MMSE-based beamforming techniques for relay broadcast channels

We propose minimum mean square error (MMSE) based beamforming techniques for a multiantenna relay network, where a base station (BS) equipped with multiple antennas communicates with a number of single antenna users through a multiantenna relay.We specifically solve three optimization problems: a) sum-power minimization problem b) mean square error (MSE) balancing problem and c)mixed quality of services (QoS) problem. Unfortunately, these problems are not jointly convex in terms of beamforming vectors at the BS and the relay amplification matrix. To circumvent this non-convexity issue, the original problems are divided into two subproblems where the beamforming vectors and the relay amplification matrix are alternately optimized while other one is fixed. Three iterative algorithms have been developed based on convex optimization techniques and general MSE duality. Simulation results have been provided to validate the convergence of the proposed algorithms

NOMA-Based UAV-Aided Networks for Emergency Communications

High spectrum efficiency (SE) requirement and massive connections are the main challenges for the fifth generation (5G) and beyond 5G (B5G) wireless networks, especially for the case when Internet of Things (IoT) devices are located in a disaster area. Non-orthogonal multiple access (NOMA)-based unmanned aerial vehicle (UAV)-aided network is emerging as a promising technique to overcome the above challenges. In this paper, an emergency communications framework of NOMA-based UAV-aided networks is established, where the disasters scenarios can be divided into three broad categories that have named emergency areas, wide areas and dense areas. First, a UAV-enabled uplink NOMA system is established to gather information from IoT devices in emergency areas. Then, a joint UAV deployment and resource allocation scheme for a multi-UAV enabled NOMA system is developed to extend the UAV coverage for IoT devices in wide areas. Furthermore, a UAV equipped with an antenna array has been considered to provide wireless service for multiple devices that are densely distributed in disaster areas. Simulation results are provided to validate the effectiveness of the above three schemes. Finally, potential research directions and challenges are also highlighted and discussed

Privacy-Preserving Multi-Class Support Vector Machine for Outsourcing the Data Classification in Cloud

Emerging cloud computing infrastructure replaces traditional outsourcing techniques and provides flexible services to clients at different locations via Internet. This leads to the requirement for data classification to be performed by potentially untrusted servers in the cloud. Within this context, classifier built by the server can be utilized by clients in order to classify their own data samples over the cloud. In this paper, we study a privacy-preserving (PP) data classification technique where the server is unable to learn any knowledge about clients’ input data samples while the server side classifier is also kept secret from the clients during the classification process. More specifically, to the best of our knowledge, we propose the first known client-server data classification protocol using support vector machine. The proposed protocol performs PP classification for both two-class and multi-class problems. The protocol exploits properties of Pailler homomorphic encryption and secure two-party computation. At the core of our protocol lies an efficient, novel protocol for securely obtaining the sign of Pailler encrypted numbers

Energy Minimization in D2D-Assisted Cache-Enabled Internet of Things: A Deep Reinforcement Learning Approach

Mobile edge caching (MEC) and device-to-device (D2D) communications are two potential technologies to resolve traffic overload problems in the Internet of Things. Previous works usually investigate them separately with MEC for traffic offloading and D2D for information transmission. In this article, a joint framework consisting of MEC and cache-enabled D2D communications is proposed to minimize the energy cost of systematic traffic transmission, where file popularity and user preference are the critical criteria for small base stations (SBSs) and user devices, respectively. Under this framework, we propose a novel caching strategy, where the Markov decision process is applied to model the requesting behaviors. A novel scheme based on reinforcement learning (RL) is proposed to reveal the popularity of files as well as users' preference. In particular, a Q-learning algorithm and a deep Q-network algorithm are, respectively, applied to user devices and the SBS due to different complexities of status. To save the energy cost of systematic traffic transmission, users acquire partial traffic through D2D communications based on the cached contents and user distribution. Taking the memory limits, D2D available files, and status changing into consideration, the proposed RL algorithm enables user devices and the SBS to prefetch the optimal files while learning, which can reduce the energy cost significantly. Simulation results demonstrate the superior energy saving performance of the proposed RL-based algorithm over other existing methods under various conditions

Physical layer security jamming: Theoretical limits and practical designs in wireless networks

Physical layer security has been recently recognized as a promising new design paradigm to provide security in wireless networks. In addition to the existing conventional cryptographic methods, physical layer security exploits the dynamics of fading channels to enhance secured wireless links. In this approach, jamming plays a key role by generating noise signals to confuse the potential eavesdroppers, and significantly improves quality and reliability of secure communications between legitimate terminals. This article presents theoretical limits and practical designs of jamming approaches for physical layer security. In particular, the theoretical limits explore the achievable secrecy rates of user cooperation based jamming whilst the centralized, and game theoretic based precoding techniques are reviewed for practical implementations. In addition, the emerging wireless energy harvesting techniques are exploited to harvest the required energy to transmit jamming signals. Future directions of these approaches, and the associated research challenges are also briefly outlined

A new design paradigm for MIMO cognitive radio with primary user rate constraint

10.1109/LCOMM.2012.030912.120319IEEE Communications Letters165706-709ICLE

Optimal subcarrier and bit allocation techniques for cognitive radio networks using integer linear programming

We propose an adaptive radio resource allocation algorithm for cognitive radio underlay networks. The proposed algorithm optimally allocates power and the available subcarriers in an OFDMA environment while ensuring interference leaked to the primary users is below a specific value. We formulate the radio resource allocation problem for cognitive radio network into integer linear programming framework. This algorithm yields an optimal solution for the proposed resource allocation problem. Simulation results have been provided to validate the performance of the algorithm