Visible light positioning (VLP) has drawn plenty of attention as a promising indoor positioning technique. However, in nonstationary environments, the performance of VLP is limited because of the highly time-varying channels. To improve the positioning accuracy and generalization capability in nonstationary environments, a cooperative VLP scheme based on federated learning (FL) is proposed in this paper. Exploiting the FL framework, a global model adaptive to environmental changes can be jointly trained by users without sharing private data of users. Moreover, a Cooperative Visible-light Positioning Network (CVPosNet) is proposed to accelerate the convergence rate and improve the positioning accuracy. Simulation results show that the proposed scheme outperforms the benchmark schemes, especially in nonstationary environments.

The sophisticated underwater environment makes it difficult to implement reliable and robust channel estimation for underwater visible light communication (UVLC). It has been observed that the UVLC channel has a block-sparse structure in the time domain, making it possible to use block-sparse recovery methods. To this end, this paper combines sparse learning theory to establish a block-sparse learning based underwater visible light channel estimation (SBL-UVCE) scheme. Specifically, classic unfold convex optimization based approximate message passing (AMP) algorithm into different network layers of a deep-unfolding neural network. Concurrently, in order to promote the network to better extract and utilize the block-sparse structure channel of UVLC, a Gaussian mixture denoiser based on minimum mean square error is introduced Simulation results have verified that the proposed scheme is significance better the existing compressed-sensing based schemes and the state-of-the-art sparse learning based scheme, especially under the circumstances of low signal-to-noise ratio (SNR) and insufficient number of observation pilots.

In order to effectively deal with the performance bottlenecks faced by underwater acoustic channel estimation, especially under harsh conditions with sophisticated background noise and insufficient spectrum resources. This paper proposed an UAC estimation method based on sparsity-aware Generative Adversarial Network in a compressed sensing framework. This method exploits the strong learning ability of the channel generator network (CGN) and establishes an explicit mapping relationship in the sample data distribution through adversarial training manner, thereby directly learning the sparse characteristics of the channel impulse response (CIR) of the UAC. Moreover, by introducing a regularized term to the traditional loss function of SA-GAN, a compound loss function is designed in this method, which aims to learn the characteristics of the channel. Simulation results show that, compared with the up-to-date UAC estimation methods, the proposed method significantly improves the channel estimation accuracy and spectral efficiency.

Semantic communications is a key enabler of the Internet of Things (IoT). By focusing on the semantic meaning of data rather than bit-level recovery, it allows intelligent agents to communicate necessary information at much lower rates. A promising technique for semantic communications is discrete neural representation learning (DNRL). The main idea is to learn discrete symbols from low-level, high dimensional sensory data, such that each symbol is grounded to a meaningful pattern in the sensory domain. This paper proposes a DNRL scheme that integrates three mechanisms into a coherent framework: contrastive learning, sparse coding, and neural index quantization. The proposed scheme is applied to public image datasets for lossy image compression with a downstream classification task. Results show that the proposed approach produces a highly compact continuous latent representation and a semantic discrete representation, with marginal degradation to the classification accuracy. The interpretability and consistency of the learned sub-symbolic discrete representations are validated by experiments of neural-net dissection, neural-net visualization, and MaxAmp-K classification test, a concept that we propose to evaluate classification performance of extremely compressed signals. Finally, the discrete representations are shown to be useful in rate-adaptive distributed sensing applications at the low-to-medium signal-to-noise ratios (SNR).

