Backscatter communication emerges as a promising solution for green Internet of Things (IoT). Current research mainly focus on On-Off Keying (OOK) and Binary Phase-Shift Keying (BPSK) modulation schemes and most overlook the crucial aspect of tag circuit sensitivity. In this paper, we derive the capacity of the M-ASK modulated backscatter system under the circuit sensitivity constraint of the tag. We conducts a comparative analysis of capacity performance across three scenarios: (i) backscatter communication systems without sensitivity constraints, (ii) backscatter systems employing different modulation orders, and (iii) conventional point-to-point communication systems. It is found that the circuit sensitivity of the passive tags has a considerable impact on the system capacity. Furthermore, high-order modulation can effectively increase the channel capacity of the backscatter communication systems.

With the rapid advancement of physical-layer security technology, the covert and secure communication has become crucial in safeguarding wireless communication systems. In this article, we propose a joint covert and secure transmission scheme for simultaneous wireless information and power transfer (SWIPT) assisted cooperative nonorthogonal multiple access (CNOMA) systems. In the CNOMA system, a greedy relay transmits the confidential information to the far user (Carol), with the assistance of the near user (Bob). Meanwhile, as a SWIPT node, Bob is self-sustained by harvesting energy from relay. What is more, a warden (Alice) and noncolluding eavesdroppers (Eves) always attempt to detect and capture the confidential information, respectively. To counteract the attacks from Alice and Eves, a jamming-assisted scheme is employed. For the proposed system model, we derive closed-form expressions for the detection error probability (DEP) and the average minimum detection error probability (AMDEP) of Alice. Additionally, closed-form expressions for the outage probability (OP) of users and the intercept probability (IP) of Eves are obtained. Furthermore, to maximize the effective covert rate (ECR) of Carol, an optimization problem is formulated, subject to covertness and security constraints. Numerical results are provided to demonstrate the impact of the system parameters on covert and secure performance, with the results showing perfect agreement with the theoretical analysis.

Reconfigurable intelligent surface (RIS)-assisted terahertz (THz) communication is emerging as a key technology to support the ultra-high data rates in future sixth-generation networks. However, the acquisition of accurate channel state information (CSI) in such systems is challenging due to the passive nature of RIS and the hybrid beamforming architecture typically employed in THz systems. To address these challenges, we propose a novel low-complexity two-phase channel estimation scheme for RIS-assisted THz systems with beam split effect. In the proposed scheme, we first estimate the full CSI over a small subset of subcarriers (SCs), then extract angular information at both the base station and RIS. Subsequently, we recover the full CSI across remaining SCs by determining the corresponding spatial directions and angle-excluded coefficients. Theoretical analysis and simulation results demonstrate that the proposed method achieves superior performance in terms of normalized mean-square error while significantly reducing computational complexity compared to existing algorithms.

In this paper, we introduce a simplified long-range (LoRa) backscatter system that enables a lightweight tag to communicate with a remote transceiver using chirp carrier and harmonic backscatter. The key idea is twofold: first, we delegate the chirp waveform generation from the tag to the transceiver, thereby maintaining lightweight tag design; second, we introduce a frequency shift on the tag during its carrier modulation to create harmonic backscatter, thereby mitigating self-interference. To refine the framework, we first detail the system model, including the design for harmonic backscatter. We then present an efficient method to address synchronization and detection issues at the transceiver. Finally, we prototype the proposed system and evaluate its performance. Experimental results demonstrate that in a corridor environment with a carrier power of 0 dBm, the tag can backscatter a signal from the transceiver at a maximum bit rate of 4 kb/s over a distance of 50 meters.

Backscatter communication (BC) has emerged as a promising paradigm for enabling energy-efficient and low-cost Internet of Things (IoT) systems. One practical challenge for BC implementation is phase-locked loop (PLL) non-ideality, which mainly includes phase noise and spurs. These non-ideal factors can introduce time-varying interference, yielding wavy backscatter signals and striped-shape constellation clusters that can substantially degrade the system performance. In this paper, we investigate the PLL non-ideality of the BC system, and propose a method to suppress its resulted time-varying interference. Specifically, we first establish the BC system model with phase noise and spurs, mathematically showing how these factors can generate time-varying interference and result in wavy signals and striped-shape constellations. We then introduce Dynamic Self-Interference Cancellation (DSIC) algorithms to mitigate the time-varying interference and implement it on a practical backscatter platform. Finally, our experimental measurements show that DSIC can reduce the bit error rates (BER) by half, demonstrating its effectiveness in mitigating the effects of PLL non-ideality and improving the system performance.

Abstract Backscatter communications, which originated from World War II, have been widely applied in the logistics domain, and recently attract emerging interest from both academic and industrial circles. Here, the backscatter communication systems equipped with retrodirective arrays that can re‐transmit the impinging signals back toward the direction of incidence are studied so as to reduce the power loss of the signals. Specifically, the authors consider the tag is equipped with retrodirective arrays to improve reliability and enhance communication range. The probability density function of channel coefficients is then derived. Next, a channel estimator based on Bayesian theory is proposed to acquire the modulus values of channel parameters and calculate its Bayesian Cramer–Rao Lower Bound. Finally, simulation results are provided to corroborate these theoretical studies.

Due to its almost zero power consumption and low-hardware costs, backscatter technology has recently gained considerable attention. However, traditional backscatter systems employ basic binary modulations to transmit data at a relatively low rate. In this article, we introduce VITAS, a new backscatter system that enables the tag to communicate with the transceiver using versatile modulations at a megabit rate. First, we present the design of the tag, which supports both binary and higher order modulations. We also develop a series of assembly instructions for the tag controller to generate modulated symbols with minimal clock cycles, thereby enhancing the data rate for a given clock frequency. Then, we design the transceiver to coordinate the backscatter communications with the tag. The transceiver incorporates a specific symbol synchronization algorithm to address the accumulated timing errors resulting from the tag unstable clock during high-rate transmission. Furthermore, we fabricate the tag on a four-layer acrlong PCB and implement the transceiver on a acrlong USRP X310. Finally, we provide experimental results to show that the VITAS system is capable of transmitting acrshort QPSK symbols at a maximum rate of 3 Mbit/s while consuming only$9.63~\mu \text{W}$(3.21 pJ/bit).

Deploying reconfigurable intelligent surfaces (RIS) near source and destination is of practical interest for improving the link quality of vehicle-to-vehicle (V2V) wireless systems. However, the accurate channel modeling and RIS tile deployment constitute a pair of challenges to evaluate the system performance such as error performance, capacity, etc. In this paper, we investigate the double-RIS channel characteristics and propose a geometry-based triple-cylinder model, where the RIS sub-surface/tile is enabled to assist V2V systems and other elements are turned off. To determine tile locations on RIS surface, we formulate an optimization problem by maximizing the end-to-end channel gain and solve it using gradient ascent (GA) method. Following this, channel correlation function, channel capacity, and outage probability are derived according to the proposed model. Five typical mobile scenarios are discussed to validate the convergence of proposed GA algorithm, where the results show that channel gain can converge to its maximum with optimized tile locations. In addition, channel correlation under different parameters and conditions are explored. Simulation results validate the enhanced channel capacity and outage probability obtained by optimizing the tile locations.

The simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) has been a promising enabler for the future wireless network due to its full-space coverage capability. In this article, we investigate the STAR-RIS assisted hybrid mmWave multiple-input-multiple-output system, where the two practical operating protocols, i.e., energy splitting (ES) and mode switching (MS), and the coupled transmission and reflection (T&R) phase-shift model for the STAR-RIS are considered. For each operating protocol, we aim to maximize the system weighted sum rate (WSR) by jointly optimizing the passive T&R coefficients at the STAR-RIS and the hybrid analog-digital precoder/combiners, subject to the discrete phase shift constraints. Specifically, we propose an efficient weighted minimum mean-square error based alternating optimization (AO) algorithm to address this highly coupled nonconvex problem. By leveraging the special ordered set of type 1 under the MS protocol, the optimization of both the discrete T&R coefficients and analog precoder/combiners can be equivalently transformed into the standard binary quadratic programming, which can be effectively solved by the mathematical programming with the equilibrium constraints-based exact penalty algorithm. The proposed penalty-based AO algorithm is also applicable to the WSR maximization under the ES protocol. In addition, to avoid high-complexity iterative process wherever possible, we develop a separate analog-digital beamforming scheme, where a fast projection-based gradient descent algorithm is applied to successively optimize discrete T&R coefficients and analog precoder/combiners to maximize the effective channel gain, and then the optimal digital precoder/combiners are obtained in semi-closed forms. Numerical simulation results demonstrate the superior WSR performance and complexity advantage of the proposed algorithms over the existing benchmark schemes.

Utilizing communication signals for indoor human activity recognition (HAS) is an important component of integrated sensing and communication (ISAC). The current majority HAS solutions adopt a single sensing strategy and only work in a simple environment. In this paper, we propose a new HAS method named WiSMLF that can flexibly select multiple sensing strategies and then use multi-level feature fusion for sensing. We first use the high frequency energy (HFE) method to categorize human activities into two types: static activities (SAs) and moving activities (MAs). Subsequently, for SAs, we adopt a joint localization and activity recognition sensing strategy, and use a multi-level feature fusion network based on visual geometry group (VGG). For MAs, we adopt a joint activity recognition and moving distance estimation sensing strategy, and use a multi-level feature fusion network based on long short-term memory (LSTM). The experimental results show that WiSMLF outperforms the existing methods especially in complex environments, and can obtain 92% higher accuracy in location, activity recognition, and distance estimation.

This paper investigates how to suppress the Age of Information (AoI) in an opportunistic channel access system, which allows multiple mobile devices to access a base station without central coordination while being aware of instant channel quality. An optimization problem is formulated to minimize the average AoI by optimizing each mobile device’s probability of contending for channel access opportunity and the threshold of offload rate. We derive the exact expression of the average AoI and generate a reformulated optimization problem. Although being non-convex, the reformulated problem is tackled by the following operations. First, we leverage the Dinkelbach method and the block coordinate descent method to convert the reformulated problem into an iterative solving procedure of two non-convex sub-problems, which optimize the contending probability and the threshold of offload rate respectively. Second, for each non-convex sub-problem, we explore the piecewise differential monotonicity for the cost function, and achieve the associated optimal solution by transforming them into standard monotonic optimization problems. Numerical results can verify the effectiveness of the proposed method through the comparison with benchmark methods.

Backscatter communications (BCs), allowing passive devices to transmit information by reflecting incident RF signals, have emerged as an attractive solution for the green Internet of Things (IoT). In the practical implementation of BC systems, we observe two common and interesting phenomena: wavy backscatter signals and striped-shape constellation clusters. These phenomena differ significantly from the traditional point-to-point communication and the theoretical BC systems, substantially degrading the system performance. Unfortunately, their causes and potential solutions remain unexplored. Motivated by this, this paper investigates the origins and designs of the corresponding solving methods. Specifically, we first reveal the causes of these phenomena: the time-varying interference stemming from the phase-locked loop (PLL) non-ideality. Then, we introduce our solutions: the dynamic self-interference cancellation (DSIC) and the data-aided decision boundary (DDB) algorithms. Finally, we implement and evaluate our solutions on a practical BC platform. Experimental results show that our solutions can reduce the bit error rate (BER) by up to two orders of magnitude, extend the communication range by over three times, and maintain linear runtime complexity, demonstrating their effectiveness and applicability in practical BC systems.

Emerging intelligent transportation services are latency-sensitive with heavy demand for computing resources, which can be supported by a multi-tier computing system composed of vehicular edge computing (VEC) servers along the roads and micro servers on vehicles. In this work, we investigate the dual Uu/PC5 interface offloading and resource allocation strategy in Cellular Vehicle-to-Everything (C-V2X) enabled multi-tier VEC system. The successful transmission probability is characterized to obtain the normalized transmission rate of PC5 interface. We aim to minimize the system latency of task processing while satisfying the resource requirements of Uu and PC5 interfaces. Due to the non-convex and variables coupling, we decompose the original problem into two subproblems, i.e., resource allocation and offloading strategy subproblems. Specifically, we derive the closed-form expressions of packet transmit frequency of PC5 interface, transmission power of Uu interface, and CPU computation frequency in the resource allocation subproblem. Moreover, for the offloading strategy subproblem, the offloading ratio matrix is obtained by proposing the PC5 interface based greedy offloading (PC5-GO) algorithm, which concludes offloading decision and ratio. Simulation results are provided that the proposed PC5-GO algorithm can significantly improve the system performance compared with other baseline schemes by 13.7% at least.

We propose a new wireless paradigm for railways – transmissive metasurface (TMS) assisted communications. It compensates for the Doppler shift brought by high mobility and reduces signal degradation due to train carriages. Specifically, the elements of the TMS panel attached to train windows manipulate the links between the base station (BS) and the onboard users. One fundamental problem with it is: does the channel introduced by TMS outperform the traditional direct channel? To answer this, we compare the gains of direct and cascaded channels and introduce the cascaded-outperform-direct probability (CODP), which is the probability that the latter exceeds the former. We then derive two simplified closed-form CODP expressions by approximating the cascaded channel gain as mixed Gaussian and Gamma distributions. Moreover, BS-related parameters impact the CODP; we show that the CODP has (i) global maximum points concerning the azimuth angle of the path from the BS to the TMS, the distance from the BS to the railway, and the BS height, respectively, and (ii) a minimum point in terms of the azimuth angle of the path from the BS to the TMS. Finally, we provide numerical results to verify our analysis and derivations.

The emerging intelligent transparent surface (ITS), unlike the intelligent reflection surface (IRS), allows incident signals to penetrate it instead of being reflected, which enables the ITS to combat the severe signal penetration loss for high-speed railway (HSR) wireless communications. This paper thus investigates the channel estimation problem where the ITS is attached to the HSR carriage window. We first propose a new transmission scheme with two pilot blocks for each frame. Second, we formulate the channels as functions of physical parameters and thus transform the problem into a parameter recovery problem. Third, we develop a successive closed-form, maximum likelihood (ML) channel estimation algorithm. Specifically, each estimate is expressed as the sum of its perfectly known value and the estimation error. By leveraging the relationship between channels for the two pilot blocks, we eliminate the unknown parameters besides Doppler shifts, which can be thereby recovered. With the reconstructed Doppler-induced phase shifts, we acquire other channel parameters. Moreover, the Cramér-Rao lower bound (CRLB) for each parameter is derived as a performance benchmark. Finally, we provide numerical results to establish the effectiveness of our proposed estimators.

Many emerging intelligent transportation services are latency-sensitive with heavy demand for computing resources, which can be handled by a multi-tier computing system composed of vehicular edge computing (VEC) servers in the roadside and micro servers carried by vehicles. In multi-tier VEC system, the offloading of vehicle-to-vehicle (V2V) can be supported using the Cellular Vehicle-to-Everything (C- V2X) links, through Uu or PC5 interfaces. In this work, we investigate the offloading and resource strategy in C- V2X enabled multi-tier VEC system. The successful transmission probability of PC5 interface is modeled to characterize the normalized transmission rate of C- V2X link. We aim to minimize the total system latency of the task processing to optimize the offloading ratio matrix and packet transmit frequency of the PC5 interface, and computation resource allocation of vehicles and VEC server. Due to the non-convex and variables coupling, the latency minimization problem is decomposed into two subproblems, i.e., resource allocation and offloading strategy subproblems, and propose a PC5 interface based greedy offloading (PC5-GO) algorithm. Specifically, for the resource allocation subproblem, we derive the closed expressions of packet transmit frequency of PC5 interface and CPU computation frequency at vehicle and VEC server. For the offloading strategy subproblem, the offloading ratio matrix is obtained by the proposed PC5-GO algorithm. Simulation results are provided that the proposed PC5-GO algorithm can significantly enhance the system performance compared with other benchmark schemes by 5.88% at least.

