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Showing papers in "IEEE Communications Letters in 2020"


Journal ArticleDOI
TL;DR: In this article, a blockchained federated learning (BlockFL) architecture is proposed, where local learning model updates are exchanged and verified by utilizing a consensus mechanism in blockchain.
Abstract: By leveraging blockchain, this letter proposes a blockchained federated learning (BlockFL) architecture where local learning model updates are exchanged and verified. This enables on-device machine learning without any centralized training data or coordination by utilizing a consensus mechanism in blockchain. Moreover, we analyze an end-to-end latency model of BlockFL and characterize the optimal block generation rate by considering communication, computation, and consensus delays.

394 citations


Journal ArticleDOI
TL;DR: This letter proposes a simple design of intelligent reflecting surface (IRS) assisted non-orthogonal multiple access (NOMA) downlink transmission, where conventional spatial division multiple access is used first at the base station and IRS-assisted NOMA is used to ensure that additional cell-edge users can also be served on these beams.
Abstract: This letter proposes a simple design of intelligent reflecting surface (IRS) assisted non-orthogonal multiple access (NOMA) downlink transmission. In particular, conventional spatial division multiple access (SDMA) is used first at the base station to generate orthogonal beams by using the spatial directions of the near users’ channels. Then, IRS-assisted NOMA is used to ensure that additional cell-edge users can also be served on these beams by aligning the cell-edge users’ effective channel vectors with the predetermined spatial directions. Both analytical and simulation results are provided to demonstrate the performance of the proposed IRS-NOMA scheme and also study the impact of hardware impairments on IRS-NOMA.

341 citations


Journal ArticleDOI
TL;DR: This letter analyzes the minimum transmit powers required by different multiple access schemes and compares them numerically, which turn out to not fully comply with the stereotyped superiority of NOMA over OMA in conventional systems without IRS, and proposes a low-complexity solution to achieve near-optimal performance.
Abstract: The integration of intelligent reflecting surface (IRS) to multiple access networks is a cost-effective solution for boosting spectrum/energy efficiency and enlarging network coverage/connections. However, due to the new capability of IRS in reconfiguring the wireless propagation channels, it is fundamentally unknown which multiple access scheme is superior in the IRS-assisted wireless network. In this letter, we pursue a theoretical performance comparison between non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) in the IRS-assisted downlink communication, for which the transmit power minimization problems are formulated under the discrete unit-modulus reflection constraint on each IRS element. We analyze the minimum transmit powers required by different multiple access schemes and compare them numerically, which turn out to not fully comply with the stereotyped superiority of NOMA over OMA in conventional systems without IRS. Moreover, to avoid the exponential complexity of the brute-force search for the optimal discrete IRS phase shifts, we propose a low-complexity solution to achieve near-optimal performance.

307 citations


Journal ArticleDOI
TL;DR: This work proposes a deep learning approach for passive beamforming design in RIS-assisted systems using a customized deep neural network trained offline using the unsupervised learning mechanism, which is able to make real-time prediction when deployed online.
Abstract: Reconfigurable intelligent surface (RIS) has recently emerged as a promising candidate to improve the energy and spectral efficiency of wireless communication systems. However, the unit modulus constraint on the phase shift of reflecting elements makes the design of optimal passive beamforming solution a challenging issue. The conventional approach is to find a suboptimal solution using the semi-definite relaxation (SDR) technique, yet the resultant suboptimal iterative algorithm usually incurs high complexity, hence is not amenable for real-time implementation. Motivated by this, we propose a deep learning approach for passive beamforming design in RIS-assisted systems. In particular, a customized deep neural network is trained offline using the unsupervised learning mechanism, which is able to make real-time prediction when deployed online. Simulation results show that the proposed approach maintains most of the performance while significantly reduces computation complexity when compared with SDR-based approach.

146 citations


Journal ArticleDOI
TL;DR: A cost-efficient convolutional neural network for robust automatic modulation classification (AMC) deployed for cognitive radio services of modern communication systems and achieves the overall 24-modulation classification rate of 93.59% at 20 dB SNR on the well-known DeepSig dataset.
Abstract: This letter proposes a cost-efficient convolutional neural network (CNN) for robust automatic modulation classification (AMC) deployed for cognitive radio services of modern communication systems. The network architecture is designed with several specific convolutional blocks to concurrently learn the spatiotemporal signal correlations via different asymmetric convolution kernels. Additionally, these blocks are associated with skip connections to preserve more initially residual information at multi-scale feature maps and prevent the vanishing gradient problem. In the experiments, MCNet reaches the overall 24-modulation classification rate of 93.59% at 20 dB SNR on the well-known DeepSig dataset.

