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In recent years, intelligent reflecting surface (IRS) has emerged as a promising technology for 6G due to its potential/ability to significantly enhance energy- and spectrum-efficiency. To this end, it is crucial to adjust the phases of reflecting elements of the IRS, and most of the research works focus on how to optimize/quantize the phase for different optimization objectives. In particular, the quantized phase shift (QPS) is assumed to be available at the IRS, which, however, does not always hold and should be fed back to the IRS in practice. Unfortunately, the feedback channel is generally bandwidth-limited, which cannot support a huge amount of feedback overhead of the QPS particularly for a large number of reflecting elements and/or the quantization level of each reflecting element. In order to break this bottleneck, in this letter, we propose a convolutional autoencoder-based scheme, in which the QPS is compressed on the receiver side and reconstructed on the IRS side. In order to solve the problems of mismatched distribution and vanishing gradient, we remove the batch normalization (BN) layers and introduce a denosing module. By doing so, it is possible to achieve a high compression ratio with a reliable reconstruction accuracy in the bandwidth-limited feedback channel, and it is also possible to accommodate existing works assuming available QPS at the IRS. Simulation results confirm the high reconstruction accuracy of the feedback/compressed QPS through a feedback channel, and show that the proposed scheme can significantly outperform the existing compression algorithms.

Traditional D2D power control methods require instantaneous interference information and so are difficult to implement in a real network due to the backhaul delay and high computational requirements. To overcome this challenge, we propose a distributed power allocation algorithm called interference feature extractor aided recurrent neural network (IFE-RNN). The core design ideas are described as follows. First, we design linear filters with various sizes termed IFEs to extract the different local interference patterns from outdated channel information. This feature extraction process enables our network to precisely learn the interference patterns around D2D links, so as to provide more effective power allocation strategies. Second, we propose to predict the real-time interference pattern based on the outputs of the IFEs and further make power decision. The prediction and decision can be modelled as a Markov decision problem (MDP) and solved by using a recurrent neural network. Third, an input reduction process is also designed to reduce the input size from $O(N^{2})$ to $O(1)$ , which speeds up the operation time and reduces the system overhead. Finally, extensive simulation results show that the proposed algorithm achieves an encouraging performance compared to the state-of-the-art power allocation algorithm.

Ambient backscatter communication (AmBC), a promising solution to future Internet of Things (IoT), has attracted increasing attention from both academia and industry in recent years. AmBC systems enable communications between RF-powered devices (e.g., tags and sensors) and AmBC receivers by utilizing ambient RF source signals from existing communication systems. Thus it offers a low-cost and energy-efficient means of communications for future IoT applications. In the current AmBC research, the impact of using channel coding on existing primary networks in terms of the bit-error-rate (BER) performance of the AmBC systems are mostly ignored. Low-density parity-check (LDPC) codes that offer fast encoding and decoding process have been proposed as promising channel codes in the fifth-generation (5 G) networks. In this paper, we investigate the BER performance of an AmBC system that uses LDPC-coded RF source signals. We have analytically derived the BER upper and lower bounds of both the RF source and tag signals for our proposed AmBC system. Simulation results show that the BERs of both signals decrease significantly, compared to conventional AmBC systems with uncoded RF source signals.

In this article, we study the receiver design for the uplink transmission of a human-type communications (HTC) and machine-type communications (MTC) (H&M) coexisted massive MIMO system. We first establish a probability model to characterize the crucial system features including channel sparsity of massive MIMO and signal sparsity of MTC packets. With the probability model, we propose to conduct joint device activity identification, channel estimation, and signal detection. We develop a message-passing-based statistical interference framework to systematically and efficiently solve the joint estimation problem for the H&M coexisted massive MIMO system. Specifically, we propose two receiver schemes based on time-slotted and non-time-slotted grant-free random access for massive machine-type device connectivity. We show that, by exploiting the channel and signal sparsity, our proposed message-passing-based algorithms significantly outperform the conventional training-based approaches in which the device activity state and the channel are estimated by sending pilots prior to data transmission, and are able to approach the genie bound with known signal support in the relatively high signal-to-noise (SNR) regime. Last but not least, we show that there exists a significant gain in terms of the number of admissible devices in the system by allowing H&M coexistence, as compared to orthogonal transmission approaches in which different time/frequency slots are assigned to HTC and MTC services.

Device-to-device (D2D) technology, which allows direct communications between proximal devices, is widely acknowledged as a promising candidate to alleviate the mobile traffic explosion problem. In this paper, we consider an overlay D2D network, in which multiple D2D pairs coexist on several orthogonal spectrum bands, i.e., channels. Due to spectrum scarcity, the number of D2D pairs is typically more than that of available channels, and thus multiple D2D pairs may use a single channel simultaneously. This may lead to severe co-channel interference and degrade network performance. To deal with this issue, we formulate a joint channel selection and power control optimization problem, with the aim to maximize the weighted-sum-rate (WSR) of the D2D network. Unfortunately, this problem is non-convex and NP-hard. To solve this problem, we first adopt the state-of-art fractional programming (FP) technique and develop an FP-based algorithm to obtain a near-optimal solution. However, the FP-based algorithm requires instantaneous global channel state information (CSI) for centralized processing, resulting in poor scalability and prohibitively high signalling overheads. Therefore, we further propose a distributed deep reinforcement learning (DRL)-based scheme, with which D2D pairs can autonomously optimize channel selection and transmit power by only exploiting local information and outdated nonlocal information. Compared with the FP-based algorithm, the DRL-based scheme can achieve better scalability and reduce signalling overheads significantly. Simulation results demonstrate that even without instantaneous global CSI, the performance of the DRL-based scheme can approach closely to that of the FP-based algorithm.

In this letter, we consider the intelligent-reflecting-surface (IRS)-assisted single-user multiple-input single-output downlink communication system, in which multiple IRSs are deployed to improve the propagation condition. Our objective is to configure the phase shift matrices of all the IRSs by exploiting the statistical channel state information (CSI). In particular, two effective algorithms are proposed, which learns the statistical CSI from the historical channel observations. Numerical results show that significant performance gain is achieved especially when the channel randomness is low.

