Fluid antenna-enabled multiple-input multiple-output (FA-MIMO) systems hold significant application potential; however, they also introduce substantial costs in channel state information (CSI) acquisition. In this paper, we propose a novel FA-assisted rectangular differential index modulation (FA-RDIM) scheme for MIMO systems, aiming to achieve high spectral efficiency while addressing the problem of the increased CSI acquisition cost. The transmitted information is mapped to modulation symbols and cyclic shifts of FA pattern indices. A multi-stage detection method is introduced, which reduces computational complexity by identifying the most likely candidates for FA pattern indices. We derive a closed-form expression for the bit error rate (BER) theoretical performance considering error propagation, and present an optimization algorithm based on rank and determinant criterion (RDC), Hamming distance (HD), and gradient descent (GD) to optimize the FA pattern vector set. Simulation results demonstrate that the proposed scheme exhibits a minimal performance loss compared to conventional coherent modulation schemes under static channel conditions, while offering a performance advantage in time-varying channels with outdated CSI.

In this paper, a two-layer game based joint time and power allocation method for an underlay Energy Harvesting Cognitive Radio (EH-CR) network is proposed. The method first models the interplay between the Primary User (PU) and the Secondary Users (SUs) as a Stackelberg game and then models the interplay among the SUs as a Supermodel game in the underlay EH-CR network. Later, a coefficient for evaluating fair ness is introduced in order to promote fairness among the SUs. Subsequently, the utility function of the primary network and the utility function of the secondary network are defined based on their individual profits. By maximizing the secondary network's utility function, the Supermodel game's Nash Equilibrium (NE) solution is achieved. Then, by substituting the NE solution of the Supermodel game into the utility function of the primary network and then maximizing the utility function of the primary network, the NE solution of the Stackelberg game is obtained. Finally, a deterministic strategy can be obtained, which is the time coefficient of equalized spectrum sensing and the equalized power allocation scheme instead of a probabilistic strategy. Simulation outcomes demonstrate that, under the condition of maintaining the communication quality of the PU, the PU's revenue when PH0 = 0.8 can be improved by 18.2% and when PH0 = 0.6 can be improved by 13.3% compared with the conventional method.

In this paper, a precoding vector based index modulation (PV-IM) scheme is proposed. In the proposed scheme, precoding vectors are designed to maximize the Euclidean distance between precoded patterns, and their indices are used for information transmission. The proposed PV-IM scheme achieves superior performance than the constellation modulation scheme in high-rate scenarios, and overcomes the shortcomings of spatial index modulation (SIM) schemes. In addition, for the reconfigurable intelligent surface (RIS)-assisted multi-user (MU) scenario, a RIS-MU-PV-IM scheme is proposed based on the PV-IM scheme, where inter-user interference (IUI) is eliminated. The achievable rate, bit error rate (BER), and computational complexity of the proposed two schemes are analyzed. The impacts of channel estimation errors to the proposed two schemes are discussed. Extensive simulations are conducted to demonstrate the superiority of the proposed schemes.

This paper proposes a novel Intelligent Reflecting Surface (IRS)-assisted interweave Cognitive Internet of Vehicles (CIoV) network under malicious jamming attacks, where the IRS enhances communication performance by establishing additional links. In order to maximize the sum transmission rate of Vehicle-to-Infrastructure (V2I) links, we propose an optimization problem that jointly optimizes wireless resource allocation, such as spectrum and transmit power for Vehicle Users (VUs) and IRS phase shift. Because this problem is non-convex and complicated, we further propose a Heterogeneous Multi-agent Transformer-enhanced Dueling Double Deep Q-Network (HMA-TD3QN) based resource allocation method, where VUs and Secondary Base Station (SBS) act as distinct heterogeneous agents can independently perform resource allocation and phase shift optimization. The Transformer neural network architecture can better adapt to long sequence input states and extract relevant features from complex input states through the attention mechanism. Simulation results indicate that the proposed HMA-TD3QN method achieves improvements of 24.42%, 20.79%, and 22.25% over the basic HMA-DQN under three different jamming strategies, highlighting the effectiveness of IRS technology in enhancing the Quality of Service (QoS) and jamming resilience of CIoV network.

