Showing papers in "IEEE Communications Letters in 2019"
TL;DR: The estimation error shows that the presented algorithm is comparable to the minimum mean square error (MMSE) with full knowledge of the channel statistics, and it is better than an approximation to linear MMSE.
Abstract: In this letter, we present a deep learning algorithm for channel estimation in communication systems. We consider the time–frequency response of a fast fading communication channel as a 2D image. The aim is to find the unknown values of the channel response using some known values at the pilot locations. To this end, a general pipeline using deep image processing techniques, image super-resolution (SR), and image restoration (IR) is proposed. This scheme considers the pilot values, altogether, as a low-resolution image and uses an SR network cascaded with a denoising IR network to estimate the channel. Moreover, the implementation of the proposed pipeline is presented. The estimation error shows that the presented algorithm is comparable to the minimum mean square error (MMSE) with full knowledge of the channel statistics, and it is better than an approximation to linear MMSE. The results confirm that this pipeline can be used efficiently in channel estimation.
373 citations
TL;DR: In this article, the authors investigate transmission optimization for intelligent reflecting surface (IRS) assisted multi-antenna systems from the physical-layer security perspective, where the design goal is to maximize the system secrecy rate subject to the source transmit power constraint and the unit modulus constraints imposed on phase shifts at the IRS.
Abstract: We investigate transmission optimization for intelligent reflecting surface (IRS) assisted multi-antenna systems from the physical-layer security perspective. The design goal is to maximize the system secrecy rate subject to the source transmit power constraint and the unit modulus constraints imposed on phase shifts at the IRS. To solve this complicated non-convex problem, we develop an efficient alternating algorithm where the solutions to the transmit covariance of the source and the phase shift matrix of the IRS are achieved in closed form and semi-closed form, respectively. The convergence of the proposed algorithm is guaranteed theoretically. Simulation results validate the performance advantage of the proposed optimized design.
356 citations
TL;DR: Simulation results show that the proposed approach allocates resources to the incoming UR LLC traffic efficiently, while satisfying the reliability of both eMBB and URLLC.
Abstract: Ultra Reliable Low Latency Communication (URLLC) is a 5G New Radio (NR) application that requires strict reliability and latency. URLLC traffic is usually scheduled on top of the ongoing enhanced Mobile Broadband (eMBB) transmissions ( i.e., puncturing the current eMBB transmission) and cannot be queued due to its hard latency requirements. In this letter, we propose a risk-sensitive based formulation to allocate resources to the incoming URLLC traffic, while minimizing the risk of the eMBB transmission ( i.e., protecting the eMBB users with low data rate) and ensuring URLLC reliability. Specifically, the Conditional Value at Risk (CVaR) is introduced as a risk measure for eMBB transmission. Moreover, the reliability constraint of URLLC is formulated as a chance constraint and relaxed based on Markov’s inequality. We decompose the formulated problem into two subproblems in order to transform it into a convex form and then alternatively solve them until convergence. Simulation results show that the proposed approach allocates resources to the incoming URLLC traffic efficiently, while satisfying the reliability of both eMBB and URLLC.
177 citations
TL;DR: In this paper, a sparse complex-valued neural network (SCNet) was proposed to approximate the uplink-to-downlink mapping function in a massive MIMO system.
Abstract: In a frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) system, the acquisition of downlink channel state information (CSI) at base station (BS) is a very challenging task due to the overwhelming overheads required for downlink training and uplink feedback. In this letter, we reveal a deterministic uplink-to-downlink mapping function when the position-to-channel mapping is bijective. Motivated by the universal approximation theorem, we then propose a sparse complex-valued neural network (SCNet) to approximate the uplink-to-downlink mapping function. Different from general deep networks that operate in the real domain, the SCNet is constructed in the complex domain and is able to learn the complex-valued mapping function by off-line training. After training, the SCNet is used to directly predict the downlink CSI based on the estimated uplink CSI without the need of either downlink training or uplink feedback. Numerical results show that the SCNet achieves better performance than general deep networks in terms of prediction accuracy and exhibits remarkable robustness over complicated wireless channels, demonstrating its great potential for practical deployments.