Given the exponential surge in data traffic and the proliferation of connected smart devices, traditional radio frequency (RF)-based wireless communication systems have to confront mounting challenges of spectrum scarcity and access congestion, particularly for networks operated in low-frequency bands. Visible light communication (VLC) technology has emerged as a promising solution, but it has own limitations, including coverage constraints and limited uplink capability, necessitating hybrid systems that leverage VLC and RF. This paper focuses on an indoor hybrid VLC-RF system extending VLC to Wi-Fi’s public spectrum, enabling VLC’s uplink via RF while enhancing system capacity. Yet, integrating VLC-RF with Wi-Fi introduces new challenges due to the coexistence of VLC-RF with existing Wi-Fi systems. To address these challenges, we propose an intelligent coexistence approach, dynamically adjusts duty cycles to ensure fairness and performance optimization between VLC-RF and Wi-Fi. Moreover, a spectrum multiplexing algorithm is introduced in the coexistence approach to enable the hybrid VLC-RF system’s multiplexing transmission on public spectrum, while preserving Wi-Fi system transmission integrity without interference, thereby further optimizing resource utilization. Extensive simulations on a meticulously constructed system-level platform validate our approach, showcasing its efficacy in enhancing system performance while maintaining equitable transmission between hybrid VLC-RF and Wi-Fi systems.

As one of the indoor communication technologies, visible light communication (VLC) has drawn great attention for its advantages such as ultra-wide unlicensed spectrum, power saving and low complexity. The nature of the visible light propagation is an open channel, which is vulnerable to wiretapping. This paper investigates a secure VLC mechanism enabled by multiple light fixtures acting as friendly jammers. The goal of the friendly jammers is to diminish the capability of the eavesdropper to infer the undisclosed information, on the premise of causing minimal impact on the legitimate receiver. For this reason, an algorithm based on reinforcement learning is proposed to dynamically optimize the friendly jamming policy in realistic nonstationary environments. In order to resolve the difficult problem of the dimensional curse and to effectively represent the continuous state and action spaces, an algorithm based on deep reinforcement learning is devised, which utilizes deep convolutional neural networks to accelerate the convergence rate of the learning process. A differentiable neural dictionary is introduced to make full use of the experiences in similar anti-eavesdropping scenarios to improve the learning capability. Simulation results demonstrate that, the proposed schemes can achieve a higher secrecy rate and a lower bit error rate than some state-of-the-art schemes.

Massive random access of devices brings great challenge to the management of radio access networks. Most of the time, the access requests in the network is sporadic. Exploiting the bursting nature, sparse active user detection (SAUD) is an efficient enabler towards efficient active user detection. However, the sparsity might be deteriorated in case of high concurrent request periods. To dynamically coordinate the access requests, a reinforcement-learning (RL)-assisted scheme of closed-loop access control utilizing the access class barring (ACB) technique is proposed, where the control policy is determined through continuous interaction between the RL agent and the environment. The proposed RL agent can be deployed at the next generation node base (gNB), supporting rapid switching between heterogeneous vertical applications, such as mMTC and uRLLC services. Moreover, a data-driven scheme of deep-RL-assisted SAUD is proposed to resolve highly complex environments with continuous and high-dimensional state and action spaces, where a replay buffer is applied for automatic large-scale data collection. An Actor-Critic framework is formulated to incorporate the strategy-learning modules into the intelligent control agent. Simulation results show that the proposed schemes can achieve superior performance in both access efficiency and user detection accuracy over the benchmark scheme for different heterogeneous services with massive access requests.

In this paper, a compressed sensing (CS) based framework of multi-target cooperative visible light positioning (VLP) is formulated to realize simultaneous high-accuracy localization of multiple targets. The light emitting diodes (LEDs) intended for illumination are utilized to locate multiple target mobile terminals equipped with photodetectors. The indoor area can be divided into a two-dimensional grid of discrete points, and the targets are located in only a few grid points, which has a sparse property. Thus, the multi-target localization problem can be transferred into a sparse recovery problem. Specifically, a CS-based framework is formulated exploiting the superposition of the received visible light signals at the multiple targets to be located via inter-target cooperation. Then it can be efficiently resolved using CS-based algorithms. Moreover, inter-anchor cooperation is introduced to the CS-based framework by the cross-correlation between the signals corresponding to different LEDs, i.e., anchors, which further improves the localization accuracy. Enabled by the proposed CS-based framework and the devised cooperation mechanism, the proposed scheme can simultaneously locate multiple targets with high precision and low computational complexity. Simulation results show that the proposed schemes can achieve centimeter-level multi-target positioning with sub-meter accuracy, which outperforms existing benchmark schemes.