Angle of arrival (AOA) estimation with tilted photodiodes (PDs) is fast, accurate and low-cost for visible light positioning (VLP) systems. However, the most popular component assembly technology, surface-mount technology (SMT), is not able to orient the PDs towards their desired tilting angles, making it challenging to manufacture the AOA estimators massively and efficiently. In this work, we propose a novel AOA estimator based on an array of horizontally placed PDs with different lenses, where the incident light angles can be estimated by the ratios of the received optical signal intensities. Such an AOA estimator can be manufactured with the SMT lines in factories and we derive closed-form expressions for the involved AOA estimation algorithm. The proposed scheme provides an SMTcompatible solution for PD-based AOA estimation, and lays theoretical foundations for the error analysis.

This paper propose a novel three-dimensional continuous fluid antenna system-enabled index modulation (3D-CFAS-IM) scheme, where the fluid antenna can move continuously within a 3D region to fully exploit spatial resources. The optimal positions of the fluid antenna are determined by maximizing the minimum Euclidean distance between different receive patterns, and their indices are used to implement IM. The achievable rate, bit error rate (BER) performance, and computational complexity of the proposed scheme are analyzed. Simulation results demonstrate that the proposed scheme achieves better BER performance and a higher achievable rate compared to existing schemes, with acceptable complexity. Additionally, the impact of channel estimation errors on system performance is investigated.

Integrated optical wireless communication and sensing scheme for the hovering civil unmanned aerial vehicles (UAVs) will play an important role in the future wireless networks. In this paper, an integrated optical mobile communication and sensing system with user mobility for hovering UAVs is proposed, which employs the power division multiplexing method to share the same beamsteering device for both communication and sensing. To support mobility, we consider new challenges including the selection of the proper beam waist, the power allocation between sensing and communication, and the adjustment time of spatial light modulator (SLM). The ergodic achievable rate (EAR) is investigated, and the optimization problem to achieve the maximum EAR is solved. Besides, the EARs for different user speeds and different adjustment time of SLM are analyzed by numerical results. Simulation results show that the EAR of the proposed system is higher than conventional schemes with fixed power allocation, and close to the free-space optical communication scheme in the ideal case.

ABSTRACT The robustness against timing offsets of filter bank multi‐carrier (FBMC) is appealing for grant‐free massive access scenarios that mainly adopt asynchronous transmissions. In this work, we propose a compressed sensing based algorithm for joint active user detection as well as timing offset and channel estimation in uplink communication under the combination of FBMC and grant‐free massive access systems, which is critical for subsequent decoding or other processes at receiver. The channel estimation part is based on generalized approximate massage passing (GAMP). The active user detection and timing offset estimation are based on loopy belief propagation (LBP) rules, where the expressions of message passing and belief distributions are derived. Besides, since the receiver may have no prior knowledge about some parameters such as noise variance and activity probability, we introduce the expectation maximization (EM) approach into the proposed algorithm. Moreover, we develop a preamble design method to improve the detection and estimation performance. Simulation results show that the proposed EM‐LBP‐GAMP algorithm can achieve satisfying performance in terms of missed activity detection probability, timing offset estimation error and normalized mean square error of channel estimation.

This paper investigates the near-field localization performance based on the hybrid reconfigurable intelligent surface (RIS), which contains both passive and active elements and is capable of simultaneously reflecting and receiving signals. The asynchronous scenario, where the transmitter and the receiver are not perfectly synchronized, is considered. The position error bound (PEB) of the hybrid RIS-assisted localization in the asynchronous scenario is derived, and the PEB expressions in different scenarios are compared. Moreover, we show that turning only a small part of the elements into active ones can significantly improve the PEB performance. We then investigate the design of the phase shift coefficients for not only the passive reflection elements but also the phase shifters connected to the active elements. The proposed Focnear scheme can efficiently improve the PEB performance compared with the random phases, and it can provide a good initial point for further optimizing the phase profiles of the hybrid RIS. Besides, we also show that increasing the interval between adjacent active elements can generally improve the PEB performance.