144 citations
TL;DR: In this letter, a cooperative non-orthogonal multiple access system with imperfect successive interference cancellation is investigated in an underlay cognitive radio network and an exact closed form of the exact outage probability for each secondary destination is derived.
Abstract: In this letter, a cooperative non-orthogonal multiple access system with imperfect successive interference cancellation is investigated in an underlay cognitive radio network. Considering that the channel coefficients between the primary source and the secondary receiving nodes follow Rayleigh distribution, we derive an exact closed form of the exact outage probability for each secondary destination. Also, asymptotic expressions for the outage probability are derived: 1) when the interference constraint goes to infinity and 2) when the transmit powers at the secondary source and relay go to infinity. The simulation results verify our analytical results.
132 citations
TL;DR: This letter considers a two-UAV scenario when one UAV transmitter delivers the confidential information to a ground node (GN), and the other UAV jammer cooperatively sends the artificial noise to confuse the ground eavesdropper for protecting the confidentiality of the data transmission.
Abstract: This letter presents a new cooperative jamming approach to secure the unmanned aerial vehicle (UAV) communication by leveraging jamming from other nearby UAVs to defend against the eavesdropping. In particular, we consider a two-UAV scenario when one UAV transmitter delivers the confidential information to a ground node (GN), and the other UAV jammer cooperatively sends the artificial noise to confuse the ground eavesdropper for protecting the confidentiality of the data transmission. By exploiting the fully controllable mobility, the two UAVs can adaptively adjust their locations over time (a.k.a. trajectories) to facilitate the secure communication and cooperative jamming. We assume that the two UAVs perfectly know the GN’s location and partially know the eavesdropper’s location a priori . Under this setup, we maximize the average secrecy rate from the UAV transmitter to the GN over one particular time period, by optimizing the UAVs’ trajectories, jointly with their communicating/jamming power allocations. Although the formulated problem is non-convex, we propose an efficient solution by applying the techniques of alternating optimization and successive convex approximation.
132 citations
TL;DR: In this article, a perturbation-based iterative algorithm is proposed to solve the problem of UAV-enabled relay system to deliver command information under ultrareliable and low-latency communication requirements.
Abstract: This letter considers the unmanned aerial vehicle (UAV)-enabled relay system to deliver command information under ultra-reliable and low-latency communication requirements. We aim to jointly optimize the blocklength allocation and the UAV’s location to minimize the decoding error probability subject to the latency requirement. The achievable data rate under finite blocklength regime is adopted. A novel perturbation-based iterative algorithm is proposed to solve this problem. Simulation results show that the proposed algorithm can achieve the same performance as the exhaustive search method, and significantly outperforms the existing algorithms.
122 citations
TL;DR: Numerical results show that the proposed NOMA-based MEC offloading scheme can significantly reduce the system energy consumption compared with a traditional orthogonal multiple access scheme.
Abstract: Mobile edge computing (MEC) has emerged as a promising technique to enhance computing capacity of mobile devices by offloading tasks to base station for remote execution. This letter considers an MEC system exploiting the non-orthogonal multiple access (NOMA) for both task uploading and result downloading. The total energy consumption is minimized by optimizing the transmit powers, transmission time allocation, and task offloading partitions. Specifically, with the obtained optimal power control solutions, the task offloading partitions and time allocation are obtained by the successive convex approximation algorithm. Numerical results show that the proposed NOMA-based MEC offloading scheme can significantly reduce the system energy consumption compared with a traditional orthogonal multiple access scheme.
119 citations
TL;DR: An alternative optimization algorithm is proposed as the solution, which jointly optimizes task offloading decision-making, bit allocation during transmission, and the UAV trajectory, and demonstrates the significant energy savings.