140 citations


Journal ArticleDOI
TL;DR: Low-complexity linear equalizers for orthogonal time frequency space (OTFS) modulation that exploit the structure of the effective channel matrix in OTFS to achieve significant complexity reduction.
Abstract: In this letter, we propose low-complexity linear equalizers for orthogonal time frequency space (OTFS) modulation that exploit the structure of the effective channel matrix in OTFS. The proposed approach exploits the block circulant nature of the OTFS channel matrix to achieve significant complexity reduction. For an $N\times M$ OTFS system, where $N$ and $M$ are the number of Doppler and delay bins, respectively, the proposed approach gives exact minimum mean square error (MMSE) and zero-forcing (ZF) solutions with just $\mathcal {O}(MN \log MN)$ complexity, while MMSE and ZF solutions using the traditional matrix inversion approach require $\mathcal {O}(M^{3}N^{3})$ complexity. The proposed approach can provide low complexity initial solutions for local search techniques to achieve enhanced bit error performance.

140 citations


Journal ArticleDOI
TL;DR: In this paper, a detailed performance analysis of the IRS aided single-input single-output (SISO) communication system is presented, taking into account of the direct link between the transmitter and receiver.
Abstract: This letter presents a detailed performance analysis of the intelligent reflecting surface (IRS) aided single-input single-output communication systems, taking into account of the direct link between the transmitter and receiver. A closed-form upper bound is derived for the ergodic capacity, and an accurate approximation is obtained for the outage probability. In addition, simplified expressions are presented in the asymptotic regime. Numerical results are provided to validate the correctness of the theoretical analysis. It is found that increasing the number of reflecting elements can significantly boost the ergodic capacity and outage probability performance, and a strong line-of-sight component is also beneficial. In addition, it is desirable to deploy the IRS close to the transmitter or receiver, rather than in the middle.

130 citations


Journal ArticleDOI
TL;DR: In this article, an IRS enhanced full-duplex MIMO two-way communication system is studied, where the system sum rate is maximized through jointly optimizing the source precoders and the IRS phase shift matrix.
Abstract: In this letter, an intelligent reflecting surface (IRS) enhanced full-duplex MIMO two-way communication system is studied. The system sum rate is maximized through jointly optimizing the source precoders and the IRS phase shift matrix. Adopting the idea of Arimoto-Blahut algorithm, the non-convex optimization problem is decoupled into three sub-problems, which are solved alternatingly. All the sub-problems can be solved efficiently with closed-form solutions. In addition, practical IRS assumptions, e.g., discrete phase shift levels, are also considered. Numerical results verify the convergence and performance of the proposed scheme.

116 citations


Journal ArticleDOI
TL;DR: Deep learning method CNN-LSTM was employed in the UWB NLOS/LOS signal classification and obtained stat e-of-art classification performance.
Abstract: Ultra-Wide-Band (UWB) was recognized as its great potential in constructing accurate indoor position system (IPS). However, indoor environments were full of complex objects, the signals might be reflected by the obstacles. Compared with the Line-Of-Sight (LOS) signal, the signal transmitting path delay contained in None-Line-Of-Sight (NLOS) signal would induce positive distance errors and position errors. Before employing ranging information from the channels to calculate the position, LOS/NLOS classification or identification was necessary for selecting the “clean” channels. In conventional method, features extracted from the UWB channel impulse response (CIR) or some other signal properties were employed as the input vector of the machine learning methods, e.g. Support Vector Machine (SVM), Multi-layer Perception (MLP). Deep learning methods represented by Convolutional neural network (CNN) and Long Short-Term Memory (LSTM) had performed superior performance in dealing with time series data classification. In this pap er, deep learning method CNN-LSTM was employed in the UWB NLOS/LOS signal classification. UWB CIR data was directly input to the CNN-LSTM. CNN was employed for exploring and extracting the features automatically, and then, the CNN outputs were fed into the LSTM for classification. Open source datasets collected from seven different sites were employed in the experiments. Classification accuracy of CNN-LSTM with different settings was compared for analyzing the performance. The results showed that CNN-LSTM obtained stat e-of-art classification performance.