Recently, visible light positioning and visible light communication are becoming a promising technology for integrated sensing and communication. However, the isolated design of positioning and communication has limited the system efficiency and performance. In this article, a visible light integrated positioning and communication (VIPAC) framework is formulated, in which the positioning task for the sensing service and the channel estimation task for the communication service are integrated into a unified architecture. First, a multi-task learning architecture, which is composed of a sparsity-aware shared network and two task-oriented sub-networks, is proposed to fully exploit the inherent sparse features of visible light channels, and achieve mutual benefits between the two tasks. The depth of the shared network can be adaptively adjusted to extract the optimal shared features, and the two sub-networks are further optimized for the two tasks, respectively. Moreover, the emerging federated learning technique is introduced to devise a multi-user cooperative VIPAC scheme, which further improves the generalization ability in spatiotemporally nonstationary environments while preserving data privacy. It is shown by theoretical analysis and simulation results that, the proposed scheme can significantly improve the performance of positioning and channel estimation in spatiotemporally nonstationary environments compared with existing benchmark schemes.

Due to the broadcast nature of visible light communication (VLC), the secrecy protection is a crucial issue. In this paper, aiming at the communication scenario of a point-to-point VLC transmission link, an anti-eavesdropping model utilizing friendly jamming is constructed, which is composed of a light-emitting diode (LED) transmitter, a legitimate receiver equipped with a photodiode (PD), and a potential eavesdropper trying to obtain the undisclosed information transmitted. In addition, the system is equipped with a friendly jammer composed of multiple LEDs. A friendly jamming scheme based on reinforcement learning (RL) is proposed, which adopts different friendly jamming actions with the dynamic change of the environment. The simulation results show that compared with the benchmark state-of-the-art method, the proposed scheme can significantly reduce the bit error rate (BER) of the legitimate receiver and improve the overall performance of the anti-eavesdropping VLC system.

Due to the inherent openness of wireless channels and the restriction of communication resources and energy supply, the privacy protection of the sensing data transmission in the security-critical Internet of Medical Things (IoMT) has become a great challenge. In order to guarantee the privacy of IoMT sensing and transmission in a wireless wiretap channel and reduce the power consumption, a privacy-aware sensing and transmission scheme with the name of sparse-learning-based encryption and recovery (SLER) is proposed. The sparse sensing signal is compressed and encrypted at the IoMT devices in the encryption stage and transmitted to the network coordinator or edge devices, where the sparse signal is accurately recovered via sparse learning in the decryption stage. The encryption stage is conducted based on compressed sensing. The decryption stage utilizes a model-based sparsity-aware deep neural network to accurately recover the sensing signal, whose sparse features are extracted to decrease the required size of measurement signals and increase the spectrum efficiency. The secrecy performance of the proposed SLER algorithm is theoretically analyzed. Experiments of electrocardiogram (ECG) signal transmission are performed as a typical IoMT application. The experimental results show that the proposed scheme can effectively guarantee the transmission secrecy against eavesdropping, while improving the spectrum efficiency and energy efficiency compared to other existing methods.

Unmanned aerial vehicles (UAVs) that are widely utilized for video capturing, processing and transmission have to address jamming attacks with dynamic topology and limited energy. In this paper, we propose a reinforcement learning (RL)-based UAV anti-jamming video transmission scheme to choose the video compression quantization parameter, the channel coding rate, the modulation and power control strategies against jamming attacks. More specifically, this scheme applies RL to choose the UAV video compression and transmission policy based on the observed video task priority, the UAV-controller channel state and the received jamming power. This scheme enables the UAV to guarantee the video quality-of-experience (QoE) and reduce the energy consumption without relying on the jamming model or the video service model. A safe RL-based approach is further proposed, which uses deep learning to accelerate the UAV learning process and reduce the video transmission outage probability. The computational complexity is provided and the optimal utility of the UAV is derived and verified via simulations. Simulation results show that the proposed schemes significantly improve the video quality and reduce the transmission latency and energy consumption of the UAV compared with existing schemes.