Ambient backscatter communication (AmBC) is a thriving paradigm of wireless communication. It enables wireless sensors such as tags to harvest energy from the ubiquitous radio frequency (RF) energy and to communicate without batteries, thus enlarging the service life and cutting down the cost of wireless sensors. It stimulates the revolution of the IoT and has received much attention from academia and industry. However, the sensitivity, a practical constraint below which the backscatter device can not be activated to transmit data, is often overlooked in basic and applied research. In this paper, we study the channel estimation problem of the AmBC system with sensitivity constraint. We first formulate the system model of the AmBC system with sensitivity constraint, then give the structure of the two-part training sequence. After that, we introduce the maximum-likelihood (ML) channel estimator and propose an optimal training design to minimize the channel estimation mean-square error. Finally, simulation results are provided to validate our analysis.

Recently, unmanned aerial vehicle (UAV)-assisted ground targets localization is widely used in search and rescue (SAR) scenes. In this article, we propose a UAV search and localization strategy, which can search and locate unknown number of victims. The proposed strategy uses the UAV as a mobile anchor to measure time of arrival (TOA) and can effectively mitigate localization errors caused by non-line-of-sight (NLOS) propagation. Generally speaking, the strategy is divided into two parts: 1) trajectory planning and 2) localization. By selecting waypoints and planning a suitable search trajectory, UAV can obtain all the measurement information in deployment area and ensure that each target has at least three anchors for localization even in the NLOS propagation environment. In the localization stage, a new estimator is proposed based on maximum-likelihood estimation (MLE), which estimates average NLOS bias together with the coordinates of target and can be well solved by the particle swarm algorithm. The simulation results show that the proposed strategy outperforms for mitigating NLOS error compared with other methods, and can effectively ensure high localization accuracy in the NLOS propagation environment.

Ambient backscatter is a new green technology that can enable batteryless tags or sensors to communicate with each other. In this paper, we combine ambient backscatter technology into traditional point-to-point wireless communication systems, and investigate its capacity and energy efficiency. Specifically, we build up the mathematical model for the new system with a sensor aided by ambient backscatter, derive its channel capacity, define the energy efficiency gain, and compare the capacity and energy efficiency performance of both traditional systems and backscatter aided systems. Simulations are then provided to corroborate our proposed studies.

Piezo-Acoustic Backscatter (PAB), one type of near-zero power backscatter, is a new technology that realizes underwater communications. In this paper, we first describe the PAB node and the channel. Then we set up a signal transmission model and formulate the underwater channel estimation problem. Based on the sparse characteristics of the channel and compressed sensing theory, we use the gradient projection for sparse reconstruction (GPSR) algorithm to solve the underwater channel estimation problem and obtain the channel estimate. Simulation results prove that in the underwater PAB system, the GPSR algorithm offers better estimation accuracy than the traditional LS algorithm.

Reconfigurable intelligent surface (RIS) is a new and revolutionary technology to achieve spectrum-, energy- and cost-efficient wireless networks. This paper studies the resource allocation for RIS-empowered device-to-device (D2D) communication underlaying a cellular network, in which an RIS is employed to enhance desired signals and suppress interference between paired D2D and cellular links. We maximize the overall network’s spectrum efficiency (SE) and energy efficiency (EE), respectively, by jointly optimizing the spectrum reuse indicators, the transmit power, the RIS’s passive beamforming and the BS’s receive beamforming. To solve both mixed-integer non-linear programming problems, we first propose an efficient and low-complexity user-pairing scheme based on relative channel strength to determine the spectrum reuse indicators. Other variables are then optimized to maximize the SE by an iterative algorithm, based on the techniques of alternating optimization, successive convex approximation, Lagrangian dual transform and quadratic transform. The EE-maximization problem is solved by an alternating algorithm integrated with Dinkelbach’s method. Numerical results show that the proposed design achieves significant SE and EE enhancements compared to traditional underlay D2D network without RIS, relay-assisted D2D network and other benchmarks.

Fast time-varying channel parameters and large penetration loss for signals passing through train carriages are two well-known challenges for wireless communications on high speed rails (HSRs). In this paper we introduce, for the first time, backscatter technology into wireless communication systems on HSRs which can address the two challenges and meanwhile have low complexity of signal processing and low cost of circuit implementation compared with traditional solutions such as relay or beamforming technology. Specifically, we propose a backscatter aided wireless transmission (BAWT) scheme and compare its performance with existing direct wireless transmission (DWT) scheme. We also propose the transceiver design for both BAWT and DWT, including joint carrier frequency offset (CFO) and channel estimator, and signal detector. We show that BAWT, instead of DWT, can obtain the channel statistical information in practical applications due to fixed train antennas and unchanged tracks, which can be utilized to facilitate channel estimation. Finally simulation results are provided to corroborate the proposed studies.

Unmanned aerial vehicle (UAV) communications are envisioned to support numerous applications in the fifth and sixth generations wireless networks. In this paper, a three-dimensional (3D) non-stationary semi-spherical geometrical model is proposed for narrowband multi-input multi-output (MIMO) UAV channels. The Gauss-Markov mobility model is used to characterize the UAV rotation for the first time, which results in time-varying elevation and orientation angles of the antenna array and further leads to channel non-stationarity. Based on the proposed model, the space-time correlation function is derived and investigated in terms of the UAV movements (including pitch, roll, and heave). These observations and conclusions can be used as a reference for the system design and performance analysis of UAV-MIMO communication systems.

The novel reconfigurable intelligent surface (RIS) is an emerging technology which facilitates high spectrum and energy efficiencies in Beyond 5G and 6G wireless communication applications. Against this backdrop, this paper investigates two-way communications via reconfigurable intelligent surfaces (RISs) where two users communicate through a common RIS. We assume that uplink and downlink communication channels between two users and the RIS can be reciprocal. We first obtain the optimal phase adjustment at the RIS. We then derive the exact outage probability and the average throughput in closed-forms for single-element RIS. To evaluate multiple-element RIS, we first introduce a gamma approximation to model a product of Rayleigh random variables, and then derive approximations for the outage probability and the average throughput. For large average signal-to-interference-plus-noise ratio (SINR) $\rho$, asymptotic analXsis also shows that the outage decreases at the rate $(\log(\rho)/\rho)$ where L is the number of elements, whereas the throughput increases with the rate $\log(\rho)$.

In this6study, the channel estimation problem is investigated for a wireless communication system assisted by a reconfigurable intelligent surface (RIS). The RIS thus creates an assistant channel, which has the features of positivity and dominance. Owing to these features, the channel estimation problem is formulated as a constrained residual sum of squares minimisation problem, which differs radically from the traditional channel estimation issue. An efficient Lagrange multiplier and dual ascent‐based estimation scheme is then designed to obtain an iterative solution for the estimator. Moreover, the Cramér–Rao lower bounds are deduced as a performance benchmark. Simulation results show that the authors' designed scheme improves the estimation accuracy up to 33%, compared with the conventional least‐square method in the low signal‐to‐noise ratio regime.

Fast time-varying channel parameters and large penetration losses for signals passing through train carriages are two well-known challenges for wireless communications on high speed rails (HSRs). In this paper, we introduce, for the first time, backscatter technology into HSR wireless communications, which can address these two challenges and yet have low complexity of signal processing and low cost of circuit implementation compared with traditional solutions such as relaying or beamforming. Specifically, we propose a backscatter aided wireless transmission (BAWT) scheme and demonstrate that it outperforms the existing direct wireless transmission (DWT) scheme. We derive the upper and lower bounds of channel capacity for BAWT and prove that it exceeds that of DWT on certain conditions. We also propose the transceiver design for both BAWT and DWT, including joint carrier frequency offset and channel estimator, and signal detector. We show that BAWT, rather than DWT, can obtain the channel statistical information in practical applications due to fixed train antennas and unchanged tracks, which can be utilized to facilitate channel estimation. Finally, simulation results are provided to corroborate the proposed solutions.