We consider a homogeneous cellular network where a multi-antenna base station (BS) in each cell transmits messages to its intended user over a common frequency band. To improve the system capacity of this multi-cell multi-input single-output (MISO) interference channel, one of the state-of-the-art algorithms, namely, downlink-beamforming coordination, allows all BSs to cooperate with one another to mitigate the effect of inter-cell interference. However, most existing algorithms are suboptimal and impractical in a dynamic wireless environment, due to the high computational complexity and the overhead involved in collecting global channel state information (CSI). In this study, we exploit deep reinforcement learning (DRL) and propose a distributed dynamic downlink-beamforming coordination (DDBC) method with partial observability of the CSI. Each BS is able to train its own deep Q-network and employs appropriate beamformer depending on its environment, which is observed through a designed limited-information exchange protocol. The simulation results show that the proposed DRL-based DDBC method, with a considerably lower system overhead, achieves a system capacity that is very close to that of the fractional programming algorithm with global and instantaneous CSI measurements. In addition, this work demonstrates the potential of utilizing DRL to solve DDBC problems in a more practical manner.

Operating LTE networks in unlicensed bands together with legacy WiFi systems is deemed as a promising technique to support explosively growing mobile traffic. In conventional LTE/WiFi spectrum sharing schemes, LTE systems need to know WiFi traffic demands for optimizing system parameters to protect WiFi systems, for which the two systems are required to cooperate with each other via signallings exchanges. However, it is difficult to establish a dedicated channel among the two independent systems for exchanging signallings. Hence, in this paper, we propose an intelligent duty-cycle medium access control protocol to realize the effective and fair spectrum sharing between LTE and WiFi systems without requiring signalling exchanges. Specifically, we first design a duty-cycle spectrum sharing framework, which allows an LTE system to share the spectrum with a WiFi system by using time sharing. After that, we develop deep reinforcement learning (DRL)-based algorithms to learn WiFi traffic demands by analyzing WiFi channel activity, e.g., the idleness/business of WiFi channels, which can be observed by the LTE system via monitoring WiFi channels. Based on the learnt knowledge, the LTE system can adaptively optimize LTE transmission time to maximize its own throughput and meanwhile to provide sufficient protection to the WiFi system. Simulation results show that, in terms of LTE throughput and WiFi protection, the performance of the proposed intelligent scheme can approach that of the genie-aided exhaustive search algorithm, which needs the perfect knowledge of WiFi traffic demands through massive signalling exchanges and is of high computational complexity.

This paper studies a full-duplex symbiotic radio network, where a full-duplex access point (FAP) transmits downlink orthogonal frequency division multiplexing signals to a legacy user (LU) and simultaneously receives signals backscattered from multiple passive backscatter devices (BDs) with capability of radio-frequency energy harvesting. A non-orthogonal-multiple-access enhanced dynamic-time-division-multiple-access (NOMA-DTDMA) transmission scheme is proposed to exploit the channel dynamics and further improve the spectrum efficiency. In order to maximize the throughput performance and ensure BD fairness, we maximize the minimum throughput among all BDs by jointly optimizing the FAP's subcarrier power allocation, the BDs' backscatter time allocation and power reflection coefficients, subject to the LU's throughput requirement, the BDs' harvested energy requirements, and other practical constraints. An efficient iterative algorithm is proposed to solve the formulated non-convex problem, by utilizing the block coordinated descent and successive convex optimization techniques. The convergence and complexity of the proposed algorithm are also analyzed. Numerical results show that the proposed NOMA-DTDMA scheme significantly outperforms the benchmark scheme of dynamic TDMA in terms of both throughput performance and BD fairness. Also, the trade-off performances between the BDs' throughput and the LU's throughput requirement as well as the BDs' harvested energy requirements are numerically verified.

International audience; Reconfigurable intelligent surface (RIS), one of the key enablers for the sixth-generation (6G) mobile communication networks, is considered by designers to smartly reconfigure the wireless propagation environment in a controllable and programmable manner. Specifically, an RIS consists of a large number of low-cost and passive reflective elements (REs) without radio frequency chains. The system gain of RIS wireless systems can be achieved by adjusting the phase shifts and amplitudes of the REs so that the desired signals can be added constructively at the receiver. However, an RIS typically has limited signal processing capability and cannot perform active transmitting/receiving in general, which leads to new challenges in the physical layer design of RIS wireless systems. In this paper, we provide an overview of the RIS-aided wireless systems, including the reflection principle, channel estimation, and system design. In particular, two types of emerging RIS systems are considered: RIS-aided wireless communications (RAWC) and RIS-based information transmission (RBIT), where the RIS plays the role of the reflector and the transmitter, respectively. We also envision the potential applications of RIS in 6G networks.

openaire: EC/H2020/813999/EU//WINDMILL We consider machine learning for intra cell beam handovers in mmWave 5GNR systems by leveraging Channel Charting (CC). We develop a base station centric approach for predicting the Signal-to-Noise-Ratio (SNR) of beams. Beam SNRs are predicted based on measured signal at the BS without the need to exchange information with UEs. In an offline training phase, we construct a beam-specific dimensionality reduction of Channel State Information (CSI) to a low-dimensional CC, annotate the CC with beam-wise SNRs and then train SNR predictors for different target beams. In the online phase, we predict target beam SNRs. K-nearest neighbors, Gaussian Process Regression and Neural Network based prediction are considered. Based on SNR difference between the serving and target beams a handover can be decided. To evaluate the efficiency of the proposed framework, we perform simulations for a street segment with synthetically generated CSI. SNR prediction accuracy of average root mean square error less than 0.3 dB is achieved.

