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.

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.

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.

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.

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.