In this paper, we investigate the two-way communication between two users assisted by a reconfigurable intelligent surface (RIS). The scheme that two users communicate simultaneously over Rayleigh fading channels is considered. The channels between the two users and RIS can either be reciprocal or non-reciprocal. For reciprocal channels, we determine the optimal phases at the RIS to maximize the signal-to-interference-plus-noise ratio (SINR). We then derive exact closed-form expressions for the outage probability and spectral efficiency for single-element RIS. By capitalizing the insights obtained from the single-element analysis, we introduce a gamma approximation to model the product of Rayleigh random variables which is useful for the evaluation of the performance metrics in multiple-element RIS. Asymptotic analysis shows that the outage decreases at (log(ρ)/ρ)Lrate where L is the number of elements, whereas the spectral efficiency increases at log(ρ) rate at large average SINR p. For non-reciprocal channels, the minimum user SINR is targeted to be maximized. For single-element RIS, closed-form solution is derived whereas for multiple-element RIS the problem turns out to be non-convex. The latter one is solved through semidefinite programming relaxation and a proposed greedy-iterative method, which can achieve higher performance and lower computational complexity, respectively.

Ambient backscatter devices (tags and readers) use existing radio frequency (RF) signals to transmit data. Most prior works consider single-antenna tags, but this paper investigates the case of multiple-antenna tags, which are capable of simultaneous energy harvesting and data transmission. However, the multi-antenna channel between the tag and the reader, and the unpredictable nature of RF signals due to uncontrollable RF sources (e.g., location and transmit power), make signal detection highly challenging. Thus, the detection process becomes a hypothesis testing problem with unknown parameters. Consequently, we design a blind detector based on the generalized likelihood ratio test (GLRT) without using channel state information (CSI), signal power and noise variance. The decision threshold and detection probability of it are also analyzed in detail. Furthermore, to maximize its detection performance, we develop the optimal backscatter antenna selection scheme. Interestingly, we show that the detector performs best when only two backscatter antennas are selected. Finally, extensive simulation results validate the analysis and illustrate the effectiveness of the proposed detector.

Ambient backscatter technology utilizes ambient radio frequency (RF) signals to enable battery-free devices (tags and readers) to communicate. Most existing studies assume single-antenna tags. However, in this paper, we consider tags with multiple antennas, which are exploited to provide transmit diversity. Channel state information (CSI) estimation is then a fundamental challenge because the tags can transmit few or no training symbols. To overcome it, we require detectors that operate without CSI. Thus, we propose and design three detectors based on the chi-squared test, F-test and Bartlett's test. The latter two are blind detectors because they require neither CSI nor the knowledge of RF source power and noise variance. We derive the detection probability bounds for the first two detectors. We also propose optimal tag antenna selection schemes to maximize the detection probabilities. Finally, simulation results are provided to corroborate our theoretical studies.

A reliable vehicle-to-vehicle (V2V) channel model is necessary for intelligent transportation systems (ITSs) design. Due to the high mobility of vehicles and the low heights of antennas, the line-of-sight (LOS) propagation paths in V2V communications are more likely to be obstructed by large vehicles such as buses. Therefore, it is worthwhile to conduct in-depth investigations on obstructed line-of sight (OLOS) propagation channels caused by vehicle obstructions. In this paper, actual V2V channel measurements with large vehicle obstructions at 5.9 GHz band are conducted. Based on the measured data, it can be found that the obstructions of large vehicles not only cause additional attenuation, but also significantly affect the angular distribution of multipath components (MPCs). In addition, a cluster-based dynamic V2V channel model is proposed for OLOS scenarios. In the proposed model, the influences of vehicle obstructions on path loss, delay and angle dispersion are intuitively embodied as changes in the statistical distribution of MPCs clusters. Finally, the rationality and accuracy of the proposed model is validated by comparing the measured and simulated channels. The results in the paper are useful for enriching the understanding of V2V channels and provide supports for vehicular communication systems design and performance evaluation.

By allowing a mobile device to offload computation- intensive tasks to a base station, mobile edge computing (MEC) is a promising solution for saving the mobile device's energy. In real applications, the offloading may span multiple fading blocks. In this paper, we investigate energy-efficient offloading over multiple fading blocks with random channel gains. An optimization problem is formulated, to find out how much data should be offloaded such that the mobile device's energy consumption is minimal. Although the formulated optimization problem is non-convex, we prove that the objective function of the problem is piecewise convex, and accordingly develop an optimal solution for the problem. Numerical results verify the correctness of our findings and the effectiveness of our proposed method.

This paper presents a novel channel estimation scheme for massive multiple input multiple output (MIMO) millimeter wave (mmWave) communication system with massive uniform linear array (ULA) at base station (BS) and hybrid architecture. Through practical channel modeling, each channel path is composed of angle information and channel gain information that can be estimated separately. We first propose a general iterative index detection-based channel estimation algorithm (IDCEA) that can obtain both direction of arrival (DOA) and channel gain of each channel path. We then design an enhanced hybrid precoding scheme from the angle domain viewpoint to reduce the inter-beam interferences. Simulation results show that the proposed channel estimation can be better than traditional methods. Finally, numerical examples are provided to corroborate the proposed studies.

In recent years, unmanned aerial vehicle (UAV) communication technology has played an important role in both military and civilian applications. However, with the rapid development of military equipment, the execution efficiency of single UAVs is often limited, for which complex combat missions cannot be completed well. Therefore, UAV swarm has become an important research trend in the field of UAVs. In this paper, we consider the problem of channel estimation and self-positioning for the UAV swarm, where multiple small UAVs are displaced by arbitrarily unknown displacements due to the dynamic moving. To explore the physical characteristics of UAV swarm, the parameters of the channel are decomposed into the direction of arrival (DOA) information, the relative position information, and the channel gain information. Utilizing the rank reduction (RARE) estimator, DOAs of the different target users can be estimated efficiently, regardless of the position of the UAVs. After obtaining the DOA information, we estimate the channel gain information using small amount of training resources, which significantly reduces the training overhead and the feedback cost. Moreover, the unknown displacements among UAVs can be self-recovered from the mixed integer nonlinear programming (MINLP). To reduce the computational complexity, we develop both the sphere decoding (SD) and the least square (LS) based methods. The deterministic Cramér-Rao bound (CRB) of the self-positioning estimation is derived in closed-form. Finally, numerical examples are provided to corroborate the proposed studies.

Recently, ambient backscatter has been introduced as an attractive technology that allows small devices, such as battery-less sensors and passive tags, to communicate by using radio-frequency (RF) signals over the air. It is worth noting that multiple-antenna tags could perform energy harvesting and backscattering simultaneously and thus are advantageous for ambient backscatter communication systems. Therefore, in this paper, we consider the ambient backscatter communication systems with multiple-antenna tags and focus on signal detection problem. Multiple antennas imply multiple channel parameters, which are difficult to estimate because the tags have limited power and can transmit few training symbols. To address this problem, a semi-blind detector is designed for readers to recover tag signals without any knowledge of the multiple channel parameters between the reader and the tag. We also derive the bounds on the detection probabilities. Moreover, an antenna selection scheme is suggested to optimize the detection performance. Finally, simulation results are provided to corroborate our theoretical studies.

Recently, ambient backscatter that utilizes surrounding radio frequency (RF) signals for both power and communications, has attracted vast interest since it can free sensors and tags from batteries and has extensive applications in Internet of Things (IoT), Existing studies about ambient backscatter often assume single antenna for each tag. Actually, as we show in this paper, equipping tags with multiple antennas can enlarge communication distance, enhance detection performance, and thus be practically useful. One key challenge of using multiple-antenna tags is the signal detection at the reader because the tag may have limited power and can transmit few training symbols. Therefore, in this paper, we design a blind detector based on F-test for the reader to recover tag signals without any knowledge of RF signals power, noise variance and all channel state information (CSI). Furthermore, we derive the lower and upper bounds of detection probabilities, and its exact expression in a special case. The optimal antenna selection scheme is also proposed to maximize the detection probability. Finally, simulation results are provided to corroborate our theoretical studies.