This paper studies semi-blind channel estimation for a reconfigurable intelligent surface (RIS) aided uplink massive multiple-input multiple-output (MIMO) system, in which the base station simultaneously estimates the channel coefficients and detects the partially unknown transmit symbols. We formulate the semi-blind channel estimation task as a trilinear inference problem. Based on the approximate message passing (AMP) principle, we develop a computationally efficient approach, called Trilinear AMP, to calculate the marginal posterior mean estimators of the trilinear inference problem. Simulation results demonstrate the effectiveness of the proposed Trilinear AMP approach.

Symbiotic radio (SR) emerges as a spectrum and energy-efficient communication paradigm for future passive Internet-of-Things (IoT). In this paper, we consider a multiuser multi-backscatter-device (BD) SR communication system to enhance the spectrum efficiency, by sharing a common time and frequency resource block. However, the receiver design becomes much more complicated due to the presence of inter-user and inter-BD interference. To address these issues, we propose a novel receiver design to perform joint channel estimation, user data decoding, and BD symbol detection. Specifically, motivated by the idea of approximate message passing, we develop a computationally efficient iterative algorithm under the Bayesian inference framework to resolve the joint estimation problem. Simulation results demonstrate the effectiveness of the proposed receiver design.

In conventional full-duplex communications, dedicated symbols are transmitted to estimate both the self-interference channel and the desired signal channel in order to perform self-interference cancellation (SIC) and to coherently detect the desired signal. However, inaccurate channel estimation will produce residual self-interference and degrade the detection performance. In this paper, we exploit a Gaussian mixture model (GMM) clustering to design a full-duplex transceiver (FDT), which is able to detect the desired signal without requiring digital-domain channel estimation and SIC. The frame structure of the designed FDT contains two successive phases: labeling phase and data transmission phase. In particular, the designed FDT performs cluster labeling in the labeling phase and performs GMM clustering based on an expectation-maximization (EM) algorithm in the data transmission phase. Furthermore, the theoretical analysis about the detection performance, computational complexity, and convergence performance for the designed FDT are studied. Finally, simulation results show that the bit error rate (BER) of the designed FDT is closed to the performance of the FDT with a maximum likelihood (ML) detector and perfect channel knowledge meanwhile is superior to the BER performance of the FDT with a ML detector and a least square (LS) or least mean square (LMS) channel estimator.

In this paper, we investigate the resource allocation problem for the symbiotic radio system, in which the passive backscatter transmission is parasitic in the primary transmission, and the cooperative receiver simultaneously detects the signals transmitted from the active primary transmitter (PT) and the backscatter device (BD). In particular, channel fading is considered, and the ergodic weighted sum rate of the primary and backscatter transmissions is maximized by jointly optimizing the transmit power at the PT and the reflection coefficient at the BD under either long-term or short-term transmit-power constraint over the fading states. Two practical transmission setups are considered, where the relationship between the primary and backscatter transmissions is either commensal or parasitic. For the commensal setup, we fix the reflection coefficient as the maximum value and derive the optimal power allocation scheme based on the standard convex optimization procedure. For the parasitic setup, the weighted sum-rate maximization problem is non-convex, and we resort to the concave–convex procedure to derive a suboptimal solution. Simulation results demonstrate that in the commensal setup, the BD may realize transmission while causing no harmful interference to the primary system; in the parasitic setup, a higher backscatter data rate can be achieved through slightly sacrificing the primary transmission rate.

Cooperative communication is a promising technique to mitigate channel impairment and improve spectral efficiency. Due to the mobility of relay nodes (RNs) and the unfavourable effects of wireless channels, channel conditions may change as time passes. Therefore, how to design proper long-term incentive mechanisms for RNs in such dynamic communication environments is an essential issue. This paper investigates the contract incentive method under asymmetric information scenario, where the relay channel condition and cost are the RNs’ private information. To capture the dynamic characteristic of the RNs’ cooperative information during the long-term cooperation, two dynamic contract mechanisms are introduced into long-term relay incentive with different RNs’ relay information structures. The optimal contract is derived to maximize the source's expected utility for both independent asymmetric information and correlated asymmetric information . Numerical results are presented to evaluate the effectiveness of the proposed dynamic contract-based incentive mechanism.

In this paper, we propose a novel receiver design for orthogonal frequency division multiplexing (OFDM) transmission over time-varying channels, where the receiver employs a turbo message passing (TMP) framework for a joint channel estimation and the data detection. We show that the proposed TMP-based receiver is able to make more efficient use of the prior information about the channel and the data for intercarrier interference cancellation than the existing iterative receivers. The proposed TMP algorithm can be further combined with a soft-input soft-output decoder for the joint channel estimation, data detection, and decoding. Numerical results are presented to demonstrate the performance superiority of our proposed TMP-based OFDM receiver.

Short packet communications (SPC) will play an important role in future Internet-of-Things networks. Conventional pilot-assisted transmission (PAT) needs significant overhead to obtain accurate channel state information (CSI) for further symbol detection and bit recovery, thereby reducing the spectral efficiency of the transmission. In this paper, a machine learning framework called Label-Assisted Transmission is proposed, in which the received signals are grouped into clusters through clustering algorithms and known labels are transmitted for cluster-symbol/bits mapping. This novel framework supports bit recovery directly without requiring the bit-symbol mapping information. When such mapping information is available, modulation constrained (MC) clustering algorithms are proposed, which exploit the unique characteristics of digital communication signals. For frequency-flat channels, this novel design needs only one known label regardless of the modulation size and the missing labels can be reconstructed using a proposed label reconstruction scheme. For frequency-selective channels with $L$ -tap time domain channel responses, only $L$ known labels are needed to reconstruct the missing labels if orthogonal frequency division multiplexing technology is adopted. The proposed clustering receiver works well even when the number of clusters is much larger than the number of received samples. The performance of the proposed framework is analyzed empirically through extensive simulations, which verify that the proposed scheme approaches the maximum likelihood detector with perfect CSI.