112 citations


Journal ArticleDOI
TL;DR: This letter derives exact closed-form expressions for the outage probability and bit error rate (BER) in terms of the Meijer’s G-function, generalized hypergeometric function, and Marcum Q-function to obtain the diversity order.
Abstract: In this letter, we propose a dual-hop reconfigurable intelligent surface (RIS)-based free space optical and radio frequency (FSO-RF) communication system, where an RIS is utilized to improve the coverage and system performance. Taking both the atmospheric turbulence and pointing errors into consideration, we derive exact closed-form expressions for the outage probability and bit error rate (BER) in terms of the Meijer’s G-function, generalized hypergeometric function, and Marcum Q-function. Furthermore, to obtain the diversity order, an asymptotic outage analysis is also obtained. Finally, the correctness of the analytical results is verified by Monte-Carlo simulation results.

97 citations


Journal ArticleDOI
TL;DR: In this paper, an IRS-assisted cognitive radio (CR) system is investigated, where an IRS is deployed to assist in the spectrum sharing between a PU link and an SU link.
Abstract: In cognitive radio (CR) communication systems, it is challenging to achieve high rate for the secondary user (SU) in the presence of strong cross-link interference with the primary user (PU). In this letter, we exploit the recently proposed intelligent reflecting surface (IRS) to tackle this problem. Specifically, we investigate an IRS-assisted CR system where an IRS is deployed to assist in the spectrum sharing between a PU link and an SU link. We aim to maximize the achievable SU rate subject to a given signal-to-interference-plus-noise ratio (SINR) target for the PU link, by jointly optimizing the SU transmit power and IRS reflect beamforming. Although the formulated problem is difficult to solve due to its non-convexity and coupled variables, we propose an efficient algorithm based on alternating optimization and successive convex approximation techniques to solve it sub-optimally, as well as some heuristic designs of lower complexity. Simulation results show that IRS is able to significantly improve the SU rate, even for the scenarios deemed highly challenging in conventional CR systems without the IRS.

Journal ArticleDOI
TL;DR: A deep residual learning framework is proposed, UcnBeamNet, to enhance the ability of approximating the iterative algorithm for sum rate maximization, where multi-branch subnets are connected in parallel to extract extra information.
Abstract: In existing works of deep learning-based resource allocation, the scalability degrades heavily with the increase of network complexity, which is due to their limited learning ability of shallow neural networks and insufficient knowledge of network. Nowadays, to address the growth of cell density, cooperative beamforming in user-centric network (UCN) is emerged, where the additional degrees of freedom of multi-antenna and cell coordination aggravate the challenges. This letter proposes a deep residual learning framework, UcnBeamNet, to enhance the ability of approximating the iterative algorithm for sum rate maximization, where multi-branch subnets are connected in parallel to extract extra information. Specifically, a weighted minimum mean square error (WMMSE)-based algorithm is derived to determine the optimal clusters and beamforming matrices; then UcnBeamNet is trained to learn the input-output mapping and provide direct insight of UCN from association matrices in addition to plural inputs. Extensive experiments demonstrate UcnBeamNet still reaches 90.38% sum-rate relative to conventional algorithm even with a large network size, and achieves more than 50, $000\times $ speed up in computational efficiency.

Journal ArticleDOI
TL;DR: The proposed scheme significantly outperforms the previous works in terms of both classification accuracy and computing time and adjusted the number of layers and added new type of layers to comply with the estimated latency standards in beyond fifth-generation (B5G) communications.
Abstract: In this letter, we propose an improved convolutional neural network (CNN)-based automatic modulation classification network (IC-AMCNet), an algorithm to classify the modulation type of a wireless signal. Since adaptive coding and modulation is widely used in wireless communication, high accuracy and short computing time of classifier is needed. Compared with the existing CNN architectures, we adjusted the number of layers and added new type of layers to comply with the estimated latency standards in beyond fifth-generation (B5G) communications. According to the simulation results, the proposed scheme significantly outperforms the previous works in terms of both classification accuracy and computing time.

