Enhancing wireless security via optimal cooperative jamming

In this work, we analyze the secrecy rate in a cooperative network, where a source node is assisted by relay nodes via cooperative jamming for delivering a secret message to the destination in the presence of an eavesdropper node. We consider the availability of both full and partial channel state information (CSI), and we take into account average power limitation at the relays as we formulate the rate maximization problem as a primal-dual problem. We derive the closed form solution for the full CSI case, and we show that the optimal solution allows the transmission of only one relay. For the partial CSI case, we define the concept of secrecy outage, where some of packets are intercepted by the eavesdropper, and we derive the secrecy outage probability and throughput in terms of average channel statistics. Due to the high nonlinearity of the secrecy throughput term, we propose a gradient update algorithm for obtaining the optimal power solutions for the partial CSI case. Our simulations demonstrate the gains of cooperative jamming over direct transmission for both full and partial CSI cases, where it is shown that the secrecy rate of the direct transmission is increased significantly, by %20−%80, when CJ is employed with our optimal power assignment algorithm

Joint Downlink Base Station Association and Power Control for Max-Min Fairness: Computation and Complexity

In a heterogeneous network (HetNet) with a large number of low power base stations (BSs), proper user-BS association and power control is crucial to achieving desirable system performance. In this paper, we systematically study the joint BS association and power allocation problem for a downlink cellular network under the max-min fairness criterion. First, we show that this problem is NP-hard. Second, we show that the upper bound of the optimal value can be easily computed, and propose a two-stage algorithm to find a high-quality suboptimal solution. Simulation results show that the proposed algorithm is near-optimal in the high-SNR regime. Third, we show that the problem under some additional mild assumptions can be solved to global optima in polynomial time by a semi-distributed algorithm. This result is based on a transformation of the original problem to an assignment problem with gains $\log(g_{ij})$, where $\{g_{ij}\}$ are the channel gains.Comment: 24 pages, 7 figures, a shorter version submitted to IEEE JSA

An Examination of the Benefits of Scalable TTI for Heterogeneous Traffic Management in 5G Networks

The rapid growth in the number and variety of connected devices requires 5G wireless systems to cope with a very heterogeneous traffic mix. As a consequence, the use of a fixed TTI during transmission is not necessarily the most efficacious method when heterogeneous traffic types need to be simultaneously serviced.This work analyzes the benefits of scheduling based on exploiting scalable TTI, where the channel assignment and the TTI duration are adapted to the deadlines and requirements of different services. We formulate an optimization problem by taking individual service requirements into consideration. We then prove that the optimization problem is NP-hard and provide a heuristic algorithm, which provides an effective solution to the problem. Numerical results show that our proposed algorithm is capable of finding near-optimal solutions to meet the latency requirements of mission critical communication services, while providing a good throughput performance for mobile broadband services.Comment: RAWNET Workshop, WiOpt 201

Reliable Multicast D2D Communication over Multiple Channels in Underlay Cellular Networks

Author's accepted manuscript© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Multicast device-to-device (D2D) communications operating underlay with cellular networks is a spectral efficient technique for disseminating data to the nearby receivers. However, due to critical challenges such as, mitigating mutual interference and unavailability of perfect channel state information (CSI), the resource allocation to multicast groups needs significant attention. In this work, we present a framework for joint channel assignment and power allocation strategy to maximize the sum rate of the combined network. The proposed framework allows access of multiple channels to the multicast groups, thus improving the achievable rate of the individual groups. Furthermore, fairness in allocating resources to the multicast groups is also ensured by augmenting the objective with a penalty function. In addition, considering imperfect CSI, the framework guarantees to provide rate above a specified outage for all the users. The formulated problem is a mixed integer nonconvex program which requires exponential complexity to obtain the optimal solution. To tackle this, we first introduce auxiliary variables to decouple the original problem into smaller power allocation problems and a channel assignment problem. Next, with the aid of fractional programming via a quadratic transformation, we obtain an efficient power allocation solution by alternating optimization. The solution for channel assignment is obtained by convex relaxation of integer constraints. Finally, we demonstrate the merit of the proposed approach by simulations, showing a higher and a more robust network throughput. Index Terms—D2D multicast communications, resource allocation, imperfect CSI, fractional programming.acceptedVersio