In reconfigurable intelligent surfaces (RISs) aided communications, the existing passive beamforming (PB) design involves polynomial complexity in the number of reflecting elements, and thus is difficult to implement due to a massive number of reflecting elements. To overcome this difficulty, we propose a reflection-angle-based cascaded channel model by adopting the generalized Snell's law, in which the dimension of the variable space involved in optimization is significantly reduced, resulting in a simplified hierarchical passive beamforming (HPB) design. We develop an efficient two-stage HPB algorithm, which exploits the angular domain property of the channel, to maximize the achievable rate of the target user. Simulation results demonstrate the appealing performance and low complexity of the proposed HPB design.

Funding Information: ACKNOWLEDGMENT This work of Gang Yang, Yating liao, and Ying-Chang Liang was supported by the National Natural Science Foundation of China (Grant No. 61631005, 62071093 and U1801261), the National Key Research and Development Program of China under Grant 2018YFB1801105 , the Fundamental Research Funds for the Central Universities under Grant ZYGX2019Z022, and the Programme of Introducing Talents of Discipline to Universities under Grant B20064. This work of Olav Tirkkonen was supported by Academy of Finland under Grant 319484. Publisher Copyright: ©2021 IEEE. Reconfigurable intelligent surfaces (RIS) are 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 sum rate of D2D users and cellular users by jointly optimizing the resource reuse indicators, the transmit power and the RIS’s passive beamforming. To solve the formulated non-convex problem, we first propose an efficient user-pairing scheme based on relative channel strength to determine the resource reuse indicators. Then, the transmit power and the RIS’s passive beamforming are jointly optimized by an iterative algorithm, based on the techniques of alternating optimization, successive convex approximation, Lagrangian dual transform and quadratic transform. Numerical results show that the proposed design outperforms the traditional D2D network without RIS.

Existing tag signal detection algorithms inevitably suffer from a high bit error rate (BER) due to the difficulties in estimating the channel state information (CSI). To eliminate the requirement of channel estimation and to improve the system performance, in this paper, we adopt a deep transfer learning (DTL) approach to implicitly extract the features of communication channel and directly recover tag symbols. Inspired by the powerful capability of convolutional neural networks (CNN) in exploring the features of data in a matrix form, we design a novel covariance matrix aware neural network (CMNet)-based detection scheme to facilitate DTL for tag signal detection, which consists of offline learning, transfer learning, and online detection. Specifically, a CMNet-based likelihood ratio test (CMNet-LRT) is derived based on the minimum error probability (MEP) criterion. Taking advantage of the outstanding performance of DTL in transferring knowledge with only a few training data, the proposed scheme can adaptively fine-tune the detector for different channel environments to further improve the detection performance. Finally, extensive simulation results demonstrate that the BER performance of the proposed method is comparable to that of the optimal detection method with perfect CSI., Accepted by IEEE Globecom 2020; Journal version "Deep Transfer Learning for Signal Detection in Ambient Backscatter Communications" has been accepted by IEEE TWC. arXiv admin note: substantial text overlap with arXiv:2009.05231

Tag signal detection is one of the key tasks in ambient backscatter communication (AmBC) systems. However, obtaining perfect channel state information (CSI) is challenging and costly, which makes AmBC systems suffer from a high bit error rate (BER). To eliminate the requirement of channel estimation and to improve the system performance, in this paper, we adopt a deep transfer learning (DTL) approach to implicitly extract the features of channel and directly recover tag symbols. To this end, we develop a DTL detection framework which consists of offline learning, transfer learning, and online detection. Specifically, a DTL-based likelihood ratio test (DTL-LRT) is derived based on the minimum error probability (MEP) criterion. As a realization of the developed framework, we then apply convolutional neural networks (CNN) to intelligently explore the features of the sample covariance matrix, which facilitates the design of a CNN-based algorithm for tag signal detection. Exploiting the powerful capability of CNN in extracting features of data in the matrix formation, the proposed method is able to further improve the system performance. In addition, an asymptotic explicit expression is also derived to characterize the properties of the proposed CNN-based method when the number of samples is sufficiently large. Finally, extensive simulation results demonstrate that the BER performance of the proposed method is comparable to that of the optimal detection method with perfect CSI., Accepted by IEEE Transactions on Wireless Communications

The performance of a device-to-device (D2D) underlay communication system is limited by the co-channel interference between cellular users (CUs) and D2D devices. To address this challenge, an intelligent reflecting surface (IRS) aided D2D underlay system is studied in this paper. A two-timescale optimization scheme is proposed to reduce the required channel training and feedback overhead, where transmit beamforming at the base station (BS) and power control at the D2D transmitter are adapted to instantaneous effective channel state information (CSI); and the IRS phase shifts are adapted to slow-varying channel mean. Based on the two-timescale optimization scheme, we aim to maximize the D2D ergodic rate subject to a given outage probability constrained signal-to-interference-plus-noise ratio (SINR) target for the CU. The two-timescale problem is decoupled into two sub-problems, and the two sub-problems are solved iteratively with closed-form expressions. Numerical results verify that the two-timescale based optimization performs better than several baselines, and also demonstrate a favorable trade-off between system performance and CSI overhead.

Cognitive radio (CR) is an effective solution to increase the spectral efficiency (SE) of wireless communications by allowing the secondary users (SUs) to share the spectrum with primary users (PUs). On the other hand, intelligent reflecting surface (IRS) is a promising approach to enhance the energy efficiency (EE) of wireless communication systems through passively reconfiguring the channel environments. In this paper, we propose an IRS enhanced downlink multiple-input single-output (MISO) CR systems to improve both SE and EE, where a single SU coexists with a primary network with multiple primary user receivers (PU-RXs). Specifically, for the MISO-CR system, we maximize the achievable rate of SU subject to a total power constraint on an SU transmitter (SU-TX) and an interference temperature (IT) constraint on PU-RXs, by jointly optimizing the beamforming vector at SU-TX and the phase shifts at the IRS. Furthermore, both perfect channel state information (CSI) and imperfect CSI are considered in the optimization. Numerical results demonstrate that the IRS can significantly improve the achievable rate of SU-RX under both the perfect and imperfect CSI conditions.

