Full-Duplex Relay with Jamming Protocol for Improving Physical-Layer Security

This paper proposes a jointly cooperative relay and jamming protocol based on full-duplex (FD) capable relay to increase the source-destination secrecy rate in the presence of different types of eavesdroppers. In this so called \textit{FD-Relay with jamming (FDJ)} protocol, the FD-Relay, first, simultaneously receives data and sends jamming to the eavesdropper, and, then, forwards the data, while the source jams the eavesdropper. Achievable secrecy rates of the proposed FDJ in presence of different eavesdropper types and self-interference (SI) are derived and compared with those of the traditional half-duplex (HD) relay. The adaptive power allocation for secrecy rate maximization in a multi-carrier scenario for both proposed FDJ and HD-Relay is formulated as a non-convex optimization problem and corresponding iterative solution algorithm is developed using the difference-of-two-concave-functions (DC) programming technique. The simulation results confirm that FDJ offers significant improvements in the secrecy rate over the HD-Relay.Comment: PIMRC 201

Joint User-Association and Resource-Allocation in Virtualized Wireless Networks

In this paper, we consider a down-link transmission of multicell virtualized wireless networks (VWNs) where users of different service providers (slices) within a specific region are served by a set of base stations (BSs) through orthogonal frequency division multiple access (OFDMA). In particular, we develop a joint BS assignment, sub-carrier and power allocation algorithm to maximize the network throughput, while satisfying the minimum required rate of each slice. Under the assumption that each user at each transmission instance can connect to no more than one BS, we introduce the user-association factor (UAF) to represent the joint sub-carrier and BS assignment as the optimization variable vector in the mathematical problem formulation. Sub-carrier reuse is allowed in different cells, but not within one cell. As the proposed optimization problem is inherently non-convex and NP-hard, by applying the successive convex approximation (SCA) and complementary geometric programming (CGP), we develop an efficient two-step iterative approach with low computational complexity to solve the proposed problem. For a given power-allocation, Step 1 derives the optimum userassociation and subsequently, for an obtained user-association, Step 2 find the optimum power-allocation. Simulation results demonstrate that the proposed iterative algorithm outperforms the traditional approach in which each user is assigned to the BS with the largest average value of signal strength, and then, joint sub-carrier and power allocation is obtained for the assigned users of each cell. Especially, for the cell-edge users, simulation results reveal a coverage improvement up to 57% and 71% for uniform and non-uniform users distribution, respectively leading to more reliable transmission and higher spectrum efficiency for VWN

Power allocation in wireless multi-user relay networks

In this paper, we consider an amplify-and-forward wireless relay system where multiple source nodes communicate with their corresponding destination nodes with the help of relay nodes. Conventionally, each relay equally distributes the available resources to its relayed sources. This approach is clearly sub-optimal since each user experiences dissimilar channel conditions, and thus, demands different amount of allocated resources to meet its quality-of-service (QoS) request. Therefore, this paper presents novel power allocation schemes to i) maximize the minimum signal-to-noise ratio among all users; ii) minimize the maximum transmit power over all sources; iii) maximize the network throughput. Moreover, due to limited power, it may be impossible to satisfy the QoS requirement for every user. Consequently, an admission control algorithm should first be carried out to maximize the number of users possibly served. Then, optimal power allocation is performed. Although the joint optimal admission control and power allocation problem is combinatorially hard, we develop an effective heuristic algorithm with significantly reduced complexity. Even though theoretically sub-optimal, it performs remarkably well. The proposed power allocation problems are formulated using geometric programming (GP), a well-studied class of nonlinear and nonconvex optimization. Since a GP problem is readily transformed into an equivalent convex optimization problem, optimal solution can be obtained efficiently. Numerical results demonstrate the effectiveness of our proposed approach

Distributed learning-based spectrum allocation with noisy observations in cognitive radio networks