Abstract: The deployment of unmanned aerial vehicles (UAVs) in wireless communication systems promises to provide services for devices with limited or without infrastructure coverage. With the emergence of diverse Internet of Things (IoT) applications, there is an ever-increasing demand for computation resources of IoT mobile devices (IMDs) in UAV communication scenarios. Motivated by this, we consider a UAV-aided edge computing scenario and study the task offloading problem between the IMDs and the UAV, aiming to minimize the overall energy consumption for accomplishing the tasks. An alternative optimization algorithm is proposed as our solution, which jointly optimizes task offloading decision-making, bit allocation during transmission, and the UAV trajectory. Numerical results demonstrate the significant energy savings of the proposed scheme.
118 citations
TL;DR: A convolutional neural network (CNN) framework for the joint design of precoder and combiners that accepts the input of channel matrix and gives the output of analog and baseband beamformers.
Abstract: Hybrid beamformer design is a crucial stage in millimeter-wave (mmWave) MIMO systems. In this letter, we propose a convolutional neural network (CNN) framework for the joint design of precoder and combiners. The proposed network accepts the input of channel matrix and gives the output of analog and baseband beamformers. Previous works are usually based on the knowledge of steering vectors of array responses which is not always accurately available in practice. The proposed CNN framework does not require such a knowledge, and it provides higher performance in capacity compared with the conventional greedy- and optimization-based algorithms.
116 citations
TL;DR: The proposed system model shows the superiority of the CR-NOMA compared with cooperative orthogonal multiple access and the derived analytical expressions are corroborated through Monte Carlo simulations.
Abstract: In this letter, a decode-and-forward cooperative underlay cognitive radio (CR) non-orthogonal multiple access (NOMA) network is studied. Exact closed-form expressions for the outage probability (OP) of two NOMA secondary destination users ( $U_{1}$ and $U_{2}$ ) are derived in the case of imperfect channel state information. Then, we find optimal power allocation factors for different distances of $U_{1}$ to satisfy the OP fairness for both users. Moreover, the proposed system model shows the superiority of the CR-NOMA compared with cooperative orthogonal multiple access. The derived analytical expressions are corroborated through Monte Carlo simulations.
TL;DR: It is shown that OTFS can have better PAPR compared to OFDM and generalized frequency division multiplexing (GFDM).
Abstract: In this letter, we analyze the peak-to-average power ratio (PAPR) of orthogonal time frequency space modulation (OTFS) waveform. Towards this, we consider modulation symbols on an $N\times M$ delay-Doppler grid, where $N$ and $M$ are the number of Doppler and delay bins, respectively. We derive an upper bound on the PAPR of the OTFS signal and show that the maximum PAPR grows linearly with $N$ (and not with $M$ , the number of subcarriers, as observed in conventional multicarrier schemes such as OFDM). We analytically characterize the complementary cumulative distribution function (CCDF) of the PAPR of OTFS with rectangular pulse for large values of $N$ . We present the simulated CCDF of the PAPR of OTFS for different pulse shapes and compare it with those of OFDM and generalized frequency division multiplexing (GFDM). It is shown that OTFS can have better PAPR compared to OFDM and GFDM.
TL;DR: An efficient model is presented in this letter, which is capable of handling the energy demands of the blockchain-enabled Internet of Vehicles by optimally controlling the number of transactions through distributed clustering.
Abstract: The blockchain is a safe, reliable, and innovative mechanism for managing numerous vehicles seeking connectivity. However, following the principles of the blockchain, the number of transactions required to update ledgers poses serious issues for vehicles as these may consume the maximum available energy. To resolve this, an efficient model is presented in this letter, which is capable of handling the energy demands of the blockchain-enabled Internet of Vehicles by optimally controlling the number of transactions through distributed clustering. Numerical results suggest that the proposed approach is 40.16% better in terms of energy conservation and 82.06% better in terms of the number of transactions required to share the entire blockchain data compared with the traditional blockchain.