Journal ArticleDOI
TL;DR: This letter considers integrating a backscatter link with a reconfigurable intelligent surface to enhance back scatter communication while assisting the direct communication, and develops two approximations that match well with the exact value.
Abstract: This letter considers integrating a backscatter link with a reconfigurable intelligent surface to enhance backscatter communication while assisting the direct communication. We derive the probability that the backscatter channel dominates in the composite channel. This probability is a useful performance measure to determine the number of reflectors. Since the exact probability lacks a closed-form solution, we develop two approximations by modeling the gain of the backscatter link with a Gaussian or Gamma distribution. We found that these approximations match well with the exact value. Importantly, with a well-designed number of reflectors, the channel gain of the backscatter link may be always stronger than that of the direct one.

Journal ArticleDOI
TL;DR: A framework integrating energy, computation and communication (ECC), and a joint beamforming design algorithm for the BS and the IoT devices to improve the overall performance of ECC are designed.
Abstract: To jointly address the critical issues of the sixth-generation (6G) cellular internet of things (IoT), i.e., energy supply, data aggregation, and information transmission, we design a framework integrating energy, computation and communication (ECC). Firstly, the base station (BS) charges massive IoT devices simultaneously via wireless power transfer (WPT) in the downlink. Then, IoT devices with the harvested energy carry out the computation task and the communication task in the uplink over the same spectrum. To improve the overall performance of ECC, we propose a joint beamforming design algorithm for the BS and the IoT devices. Finally, simulation results validate the effectiveness of the proposed algorithm in 6G cellular IoT.

Journal ArticleDOI
TL;DR: This letter studies the energy-efficient unmanned aerial vehicle (UAV) communications to support ground nodes (GNs) and introduces a constraint named as information causality constraint (ICC), to guarantee that the UAV receives information from BS in any time slot and forward the only received information to GNs in remaining time slots.
Abstract: This letter studies the energy-efficient unmanned aerial vehicle (UAV) communications to support ground nodes (GNs). The system considers the UAV working as a relay while there is a base station (BS) on the ground. We analyze the UAV energy consumption model to design the energy-efficient UAV trajectory path. We formulate the energy-efficient UAV relaying communication, which considers both throughput and UAV propulsion energy consumption. We optimize joint transmit power of UAV and BS; UAV trajectory, acceleration, and flying speed to maximize the energy-efficient UAV relaying problem. We also introduce a constraint named as information causality constraint (ICC). The main idea of ICC is to guarantee that the UAV receives information from BS in any time slot and forward the only received information to GNs in remaining time slots. The formulated energy-efficiency maximization problem is not convex. Thus, we solve it sub-optimally using the iterative method. Finally, we present the simulation results to validate the efficacy of the proposed algorithm.

Journal ArticleDOI
TL;DR: The phase-amplitude-frequency relationship of the reflected signals is investigated and a practical model of reflection coefficient for an IRS-aided wideband system is proposed and Simulation results illustrate the importance of the practical model on the IRS designs and validate the effectiveness of the proposed model.
Abstract: Intelligent reflecting surface (IRS) has emerged as a revolutionizing solution to enhance wireless communications by intelligently changing the propagation environment. Prior studies on IRS are based on an ideal reflection model with a constant amplitude and a variable phase shift. However, it is difficult and unrealistic to implement an IRS satisfying such ideal reflection model in practical applications. In this letter, we aim to investigate the phase-amplitude-frequency relationship of the reflected signals and propose a practical model of reflection coefficient for an IRS-aided wideband system. Then, based on this practical model, joint transmit power allocation of each subcarrier and IRS beamforming optimization are investigated for an IRS-aided wideband orthogonal frequency-division multiplexing (OFDM) system. Simulation results illustrate the importance of the practical model on the IRS designs and validate the effectiveness of our proposed model.

