision of such networks requires proper resource allocation across the radio access links in conjunction with appropriate traffic engineering within the backhaul network. In this paper we propose an efficient distributed algorithm for the joint resource allocation across the wireless links and the flow control within the backhaul network. The proposed algorithm, which maximizes the minimum rate among all the users and/or flows, is based on a decomposition approach that leverages both the Alternating Direction Method of Multipliers (ADMM) and the WMMSE algorithm, and is shown to be globally convergent to a stationary solution of the joint flow control and resource allocation problem. Moreover, this algorithm is easily parallelizable and can be extended to the multi-antenna scenario.

We consider a cloud-based heterogeneous network of base stations (BSs) connected via a backhaul network of routers and wired/wireless links with limited capacity. The optimal provision of such networks requires proper resource allocation across the radio access links in conjunction with appropriate traffic engineering within the backhaul network. In this paper, we propose an efficient algorithm for joint resource allocation across the wireless links and flow control over the entire network. The proposed algorithm, which maximizes the min-rate among all the transmitted commodities, is based on a decomposition approach that leverages both the alternating direction method of multipliers (ADMM) and the weighted-MMSE (WMMSE) algorithm. We show that this algorithm is easily parallelizable and converges globally to a stationary solution of the joint optimization problem. The proposed algorithm can also be extended to networks with multi-antenna nodes and other utility functions.

In a densely deployed Heterogeneous network (HetNet), the number of pico/micro base stations (BS) can be comparable or more than the number of the users. To reduce the operational overhead of the HetNet, selection of serving BSs becomes an important design issue. In this work, we propose to jointly optimize the transceiver and active BSs to trade off the overall spectrum efficiency with the operational overhead. We formulate this problem as a regularized sum rate maximization problem and solve it using sparse optimization techniques. The proposed algorithm is guaranteed to converge to a local optimal solution. The efficiency and the efficacy of the algorithm are demonstrated via realistic numerical simulations.

We investigate a generalized control problem via the Circle criterion borrowed from system theory; the generalized controller can be cast into existing canonical controllers depending on parameterizations. We first show a fuzzy version of the Circle criterion and then provide a synthesis result based on the Circle criterion, establishing closed-loop stabilizability for state-feedback controllers. In this paper, we show how to incorporating the membership information by approximating the cone of copositive matrices via SOS that is solvable by SOSTOOLS. Finally the validity and applicability of the approach are illuminated by examples.

This paper considers a downlink wireless system where a multiple-antenna transmitter (Alice) aims to discriminate the reception performances between a legitimate receiver (Bob) and a set of unauthorized receivers (Eves). To this end, there has been great interest in the use of artificial noise (AN) together with transmit beamforming in order to effectively interfere Eves' reception. However, most of the existing works do not optimize the AN but simply allocate it in the left null space of the Alice-to-Bob channel. In the paper, we propose to jointly optimize the beamforming vector and the AN covariance matrix by minimizing the total transmit power subject to a target signal-to-interference-plus-noise ratio (SINR) constraint on Bob and limited SINR constraints on all Eves. While the considered beamforming problem is not convex and may be difficult to solve in general, it can be effectively handled by a convex approximation method called semidefinite program (SDP) relaxation. In addition to showing how SDP relaxation can be applied to this problem, we prove using the KKT optimality that SDP relaxation provides a global optimum solution of the proposed beamforming problem when Alice has perfect information of the channel from Alice to Bob. Simulation results are presented to demonstrate the effectiveness of the proposed beamforming method.

In this paper, IDS (Intrusion Detection System) nodes are deployed in MANETs in order to mitigate black hole attacks. The IDS nodes must be set in sniff mode in order to perform the so-called ABM (Anti-Blackhole Mechanism) function, which is mainly used to estimate a suspicious value of a node according to the abnormal difference between the routing messages transmitted from the node. When a suspicious value exceeds a threshold, an IDS nearby will broadcast a block message, informing all nodes on the network, asking them to cooperatively isolate the malicious node.