Massive access is recognized as one of the main use cases of future wireless networks. The characteristic of sporadic transmission in massive access makes the processes of channel estimation (CE) and active user detection (AUD) essential prerequisites for successful data decoding. In this article, we study joint CE and AUD in massive access system with cell-free structure. Specifically, we first establish the expectation maximization approximate message passing (EM-AMP) framework tailored for cell-free structure as a benchmark. Then, we investigate three new methods that exploit coarse user location information in different ways, namely, the variance bounding method, the variance fusion method, and the proposed EM on location method, where the last two methods can also generate finer location estimation as byproduct to CE and AUD at the cost of higher complexity. For single user scenario, we theoretically prove the optimality of the proposed method in channel variance estimation, validating the foundation of the proposed method. For multiuser scenario, we conduct various simulations to compare the performance of different methods. Our findings illustrate that harnessing coarse user location information yields substantial enhancements in CE and AUD performance. Moreover, the proposed method exhibits superior localization accuracy compared to the variance fusion method, all while maintaining comparable complexity, making it a good candidate for applications with both communication and sensing requirements.

Cognitive radio (CR) is regarded as the key technology of the 6th-Generation (6G) wireless network. Because 6G CR networks are anticipated to offer worldwide coverage, increase cost efficiency, enhance spectrum utilization, and improve device intelligence and network safety. This article studies the secrecy communication in an energy-harvesting (EH)-enabled Cognitive Internet of Things (EH-CIoT) network with a cooperative jammer. The secondary transmitters (STs) and the jammer first harvest the energy from the received radio frequency (RF) signals in the EH phase. Then, in the subsequent wireless information transfer (WIT) phase, the STs transmit secrecy information to their intended receivers in the presence of eavesdroppers while the jammer sends the jamming signal to confuse the eavesdroppers. To evaluate the system secrecy performance, we derive the instantaneous secrecy rate and the closed-form expression of secrecy outage probability (SOP). Furthermore, we propose a deep reinforcement learning (DRL)-based framework for the joint EH time and transmission power allocation problems. Specifically, a pair of ST and jammer over each time block is modeled as an agent which is dynamically interacting with the environment by the state, action, and reward mechanisms. To better find the optimal solutions to the proposed problems, the long short-term memory (LSTM) network and the generative adversarial networks (GANs) are combined with the classical DRL algorithm. The simulation results show that our proposed method is highly effective in maximizing the secrecy rate while minimizing the SOP compared with other existing schemes.

This article studies the topic of probabilistic on-off division multiple access (ODMA) system design under multiple input-multiple output (MIMO) and massive MIMO with hybrid analog/digital architectures for unsourced random access (URA). Though probabilistic ODMA in Gaussian multiple access channel (GMAC) manifests desirable capacity, probabilistic ODMA extension towards MIMO and hybrid massive MIMO encounters problems have not been discussed. Specifically, a portion of data bits are utilized to select on-off patterns and not transmitted. These pattern-correlated bits and others are restored iteratively with pilot-free transmission in the so-called process of joint pattern and data detection. Discrepant to the state-of-arts, such as interleaving division multiple access (IDMA), where prior of patterns is an essential prerequest for the later detection, probabilistic ODMA detects pattern and data simultaneously with only linearly modulated signals. In this work, two probabilistic ODMA transceiver designs are proposed by different ranks of signal component matrix in MIMO and hybrid massive MIMO. Overall, our proposed designs retain the simplicity of the original ODMA and further exploits the sparsity of on-off patterns. For MIMO, on-off patterns are correlated with common codebook and a receiver is designed in the spirit of uncoupled URA. For hybrid massive MIMO, a novel probabilistic ODMA transceiver achieves probabilistic channel estimation via low-rank matrix completion aided by the sparsity of on-off patterns. Meanwhile, joint pattern and data detection is accomplished by iterative treating interference as noise (TIN) strategy. Extensive simulations are conducted under fair comparisons with state-of-the-arts to verify the validity of the proposed schemes.

Reconfigurable intelligent surface (RIS)-assisted spatial index modulation (SIM) is a promising candidate for future wireless communication systems due to its high energy and spectrum efficiencies. When only one transmit antenna and one receive antenna are active in the RIS-assisted SIM, we develop a phase-alignment algorithm based on the signature constellation, which utilizes the signature constellation technology to achieve a good bit error rate (BER) performance with a low complexity. Furthermore, in the general scenario, we propose a novel optimization algorithm for RIS reflection coefficients, called maximum Euclidean distance-based low BER (MED-LBER) algorithm. The proposed algorithm aims to maximize the Euclidean distance between different received signals to improve the system performance. The achievable rate, BER performances, and computational complexities of the proposed algorithms are analyzed in detail. The impacts of imperfect channels on the performance of the proposed algorithms are studied. Extensive simulations are carried out to compare the performance of the proposed algorithms with existing algorithms, and demonstrate their superior BER performance.

