Yu-Dong Yao

Cellular-Base-Station-Assisted Device-to-Device Communications in TV White Space

This paper presents a systematic approach to exploiting TV white space (TVWS) for device-to-device (D2D) communications with the aid of the existing cellular infrastructure. The goal is to build a location-specific TVWS database, which provides a lookup table service for any D2D link to determine its maximum permitted emission power (MPEP) in an unlicensed digital TV (DTV) band. To achieve this goal, the idea of mobile crowd sensing is first introduced to collect active spectrum measurements from massive personal mobile devices. Considering the incompleteness of crowd measurements, we formulate the problem of unknown measurements recovery as a matrix completion problem and apply a powerful fixed point continuation algorithm to reconstruct the unknown elements from the known elements. By joint exploitation of the big spectrum data in its vicinity, each cellular base station further implements a nonlinear support vector machine algorithm to perform irregular coverage boundary detection of a licensed DTV transmitter. With the knowledge of the detected coverage boundary, an opportunistic spatial reuse algorithm is developed for each D2D link to determine its MPEP. Simulation results show that the proposed approach can successfully enable D2D communications in TVWS while satisfying the interference constraint from the licensed DTV services. In addition, to our best knowledge, this is the first try to explore and exploit TVWS inside the DTV protection region resulted from the shadowing effect. Potential application scenarios include communications between internet of vehicles in the underground parking and D2D communications in hotspots such as subway, game stadiums, and airports.

On Secrecy Performance of Antenna-Selection-Aided MIMO Systems Against Eavesdropping

In this paper, we consider a multiple-input-multiple-output (MIMO) system consisting of one source, one destination, and one eavesdropper, where each node is equipped with an arbitrary number of antennas. To improve the security of source-destination transmissions, we investigate the antenna selection at the source and propose the optimal antenna selection (OAS) and suboptimal antenna selection (SAS) schemes, depending on whether the source node has the global channel state information (CSI) of both the main link (from source to destination) and the wiretap link (from source to eavesdropper). Moreover, the traditional space-time transmission (STT) is studied as a benchmark. We evaluate the secrecy performance of STT, SAS, and OAS schemes in terms of the probability of zero secrecy capacity. Furthermore, we examine the generalized secrecy diversity of the STT, SAS, and OAS schemes through an asymptotic analysis of the probability of zero secrecy capacity as the ratio between the average gains of the main and wiretap channels tends to infinity. This is different from the conventional secrecy diversity that assumes an infinite signal-to-noise ratio (SNR) received at the destination under the condition that the eavesdropper has a finite received SNR. It is shown that the generalized secrecy diversity orders of the STT, SAS, and OAS schemes are the product of the number of antennas at source and destination. Additionally, numerical results show that the proposed OAS scheme strictly outperforms both the STT and the SAS schemes in terms of the probability of zero secrecy capacity.

Energy-Efficiency-Based Optimal Relay Selection Scheme With a BER Constraint in Cooperative Cognitive Radio Networks

Due to the energy constraint of each cognitive user and potential secondary transmission errors, energy efficiency is very important for cognitive nodes in spectrum sensing and cooperative transmissions. In this paper, an energy awareness optimal relay selection (EAORS) scheme is proposed, in which the number of optimal relays is determined by a weighted objective function considering energy consumption in both spectrum sensing and the cooperative transmission phases, under the constraints of the global misdetection requirement and secondary cooperative transmission bit error rate (BER). In the proposed EAORS scheme, we investigate the tradeoff between detection accuracy and energy efficiency and construct the objective function as a weighted convex function of relay numbers with the consideration of global missing detection probability and cooperative transmission BER. This nonlinear convex problem is solved by applying numerical analysis to obtain the optimal relay numbers. Energy consumption analysis is conducted to show the effectiveness of the proposed EAORS scheme. Numerical results show that the proposed scheme outperforms a compressed sensing (CS)-based energy-efficient cooperative detection scheme and provides a tradeoff between detection accuracy and energy efficiency.

Traffic-Aware Online Network Selection in Heterogeneous Wireless Networks

We focus on the network selection problem in heterogeneous wireless networks. Many traditional approaches select the best network according to quality of service (QoS)-related criteria, which neglects diverse user demands. We aim to select networks maximizing the quality of experience (QoE) of users. When the availability and dynamics of network state information (NSI) are considered, most of the existing approaches cannot make effective selection decisions since they are vulnerable to the uncertainty in NSI. To address this issue, we introduce the idea of online learning for network selection. In this paper, we formulate the network selection problem as a continuous-time multiarmed bandit (CT-MAB) problem. A traffic-aware online network selection (ONES) algorithm is designed to match typical traffic types of users with respective optimal networks in terms of QoE. Moreover, we found that the correlation among multiple traffic network selections can be exploited to improve the learning capability. This motivates us to propose another two more efficient algorithms: the decoupled ONES (D-ONES) algorithm and the virtual multiplexing ONES (VM-ONES) algorithm. Simulation results demonstrate that our ONES algorithms attain around 10% gain in QoE reward rate over nonlearning-based algorithms and learning-based algorithms without QoE considerations.