TL;DR: In this paper, a new convolutional neural network (NN) based architecture was proposed to enhance the accuracy of quantized CSI feedback in MIMO communications, and the proposed NN architecture invokes a module named LSTM that admits the NN to benefit from exploiting temporal and frequency correlations of wireless channels.
Abstract: In a multiple-input multiple-output (MIMO) system, the availability of channel state information (CSI) at the transmitter is essential for performance improvement. Recent convolutional neural network (NN)-based techniques show competitive ability in realizing CSI compression and feedback. By introducing a new NN architecture, we enhance the accuracy of quantized CSI feedback in MIMO communications. The proposed NN architecture invokes a module named long short-term memory that admits the NN to benefit from exploiting temporal and frequency correlations of wireless channels. Compromising performance with complexity, we further modify the NN architecture with a significantly reduced number of parameters to be trained. Finally, experiments show that the proposed NN architectures achieve better performance in terms of both CSI compression and recovery accuracy.
TL;DR: In this article, the authors show that end-to-end learning of communication systems through deep neural network autoencoders can be extremely vulnerable to physical adversarial attacks and elaborate how an attacker can craft effective physical black-box adversarial attack.
Abstract: We show that end-to-end learning of communication systems through deep neural network autoencoders can be extremely vulnerable to physical adversarial attacks. Specifically, we elaborate how an attacker can craft effective physical black-box adversarial attacks. Due to the openness (broadcast nature) of the wireless channel, an adversary transmitter can increase the block-error-rate of a communication system by orders of magnitude by transmitting a well-designed perturbation signal over the channel. We reveal that the adversarial attacks are more destructive than the jamming attacks. We also show that classical coding schemes are more robust than the autoencoders against both adversarial and jamming attacks.
TL;DR: This work investigates a low complexity linear minimum mean square error receiver which exploits sparsity and quasi-banded structure of matrices involved in the demodulation process which results in a log-linear order of complexity without any performance degradation of BER.
Abstract: Orthogonal time frequency space modulation is a two dimensional (2D) delay-Doppler domain waveform. It uses inverse symplectic Fourier transform (ISFFT) to spread the signal in time-frequency domain. To extract diversity gain from 2D spreaded signal, advanced receivers are required. In this work, we investigate a low complexity linear minimum mean square error receiver which exploits sparsity and quasi-banded structure of matrices involved in the demodulation process which results in a log-linear order of complexity without any performance degradation of BER.
TL;DR: A fundamental definition of fairness is introduced, which measures the difference between the rates that can be achieved by the users and the fair rates suggested by the power distribution among them, and an asymptotic analysis of fairness at high and low signal-to-noise ratio (SNR) values is provided.
Abstract: Fairness in non-orthogonal multiple access (NOMA) may be defined in different ways, taking into account the physical layer alone or also including the process of radio resource management. The latter has been frequently used in the recent literature, implying a significant advantage of this technique compared with conventional orthogonal multiple access (OMA). In this letter, we look at the fairness issue from another angle and introduce a fundamental definition of fairness, which measures the difference between the rates that can be achieved by the users and the fair rates suggested by the power distribution among them. With this definition, the fairness issue is inexistent in OMA and it appears as a NOMA-specific problem due to interference between users and the detection process. The new fairness index we advocate incorporates the power distribution of different users. Thus, in a cell with a non-uniform power distribution, fairness implies that a user with a higher power should get a higher rate, and hence, the index should measure the rate of each user by accounting for the fraction of total power allocated to it. Our index achieves its maximum value of 1 only when all users get their fair rates , and it goes to zero when one user gets all resources in the case of uniform power distribution. We provide an asymptotic analysis of fairness at high and low signal-to-noise ratio (SNR) values and give a comprehensive illustration in the two-user case.