In this paper, we consider a smart jammer that only attacks the channel if it detects activities of legitimate devices on that channel. To cope with smart jamming attacks, we propose an intelligent deception strategy in which the legitimate device will send fake transmissions to lure the jammer. Then, if the jammer launches attacks to the channel, the legitimate device can either backscatter the jamming signals to transmit data or harvest energy from the jamming signals for future active transmission. In this way, we can not only undermine the attack ability of the jammer, but also leverage jamming attacks as means to enhance system performance. In addition, to find an optimal defense strategy for the legitimate device under uncertainty of wireless environment as well as incomplete information from the jammer, we develop Q-learning and deep Q-learning algorithms based on the Markov decision process. Through simulation results, we demonstrate that our proposed solution is able to not only deal with smart jamming attacks, but also successfully leverage jamming attacks to improve the system performance.

This paper focuses on learning the representing dictionaries for sparse channel estimation in frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. To overcome the energy leakage problem in traditional sparse channel estimation, we propose a geographical dictionary- based spatial channel model to efficiently represent the cell-specific geographical characteristics. Based on that, the properties, especially the block structure, of the expected dictionaries are analyzed, and we design a data-driven joint block-structured dictionary learning algorithm (JBSDL) to obtain the expected representing dictionaries. The simulation environment is generated according to 3GPP standard, and we systematically study the properties of the learned dictionaries, which reveals the physical meaning of the dictionary learning results in massive MIMO systems. The proposed method demonstrates superior downlink channel estimation performance through the simulations.

In this paper, we are interested in symbiotic radio networks, in which an Internet-of-Things (IoT) network parasitizes in a primary network to achieve spectrum-, energy-, and infrastructure-efficient communications. Specifically, the BS serves multiple cellular users using time division multiple access (TDMA) and each IoT device is associated with one cellular user for information transmission. We focus on the user association problem, whose objective is to link each IoT device to an appropriate cellular user by maximizing the sum rate of all IoT devices. However, the difficulty in obtaining the full real-time channel information makes it difficult to design an optimal policy for this problem. To overcome this issue, we propose two deep reinforcement learning (DRL) algorithms, both use the historical information to infer the current information in order to make appropriate decisions. One algorithm, centralized DRL, makes decisions for all IoT devices at one time with global information. The other algorithm, distributed DRL, makes a decision only for one IoT device at one time using local information. Finally, simulation results show that the two DRL algorithms achieve comparable performance as the optimal user association policy which requires perfect real-time information, and the distributed DRL algorithm has the advantage of scalability., 12 pages, 11 figures

Unmanned aerial vehicles (UAVs) have recently been proposed as moving base stations to collect data from ground IoT nodes in remote areas. Since IoT nodes are normally battery-limited, energy efficiency is an important metric in IoT systems. In order to improve energy efficiency in UAV-enabled IoT systems, it is necessary to allocate both channels and transmit power properly for IoT nodes. Motivated by the superior performance of deep reinforcement learning (DRL) in decision-making tasks, we propose a DRL-based channel and power allocation framework in a UAV-enabled IoT system. With the proposed framework, the UAV-BS is able to intelligently allocate both channels and transmit power for uplink transmissions of IoT nodes to maximize the minimum energy-efficiency among all the IoT nodes. Simulation results validate the effectiveness of the proposed algorithm and show its superiority over the- state-of-the-arts.

As a promising candidate to alleviate the mobile traffic explosion, device-to-device (D2D) technology enables the direct communications between proximal devices. To mitigate the mutual interference among D2D pairs and improve the spectrum efficiency, this paper investigates a weighted-sum-rate (WSR) maximization problem in multi-channel D2D networks. Particularly, we propose a deep reinforcement learning based scheme for each D2D pair to make decisions independently on the channel selection and power control. In contrast to the conventional methods that require instantaneous global network information, the proposed scheme only needs local information and outdated feedbacks. Simulation results demonstrate that, in terms of the WSR, the proposed scheme outperforms the conventional suboptimal fractional programming algorithm that requires the instantaneous global network information.

In this work, an analytical framework for deriving the exact moments of multiple-input- multiple-output (MIMO) mutual information in the high-signal-to-noise ratio (SNR) regime is proposed. The idea is to make efficient use of the matrix-variate densities of channel matrices instead of the eigenvalue densities as in the literature. The framework is applied to the study of the emerging models of MIMO Rayleigh product channels and Jacobi MIMO channels, which include several well-known channels models as special cases. The corresponding exact moments of any order for the high-SNR mutual information are derived. The explicit moment expressions are utilized to construct simple yet accurate approximations to the outage probability. Despite the high-SNR nature, simulation shows usefulness of the approximations with finite SNR values in the scenario of low outage probability relevant to MIMO communications.

Dynamic spectrum sharing is considered as one of the key features in the next-generation communications. In this correspondence, we investigate the dynamic tradeoff between the sensing performance and the achievable throughput, in the presence of time-varying fading (TVF) channels. We first establish a unified dynamic state-space model (DSM) to characterize the involved dynamic behaviors, where the occupancy states of primary user (PU) and the fading channel gains are modeled as two Markov chains. On this basis, a promising dynamic sensing schedule framework is proposed, whereby the sensing duration is adaptively adjusted based on the estimated real-time TVF channel. We formulate the sensing-throughput tradeoff problem mathematically, and further show that there exists the optimal sensing duration maximizing the throughput for the secondary user (SU), which will change dynamically with channel gains. Relying on our designed recursive sensing paradigm that is able to blindly acquire varying channel gains as well as the PU states, the sensing duration can be then adjusted in line with the evolving channel gains. Numerical simulations are provided to validate our dynamic sensing schedule algorithm, which can significantly improve the SU's throughput by reconfiguring the sensing duration according to dynamic channel conditions.