In this paper, we consider the problem of self- positioning for the unmanned aerial vehicle (UAV) swarm, where multiple small UAVs are arranged by unknown displacement due to the dynamic moving. These multiple small UAVs also formulate a virtual massive antenna array that can estimate the direction of arrivals (DOAs) of target users efficiently, regardless of the relative position of the UAVs. After obtaining the DOA information, the unknown displacements among UAVs can also be self-recovered, automagically realizing the important functionality of self-positioning for UAV swarm. The self-positioning problem falls into the category of the mixed integer nonlinear programming (MINLP). To reduce the computational complexity, we develop a novel self-positioning algorithm based on least square (LS) method. Moreover, the deterministic Cramer-Rao bound (CRB) of the self-positioning estimation is derived in closed- form. Finally, numerical examples are provided to corroborate the proposed studies.

By allowing mobile device to offload all or part of a latency-constrained computational task to a base station at the edge of a network, mobile edge computing (MEC) is a promising technique to help the mobile device save its energy consumption. In this paper, we consider the scenario with one mobile device and multiple edge base stations, which is usual in practice. Nonorthogonal multiple access (NOMA) technique is implemented. An optimization problem is formulated, in which the transmission power to every edge base station and the amount of data to be offloaded for computation is optimized to minimize the total energy consumption of the mobile device. However, the formulated optimization problem is non-convex. To get the global optimal solution, we decompose the formulated optimization problem into two levels. In the lower level, a convex optimization problem is required to be solved. By finding out some special property of the lower level optimization problem, the upper level optimization problem can be formulated as a monotonic optimization problem, whose global optimal solution is achievable. Numerical results verify the effectiveness of our proposed method.

This paper proposes a novel direction-of-arrival (DOA)-aided channel estimation for a hybrid millimeter-wave (mm-wave) massive multiple-input multiple-output system with a uniform planar array at the base station. To explore the physical characteristics of the antenna array in mm-wave systems, the parameters of each channel path are decomposed into the DOA information and the channel gain information. We first estimate the initial DOAs of each uplink path through the 2-D discrete Fourier transform and enhance the estimation accuracy via the angle rotation technique. We then estimate the channel gain information using a small amount of training resources, which significantly reduces the training overhead and the feedback cost. More importantly, to examine the estimation performance, we derive the theoretical bounds of the mean squared errors (MSEs) and the Cramér-Rao lower bounds (CRLBs) of the joint DOA and channel gain estimation. The simulation results show that the performances of the proposed methods are close to the theoretical MSEs' analysis. Furthermore, the theoretical MSEs are also close to the corresponding CRLBs.

This paper proposes a novel direction of arrival (DOA) estimation for hybrid millimeter wave (mmWave) massive MIMO systems with the uniform planar array (UPA) at base station (BS). To explore the physical characteristics of antenna array in mmWave systems, the parameters of each channel path are decomposed into the DOA information and the channel gain information. We first estimate the initial DOAs of each uplink path through the two dimension discrete Fourier transform (2D-DFT) efficiently, and then the estimation accuracy can be further enhanced via the angle rotation technique. To examine the estimation performance, we derive the theoretical bounds of the mean squared error (MSE) performance of the DOA estimation in high signal-to-noise ratio (SNR) region. Simulation results are provided to corroborate the proposed studies, and show that the proposed DOA estimation method is close to the theoretical MSE performance.

This paper proposes a practical channel estimation scheme for downlink 60GHz indoor systems with the massive uniform rectangular array (URA) at base station (BS). Through array signal processing theory, the parameter of each channel path can be decomposed into the angular information and the channel gain information that can be estimated separately. We first prove that the two dimensional Discrete Fourier transform (2D-DFT) with phase rotation operation can be applied to efficiently estimate the angular information. Then, the channel gain information could be easily obtained with small amount of training resources in a linear manner. Interestingly, since uplink and the downlink angular information is reciprocal, the proposed method applicable for both TDD and FDD systems. Simulation results are provided to corroborate the proposed studies.

In this paper, we consider a point-to-point single antenna communication system where the receiver decodes information and harvests energy at the same time. Two practical schemes, namely power splitting scheme and time switching scheme, are investigated, in which the receiver splits or switches signals for information decoding and energy harvesting. We derive the characteristics of the formulated problem which maximizes outage received power under the outage capacity probability and outage received power probability constraints. Moreover, the numerical results prove the correctness of the characteristics of the optimization problem. In particular, it illustrates that the PS scheme outperforms the TS scheme.

In this paper, we consider a wireless communication pair powered with radio frequency (RF) signal, in which the transmitter first harvest energy from an energy access point and then transmit information to the destination. Optimization problems aiming at maximizing throughput are investigated with a consideration of two types of power constraints at the transmitter: i) short-term power constraint, which corresponds to the case that no battery capacity is assumed at the transmitter and ii) long-term power constraint, which corresponds to the case that battery capacity at the transmitter is large enough. The formulated optimization problems are non-convex but are transformed to be convex ones in the first step. For the optimization problem with short-term power constraint, a fast algorithm with a combination of bisection search and closed-form solution is proposed for the first time. For the optimization problem with long-term power constraint, an online calculation method is designed for the first time. Numerical results prove the effectiveness of the proposed algorithms.

Ambient backscatter is a new technology that utilizes existing wireless signals of other communication systems to enable small devices to communicate with each other. One key problem for ambient backscatter is that if it will result in interference on existing communication systems. We investigate this problem in this paper. Specifically, we build up the theoretical system model for the ambient backscatter systems, design a maximum likelihood (ML) detector for the legacy receiver, and also derive the corresponding bit error rate (BER) expressions. Finally, we evaluate the interference of the backscattered signals on existing communication systems via simulations. It is found that channel estimation results, distances and angles between the tag, the radio frequency (RF) source and the legacy receiver will impact the BER performance.

This paper investigates joint maximum likelihood (ML) Doppler shift and channel estimation problem for high speed railway (HSR) wireless communication systems with applying massive uniform linear array (ULA). For characterizing the performance of the considered scenarios, we provide a tractable framework for analyzing the performance of on Doppler shift estimation. Furthermore, the analytical expressions of ML channel estimation are derived, under the joint consideration of Doppler shift. Both analysis match their corresponding Cramer-Rao Bounds (CRBs) well. Finally, simulation results are provided to corroborate our proposed studies.

This paper proposes a practical channel estimation for 60-GHz indoor systems with the massive uniform rectangular array at base station. Through antenna array theory, the parameters of each channel path can be decomposed into the angular information and the channel gain information. We first prove that the true direction of arrivals of each uplink path can be extracted via an efficient array signal processing method. Then, the channel gain information could be obtained linearly with small amount of training resources, which significantly reduces the training overhead and the feedback cost. More importantly, the proposed scheme unifies the uplink/downlink channel estimations for both the time duplex division and frequency duplex division systems, making itself particularly suitable for protocol design. Compared with the existing channel estimation algorithms, the newly proposed one does not require any knowledge of channel statistics and can be efficiently deployed by the 2-D fast Fourier transform. Meanwhile, the number of user terminals simultaneously served can be increased from a sophisticatedly designed angle division multiple access scheme. Simulation results are provided to corroborate the proposed studies.