TL;DR: This letter study and discuss the applicability of merging deep learning (DL) models, i.e., convolutional neural network (CNN), recurrent neural network and reinforcement learning (RL), with IoT and information-centric networking which is a promising future Internet architecture, combined all together with the EC concept.
Abstract: Various Internet solutions take their power processing and analysis from cloud computing services. Internet of Things (IoT) applications started discovering the benefits of computing, processing, and analysis on the device itself aiming to reduce latency for time-critical applications. However, on-device processing is not suitable for resource-constraints IoT devices. Edge computing (EC) came as an alternative solution that tends to move services and computation more closer to consumers, at the edge. In this letter, we study and discuss the applicability of merging deep learning (DL) models, i.e., convolutional neural network (CNN), recurrent neural network (RNN), and reinforcement learning (RL), with IoT and information-centric networking which is a promising future Internet architecture, combined all together with the EC concept. Therefore, a CNN model can be used in the IoT area to exploit reliably data from a complex environment. Moreover, RL and RNN have been recently integrated into IoT, which can be used to take the multi-modality of data in real-time applications into account.
TL;DR: This letter addresses the sum-rate maximization for a downlink non-orthogonal multiple access (NOMA) system in the presence of imperfect successive interference cancellation (SIC), and optimize the circularity coefficient of the IGS-based NOMA system to maximize its sum- rate subject to quality-of-service requirements.
Abstract: This letter addresses the sum-rate maximization for a downlink non-orthogonal multiple access (NOMA) system in the presence of imperfect successive interference cancellation (SIC). We assume that the NOMA users adopt improper Gaussian signaling (IGS), and hence derive new expressions of their rates under residual interference from imperfect SIC. We optimize the circularity coefficient of the IGS-based NOMA system to maximize its sum-rate subject to quality-of-service requirements. Compared to the NOMA with proper Gaussian signaling, simulation results show that the IGS-based NOMA system demonstrates considerable sum-rate performance gain under imperfect SIC.
TL;DR: This letter investigates how a rotary-wing unmanned-aerial vehicle acts as a wireless base station to provide emergency communication service for a post-disaster area with unknown user distribution and proposes two efficient path planning algorithms.
Abstract: In this letter, we investigate how a rotary-wing unmanned-aerial vehicle (UAV) acts as a wireless base station to provide emergency communication service for a post-disaster area with unknown user distribution. The formulated optimization task is to find out the optimal path starting and ending at the same point to serve as many as possible users under limited battery capacity. We show that this problem can be transformed to an extended multi-armed bandit (MAB) problem, for which we propose two efficient path planning algorithms. Simulation results show that, in terms of the total number of served users, our proposed algorithms outperform the straightforward helical path that scans the entire area by circles with increasing radius.
TL;DR: Cooperative device-to-device (D2D) communication in an uplink cellular network, where D2D users act as relays for cellular users, is investigated and optimal spectrum and power allocation are obtained to maximize the total average achievable rate under the outage probability constraint.
Abstract: In this letter, we investigate cooperative device-to-device (D2D) communication in an uplink cellular network, where D2D users act as relays for cellular users. We derive the outage probability of a cellular user and the average achievable rate from a D2D transmitter to a D2D receiver in analytic form. We obtain optimal spectrum and power allocation to maximize the total average achievable rate under the outage probability constraint. The validity of the analysis is verified by computer simulations.
TL;DR: An online fully complex extreme learning machine (C-ELM)-based channel estimation and equalization scheme with a single hidden layer feedforward network (SLFN) for orthogonal frequency-division multiplexing (OFDM) systems against fading channels and the nonlinear distortion resulting from an high-power amplifier (HPA).