Ambient backscatter communication (AmBC) enables radio-frequency (RF) powered backscatter devices (BDs) (e.g., sensors, tags) to modulate their information bits over ambient RF carriers in an over-the-air manner. This technology also called "modulation in the air", thus has emerged as a promising solution to achieve green communications for future Internet-of-Things. This paper studies an AmBC system by leveraging the ambient orthogonal frequency division multiplexing (OFDM) modulated signals in the air. We first model such AmBC system from a spread-spectrum communication perspective, upon which a novel joint design for BD waveform and receiver detector is proposed. The BD symbol period is designed to be in general an integer multiplication of the OFDM symbol period, and the waveform for BD bit `0' maintains the same state within a BD symbol period, while the waveform for BD bit `1' has a state transition in the middle of each OFDM symbol period within a BD symbol period. In the receiver detector design, we construct the test statistic that cancels out the direct-link interference by exploiting the repeating structure of the ambient OFDM signals due to the use of cyclic prefix. For the system with a single-antenna receiver, the maximum-likelihood detector is proposed to recover the BD bits, for which the optimal threshold is obtained in closed-form expression. For the system with a multi-antenna receiver, we propose a new test statistic, and derive the optimal detector. Moreover, practical timing synchronization algorithms are proposed, and we also analyze the effect of various system parameters on the system performance. Finally, extensive numerical results are provided to verify that the proposed transceiver design can improve the system bit-error-rate (BER) performance and the operating range significantly, and achieve much higher data rate, as compared to the conventional design., Comment: 32 pages, 10 figures, journal paper

Full-duplex relaying has the potential to improve the spectral efficiency (SE) of cooperative communication systems. Due to residual self-interference (RSI), increase of the relay power does not always contribute to SE improvement. To fully utilize full-duplex relaying in cooperative communications, the effect of RSI on the SE achieved by different relay schemes need to be investigated. In this paper, we study bounds on the SE of full-duplex relay channel with decode-forward (DF) relaying, compress-forward (CF) relaying, and amplify-forward (AF) relaying in the presence of RSI. For respective schemes, optimal relay power and the corresponding maximal SE are derived in closed-form. Bounds on the relay energy-efficiency (REE) are presented for different schemes, which are useful for system design under per-node energy efficiency constraint. Based on the SE performance, the conditions of employing full-duplex relay, criteria for selecting relay scheme among DF, CF, and AF schemes, and the conditions of adopting hybrid full-duplex or half-duplex mode are elaborated regarding to RSI strength. In summary, this paper investigates the relationship among SE, REE, and system design by taking into account the effect of RSI for a general class of cooperation schemes.

The unknown noise variance and time-variant fading channels make the spectrum sensing design a challenging task for cognitive radios. Most existing sensing methods suffer from the information uncertainty and can hardly acquire promising performances in the adverse situations. To address this challenge, in this paper, we first formulate a dynamic state-space model for spectrum sensing, in which the unknown noise variance and time-variant flat fading channels are all taken into considerations. The dynamic behaviors of both primary user states and fading channels are characterized by two discrete state Markov chains. Based on this model, a novel spectrum sensing scheme is designed to recursively estimate the occupancy state of primary users, by estimating the time-variant fading channel gain and noise parameters jointly. The joint estimation is primarily premised on a maximum a posteriori probability criterion and the marginal particle filtering schemes. Simulation results are provided to demonstrate the advantages of our proposed method, which can significantly improve the sensing performance over time-variant flat fading channels, even with unknown noise variance.

The rising popularity of wireless services resulting in spectrum shortage has motivated dynamic spectrum sharing to facilitate efficient usage of the underutilized spectrum. Wideband spectrum sensing is a critical functionality to enable dynamic spectrum access by enhancing the opportunities of exploring spectral holes, but entails a major implementation challenge in compact commodity radios that only have limited energy and computation capabilities. In contrast to the traditional sub-Nyquist approaches where a wideband signal or its power spectrum is first reconstructed from compressed samples, this paper proposes a sub-Nyquist wideband spectrum sensing scheme that locates occupied channels blindly by recovering the signal support, based on the jointly sparse nature of multiband signals. Exploiting the common signal support shared among multiple secondary users (SUs), an efficient cooperative spectrum sensing scheme is developed, in which the energy consumption on wideband signal acquisition, processing, and transmission is reduced with detection performance guarantee. Based on subspace decomposition, the low-dimensional measurement matrix, computed at each SU from local sub-Nyquist samples, is deployed to reduce the transmission and computation overhead while improving noise robustness. The theoretical analysis of the proposed sub-Nyquist wideband sensing algorithm is derived and verified by numerical analysis and further tested on real-world TV white space signals. It shows that the proposed scheme can achieve good detection performance as well as reduce the computation and implementation complexity, in comparison with the conventional cooperative wideband spectrum sensing schemes.

In this paper, we propose a novel robust resource allocation algorithm for downlink non-orthogonal multiple access (NOMA) cognitive radio networks with the underlay spectrum sharing mode. To characterize the effect of channel uncertainty, a robust energy efficiency (EE) maximization optimization problem is formulated under the constraints of the maximum interference temperature threshold of primary user, the maximum transmit power of secondary base station, and the outage probability of secondary user’s rate. The non-linear optimization problem is converted into a convex form by using a new parameter transformation. Moreover, the analytical solution of power allocation is obtained by using the Lagrange dual theory. The provided results demonstrate the proposed robust resource allocation scheme can always achieve better EE and outage performance than that of the traditional orthogonal multiple access scheme and the non-robust NOMA scheme.

Ambient backscatter communication (AmBC) is a promising technology for energy-and spectrum-efficient Internet of Things (IoT) communication. In an AmBC system, due to the strong direct link interference and the difficulty in acquiring the channel state information (CSI), the detection of IoT device symbol becomes difficult. In this paper, we propose a semi-blind receiver, which includes the semi-blind channel estimator and the direct-link-averaging detector (DL-AD). The semi-blind channel estimator only relies on two pilot symbols from the IoT device to indicate different CSI without the RF source pilots, and can eliminate the effect of the RF source symbols. The DL-AD is based on the log likelihood ratio test. The simulation results show that the semi-blind receiver has very similar error probability performance compared with the constellation learning (CL) receiver that we proposed earlier, and it is simpler than the CL receiver. We also propose an iterative version of the semiblind receiver that has improved performance as shown in simulations.