We study a novel communication technique, ambient backscatter, that utilizes radio frequency signals transmitted from an ambient source as both energy supply and information carrier to enable communications between low-power devices. Different from existing noncoherent schemes, we here design the semi-coherent detection, where channel-related parameters can be obtained from unknown data symbols and a few pilot symbols. In order to obtain a benchmark for overall detection, we first derive a maximum likelihood detector assuming a complex Gaussian ambient source, and the closed-form bit error rate (BER) is computed. To release the dependence on prior knowledge of the ambient source, we next derive a type of robust design, called an energy detector, with the ambient signal being either complex Gaussian or phase shift keying (PSK). The closed-form detection thresholds, analytical BERs, and outage probability are provided correspondingly. Interestingly, the complex Gaussian source would cause an error floor, while the PSK source does not, which brings nontrivial indication of constellation design as opposed to popular Gaussian-embedded literatures. We also propose an effective approach to estimate detection-required parameters rather than channels themselves. Numerical simulations are finally presented to verify theoretical results.

Ambient backscatter, an emerging communication mechanism where battery-free devices communicate with each other via backscattering ambient radio frequency (RF) signals, has achieved much attention recently because of its desirable application prospects in the Internet of Things. In this paper, we formulate a practical transmission model for an ambient backscatter system, where a tag wishes to send some low-rate messages to a reader with the help of an ambient RF signal source, and then provide fundamental studies of noncoherent symbol detection when all channel state information of the system is unknown. For the first time, a maximum likelihood detector is derived based on the joint probability density function of received signal vectors. In order to ease availability of prior knowledge of the ambient RF signal and reduce computational complexity of the algorithm, we design a joint-energy detector and derive its corresponding detection threshold. The analytical bit error rate (BER) and BER-based outage probability are also obtained in a closed form, which helps with designing system parameters. An estimation method to obtain detection-required parameters and comparison of computational complexity of the detectors are presented as complementary discussions. Simulation results are provided to corroborate theoretical studies.

We study the problem of signal detection for the ambient backscatter system (ABS) when data are transmitted with differential modulation. An implementation of the maximum likelihood (ML) detection algorithm is proposed. To reduce the computational complexity, we further design a suboptimal detector and derive its bit error rate (BER) closed-form expression. Moreover, both the upper and the lower bounds of the BER, which can tell more insight of how system parameters affect the detection performance, are obtained. Simulations are then provided to corroborate the studies.

In this paper, we consider the sequential detection problem in a new cognitive radio (CR) scenario when the primary user (PU) works with more than one transmit power levels. The targets of the secondary user (SU) is not only to detect the presence of PU but also to recognize PU's transmit power levels. We formulate a valid sequential detection approach via the modified Neyman Pearson (NP) criterion and then derive the closed-form decision region for each PU's transmit power level. Moreover, the average sample number (ASN), a key metric for any sequential detection method, is analytically derived in closed-form to facilitate the performance evaluation. Finally simulation result is presented to verify the correctness of the proposed studies.

In this paper, we explore the underlay cognitive relay networks in the presence of hardware impairment. To avoid interfering with the primary user's communications, the transmission powers of the secondary source and relays are adaptively adjusted by considering the following three practical effects: i) the maximum transmission power constraint at the secondary transmission nodes, ii) the interference power constraint at the primary receiver, and iii) imperfect hardware. By taking the correlations among the received signal-to-interference-plus-noise ratios into account, exact closed form expressions for the outage probabilities are respectively derived for the cases with and without a direct secondary link over Rayleigh fading channels. We further conduct an asymptotic outage probability to evaluate the impact of hardware impairment on diversity order, and show both cases with and without direct secondary link can achieve the full diversity order. Finally, simulation results are presented to verify the correctness of our analytical derivations.

The paper presents a new channel estimation scheme for downlink 60GHz indoor environment with Massive Multiple Input Multiple Output (Massive MIMO) systems by exploiting the physical characteristics of the uniform planar array (UPA) and 2-dimensional discrete Fourier Transform (2D-DFT) operation. The proposed channel estimation algorithm can be carried out with very small number of training resources, which significantly reduces the training overhead and the feedback cost. Meanwhile, the number of user terminals (UTs) served simultaneously can be increased by exploiting the spatial pattern of different UTs. Compared to the existing channel estimation algorithms, the newly proposed one does not require any knowledge of channel statistics and can be efficiently deployed by the fast Fourier transform (FFT). Finally, simulation results are provided to corroborate the proposed studies.

Time-varying channel estimation is a well-known challenge for wireless communications on high-speed railways. Existing estimation algorithms include interpolation, basis expansion model (BEM) and autoregressive models. These methods remain unchanged for every scenario on high speed railways (HSRs), such as viaducts, tunnels, cutting and open areas. Thus their estimation performance are not consistent and also not satisfactory in many cases. Noting that every train on one high-speed railway follows the same track and therefore the channel parameters are correlated at each fixed position. Based on this observation, we propose a new BEM that explores the correlation between current channels parameters and past ones. More importantly, we analyze its channel estimation performance and justify our suggestions. Finally, simulation results are provided to corroborate our proposed studies.

Ambient backscatter, a new communication technology, can permit battery-free devices to communicate with other devices through reflecting the ambient radio frequency signals. One challenge for ambient backscatter communication system is to recover the backscattered information bits hidden in the received signals. Existing solutions are mainly based on energy detector and thus provide poor performance at low signal noise ratio (SNR). To solve this problem, a detection algorithm based on statistical covariances is suggested in this paper. Specifically, we calculate the distributions of two covariance-based statistics, design the detection rule, and then derive the closed- form expressions for detection probability and bit error rate (BER). It is found that our proposed algorithm outperforms the energy detector at low SNR regions. Finally, the simulation results are provided to corroborate our theoretical studies.

In this study, a problem of efficient spectrum sensing and power allocation is studied. A network of cognitive radio adopting the technique of two‐way relay is considered. A relay node, sitting between two secondary users, carries out spectrum sensing and helps the two secondary users to realise bidirectional relay communications. For such a system, an optimisation problem which targets at maximising the average rate between the two secondary users while guaranteeing the detection probability above a predefined threshold, is formulated. Although the problem is shown to be non‐convex which means the global optimal solution is hard to be obtained, the authors solve the formulated problem global optimally by using a combination of bilevel optimisation and monotonic programming. Numerical results are provided to present the effectiveness of the authors’ proposed algorithm.

Existing studies on cognitive two-way relay networks assume that the primary users (PUs) transmit data with two power levels. In practice, it is possible that the PUs may adopt multiple power levels to maximize their throughputs or to compensate for wireless link fading. In such a scenario, there exist two key problems: one is the detection accuracy for the secondary users (SUs), and the other is the throughput tradeoff between PUs and SUs. To address the detection accuracy challenge, we propose a cooperative soft-combination-based energy detector and demonstrate its benefit over other hard-combination-based detectors. To solve the throughput tradeoff problem, we derive the throughput expressions for both PUs and SUs and analyze the impact of our detection accuracy on the throughputs. It is shown that the SU's throughput varies with the number of the PU's transmit power levels and achieves a maximum value when the number is small. We also find that increasing the detection capability of the SU reduces its own throughput, while increases the PU's throughput.

Ambient backscatter technology that utilizes the ambient radio frequency signals to enable the communications of battery-free devices has attracted much attention recently. In this paper, we study the problem of signal detection for an ambient backscatter communication system that adopts the differential encoding to eliminate the necessity of channel estimation. Specifically, we formulate a new transmission model, design the data detection algorithm, and derive two closed-form detection thresholds. One threshold is used to approximately achieve the minimum sum bit error rate (BER), while the other yields balanced error probabilities for “0” bit and “1” bit. The corresponding BER expressions are derived to fully characterize the detection performance. In addition, the lower and the upper bounds of BER at high signal-to-noise ratio regions are also examined to simplify a performance analysis. Simulation results are then provided to corroborate the theoretical studies.