This paper studies the medium access design for secondary users (SUs) from a game-theoretic learning perspective. In consideration of the random return of primary users (PUs), a distributed SU access approach is presented based on an adaptive carrier sense multiple access (CSMA) scheme, in which each SU accesses multiple idle frequency slots of a licensed frequency band with adaptive activity factors. The problem of finding optimal activity factors of SUs is formulated as a potential game, and the existence, feasibility, and optimality of Nash equilibrium (NE) are analyzed. Furthermore, to achieve NEs of the formulated game, learning-based algorithms are developed in which each SU independently adjusts its activity factors. Convergence properties of best-response dynamics and log-linear dynamics are studied. Subsequently, by learning other SUs' behavior from locally available information, the convergence with probability of one to an arbitrarily small neighborhood of the globally optimal solution is investigated by both analysis and simulation

QoS-Aware Utility-Based Resource Allocation in Mixed-Traffic Multi-User OFDM Systems

This paper deals with the joint subcarrier and power allocation problem in a downlink multi-user orthogonal frequency division multiplexing system subject to user delay and minimum rate quality-of-service (QoS) requirements over a frequency-selective multi-carrier fading channel. We aim to maximize the utility-pricing function, formulated as the difference between the achieved spectral efficiency and the associated linear cost function of transmit power scaled by a system-dependent parameter. For a homogeneous system, we show that the joint resource allocation can be broken down into sequential problems while retaining the optimality. Specifically, the optimal solution is obtained by first assigning each subcarrier to the user with the best channel gain. Subsequently, the transmit power for each subcarrier is adapted according to water-filling policy if the global optimum is feasible, else it is given by a nonwater-filling power adaptation. For a heterogeneous system, an optimal solution needs exhaustive search and hence, we resort to two reduced-complexity sub-optimal algorithms. Algorithm-I is a simple extension of the aforementioned optimal algorithm developed for a homogeneous system, while Algorithm-II further takes into consideration the heterogeneity in user QoS requirements for performance enhancement. Simulation results reveal the impacts of user QoS requirements, number of subcarriers and number of users on the system transmit power

Reconfigurable and traffic-aware MAC design for virtualized wireless networks via reinforcement learning

In this paper, we present a reconfigurable MAC scheme where the partition between contention-free and contention-based regimes in each frame is adaptive to the network status leveraging reinforcement learning. In particular, to support a virtualized wireless network consisting of multiple slices, each having heterogeneous and unsaturated devices, the proposed scheme aims to configure the partition for maximizing network throughput while maintaining the slice reservations. Applying complementary geometric programming (CGP) and monomial approximations, an iterative algorithm is developed to find the optimal solution. For a large number of devices, a scalable algorithm with lower computational complexity is also proposed. The partitioning algorithm requires the knowledge of the device traffic statistics. In the absence of such knowledge, we develop a learning algorithm employing Thompson sampling to acquire packet arrival probabilities of devices. Furthermore, we model the problem as a thresholding multi-armed bandit (TMAB) and propose a threshold-based reconfigurable MAC algorithm, which is proved to achieve the optimal regret bound

Efficient LTE/WiFi coexistence in unlicensed spectrum using virtual network entity: Optimization and performance analysis

Long term evolution (LTE) operation in the unlicensed spectrum is a promising solution to address the scarcity of licensed spectrum for cellular networks. Although this approach brings higher capacity for LTE networks, the WiFi performance operating in this band can be significantly degraded. To address this issue, we consider a coordinated structure, in which both networks are controlled by a higher-level network entity. In such a model, LTE users can transmit in the assigned time-slots, while WiFi users can compete with each other by using p-persistent CSMA in their exclusive time-share. In an unsaturated network, at each duty cycle, the TDMA scheduling for LTE users and p values for WiFi users should be efficiently updated by the central controller. The corresponding optimization problem is formulated and an iterative algorithm is developed to find the optimal solution using complementary geometric programming (CGP) and monomial approximations. Aiming to address Quality-ofService (QoS) assurance for LTE users, an upper bound for average delay of these users are obtained. This analysis could be a basis for admission control of LTE users in unlicensed bands. The simulation results reveal the performance gains of the proposed algorithm in preserving the WiFi throughput requirement