Light-emitting diodes (LEDs) are widely used in visible light communication (VLC) systems, but their bandwidth is generally incomparable to the laser diodes (LDs). Though LDs are exploited in free space optical systems to provide high data rate, such systems generally fail for mobile users due to the challenges in acquisition, tracking and pointing. Recently, various centimeter-level visible light positioning (VLP) systems have been realized, making it possible to realize VLP-assisted communication systems. In this work, we design a novel VLP-assisted indoor laser communication system, where an LED provides the positioning signal for the mobile user and a narrow-beam laser carries high-speed data stream. The estimated position is updated regularly to continuously orient the laser boresight to the user. The error of the VLP system and the outage probability of the VLP-assisted laser communication system are both derived in closed forms. According to the analytical and simulation results, the outage probability is related to the changes of the system parameters, such as the transmission power of the laser and the user position. Moreover, the outage probability can be minimized by optimizing the laser’s beam divergence angle. An experimental platform is built to verify the feasibility of our proposed system.

The reconfigurable intelligent surface (RIS) has drawn considerable attention for its ability to enhance the performance of not only the wireless communication but also the indoor localization with low-cost. This paper investigates the performance limits of the RIS-based near-field localization in the asynchronous scenario, and analyzes the impact of each part of the cascaded channel on the localization performance. The Fisher information matrix (FIM) and the position error bound (PEB) are derived. Besides, we also derive the equivalent Fisher information (EFI) for the position-related intermediate parameters. Enabled by the derived EFI, we verify that both the ranging and bearing information of the user can be obtained when the near-field model is considered for the RIS-User equipment (UE) part of the channel, while only the direction of the UE can be inferred in the far-field scenario. This result is well known in the scenario that the curvature of arrival (COA) is directly sensed by the traditional active large-scale array, and we prove that it still holds when the COA is sensed passively by the large RIS. For the base station (BS)-RIS part of the channel, we reveal that this part of the channel determines the type of the gain provided by the BS antenna array. Besides, in the single-carrier, single snapshot case, it requires both the BS-RIS and the RIS-UE part of the channel works in the near-field scenario to localize the UE. We also show that the well-known focusing control scheme for RIS, which maximizes the received SNR, is not always a good choice and may degrade the localization performance in the asynchronous scenario. The simulation results validate the analytic work. The impact of the focusing control scheme on the PEB performances under synchronous and asynchronous conditions is also investigated.

Energy efficiency (EE) is currently one of the primary concerns in wireless communication systems. In future 6G systems, achieving high energy-efficient communication with T-bit transmission rate requirements will heavily depend on resource and power parameters. This paper proposes a novel energy efficiency model that incorporates wireless resources and power for a multi-user multiple-input-multiple-output (MIMO) wireless communication system. Firstly, we analyze the equivalency between frequency and spatial dimensions and introduce a two-dimensional resource domain. Additionally, we propose an energy efficiency model incorporating the wireless resource and power. Using the model, we analyze the interaction between the two parameters aiming at the maximize energy efficiency and present an energy-efficient algorithm. Furthermore, We propose two methods for judging high energy efficiency communication of each user based on the model. Finally, our numerical results illustrate the performance of energy efficiency for each user and the advantages and disadvantages of each measurement method in the multi-user system.

The robustness of filter bank multi-carrier (FBMC) against delays is appealing for asynchronous grant-free massive connectivity systems. In this paper, we study the joint activity detection, delay and channel estimation (JADDCE) problem for filter bank multi-carrier (FBMC)-based uplink massive connectivity with asynchronous transmission and frequency-selective fading (FSF) channels. We formulate JADDCE as a compressed sensing (CS) problem to fully exploit the sparsity structure and propose an efficient algorithm based on generalized approximate message passing (GAMP) to solve it. Besides, since parameters such as noise variance and activity probability may not be perfectly known by the receiver, we introduce the expectation maximization (EM) method into the algorithm and derive the updating rules of the unknown parameters. We also utilize the analysis framework based on average mutual information (AMI) to find theoretical upper-bound of the channel estimation performance. Simulation results show satisfying detection and estimation performance of the proposed algorithm under FSF channels. Besides, the channel estimation performance can approach the theoretical upper-bound.