Journal ArticleDOI
TL;DR: This letter proposes a CNN-LSTM detector which first uses the CNN to extract the energy-correlation features from the covariance matrices generated by the sensing data, then the series of energy-Correlation features corresponding to multiple sensing periods are input into the LSTM so that the PU activity pattern can be learned.
Abstract: For most existing spectrum sensing detectors, the design of their test statistics relies on certain signal-noise model assumptions and hence, their detection performance heavily depends on the accuracy of the assumed models. Therefore, recently, much attention in the research of spectrum sensing is focused on deep learning which is free from model assumptions. Note that, in deep learning, the convolutional neural networks (CNNs) and the long-short term memory (LSTM) networks have the powerful capabilities in extracting spatial and temporal features of the input, respectively. In this letter, we propose a CNN-LSTM detector which first uses the CNN to extract the energy-correlation features from the covariance matrices generated by the sensing data, then the series of energy-correlation features corresponding to multiple sensing periods are input into the LSTM so that the PU activity pattern can be learned. The purpose of learning PU activity pattern is to further promote the detection probability. With sufficient simulations, the superiority of the CNN-LSTM detector is proven in scenarios with and without noise uncertainty.

Journal ArticleDOI
TL;DR: In this letter, the ergodic capacity of the intelligent reflecting surface (IRS)-assisted communication system with quantization phase errors is investigated, the impact of phase errors on the capacity degradation is quantified, and the minimum number of the reflectors to achieve a given rate threshold is obtained.
Abstract: In this letter, we investigate the ergodic capacity of the intelligent reflecting surface (IRS)-assisted communication system with quantization phase errors, which is different from existing works assuming ideal continuous or discrete phases. The ergodic capacity, however, does not admit an exact closed-form expression if not impossible. In order to gain insight into the capacity performance, the impact of phase errors on the capacity degradation is quantified, and the minimum number of the reflectors to achieve a given rate threshold is obtained. Simulation results verify the effectiveness of the IRS-assisted system and the capacity scaling law.

Journal ArticleDOI
TL;DR: In this letter, a constellation density matrix (CDM) based modulation classification algorithm is proposed to identify different orders of ASK, PSK, and QAM to demonstrate better performance compared to many existing classifiers in the literature.
Abstract: Deep learning (DL) is a newly addressed area of research in the field of modulation classification. In this letter, a constellation density matrix (CDM) based modulation classification algorithm is proposed to identify different orders of ASK, PSK, and QAM. CDM is formed through local density distribution of the signal’s constellation generated using LabVIEW for a wide range of SNR. Two DL models, ResNet-50 and Inception ResNet V2 are trained through color images formed by filtering the CDM. Classification accuracy achieved demonstrates better performance compared to many existing classifiers in the literature.

Journal ArticleDOI
TL;DR: This letter study an IRS-assisted multiple-input single-output (MISO) system where a base station with multiple antennas arranged in a uniform rectangular array serves a single-antenna user with the help of an IRS with multiple elements arranged in an URA to reveal important design insights.
Abstract: Intelligent reflecting surface (IRS) is gradually being recognized as a promising technology for improving spectral and energy efficiency of wireless systems. In this letter, we study an IRS-assisted multiple-input single-output (MISO) system where a base station (BS) with multiple antennas arranged in a uniform rectangular array (URA) serves a single-antenna user with the help of an IRS with multiple elements arranged in a URA. We consider a Rician fading model, where the non-line of sight (NLoS) components vary slowly and the line of sight (LoS) components do not change. To reduce costs for channel estimation and phase adjustment, we adopt fixed maximum-ratio transmission (MRT) at the BS, fixed phase shifts at the IRS and constant rate transmission. First, we obtain the expression of the outage probability. Then, we minimize the outage probability by optimizing the phase shifts of the IRS. Finally, we obtain the expression of the asymptotically optimal outage probability in the high signal-to-noise ratio (SNR) regime. We also characterize the impacts of several key system parameters on the optimal outage probability to reveal important design insights.

