Beam domain secure transmission for massive MIMO communications

We investigate the optimality and power allocation algorithm of beam domain transmission for single-cell massive multiple-input multiple-output (MIMO) systems with a multi-antenna passive eavesdropper. Focusing on the secure massive MIMO downlink transmission with only statistical channel state information of legitimate users and the eavesdropper at base station, we introduce a lower bound on the achievable ergodic secrecy sum-rate, from which we derive the condition for eigenvectors of the optimal input covariance matrices. The result shows that beam domain transmission can achieve optimal performance in terms of secrecy sum-rate lower bound maximization. For the case of single-antenna legitimate users, we prove that it is optimal to allocate no power to the beams where the beam gains of the eavesdropper are stronger than those of legitimate users in order to maximize the secrecy sum-rate lower bound. Then, motivated by the concave-convex procedure and the large dimension random matrix theory, we develop an efficient iterative and convergent algorithm to optimize power allocation in the beam domain. Numerical simulations demonstrate the tightness of the secrecy sum-rate lower bound and the near-optimal performance of the proposed iterative algorithm

Power Efficient Resource Allocation for Full-Duplex Radio Distributed Antenna Networks

In this paper, we study the resource allocation algorithm design for distributed antenna multiuser networks with full-duplex (FD) radio base stations (BSs) which enable simultaneous uplink and downlink communications. The considered resource allocation algorithm design is formulated as an optimization problem taking into account the antenna circuit power consumption of the BSs and the quality of service (QoS) requirements of both uplink and downlink users. We minimize the total network power consumption by jointly optimizing the downlink beamformer, the uplink transmit power, and the antenna selection. To overcome the intractability of the resulting problem, we reformulate it as an optimization problem with decoupled binary selection variables and non-convex constraints. The reformulated problem facilitates the design of an iterative resource allocation algorithm which obtains an optimal solution based on the generalized Bender's decomposition (GBD) and serves as a benchmark scheme. Furthermore, to strike a balance between computational complexity and system performance, a suboptimal algorithm with polynomial time complexity is proposed. Simulation results illustrate that the proposed GBD based iterative algorithm converges to the global optimal solution and the suboptimal algorithm achieves a close-to-optimal performance. Our results also demonstrate the trade-off between power efficiency and the number of active transmit antennas when the circuit power consumption is taken into account. In particular, activating an exceedingly large number of antennas may not be a power efficient solution for reducing the total system power consumption. In addition, our results reveal that FD systems facilitate significant power savings compared to traditional half-duplex systems, despite the non-negligible self-interference.Comment: Submitted for possible journal publicatio

Compressive Massive Random Access for Massive Machine-Type Communications (mMTC)

In future wireless networks, one fundamental challenge for massive machine-type communications (mMTC) lies in the reliable support of massive connectivity with low latency. Against this background, this paper proposes a compressive sensing (CS)-based massive random access scheme for mMTC by leveraging the inherent sporadic traffic, where both the active devices and their channels can be jointly estimated with low overhead. Specifically, we consider devices in the uplink massive random access adopt pseudo random pilots, which are designed under the framework of CS theory. Meanwhile, the massive random access at the base stations (BS) can be formulated as the sparse signal recovery problem by leveraging the sparse nature of active devices. Moreover, by exploiting the structured sparsity among different receiver antennas and subcarriers, we develop a distributed multiple measurement vector approximate message passing (DMMV-AMP) algorithm for further improved performance. Additionally, the state evolution (SE) of the proposed DMMV-AMP algorithm is derived to predict the performance. Simulation results demonstrate the superiority of the proposed scheme, which exhibits a good tightness with the theoretical SE.Comment: This paper has been accepted by 2018 IEEE GlobalSI

UAV-Enabled Mobile Edge Computing: Offloading Optimization and Trajectory Design

With the emergence of diverse mobile applications (such as augmented reality), the quality of experience of mobile users is greatly limited by their computation capacity and finite battery lifetime. Mobile edge computing (MEC) and wireless power transfer are promising to address this issue. However, these two techniques are susceptible to propagation delay and loss. Motivated by the chance of short-distance line-of-sight achieved by leveraging unmanned aerial vehicle (UAV) communications, an UAV-enabled wireless powered MEC system is studied. A power minimization problem is formulated subject to the constraints on the number of the computation bits and energy harvesting causality. The problem is non-convex and challenging to tackle. An alternative optimization algorithm is proposed based on sequential convex optimization. Simulation results show that our proposed design is superior to other benchmark schemes and the proposed algorithm is efficient in terms of the convergence.Comment: This paper has been accepted by IEEE ICC 201

Computation Rate Maximization in UAV-Enabled Wireless Powered Mobile-Edge Computing Systems

Mobile edge computing (MEC) and wireless power transfer (WPT) are two promising techniques to enhance the computation capability and to prolong the operational time of low-power wireless devices that are ubiquitous in Internet of Things. However, the computation performance and the harvested energy are significantly impacted by the severe propagation loss. In order to address this issue, an unmanned aerial vehicle (UAV)-enabled MEC wireless powered system is studied in this paper. The computation rate maximization problems in a UAV-enabled MEC wireless powered system are investigated under both partial and binary computation offloading modes, subject to the energy harvesting causal constraint and the UAV's speed constraint. These problems are non-convex and challenging to solve. A two-stage algorithm and a three-stage alternative algorithm are respectively proposed for solving the formulated problems. The closed-form expressions for the optimal central processing unit frequencies, user offloading time, and user transmit power are derived. The optimal selection scheme on whether users choose to locally compute or offload computation tasks is proposed for the binary computation offloading mode. Simulation results show that our proposed resource allocation schemes outperforms other benchmark schemes. The results also demonstrate that the proposed schemes converge fast and have low computational complexity.Comment: This paper has been accepted by IEEE JSA