Abstract: Machine learning-based channel estimation and equalization methods may improve the robustness and bit error rate (BER) performance of communication systems. However, the implementation of these methods has been blocked by some limitations, mainly including channel model-based offline training and high-computational complexity for training deep neural network (DNN). To overcome those limitations, we propose an online fully complex extreme learning machine (C-ELM)-based channel estimation and equalization scheme with a single hidden layer feedforward network (SLFN) for orthogonal frequency-division multiplexing (OFDM) systems against fading channels and the nonlinear distortion resulting from an high-power amplifier (HPA). Computer simulations show that the proposed scheme can acquire the information of channels accurately and has the ability to resist nonlinear distortion and fading without pre-training and feedback link between receiver and transmitter. Furthermore, the robustness of the proposed scheme is well investigated by extensive simulations in various fading channels, and its excellent generalization ability is also discussed and compared with the DNN.
TL;DR: This letter proposes a convolutional neural network-based deep learning algorithm for spectrum sensing that outperforms the estimator-correlator detector and the hidden Markov model-based detector in terms of correct detection probability.
Abstract: In cognitive radio, most spectrum sensing algorithms are model-based and their detection performance relies heavily on the accuracy of the assumed statistical model. In this letter, we propose a convolutional neural network-based deep learning algorithm for spectrum sensing. Compared with model-based spectrum sensing algorithms, our proposed deep learning approach is data-driven and requires neither signal-noise probability model nor primary user (PU) activity pattern model. The proposed algorithm simultaneously takes in the present sensing data and historical sensing data, with which the inherent PU activity pattern can be learned to benefit the detection of PU activity. With extensive numerical simulations, results show that the proposed algorithm outperforms the estimator-correlator detector and the hidden Markov model-based detector in terms of correct detection probability.
TL;DR: This letter analyzes error performance of cooperative-NOMA, and the exact end-to-end bit error probability is derived in the closed-form and validated via simulations.
Abstract: The demands for high spectral efficiency and massive connections led the researchers to investigate new multiple access techniques for the future wireless networks. Non-Orthogonal Multiples Access (NOMA) is one of them. Hence, the integration of NOMA technique with the other physical layer techniques, such as MIMO, cooperative communication, and cognitive radio, has recently taken considerable attention. Whereas these numerous studies are mostly devoted to reveal the overall capacity and outage performances of NOMA involved systems, the error performances have not been investigated in the literature. In this letter, we analyze error performance of cooperative-NOMA, and the exact end-to-end bit error probability is derived in the closed-form. Then, the derived expressions are validated via simulations. Finally, the effect of the power allocation coefficient on the error performance is discussed.
TL;DR: This letter investigates joint radio resource allocation and edge offloading decision optimization in a multi-cell orthogonal frequency-division multiple access cellular network in order to minimize system’s energy consumption and uses the variable relaxation and majorization minimization method.
Abstract: In this letter, we investigate joint radio resource allocation and edge offloading decision optimization in a multi-cell orthogonal frequency-division multiple access cellular network in order to minimize system’s energy consumption. The discrete subchannel allocation and offloading decision variables as well as the interference incorporated in the data rate function make the stated optimization problem a mixed integer non-linear programming. To address this problem and obtain a locally optimal solution, we use the variable relaxation and majorization minimization method. Via simulation results, we demonstrate that our proposed solution results in a considerable reduction of system’s power consumption.
TL;DR: This letter introduces discrete normalized advantage functions (DNAF) into DQL and exploits a deterministic policy gradient descent (DPGD) algorithm to avoid the unnecessary calculation of the state-value function term and an advantage term for every state-action pair.
Abstract: Network slicing promises to provision diversified services with distinct requirements in one infrastructure. Deep reinforcement learning (e.g., deep $\mathcal {Q}$ -learning, DQL) is assumed to be an appropriate algorithm to solve the demand-aware inter-slice resource management issue in network slicing by regarding the varying demands and the allocated bandwidth as the environment state and the action , respectively. However, allocating bandwidth in a finer resolution usually implies larger action space, and unfortunately DQL fails to quickly converge in this case. In this letter, we introduce discrete normalized advantage functions (DNAF) into DQL, by separating the $\mathcal {Q}$ -value function as a state-value function term and an advantage term and exploiting a deterministic policy gradient descent (DPGD) algorithm to avoid the unnecessary calculation of $\mathcal {Q}$ -value for every state-action pair. Furthermore, as DPGD only works in continuous action space, we embed a k-nearest neighbor algorithm into DQL to quickly find a valid action in the discrete space nearest to the DPGD output. Finally, we verify the faster convergence of the DNAF-based DQL through extensive simulations.