With the increasing demand of vehicular data transmission, the utilization of cellular resources in low frequency bands is facing great challenges to meet the growing throughput requirements of cellular vehicle-to-everything (C-V2X) users. To solve this problem, we expand certain aspects of the vehicular business to the unlicensed spectrum, which enables C-V2X users to access unlicensed channels fairly and thus will greatly increase system capacity. Moreover, this approach also introduces coexistence issues between C-V2X users and unlicensed users. In this paper, a C-V2X and Wi-Fi coexistence scheme based on reinforcement learning is proposed while considering the system throughput and fairness. A Q-learning algorithm is utilized to determine the optimal duty cycle selection strategy in a multi-unlicensed-channels scenario. Simulation results show that compared with existing coexistence schemes, the proposed scheme can improve throughput performance considerably while ensuring fairness.

Unmanned aerial vehicle (UAV) networks are vulnerable to jamming attacks because of the high mobility, limited battery and scarce spectrum resources of UAVs. In this paper, we propose a reinforcement learning based UAV relay scheme to improve the anti-jamming capability and save energy consumption of the UAV network. Based on the real-time channel conditions and the historical relay experiences, the proposed scheme enables UAVs to improve the policy of relay power and strategies without knowing the UAV network and channel model. Simulation results show that the proposed UAV relay scheme reduces the bit error rate of the messages and reduces the energy consumption of the UAV network compared with the state-of-the-art benchmark.

The dense deployment of small cells in 5G cellular networks raises the issue of controlling downlink inter-cell interference under time-varying channel states. In this paper, we propose a reinforcement learning based power control scheme to suppress downlink inter-cell interference and save energy for ultra-dense small cells. This scheme enables base stations to schedule the downlink transmit power without knowing the interference distribution and the channel states of the neighboring small cells. A deep reinforcement learning based interference control algorithm is designed to further accelerate learning for ultra-dense small cells with a large number of active users. Analytical convergence performance bounds including throughput, energy consumption, inter-cell interference, and the utility of base stations are provided and the computational complexity of our proposed scheme is discussed. Simulation results show that this scheme optimizes the downlink interference control performance after sufficient power control instances and significantly increases the network throughput with less energy consumption compared with a benchmark scheme.

Unmanned aerial vehicles (UAVs) are widely utilized to capture and compress videos of the target area and then transmit the processed videos to the control station (CS) on the ground. The media transmissions in the UAV-aided network face many challenges due to the highly dynamic network topology and limited resources such as bandwidth and energy. This paper introduces a media transmission scheme in the UAV-aided network utilizing reinforcement learning algorithms to efficiently process and transmit the captured video, which is able to improve the quality-of-experience (QoE) and reduce the energy consumption. Exploiting the proposed reinforcement learning algorithm, the UAV dynamically selects the quantization parameter in the source coding process and determines the transmit power without knowing the video transmission model. Simulation results demonstrate that the proposed scheme is capable of achieving a higher video quality and utility with lower energy consumption compared with the state-of-the-art schemes.

Narrowband Internet-of-Things (NB-IoT) is a competitive 5G technology for massive machine-type communication scenarios, but meanwhile introduces narrowband interference (NBI) to existing broadband transmission such as the Long Term Evolution (LTE) systems in enhanced mobile broadband (eMBB) scenarios. In order to facilitate the harmonic and fair coexistence in wireless heterogeneous networks, it is important to eliminate NB-IoT interference to LTE systems. In this paper, a novel sparse machine learning-based framework and a sparse combinatorial optimization problem is formulated for accurate NBI recovery, which can be efficiently solved using the proposed iterative sparse learning algorithm called sparse cross-entropy minimization (SCEM). To further improve the recovery accuracy and convergence rate, regularization is introduced to the loss function in the enhanced algorithm called regularized SCEM. Moreover, exploiting the spatial correlation of NBI, the framework is extended to multiple-input multiple-output systems. Simulation results demonstrate that the proposed methods are effective in eliminating NB-IoT interference to LTE systems, and significantly outperform the state-of-the-art methods.