Ambient backscatter, an emerging green communication technology, has aroused great interest from both academia and industry. One open problem for ambient backscatter communication (AmBC) systems is channel estimation for a massive-antenna reader. In this paper, we focus on channel estimation problem in AmBC systems with uniform linear array (ULA) at the reader which consists of large number of antennas. We first design a two-step method to jointly estimate channel gains and direction of arrivals (DoAs), and then refine the estimates through angular rotation. Additionally, Cramer-Rao lower bounds (CRLBs) are derived for both the modulus of the channel gain and the DoA estimates. Simulations are then provided to validate the analysis, and to show the efficiency of the proposed approach., 5 figures, submitted to IEEE Transactions on Vehicular Technology, 29 March, 2019

Reconfigurable intelligent surfaces (RIS) is a promising solution to build a programmable wireless environment via steering the incident signal in fully customizable ways with reconfigurable passive elements. In this paper, we consider a RIS-aided multiuser multiple-input single-output (MISO) downlink communication system. Our objective is to maximize the weighted sum-rate (WSR) of all users by joint designing the beamforming at the access point (AP) and the phase vector of the RIS elements, while both the perfect channel state information (CSI) setup and the imperfect CSI setup are investigated. For perfect CSI setup, a low-complexity algorithm is proposed to obtain the stationary solution for the joint design problem by utilizing the fractional programming technique. Then, we resort to the stochastic successive convex approximation technique and extend the proposed algorithm to the scenario wherein the CSI is imperfect. The validity of the proposed methods is confirmed by numerical results. In particular, the proposed algorithm performs quite well when the channel uncertainty is smaller than 10%., Comment: 13 pages, 8 figure

Modulation classification using deep neural networks has recently received increasing attention due to its capability in learning rich features of data. In this paper, we propose a low- complexity blind data-driven modulation classifier. Our classifier operates robustly over Rayleigh fading channels under uncertain noise conditions modeled using a mixture of three types of noise, namely, white Gaussian noise, white non- Gaussian noise and correlated non-Gaussian noise. The proposed classifier consists of several layers of recurrent neural networks (RNN) which is well-suited for learning representations from time-correlated data. The classifier is trained using the labeled raw signal samples generated under different noise conditions. Simulation results show that the performance of our proposed classifier approaches that of maximum likelihood classifiers with perfect channel knowledge and outperforms existing expectation maximum (EM) and expectation conditional maximum (ECM) classifiers which iteratively estimate channel and noise parameters.

Spatial modulation (SM) is regarded as a promising multiple-input multiple-output (MIMO) technique since it exploits the additional spatial domain for transmitting information. For transceiver design in SM, the traditional schemes first estimate the channel state information (CSI) based on pilots for further decoding, which introduce significant overhead, thereby reducing the spectral efficiency of the transmission. In this paper, to avoid the channel estimation, we convert the signal detection problem into a cluster problem for SM system. Utilizing the inter-structure of the SM system, we propose a structured Gaussian mixture model (SGMM) with a reduced number of parameters to model the received signals. We also develop an SGMM-based expectation maximization algorithm (SGMM-EM) to cluster the received signals. The complexity of SGMM-EM algorithm is reduced significantly compared with traditional clustering algorithm. After clustering all the received signals, a label mapping method is developed to map each transmitted symbol to one of the clusters. Simulation results show that, with only a small number of iterations, the performance of our proposed scheme approaches to that of the optimal receiver with perfect CSI.

In conventional full-duplex communications, dedicated symbols are transmitted to estimate both the self-interference channel and the desired signal channel in order to perform self-interference cancellation (SIC) and to detect the desired signal. However, inaccurate channel estimation will produce residual self-interference and degrade the detection performance. In this paper, we exploit Gaussian mixture model (GMM) clustering to design a full-duplex transceiver (FDT), which is able to detect the desired signal without requiring digital domain channel estimations and SIC. The frame structure of the designed FDT contains two successive phases: labelling phase and data transmission phase. In particular, the designed FDT performs cluster labelling in the labelling phase and performs GMM clustering based on expectation-maximization (EM) algorithm in the data transmission phase. Furthermore, the labelling symbol design is optimized for the overhead reduction and clustering accuracy improving. Finally, simulation results show that, the bit error rate (BER) of the designed FDT is closed to the performance of the FDT with a maximum likelihood (ML) detector and perfect channel knowledge, meanwhile is superior to the BER performance of the FDT with a ML detector and a least square (LS) channel estimator.

In this paper, we propose a novel receiver design for orthogonal frequency division multiplexing (OFDM) transmission over doubly (time- and frequency-) spread channels, where the receiver employs a turbo message passing (TMP) algorithm for joint channel estimation and data detection. We show that the proposed TMP-based receiver is able to make more efficient use of the prior information about the channel and the data for inter-carrier interference (ICI) cancellation than the existing iterative receivers. The proposed TMP algorithm can be further combined with a soft-input soft-output (SISO) decoder for joint channel estimation, data detection and decoding. Numerical results are presented to demonstrate the performance superiority of our proposed TMP-based OFDM receiver.

To ensure that the data aggregation, data storage, and data processing are all performed in a decentralized but trusted manner, we propose to use the blockchain with the mining pool to support IoT services based on cognitive radio networks. As such, the secondary user can send its sensing data, i.e., transactions, to the mining pools. After being verified by miners, the transactions are added to the blocks. However, under the dynamics of the primary channel and the uncertainty of the mempool state of the mining pool, it is challenging for the secondary user to determine an optimal transaction transmission policy. In this paper, we propose to use the deep reinforcement learning algorithm to derive an optimal transaction transmission policy for the secondary user. Specifically, we adopt a Double Deep-Q Network (DDQN) that allows the secondary user to learn the optimal policy. The simulation results clearly show that the proposed deep reinforcement learning algorithm outperforms the conventional Q-learning scheme in terms of reward and learning speed.