TL;DR: Deep learning is applied to estimate the uplink channels for mixed analog-to-digital converters (ADCs) massive multiple-input multiple-output (MIMO) systems, where a portion of antennas are equipped with high-resolution ADCs while others employ low-resolution ones at the base station.
Abstract: In this letter, deep learning is applied to estimate the uplink channels for mixed analog-to-digital converters (ADCs) massive multiple-input multiple-output (MIMO) systems, where a portion of antennas are equipped with high-resolution ADCs while others employ low-resolution ones at the base station. A direct-input deep neural network (DI-DNN) is first proposed to estimate channels by using the received signals of all antennas. To eliminate the adverse impact of the coarsely quantized signals, a selective-input prediction DNN (SIP-DNN) is developed, where only the signals received by the high-resolution ADC antennas are exploited to predict the channels of other antennas as well as to estimate their own channels. Numerical results show the superiority of the proposed DNN based approaches over the existing methods, especially with mixed one-bit ADCs, and the effectiveness of the proposed approaches on different ADC resolution patterns.
TL;DR: An iterative algorithm based on the successive convex approximation and alternating methods is developed that can significantly improve the minimum secrecy rate compared with the traditional flight scheme.
Abstract: This letter investigates a joint optimization problem of unmanned aerial vehicle (UAV) flight trajectory, downlink transmission power, and ground terminals (GTs) association under wiretap channels. Specifically, we consider a scenario where a UAV serves a group of GTs and maximizes the minimum secrecy rate to ensure the fairness among GTs. To solve the nonconvex optimization problem, we develop an iterative algorithm based on the successive convex approximation and alternating methods. The simulation results demonstrate that the proposed algorithm is effective and can significantly improve the minimum secrecy rate compared with the traditional flight scheme.
TL;DR: This study shows that for each iteration, the energy-efficient WP-BackCom network is equivalent to either the network in which the transmitter always operates in the active state, or thenetwork in which $S$ adopts the maximum allowed power.
Abstract: In this letter, we consider a wireless-powered backscatter communication (WP-BackCom) network, where the transmitter first harvests energy from a dedicated RF energy source ( $S$ ) in the sleep state. It subsequently backscatters information and harvests energy simultaneously through a reflection coefficient. Our goal is to maximize the achievable energy efficiency of the WP-BackCom network via jointly optimizing time allocation, reflection coefficient, and transmit power of $S$ . The optimization problem is non-convex and challenging to solve. We develop an efficient Dinkelbach-based iterative algorithm to obtain the optimal resource allocation scheme. This study shows that for each iteration, the energy-efficient WP-BackCom network is equivalent to either the network in which the transmitter always operates in the active state, or the network in which $S$ adopts the maximum allowed power.
TL;DR: This paper transforms the non-convex problem into a convex one and applies convex optimization to solve the problem of jointly optimizing computation offloading, data compression and resource allocation under the latency constraint and finite MEC computation capacity.
Abstract: In this letter, we consider a multiuser mobile-edge computing (MEC) system with latency constraint. In order to meet the latency requirement and save energy consumption, each user can partially offload the task to the MEC server for edge computing. Data compression is applied to compress the offloaded data before transmission to reduce the data size. The problem of jointly optimizing computation offloading, data compression and resource allocation to minimize energy consumption under the latency constraint and finite MEC computation capacity is considered. We transform the non-convex problem into a convex one and apply convex optimization to solve it. The simulation results demonstrate that our proposed scheme significantly outperforms the benchmark schemes.