The inherent broadcast characteristics of the visible light communication (VLC) channel makes VLC downlinks susceptible to unauthorized terminals in many actual VLC scenarios, such as offices and shopping centers. This paper considers a multiple-input-single-output (MISO) VLC scenario with multiple light fixtures acting as the transmitter, a VLC receiver as the legitimate user, and an eavesdropper attempting to intercept the undisclosed information. To improve the confidentiality of VLC links, a physical-layer anti-eavesdropping framework is proposed to obscure the unauthorized eavesdroppers and diminishes their capability of inferring the information through smart beamforming over the MISO VLC wiretap channel. To cope with the intractable problem of finding the theoretically optimal solution of the secrecy rate and utility for the MISO VLC wiretapping channel, a reinforcement learning (RL)-based VLC beamforming control scheme is proposed to achieve the optimal beamforming policy against the eavesdropper. Furthermore, a deep RL-based VLC beamforming control scheme is proposed to handle the curse of dimensionality for both observation space and action space and avoid the quantization error of the RL-based algorithm. Simulation results show that the proposed learning-based VLC beamforming control schemes can significantly decrease the bit error rate of the legitimate receiver and increase the secrecy rate and utility of the anti-eavesdropping MISO VLC system, compared with the benchmark strategy.

Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems have to address smart jammers that use smart radio devices to choose the jamming policy with the goal of interrupting the ongoing transmissions. In this paper, we propose a reinforcement learning based power control strategy for the downlink mmWave massive MIMO systems. More specifically, we present a fast policy hill-climbing based power control algorithm for a base station to choose the transmit power over multiple antennas. Based on the signal-to-interference-plus-noise ratio (SINR) of the signals and the jamming strength, we evaluate the impact of the number of transmit antennas on the communication performance. Simulation results verify that the proposed schemes can increase the average SINR, sum data rate and the utility of the mmWave massive MIMO against smart jamming compared with the benchmark strategy.

The densification deployment of small cells emerging into 5G cellular networks can achieve high capacity, but is faced with the challenge of how to manage energy consumption and inter-cell interference well in time-varying channels. In this paper, we propose a reinforcement learning based downlink power control algorithm to manage interference for the ultra-dense small cell networks. More specifically, base stations of the small cells use Q-learning to select the downlink transmit powers. A transfer learning method called hotbooting is applied to further accelerate the learning speed and save the energy consumption based on the estimated user density without being aware of the network and channel model of the other small cells. Simulation results demonstrate this scheme significantly improves the network throughput and saves the energy consumption compared with the benchmark, a data-driven based transmission power adaptation scheme.

As the underwater applications require reliable, fast and efficient communications, the underwater visible light communication (VLC) is attracting more and more attention. Due to the complicated channel condition of the underwater environment, accurate channel estimation is still challenging for underwater VLC systems. This paper investigates the underwater VLC channel estimation problem by following two main assumptions. Firstly, the number of the channel paths is assumed to be much smaller than the channel length, which will obtain the sparsity property in the distance domain. Secondly, the attenuation coefficient is assumed to be approximately linear with the frequency, which is true when the modulation bandwidth is much more narrow compared with the carrier frequency. The compressive sensing (CS) framework is formulated based on the two assumptions, whose sensing matrix has a large coherence. In this condition, the Bayesian CS algorithm is adopted instead of the conventional schemes, and the subspace scheme is also investigated for comparison. The simulation results demonstrate that the proposed scheme outperforms the conventional ones in terms of mean square error (MSE), hence better system performance can be expected.

Visible light communication (VLC), as a promising alternative to the next generation wireless communication technologies, has drawn extensive research interests. In this paper, a novel hybrid intensity modulated direct detection (IM/DD) communication system is proposed, which integrates the multi-carrier and single-carrier modulation to enhance spectrum efficiency and support different qualities of services (QoS). In the proposed method, the negative hybrid asymmetrically clipped optical orthogonal frequency division multiplexing (NHACO-OFDM) is proposed based on the conventional HACO-OFDM scheme, and then HACO-OFDM and NHACO-OFDM signal are combined with on-off keying (OOK) for simultaneous transmission. Simulation results demonstrate that the hybrid transmitted signals can be recovered completely at the receiver with higher spectrum efficiency, meanwhile the proposed system can support multi-service requirements and adapt to the different receivers with various complexities.