Journal ArticleDOI
TL;DR: Numerical observations demonstrate that the mixed-ADC architecture with a small fraction of perfect ADCs has a great potential to boost the SE compared to the case with pure low-resolution ADCs, and is superior to the full perfect ADC counterpart in terms of energy efficiency (EE).
Abstract: In this letter, we introduce a mixed analog-to-digital converter (ADC) receiver architecture to cell-free massive multi-input multi-output (mMIMO) system under Rician fading channels. In particular, the mixed-ADC architecture permits some of receiver’s antennas to be implemented with economical low-resolution ADCs, while the rests are equipped with high-priced perfect ADCs. Leveraging on the additive quantization noise model (AQNM), a tight approximate uplink spectral efficiency (SE) expression with matched filtering receiver is derived, which provides us with a tool for easily quantifying the impacts of the Rician ${K}$ -factor, the quantization bit of low-resolution ADCs, and the proportion of the perfect ADCs in the mixed-ADC architecture. Numerical observations demonstrate that the mixed-ADC architecture with a small fraction of perfect ADCs has a great potential to boost the SE compared to the case with pure low-resolution ADCs. Moreover, the mixed-ADC architecture is superior to the full perfect ADCs counterpart in terms of energy efficiency (EE).

Journal ArticleDOI
TL;DR: To tackle the non-convex optimization problem, an efficient algorithm is developed by capitalizing on alternating optimization and majorization-minimization techniques and substantially outperforms conventional non-robust methods.
Abstract: In this work, we study the statistically robust beamforming design for an intelligent reflecting surfaces (IRS) assisted multiple-input single-output (MISO) wireless system under imperfect channel state information (CSI), where the channel estimation errors are assumed to be additive Gaussian. We aim at jointly optimizing the transmit/receive beamformers and IRS phase shifts to minimize the average mean squared error (MSE) at the user. In particular, to tackle the non-convex optimization problem, an efficient algorithm is developed by capitalizing on alternating optimization and majorization-minimization techniques. Simulation results show that the proposed scheme achieves robust MSE performance in the presence of CSI error, and substantially outperforms conventional non-robust methods.

Journal ArticleDOI
TL;DR: This letter examines the bit error rate (BER) performance of downlink non-orthogonal multiple access networks for binary phase-shift keying modulation through simulations and software-defined radio-based real-time tests.
Abstract: This letter examines the bit error rate (BER) performance of downlink non-orthogonal multiple access networks for binary phase-shift keying modulation. Exact BER expression is derived for each user in closed-form under additive white Gaussian noise and Rayleigh fading channels in perfect and imperfect successive interference cancellation (SIC) cases. Next, in perfect SIC case, the asymptotic BER expression in a high signal-to-noise ratio (SNR) region is obtained to express the behavior of the network with diversity and array gains. On the other hand, in imperfect SIC case, the upper bound for BER is attained, and at high SNR values, the BER reveals an error floor, and hence a zero diversity gain is achieved. Then, a feasible range of power allocation coefficients is found such that a good BER performance can be provided for each user. Finally, through simulations and software-defined radio-based real-time tests, analytical results are validated.

Journal ArticleDOI
TL;DR: In this paper, a joint optimization problem over active beamforming, passive beamforming and power allocation for a downlink intelligent reflecting surface (IRS) enhanced millimeter-wave (mmWave) NOMA system is considered.
Abstract: In this letter, a downlink intelligent reflecting surface (IRS) enhanced millimeter-wave (mmWave) non-orthogonal multiple access (NOMA) system is considered. A joint optimization problem over active beamforming, passive beamforming and power allocation is formulated. Due to the highly coupled variables, the formulated optimization problem is non-convex. To solve this problem, an alternative optimization and successive convex approximation based iterative algorithm is proposed. Numerical results illustrate that: 1) the proposed scheme offers significant sum-rate gains, which confirms the effectiveness of introducing IRS for mmWave-NOMA systems; 2) the proposed algorithm with discrete phase shifts can achieve close performance to that of continuous phase shifts.

Journal ArticleDOI
TL;DR: A new pilot pattern and a sparse Bayesian learning (SBL)-based channel estimation algorithm are proposed for orthogonal time frequency space modulation and the expected maximum (EM) algorithm is used to update the parameters in the prior model.
Abstract: Orthogonal time frequency space (OTFS) modulation has superior performance than traditional orthogonal frequency division multiplexing (OFDM) in fast time-varying scenarios. However, due to the effect of Doppler shift, higher pilot overhead and pilot power are required to estimate the channel state information. According to the sparseness of the channel in the delay-Doppler domain, this letter proposes a new pilot pattern and a sparse Bayesian learning (SBL)-based channel estimation algorithm. There is no guard pilot in the pilot pattern, and the pilot has the same power as the data. Based on the new pilot pattern, we first convert the channel estimation problem to a sparse signal recovery problem. Then, we introduce a sparse Bayesian learning framework and construct a sparse signal prior model as a hierarchical Laplace prior. Finally, the expected maximum (EM) algorithm is used to update the parameters in the prior model. Numerical simulation highlights the superiority of the proposed algorithm in terms of pilot overhead, pilot power consumption, and anti-noise interference.