We investigate the energy efficiency (EE) in cognitive radio networks, where cognitive users are allowed to access a licensed frequency band opportunistically, provided that the licensed band is vacant. In particular, we study the impact of imperfect spectrum sensing and formulate the average EE maximization problem in fading channels as a joint optimization problem of the spectrum sensing duration and the transmit power of cognitive users. Meanwhile, we consider the constraints of both the collision probability between the primary and cognitive transmissions and the outage probability of cognitive transmissions. However, the joint optimization problem subject to the constraints is complicated and it is computationally hard to obtain the optimal solution. Alternatively, we develop two algorithms, i.e., a linear search algorithm and an iterative-based algorithm, with considerable complexity to solve the problem. Numerical results verify the correctness of both algorithms and show that the proposed algorithms can achieve the performance close to that of the exhaustive search algorithm and outperform the state of arts.

The millimeter wave (mmWave) radio band is promising for the next-generation heterogeneous cellular networks (HetNets) due to its large bandwidth available for meeting the increasing demand of mobile traffic. However, the unique propagation characteristics at mmWave band cause huge redundant handoffs in mmWave HetNets that brings heavy signaling overhead, low energy efficiency and increased user equipment (UE) outage probability if conventional Reference Signal Received Power (RSRP) based handoff mechanism is used. In this paper, we propose a reinforcement learning based handoff policy named SMART to reduce the number of handoffs while maintaining user Quality of Service (QoS) requirements in mmWave HetNets. In SMART, we determine handoff trigger conditions by taking into account both mmWave channel characteristics and QoS requirements of UEs. Furthermore, we propose reinforcement-learning based BS selection algorithms for different UE densities. Numerical results show that in typical scenarios, SMART can significantly reduce the number of handoffs when compared with traditional handoff policies without learning.

In this paper, we propose an ambient backscatter multiple-access system, in which a receiver (Rx) simultaneously detects the information sent from an active transmitter (Tx) and a passive Tag. Specifically, the information-carrying signal sent by the Tx arrives at the Rx through two wireless channels: one is the direct Tx-Rx channel, and the other is the backscatter channel, i.e., the Tx- Tag-Rx channel, which further carries the Tag's information due to the multiplicative backscatter operation at the Tag. The proposed multiple-access scheme introduces a new channel model named as the multiplicative multiple-access channel (M-MAC), which has not been addressed before. We study the achievable rate region and the capacity region of the M-MAC, and prove that the achievable rate region of the M-MAC is strictly larger than that of the conventional time-sharing one (i.e., the M- MAC capacity region is strictly convex) in many cases, including the high SNR case and the typical case that the direct channel is much stronger than the backscatter channel. Moreover, the numerical results have also validated this phenomenon under a practical range of SNR and channel conditions. The proposed multiple-access scheme is an attractive technique to improve the throughput of ambient backscatter communication systems.

Blind modulation classification is a fundamental step before signal detection for cognitive radio networks where the users may not have the complete knowledge of the modulation scheme due to the flexibility of operating dynamically in multiple frequency bands. In this paper, a modulation-constrained (MC) clustering classifier is proposed for recognizing the modulation scheme with unknown channel matrix and noise variance for MIMO systems. By recognizing the fact that the received signals within an observation interval form into clusters and exploiting the intrinsic relationships of different digital modulation schemes, the modulation classification problem is transformed into a clustering problem without direct channel estimation for each modulation scheme and the maximum likelihood criterion is applied for the final classification decision. A central component of the proposed classifier is a method called centroid reconstruction, which exploits the structural relationships in constellation diagrams to reconstruct cluster centroids with fewer number of parameters. Furthermore, a method to initialize the cluster centroids is also proposed. The proposed MC classifier together with centroid reconstruction and initialization methods not only reduce the number of parameters to be estimated, but also help to initialize the centroids for enhanced convergence of expectation- maximization (EM) algorithm. Simulation results show that our algorithm can perform excellently even at low SNR and with very short observation interval length.

Dynamic spectrum sharing will facilitate the interference coordination in device-to-device (D2D) communications. In the absence of network level coordination, the timing synchronization among D2D users will be unavailable, leading to inaccurate channel state estimation and device detection, especially in time-varying fading environments. In this study, we design an asynchronous device detection/discovery framework for cognitive-D2D applications, which acquires timing drifts and dynamical fading channels when directly detecting the existence of a proximity D2D device (e.g. or primary user). To model and analyze this, a new dynamical system model is established, where the unknown timing deviation follows a random process, while the fading channel is governed by a discrete state Markov chain. To cope with the mixed estimation and detection (MED) problem, a novel sequential estimation scheme is proposed, using the conceptions of statistic Bayesian inference and random finite set. By tracking the unknown states (i.e. varying time deviations and fading gains) and suppressing the link uncertainty, the proposed scheme can effectively enhance the detection performance. The general framework, as a complimentary to a network-aided case with the coordinated signaling, provides the foundation for development of flexible D2D communications along with proximity-based spectrum sharing.\ud

Ambient backscatter communication (AmBC) has attracted much attention recently due to its low-cost and low power consumption in connecting billions of devices for future Internet-of-Things (IoT). Signal detection in AmBC can be a challenging issue because of the difficulty in estimating relevant channels, and the spectrum sharing nature of the AmBC system. In this paper, a novel machine learning inspired signal detection method is proposed for AmBC systems. This method exploits the features of the received signals directly and groups them into clusters through unsupervised learning. Furthermore, labeled bits from tag are transmitted for cluster-bit mapping to assist signal detection without estimating the channel coefficients and the noise power. Two detection approaches are proposed for the cases that the spreading gain N ≫ 1 and N = 1, where the features follow different mixture distributions. The detection thresholds are derived to optimize the detection performance using the learned parameters. Finally, extensive simulation results are provided to verify the performance of the proposed schemes.