This paper proposes a novel doubly selective channel estimation scheme for MIMO-OFDM systems with both time- and frequency-domain training (TFDT) based on structured compressive sensing (SCS). The characteristics of the doubly selective channel is considered in three dimensions, i.e., sample-domain, tap-domain, and antenna-domain. The tap-domain channel impulse responses (CIRs) are jointly sparse, while the sample-domain CIRs can be expanded by specific bases. We utilize the received pseudo-random noise (PN) sequence to acquire the common support of the channel. After a cyclic reconstruction process, the channel estimation can be performed under an SCS model by exploiting the proposed pilot pattern and the spatial correlation among antennas. Simulation results demonstrate that the proposed scheme has a superior performance than that of conventional counterparts.

The non-orthogonal multiple access (NOMA) technology has been proposed and regarded as one of the potential promising technologies for the future 5G network. The extension of generalized spatial modulation multiple-input multiple-output (MIMO) to NOMA system improves both the spectral and energy efficiencies of the system, while maintaining the massive connectivity and low latency advantages, but it puts forward challenges for the multi-user and signal detection as well. In this paper, we propose a joint user activity and signal detection scheme based on the block-sparse compressive sensing (BS-CS) method in the uplink NOMA system, in which the generalized spatial modulation MIMO technology is used. By exploiting the structure and sparsity of the multi-user generalized spatial modulation signals, we formulate the detection problem into a block-sparse recovery problem. Then a BS-CS based detection algorithm, enhanced structured block-sparse compressive sampling matching pursuit (ESB-CoSaMP), is proposed to detect the active users and transmitted data efficiently. Moreover, the information of active antennas at each user is exploited in ESB-CoSaMP to further improve the accuracy. Simulations show that the proposed detection scheme outperforms the conventional CS and BS-CS based schemes.

In the next generation of cellular networks, millimeter wave (mmWave) communications will play an important role. With the utilization of mmWave communications, the massive multiple-input multiple-output (MIMO) technique can be effectively employed, which will significantly improve system capacity. However, an effective channel estimation scheme is the prerequisite of system stability and in great need for improvement in massive MIMO systems. In this paper, a channel estimation scheme based on training sequence (TS) design and optimization with high accuracy and spectral efficiency is investigated in the framework of structured compressive sensing. As a new perspective to optimize the block coherence of the sensing matrix, the auto-coherence and cross-coherence of the blocks are proposed and specified as two kinds of key merit factors. In order to optimize the two factors, specific TS is designed and obtained from the inverse discrete Fourier transform of a frequency domain binary training sequence, and a genetic algorithm is adopted afterwards to optimize the merit factors of the TS. It is demonstrated by the simulation results that the block coherence of the sensing matrix can be significantly reduced by the proposed TS design and optimization method. Moreover, by using the proposed optimized TS's, the channel estimation outperforms the conventional TS design obtained by the brute force search in terms of the correct recovery probability, mean square error, and bit error rate, and can also approach the Cramer-Rao lower bound.

Narrowband Internet-of-Things (NB-IoT) is one of the emerging 5G technologies, but might introduce narrowband interference (NBI) to existing broadband systems, such as long-term evolution advanced (LTE-A) systems. Thus, the mitigation of the NB-IoT interference to LTE-A is an important issue for the harmonic coexistence and compatibility between 4G and 5G. In this paper, a newly emerged sparse approximation technique, block sparse Bayesian learning (BSBL), is utilized to estimate the NB-IoT interference in LTE-A systems. The block sparse representation of the NBI is constituted through the proposed temporal differential measuring approach, and the BSBL theory is utilized to recover the practical block sparse NBI. A BSBL-based method, partition estimated BSBL, is proposed. With the aid of the estimated block partition beforehand, the Bayesian parameters are obtained to yield the NBI estimation. The intra-block correlation (IBC) is considered to facilitate the recovery. Moreover, exploiting the inherent structure of the identical IBC matrix, another method of informative BSBL is proposed to further improve the accuracy, which does not require prior estimation of the block partition. Reported simulation results demonstrate that the proposed methods are effective in canceling the NB-IoT interference in LTE-A systems, and significantly outperform other conventional methods.