On the Fundamental Limits of MIMO Massive Multiple Access Channels

In this paper, we study the multiple-antenna wireless communication networks, where a large number of devices simultaneously communicate with an access point. The capacity region of multiple-input multiple-output massive multiple access channels (MIMO mMAC) is investigated. While joint typicality decoding is utilized to establish the achievability of capacity region for conventional MAC with fixed number of users, the technique is not directly applicable for MIMO mMAC. Instead, an information-theoretic approach based on Gallager's error exponent analysis is exploited to characterize the \textcolor[rgb]{0,0,0}{finite dimension region} of MIMO mMAC. Theoretical results reveal that the finite dimension region of MIMO mMAC is dominated by sum rate constraint only, and the individual user rate is determined by a specific factor that corresponds to the allocation of sum rate. The rate in conventional MAC is not achievable with massive multiple access, which is due to the fact that successive interference cancellation cannot guarantee an arbitrary small error decoding probability for MIMO mMAC. The results further imply that, asymptotically, the individual user rate is independent of the number of transmit antennas, and channel hardening makes the individual user rate close to that when only statistic knowledge of channel is available at receiver. The finite dimension region of MIMO mMAC is a generalization of the symmetric rate in Chen \emph{et al.} (2017).Comment: Accepted by ICC'201

MIMO-OFDM Scheme design for Medium Voltage Underground Cables based Power Line Communication

Power line communication (PLC) provides intelligent electrical functions such as power quality measurement, fault surveys, and remote control of electrical network. However, most of research works have been done in low voltage (LV) scenario due to the fast development of in-home PLC. The aim of this paper is to design a MIMO-OFDM based transmission link under medium voltage (MV) underground power line channel and evaluate the performance. The MIMO channel is modeled as a modified multipath model in the presence of impulsive noise and background noise. Unlike most literatures on MIMO power line transmission, we adopt spatial multiplexing instead of diversity to increase the transmission rate in this paper. The turbo coding method originally designed for LV power line communication is used in the proposed transmission system. By comparing the BER performance of MIMO-OFDM system with and without the turbo coding, we evaluate its applicability in MV power line communication. The effect of frequency band varying on the PLC system's performance is also investigated.Comment: To appear in IEEE WCSP'1

Optimal Detection of UAV's Transmission with Beam Sweeping in Wireless Networks

In this work, an detection strategy based on multiple antennas with beam sweeping is developed to detect UAV's potential transmission in wireless networks. Specifically, suspicious angle range where the UAV may present is divided into different sectors to potentially increase detection accuracy by using beamforming gain. We then develop the optimal detector and derive its detection error probability in a closed-form expression. We also utilize the Pinsker's inequality and Kullback-Leibler divergence to yield low-complex approximation for the detection error probability, based on which we obtain some significant insights on the detection performance. Our examination shows that there exists an optimal number of sectors that can minimize the detection error probability in some scenarios (e.g., when the number of measurements is limited). Intuitively, this can be explained by the fact that there exists an optimal accuracy of the telescope used to find an object in the sky within limited time period

Two High-performance Schemes of Transmit Antenna Selection for Secure Spatial Modulation

In this paper, a secure spatial modulation (SM) system with artificial noise (AN)-aided is investigated. To achieve higher secrecy rate (SR) in such a system, two high-performance schemes of transmit antenna selection (TAS), leakage-based and maximum secrecy rate (Max-SR), are proposed and a generalized Euclidean distance-optimized antenna selection (EDAS) method is designed. From simulation results and analysis, the four TAS schemes have an decreasing order: Max-SR, leakage-based, generalized EDAS, and random (conventional), in terms of SR performance. However, the proposed Max-SR method requires the exhaustive search to achieve the optimal SR performance, thus its complexity is extremely high as the number of antennas tends to medium and large scale. The proposed leakage-based method approaches the Max-SR method with much lower complexity. Thus, it achieves a good balance between complexity and SR performance. In terms of bit error rate (BER), their performances are in an increasing order: random, leakage-based, Max-SR, and generalized EDAS

Pilot Spoofing Attack by Multiple Eavesdroppers

In this paper, we investigate the design of a pilot spoofing attack (PSA) carried out by multiple single-antenna eavesdroppers (Eves) in a downlink time-division duplex (TDD) system, where a multiple antenna base station (BS) transmits confidential information to a single-antenna legitimate user (LU). During the uplink channel training phase, multiple Eves collaboratively impair the channel acquisition of the legitimate link, aiming at maximizing the wiretapping signal-to-noise ratio (SNR) in the subsequent downlink data transmission phase. Two different scenarios are investigated: (1) the BS is unaware of the PSA, and (2) the BS attempts to detect the presence of the PSA. For both scenarios, we formulate wiretapping SNR maximization problems. For the second scenario, we also investigate the probability of successful detection and constrain it to remain below a pre-designed threshold. The two resulting optimization problems can be unified into a more general non-convex optimization problem, and we propose an efficient algorithm based on the minorization-maximization (MM) method and the alternating direction method of multipliers (ADMM) to solve it. The proposed MM-ADMM algorithm is shown to converge to a stationary point of the general problem. In addition, we propose a semidefinite relaxation (SDR) method as a benchmark to evaluate the efficiency of the MM-ADMM algorithm. Numerical results show that the MM-ADMM algorithm achieves near-optimal performance and is computationally more efficient than the SDRbased method.Comment: Accepted by IEEE Transaction on Wireless Communication