Design and Analysis of Distributed Hopping-Based Channel Access in Multi-Channel Cognitive Radio Systems with Delay Constraints

To support delay-sensitive traffic in multi-channel cognitive radio systems, designing a channel access scheme faces two major challenges, namely, the long waiting time due to continuous channel occupancy of primary users (PUs) and the performance degradation due to transmission collisions among secondary users (SUs). To address both issues, we propose a two-phase channel access scheme, which consists of a distributed channel negotiation phase and a hopping-based channel access phase for each SU. Specifically, in its first phase, an SU attempts to negotiate a specific initial slot/channel differing from the ones chosen by other SUs. Then, in its second phase, the SU chooses a channel in each time slot in a hopping-based manner to transmit data, where the hopping starts from its initial channel and follows a common hopping sequence. Virtual channels are introduced to accommodate the situation when the number of SUs is larger than that of actual channels. The average maximal waiting time due to the channel negotiation phase is derived, and the effective capacity of the service process for each SU in the channel access phase is analyzed. Numerical results show that the proposed scheme can support a higher traffic load under the statistical delay constraint, as compared with fixed or random channel access schemes.

Blind Decoding Based on Independent Component Analysis for a Massive MIMO Uplink System in Microcell Rician/Rayleigh Fading Channels

In a massive multiple-input-multiple-output (MIMO) uplink system, the pilot sequence reuse in neighboring cells causes pilot contamination, causing the decoding performance to degrade significantly. In this paper, a blind decoding method based on independent component analysis (ICA) is proposed without using pilot sequences. The proposed blind decoding method uses ICA to separate the received signals (from in-cell and neighboring cells) and estimate channels. The energy levels of the estimated channels are used to differentiate an in-cell signal from neighboring cell signals. The analytical performance results of the blind decoding method are derived. Numerical results show that the proposed blind decoding scheme outperforms minimum-mean-square-error (MMSE) decoding and zero-forcing (ZF) decoding with imperfect channel state information (CSI). The proposed scheme has only a negligible performance loss compared with MMSE decoding and ZF decoding with perfect CSI.

Cooperative Spectrum Sensing With Random Access Reporting Channels in Cognitive Radio Networks

In cognitive radio (CR) networks, cooperative spectrum sensing is utilized to improve the sensing performance to avoid potential interference to primary users (PUs) and increase spectrum access opportunities for secondary users (SUs). A cooperative spectrum sensing process is divided into three phases: individual sensing/detection, reporting/fusion, and data transmission. In the reporting phase, one or more reporting channels are needed to transmit individual sensing results to a fusion center (FC), and global spectrum sensing results are determined at the FC. The number of required reporting channels depends on the number of spectrum sensors or SUs, which relates to reporting channel efficiency and channel scheduling complexity. That is to say, the reporting channel design can be a challenge, especially when fixed assignment scheduling is used. Therefore, in this paper, we design a reporting channel scheme based on random access protocols, including slotted Aloha and reservation-Aloha. Performance evaluations in terms of PU detection probabilities and false alarm probabilities considering the proposed reporting channels are presented. In addition, the impact of soft/unquantized spectrum sensors or detectors (SUs) and malicious SUs is considered in this paper. Analytical and simulation results illustrate the effectiveness of the proposed reporting channel scheduling methods in improving the cooperative spectrum sensing performance.

Massive Machine-to-Machine Communications in Cellular Network: Distributed Queueing Random Access Meets MIMO

Machine-type communications are emerging as a new paradigm for enabling a broad range of applications from the massive deployment of sensor devices to mission-critical services. To support massive machine-to-machine (M2M) communications with delay constraints in cellular networks, we design an efficient random access and data transmission system known as distributed queueing random access-multiple-input multiple-output (DQRA-MIMO) data transmission system. This system has the advantages of both efficient collision resolution of DQRA protocol and the efficient data transmission of MIMO technology. To obtain higher throughput under delay constraint and limited time-frequency resources, we match the ability of collision resolution with the capability of MIMO transmission by optimally configuring system parameters. The closed-form expression of throughput is derived, which is a function of the total user equipments’ traffic arrival rate, average packet number of each arrival, number of base station antennas, and number of access request (AR) slots. An optimization problem is formulated to maximize the throughput to obtain the optimal number of AR slots given a certain delay constraint for M2M traffic. Numerical and simulation results reveal that, for a given requirement of average delay, the proposed optimized DQRA-MIMO system, which dynamically adjusts time-frequency resource division to maximize throughput, can provide a higher throughput than that of a baseline approach.

Secondary User Access Control in Cognitive Radio Networks

Spectrum sharing and aggregation among authorized secondary users (A-SUs) are important tasks in operating effective cognitive radio networks. Furthermore, in protecting spectrum sharing/aggregation against unauthorized secondary users (UA-SUs), secondary user access control (SUAC) is needed, which is investigated in this paper. A jamming signal is injected to degrade the spectrum sensing performance of UA-SUs, while reliable spectrum sensing performance for A-SUs can be achieved through an oblique projection-based jamming cancellation method. An orthogonal frequency division multiplexing-based transmission model is considered in this paper. The generalized likelihood ratio test algorithm is used for both authorized and unauthorized SUs in spectrum sensing. Numerical results show the effectiveness of the proposed SUAC in degrading the spectrum sensing performance of the unauthorized SUs.

Coordinated jamming and communications in an MC-CDMA system

A coordinated jamming and communications technique for degrading enemy user transmission performance in a multicarrier code-division multiple-access system is introduced. A linear minimum mean square error (MMSE) algorithm with jamming matrix estimation in single-tone jamming is presented. Furthermore, a blind linear MMSE multiuser detection algorithm in multitone jamming is investigated. Simulation results show that friendly users are able to achieve reliable communications, while the transmission performance of enemy users is significantly degraded.