In this paper, a novel impulsive noise (IN) cancel- lation scheme based on structured compressed sensing (SCS) for multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM) power line communication systems is proposed. The SCS theory is introduced to IN recovery for the first time in this paper to the best of the authors' knowledge, and the gap of lack of research on the IN mitigation for MIMO PLC systems is filled. First, the measurements matrix of the IN is obtained, and the SCS optimization framework is formulated through the proposed spatially multiple measuring method, by fully exploiting the spatial correlation of the IN signals at different receive antennas. To efficiently reconstruct the IN signal, an enhanced SCS-based greedy algorithm, structured a priori aided sparsity adaptive matching pursuit (SPA-SAMP), is proposed, which significantly improves the accuracy and robustness compared with the state-of-art methods. Theoretical analysis and computer simulations validate that the proposed scheme outperforms conventional methods in the typical MIMO PLC system.

A block sparse Bayesian learning (BSBL) based approach of narrowband interference (NBI) cancellation for cyclic prefixed orthogonal frequency division multiplexing based smart grid communications is proposed in this paper. The BSBL theory is firstly introduced to recover the practical block sparse NBI with a frequency offset compared with the sub-carriers. The block sparse representation of the NBI is constituted through the proposed temporal differential measuring approach. A BSBL based method, estimated partitioned BSBL, is proposed for NBI recovery. The intra-block correlation is firstly considered to facilitate the recovery of block sparse NBI. Reported simulation results demonstrate that the proposed methods are effective and significantly outperform conventional counterparts.

Visible light communication (VLC) using the light emitting diode (LED) will become an appealing alternative to the radio frequency communication technology for indoor wireless broadband access. However, VLC needs a ubiquitous network as its backbone to avoid becoming an information isolated island. Power line communication (PLC) systems could easily solve the informative problem of VLC while powering the LED lamps at the same time, which is considered as a good partner of VLC for the cost-effective implementation. In this paper, a novel and cost-effective framework of ubiquitous indoor broadband access based on deeply integrated VLC and PLC technology with only low-cost modification to the current infrastructure is therefore proposed. The broadband access network supports duplex transmission through each LED using the decode-and-forward (DF) working mode. This paper will present our recent research progress in this area, including a prototyping of duplex voice communications network based on hybrid PLC and VLC in our lab. Our research and development plan in this area for the near future will also be covered.

In practical multiple input multiple output (MIMO) systems, accurate knowledge of the channel state information (CSI) is a prerequisite to guarantee the system performance. The conventional CSI acquisition methods for MIMO system usually rely on the orthogonal (either time‐ or frequency‐domain) training sequences (TSs) to estimate the channel associated with each transmit–receive antenna pair, which is not spectrally efficient. This study proposes a non‐orthogonal time‐domain training‐based CSI acquisition approach for MIMO systems under the framework of structured compressive sensing. By exploiting the spatial–temporal correlations of the sparse MIMO channels, a spatially–temporally spARsity‐adaptivE‐simultaneous orthogonal matching pursuit algorithm is proposed, which could use the inter‐block interference free region of very small dimension within the received TS to recover the multiple channels. Furthermore, the proposed algorithm could utilise the priori channel partial common support to improve the recovery probability and reduce the complexity. Simulation results show that the proposed scheme has better performance and higher spectral efficiency than the conventional MIMO schemes, which might be an appealing solution for the future wireless communications.