This paper studies the intercept behavior of an industrial wireless sensor network (WSN) consisting of a sink node and multiple sensors in the presence of an eavesdropping attacker, where the sensors transmit their sensed information to the sink node through wireless links. Due to the broadcast nature of radio wave propagation, the wireless transmission from the sensors to the sink can be readily overheard by the eavesdropper for interception purposes. In an information-theoretic sense, the secrecy capacity of the wireless transmission is the difference between the channel capacity of the main link (from sensor to sink) and that of the wiretap link (from sensor to eavesdropper). If the secrecy capacity becomes nonpositive due to the wireless fading effect, the sensor's data transmission could be successfully intercepted by the eavesdropper and an intercept event occurs in this case. However, in industrial environments, the presence of machinery obstacles, metallic frictions, and engine vibrations makes the wireless fading fluctuate drastically, resulting in the degradation of the secrecy capacity. As a consequence, an optimal sensor scheduling scheme is proposed in this paper to protect the legitimate wireless transmission against the eavesdropping attack, where a sensor with the highest secrecy capacity is scheduled to transmit its sensed information to the sink. Closed-form expressions of the probability of occurrence of an intercept event (called intercept probability) are derived for the conventional round-robin scheduling and the proposed optimal scheduling schemes. Also, an asymptotic intercept probability analysis is conducted to provide an insight into the impact of the sensor scheduling on the wireless security. Numerical results demonstrate that the proposed sensor scheduling scheme outperforms the conventional round-robin scheduling in terms of the intercept probability.

In this paper, the problem of joint transmit power and bandwidth allocation over multiple channels is investigated for a secondary user in underlay mode, when partial information of interference channel is known. The target is to maximize the capacity of a secondary user under a probabilistic constraint of the interference to the primary user. A robust optimization problem is formulated, which is nondeterministic and cannot be solved directly. We then transform the original optimization problem into an equivalent convex optimization problem. For general case, an optimal solving algorithm, which is a combination of analytical and bisection-search methods, is given. For some special cases, simple and optimal solving algorithms are also devised. Numerical results are presented to show that our proposed optimal algorithm has low computation complexity when the number of channels is not large and can achieve global optimal utility in general case and our proposed simple algorithms have much lower computation complexity and can achieve global optimal utility in special cases.

In this paper, we propose a cognitive transmission scheme for Amplify-and-Forward (AF) two-way relay networks (TWRNs) and investigate its joint sensing and transmission performance. Specifically, we derive the overall false alarm probability, the overall detection probability, the outage probability of the cognitive TWRN over Rayleigh fading channels. Furthermore, based on these probabilities, the spectrum hole utilization efficiency of the cognitive TWRN is defined and evaluated. It is shown that smaller individual or overall false alarm probability can result in less outage probability and thus larger spectrum hole utilization efficiency for cognitive TWRN, and however produce more interference to the primary users. Interestingly, it is found that given data rate, more transmission power for the cognitive TWRN does not necessarily obtain higher spectrum hole utilization efficiency. Moreover, our results show that a maximum spectrum hole utilization efficiency can be achieved through an optimal allocation of the time slots between the spectrum sensing and data transmission phases. Finally, simulation results are provided to corroborate our proposed studies. Copyright © 2016 John Wiley & Sons, Ltd.

Ambient backscatter is a new communication technology that utilizes ambient radio frequency signals to enable battery-free devices to communicate with each other. In this paper, we study the problem of signal detection and bit error rate (BER) performance for this new communication system where the differential encoding is adopted to eliminate the necessity of channel estimation. We formulate a new transmission model, design the data detection approach, and derive the optimal/approximate closed-form detection thresholds. In addition, the performance at high signal-to-noise region (SNR) is also analyzed, where the lower and the upper bounds of BERs are found. Simulation results are then provided to corroborate our theoretical studies.

In this paper, we propose a cognitive transmission scheme for Amplify-and-Forward (AF) two-way relay networks (TWRNs) and investigate its joint sensing and transmission performance. Specifically, we derive the overall false alarm probability, the overall detection probability, the outage probability of the cognitive TWRN over Rayleigh fading channels. Furthermore, based on these probabilities, the spectrum hole utilization efficiency of the cognitive TWRN is defined and evaluated. It is shown that smaller individual or overall false alarm probability can result in less outage probability and thus larger spectrum hole utilization efficiency for cognitive TWRN, and also produce more interference to the primary users. Interestingly, it is found that given data rate, more transmission power for the cognitive TWRN does not necessarily obtain higher spectrum hole utilization efficiency. Moreover, our results show that a maximum spectrum hole utilization efficiency can be achieved through an optimal allocation of the time slots between the spectrum sensing and data transmission phases. Finally, simulation results are provided to corroborate our proposed studies.

In this paper, we study the cognitive radio networks where the primary users transmit data with multiple power levels. A cooperative sensing algorithm with soft combination based on energy detection is proposed to improve the detection accuracy. We compare the detection performance between our proposed cooperative soft combination and existing hard combination, such as averaging combination, maximum combination and minimum combination. It is found that the suggested algorithm outperforms other existing hard combination schemes. It is also shown that the increasing number of the cooperative users can result in better detection performance at low signal-to-noise ratio (SNR), while will obtain limited contribution for detection in high SNR. In the end, we validate our work by various simulation results.

One open problem for wireless communication in the high-speed railway environment is channel capacity. In this paper, we study the problem of ergodic capacity for the time-selective and frequency-selective wireless channels in the high-speed railway environment. Firstly, we build up the channel model for the wireless communication between mobile users on the high-speed train and the base station along the railway via the train antennas. Next both the upper bound and the lower bound for the ergodic channel capacity are derived on the basis of this model. Furthermore, the closed-form expression for the ergodic channel capacity is obtained at high SNR. Finally, simulation results are provided to corroborate our proposed studies.

In this paper, we introduce a three-phase transmission scheme for cloud radio networks against eavesdropper. We study the security and reliability performance of this scheme in the form of intercept probability and outage probability, respectively. Channel estimation error is considered in our study instead of assumption of perfect channel state information. It is found that the security-reliability performance improves as the number of relay increases. Moreover, the length of training symbol considerably influences the performance. Simulation results are provided to corroborate our studies.

In this work, a two-phase optimal-relay transmission scheme for relay networks against eavesdropper is proposed. The security and the reliability performance of this scheme are evaluated in the form of the intercept probability and the outage probability, respectively. In addition, the closed-form expression of security-reliability tradeoff (SRT) is obtained to show the compromise of the security and reliability performance of the system. Specifically, instead of common assumptions of perfect global channel state information in most literature, the channel estimation error and its effects on performance analysis are considered in this paper. It is shown that the security (or reliability) can be enhanced by degrading the reliability (or security) requirement, and the performance can be improved by increasing the number of relays. Finally, simulation results are provided to corroborate our proposed studies.

This paper reports (i) a set of measurements of the wireless propagation channel at 930 MHz, conducted along the "Zhengzhou-Xian" high-speed railway of China in various railway viaduct scenarios, and (ii) an analysis and modeling of the small-scale and large-scale channel parameters based on those measurements. The environment can be categorized into four cases, covering viaducts with different heights and in different suburban environments. Small values of fade depth, level crossing rates, and average fade duration are observed. Akaike's Information Criterion (AIC)-based evaluation indicates that the Ricean distribution is the best to describe small-scale amplitude fading. An analysis of the envelope autocovariance function shows that the coherence distance is less than 10 cm. The Ricean K-factor is modeled as a piecewise-linear function of distance. Moreover, a breakpoint path loss model is developed and shadow fading is investigated using the same break point as for the distance-dependent K-factor model. The Suzuki distribution is found to offer a good fit for the composite multipath/shadowing channels. We find that the viaduct height H, together with the number of surrounding scatterers, significantly affects the small- and large-scale channel parameters. These results are applicable to both normal-speed and high-speed railways, and will be useful in the modeling of railway viaduct channels and the design of railway wireless communication systems.