Journal ArticleDOI
Rongpeng Li1, Chujie Wang1, Zhifeng Zhao, Guo Rongbin, Honggang Zhang1 
TL;DR: This letter considers a scenario that contains several slices in a radio access network with base stations that share the same physical resources (e.g., bandwidth or slots), and incorporates the long short-term memory (LSTM) into A2C, and puts forward an LSTM-A2C algorithm to track the user mobility and improve the system utility.
Abstract: Network slicing aims to efficiently provision diversified services with distinct requirements over the same physical infrastructure. Therein, in order to efficiently allocate resources across slices, demand-aware inter-slice resource management is of significant importance. In this letter, we consider a scenario that contains several slices in a radio access network with base stations that share the same physical resources (e.g., bandwidth or slots). We primarily leverage advantage actor-critic (A2C), one typical deep reinforcement learning (DRL) algorithm, to solve this problem by considering the varying service demands as the environment state and the allocated resources as the environment action. However, given that the user mobility toughens the difficulty to perceive the environment, we further incorporate the long short-term memory (LSTM) into A2C, and put forward an LSTM-A2C algorithm to track the user mobility and improve the system utility. We verify the performance of the proposed LSTM-A2C through extensive simulations.

Journal ArticleDOI
TL;DR: A new method for two-dimensional (2-D) direction-of-arrival (DOA) estimation using two parallel nested arrays that exploits the difference coarray to increase the array aperture and degrees of freedom and can achieve parameter automatic pairing.
Abstract: In this letter, we propose a new method for two-dimensional (2-D) direction-of-arrival (DOA) estimation using two parallel nested arrays. In this method, an augmented covariance matrix is firstly constructed using the outputs of two parallel difference coarrays. Based on the augmented covariance matrix, the 2-D DOA estimation problem is then converted into two one-dimensional estimation problems. Finally, the azimuth and elevation angle estimates are derived using the estimated direction cosines. Unlike the traditional methods, our algorithm exploits the difference coarray to increase the array aperture and degrees of freedom. Moreover, it does not require any peak searching and can achieve parameter automatic pairing. Numerical simulations are conducted to verify the performance of the proposed method.

Journal ArticleDOI
TL;DR: By characterizing source-to-relay and relay- to-destination channel models of the considered unmanned aerial vehicle (UAV)-assisted free-space optical (FSO) relay link, the optimal 3D coordinates of UAV relay as well as optimal optical beam pattern are derived.
Abstract: In this letter, by characterizing source-to-relay and relay-to-destination channel models of the considered unmanned aerial vehicle (UAV)-assisted free-space optical (FSO) relay link, we derive the optimal 3D coordinates of UAV relay as well as optimal optical beam pattern in order to minimize the outage probability. Moreover, we study the impact of physical parameters (such as strength of orientation fluctuations of UAV, height and position of obstacles) on the optimal location of UAV relay.

Journal ArticleDOI
TL;DR: A semidefinite programming (SDP) based localization algorithm to effectively solve the MLE problem and the accuracy of the proposed algorithm is close to the Cramér-Rao lower bound and has a superior performance than existing solutions.
Abstract: In time-difference-of-arrival (TDOA) localization systems, the TDOA measurements and range-difference-of-arrival (RDOA) values can be used interchangeably if the signal propagation speed is known. In some scenarios like underwater acoustic localization, the signal propagation speed is often unknown. This letter addresses joint source location and propagation speed (unknown) estimation using signal TDOA measurements in the presence of sensor position errors. The maximum likelihood estimator (MLE) for this problem is optimal, but it requires a suitable initial value to obtain the global solution or needs exhaustive grid search. We develop a semidefinite programming (SDP) based localization algorithm to effectively solve the MLE problem. The accuracy of the proposed algorithm is close to the Cramer-Rao lower bound and has a superior performance than existing solutions.