Power line communication (PLC) techniques present a no extra wire solution for the communication purpose in a smart grid due to the ubiquity and low cost. Moreover, the through-the-grid property of PLC has naturally extended its possible applications, including but not limited to the automatic meter reading, line quality monitoring, online diagnostics, and network tomography. To guarantee the performance of communications as well as other applications in PLC systems, accurate channel state information (CSI) acquisition should be performed regularly. However, the conventional pilot-based CSI acquisition approaches in PLC systems have not made full use of the channel characteristics and hence suffer from a low spectral efficiency. In this paper, by exploiting the parametric sparsity and discretizing the electrical length in the well-known PLC channel model, we formulate the non-sparse (either time domain or frequency domain) PLC channel into a compressive sensing (CS) applicable problem. Furthermore, we propose a spectrally efficient CSI acquisition scheme under the framework of Bayesian CS and extend it to the multiple-input multiple-output PLC by investigating the channel spatial correlation. Compared with the existing sparse CSI acquisition schemes for PLC, such as the annihilating filter-based and the estimating signal parameters via rotational invariance technique-based ones, the proposed scheme has better mean square error performance and noise robustness.

In this paper, a novel impulsive noise (IN) cancellation scheme based on priori aided compressive sensing (CS) for OFDM-based communications systems is proposed. The IN is reconstructed from the frequency-domain measurements at the null sub-carriers based on the CS theory using greedy algorithms. With the aid of the a priori partial support obtained from the proposed time-domain thresholding method, we propose the enhanced greedy algorithm of priori aided sparsity adaptive matching pursuit (PA-SAMP) to improve the accuracy and robustness of the IN recovery. Theoretical analysis and computer simulations validate that the proposed method outperforms conventional CS-based and other classical IN mitigation methods for OFDM-based communications systems.

The dual pseudo noise padded (DPNP) time‐domain synchronous‐orthogonal frequency division multiplexing (TDS‐OFDM) which utilises the second pseudo noise (PN) sequence for channel estimation is able to reduce the complexity and improve the accuracy of channel estimation compared to the classical TDS‐OFDM. However, because of the duplicate PN sequence, DPNP TDS‐OFDM will suffer from severer out‐of‐band emission, which is a headache issue for the conventional TDS‐OFDM. In this study, a novel out‐of‐band suppression approach based on a new PN design criterion as well as a windowing operation is proposed for the DPNP TDS‐OFDM systems. The frame structure and system model are also modified to accommodate to the approach, while the overlap‐and‐add approach is adopted to guarantee the spectral efficiency. Both simulation and experimental results show that this approach could achieve satisfactory performance with much less complexity and no spectral efficiency loss compared to the classical DPNP TDS‐OFDM system. In addition, an iterative channel estimation method is also proposed for the modified frame structure to combat against the long channel delay spread.

A novel compressive sensing (CS) based narrowband interference (NBI) cancellation approach for power line communication (PLC) systems is proposed in this paper. The repeated training sequences of the preamble in PLC systems are utilized for differential measuring to acquire the measuring vector. Under the CS framework, the sparse high-dimensional NBI signal can be reconstructed from the measuring vector of much smaller size obtained through the proposed CS-based differential measuring (CSDM) method. It is verified by theoretical analysis and simulations that the proposed method outperforms conventional anti-NBI methods under the PLC channel.

In this paper, an optimized time-frequency interleaving scheme is proposed to combat against the narrowband interference (NBI) and time-domain impulsive noise (TIN) for orthogonal frequency division multiplexing (OFDM) systems in power line communications (PLC). To improve both the anti-NBI and anti-TIN performance, we propose two criteria based on which the optimized interleaving scheme is designed: (i) to increase the number of different OFDM blocks for one forward error correction (FEC) codeword, which is aimed at TIN mitigation; and (ii) to increase the number of different sub-carriers mapped to the data cells in one FEC codeword, which is aimed at NBI mitigation. Simulations show that the proposed interleaving scheme can effectively reduce the impairments caused by NBI and/or TIN in OFDM systems under PLC environments, and hence achieve better performance than the conventional block interleaving scheme.