Two transmission schemes exist in wireless communication system for high-speed railway: the direct transmission and relay-based transmission. The main purpose of this paper is to evaluate the performance of these two transmission schemes. Specifically, the closed-form expressions of ergodic capacity for each transmission scheme are derived. By comparing these obtained capacity expressions, we find that the relay-based transmission has better performance in the low and intermediate SNR, while the direct transmission becomes a better choice at high SNR. It is also shown that the signal amplitude loss resulted from signal traversing through the carriage is a key parameter in determining the ergodic capacity for direct transmission scheme. Finally, the simulation results demonstrate an exact match with our theoretical findings.

In this paper, the estimation of doubly selective channel is considered for amplify-and-forward (AF) relay networks. The complex exponential basis expansion model (CE-BEM) is chosen to describe the time-varying channel, from which the infinite channel parameters are mapped onto finite ones. Since direct estimation of these coefficients encounters high computational complexity and large spectral cost, we develop an efficient estimator targeting at some specially defined channel parameters. The training sequence design that can minimize the channel estimation mean-square error is also proposed.

In this paper, we consider the problem of channel estimation for two-way relay networks (TWRN) under time-selective environment. We first parameterize the time-varying channels by the basis expansion model (BEM) and then propose a novel pilot symbol aided modulation (PSAM) for TWRN. A linear approach to estimate the cascaded channels is designed and the optimal training sequences are derived based on minimizing the mean-square error (MSE) criterion. Moreover, we develop an algorithm to recover the individual channel knowledge with which both the channel estimation accuracy and the system performance can be improved. Various simulations are provided to corroborate the proposed studies.

In this paper, we apply the data-dependent superimposed training (DDST) in amplify-and-forward (AF) relay networks with cyclic-prefix single carrier (CPSC) modulation. We consider various issues such as channel estimation, training design and data detection. A sub-optimal training sequence that can minimize the upper bound of the mean square error of the estimator is derived. Since the DDST estimator can only find the overall channel information, we further propose a doubly cooperative estimator (DCE) to track the individual channel knowledge at the cost of some performance loss. Simulations are then provided to corroborate the proposed studies.

In this paper, channel estimation and training sequence design are considered for amplify-and-forward (AF)-based two-way relay networks (TWRNs) in a time-selective fading environment. A new complex-exponential basis expansion model (CE-BEM) is proposed to represent the mobile-to-mobile time-varying channels. To estimate such channels, a novel pilot symbol-aided transmission scheme is developed such that a low complex linear approach can estimate the BEM coefficients of the convoluted channels. More essentially, two algorithms are designed to extract the BEM coefficients of the individual channels. The optimal training parameters, including the number of the pilot symbols, the placement of the pilot symbols, and the power allocation to the pilot symbols, are derived by minimizing the channel mean-square error (MSE). The selections of the system parameters are thoroughly discussed in order to guide practical system design. Finally, extensive numerical results are provided to corroborate the proposed studies.

Joint estimation of the carrier frequency offset (CFO) and the channel is developed for a two-way relay network (TWRN) that comprises two source terminals and an amplify-and-forward (AF) relay. The terminals use orthogonal frequency division multiplexing (OFDM). New zero-padding (ZP) and cyclic-prefix (CP) transmission protocols, which maintain the carrier orthogonality and ensure low estimation and detection complexity, are proposed. Both protocols lead to the same estimation problem which can be solved by the nulling-based least square (LS) algorithm and perform identically when the block length is large. We present detailed performance analysis by proving the unbiasedness of the LS estimators at high signal-to-noise ratio (SNR) and by deriving the closed-form expression of the mean-square-error (MSE). Simulation results are provided to corroborate our findings.

In this paper, channel estimation for two-way relay networks (TWRNs) over time-varying channels is investigated. We consider the amplify-and-forward (AF) relaying scheme and adopt the complex-exponential basis expansion model (CE-BEM) that represents the time-varying channel by a finite number of parameters. We develop the estimation methods for both the cascaded channels and the individual channels and also apply the total least square (TLS) algorithm to improve the estimation accuracy. Moreover, the training design is discussed and a heuristic criterion is proposed to minimize the condition number of the estimation matrix. The simulation results verify the goodness of the criterion.

This paper proposes a superimposed training strategy to estimate the individual frequency and channel parameters in an amplify-and-forward (AF) two-way relay network (TWRN). Two efficient suboptimal estimation algorithms and an iterative process to further improve the performance are proposed. The estimation Cramér-Rao Bound (CRB) on the proposed estimation strategy is also derived. The simulations confirm that the iterative estimation process converges rapidly and that the resultant estimation mean square error (MSE) approaches the CRB, especially for the case when the carrier frequency offset between the two source terminals is small.

In this paper, we study the problem of joint carrier frequency offset (CFO) and channel estimation for amplify-andforward (AF) two-way relay network (TWRN) that comprises two source terminals and one relay node. Both the system design and the estimation problem become more challenging when CFO is non-zero in a frequency-selective environment, as compared to the conventional point-to-point communication systems. By introducing some redundancy, we propose a cyclic prefix (CP) based OFDM modulation for TWRN that is capable of maintaining the advantage of using multi-carrier transmission and at the same time facilitates the system initialization, e.g., synchronization and channel estimation. We then apply a least square (LS) approach to solve the estimation problem. The approximated Cramér-Rao Bound (CRB) has been derived as the performance benchmark of the proposed estimator. Finally, simulations are provided to corroborate the theoretical studies. ©2010 IEEE.

Existing works on joint carrier frequency offset (CFO) and channel estimation in two-way relay networks (TWRN) only deal with the composite channel parameters and the mixed CFO values. In this paper, we design a superimposed pilot based training strategy such that the individual frequency and channel parameters can be obtained at the source terminals. We consider the amplify-and-forward (AF) relaying scheme and discuss the zero-padding (ZP) based orthogonal frequency division multiplexing (OFDM) modulation in order to cope with the frequency selective fading channels. We build up the system model and propose the joint estimation method. An iterative process is also proposed to further improve the estimation accuracy. To make the study complete, we also derive the Cram\'er-Rao Bound (CRB) and compare with the mean square error of our algorithms. Finally, simulation results are provided to corroborate our studies.

In this paper, we study the problem of joint carrier frequency offset (CFO) and channel estimation for two-way relay network (TWRN). We consider the frequency selective fading channels and adopt the zero padding (ZP) based orthogonal frequency division multiplexing (OFDM) as the modulation of the transmission. Due to the mixture of the first and the second transmission phases, the joint estimation problem becomes much challenging than that in the traditional point-to-point communication systems. By introducing some redundancy, we modify the structure of ZP-OFDM to cope with non-zero frequency synchronization errors. We then propose a nulling-based least square (NLS) method for joint CFO and channel estimation. A detailed performance analysis of NLS has been conducted, where we prove that the unbiasedness of NLS and derive the closed-form estimation mean-square-error (MSE) at high signal-to-noise ratio (SNR). Finally, simulations are provided to corroborate the proposed studies.

In this paper, we study the problem of joint carrier frequency offset (CFO) and channel estimation for two-way relay network (TWRN) that comprises two source terminals and one relay node. We build up the signal model, from which we identify the CFO and channels at the two source terminals. As the very first attempt to discuss the joint CFO and the channel estimation for TWRN, we consider relay node that purely amplifies and forwards, which is also known as the repeater. The new model is different from the traditional ones in that the unknown CFO is combined with only part of the channel parameters. We then propose two joint estimation methods, i.e., the approximate maximum-likelihood (ML) method and the nulling-based method. The Cramer-Rao Bounds (CRB) of both methods are derived in closed-form. Simulations are then provided to corroborate the proposed studies.

Super-imposed pilots can be used as an alternative to traditional pilots that are used for channel estimation. Superimposed pilots improve bandwidth efficiency. We apply it to the amplify and forward (AF) relay systems. In this paper we give the channel estimation (CE) results, analyze the system performance, study the power allocation methods and extend our result to multi-hop relay systems. Our main contribution is that we suggest and prove the existence of minimum bit error rate (BER) as a function of pilot signal power, find the best power allocation ratio value that can reach the minimum BER, analyze parameters' influence on the ratio value, and extend our result to multi-hop systems.