Millimeter-wave (mmWave) communication using lens antenna array has emerged as a promising techique for the next generation wireless communication systems, which has the advantage of considerably reducing the number of required radio-frequency (RF) chains. However, the beamspace channel estimation is a challenging problem due to the fact that the number of RF chains is much smaller than that of transmit/receive antennas at the base station (BS). In this paper, motivated by the sparsity structure of lens-antenna based beamspace channels, we first model beamspace channel estimation problem as a sparse signal recovery problem, which can be solved by the compressed sensing (CS) algorithms. Then, we propose a simplified learned approximate message passing (SL-AMP) network by pre-learning the prior parameters of beamspace channel. Specifically, we use the Wasserstein generative adversarial net (GAN) to learn the underlying channel distribution and generate the corresponding samples to address the lack of training data problem. Next we choose the Bernoulli Gaussian mixture distribution to capture the channel prior and derive the corresponding shrinkage function. Finally, simulations results show that the proposed SL-AMP network can achieve relatively better performance with much lower training complexity compared with the existing LAMP network.

This work studies the unsourced random access (URA) problem via random dictionary learning over Rayleigh block-fading channels with multiple receive antennas. We propose a novel pilot-free URA equipped with a concatenated URA coding structure enabled by on-off patterns. The inherent collision problem in URA is mitigated with slotted encoding structure. Other problems of state-of-the-arts such as low code rate and inefficient transmission are well-tackled. Distinctively, activity detection is not only accomplished in the original pilot-free manner but also enhanced with softer verdict. Facing the potential false alarm (FA) error, a joint channel pruning and dictionary learning procedure maintains FA under a low level. The learning process updates the noise variance of pseudo noise with explicit models to obtain channel estimation. Subsequently, for the first time, this work realizes joint on-off pattern detection and data restoration for on-off division multiple access (ODMA) under multiple-input and multiple-output (MIMO). Also, a successive interference cancellation (SIC) structure is adopted as an iterative approach to produce desirable performance. Moreover, we analyze the approximation of error ceiling and the performance lower-bound determined by feasible deterministic parameters. Finally, numerical simulations with fair comparisons validate the viability of the proposed URA scheme.

Massive machine-type communications (mMTC) or massive access is a critical scenario in the fifth generation (5G) and the future cellular network. With the surging density of devices from millions to billions, unique pilot allocation becomes inapplicable in the user ID-incorporated grant-free random access protocol. Unsourced random access (URA) manifests itself by focusing only on unwrapping the received signals via a common codebook. In this paper, we propose a URA protocol for a massive access cellular system with multi-user single input multiple output (MIMO). The proposed scheme encompasses a codebook enabling construction of sparse transmission frame, a receiver equipped with dictionary learning and error-correcting codes and a collision resolution strategy for the collided codeword. Discrepant to the existing schemes with necessary overhead for preamble signals, no overhead or pre-defined pilot sequences are needed in the proposed scheme, which is favorable for energy-efficient transmission and latency reduction. Numerical results verify the viability of the proposed scheme in practical massive access scenario.

In this paper, a novel random scattering transceiver design for unsourced random access (URA) is investigated. Distinctive from the state-of-the-art such as coded compressed sensing (CCS) kind and interleaving division multiple access (IDMA) kind, the data is split into two portions and the back-and-forth interleaving/de-interleaving for soft information update is dismantled. A portion of the data is encoded by compressed sensing (CS) and the rest is encoded by convolutional code LDPC (CC-LDPC). Based on the principle of probabilistic data association (PDA), a receiver with a turbo PDA multi-user detector (MUD) is designed by the random scattering transmission. Meanwhile, the proposed turbo PDA detector can also cooperate with the CC-LDPC decoder. A sliding window decoding structure is embedded in the proposed receiver. Moreover, given that permeable collision in URA can undermine the system performance, this paper treats collision as interference and elaborates different slot-wise MUD signal models combining the feature of random scattering, based on which a collision resolution procedure is proposed. Empirically, the proposed scheme shows faster system performance convergence and more accurate MUD performance than the IDMA kind. Numerical results with fair comparisons validate the viability of the proposed scheme.

In this paper, we consider the channel modeling of a heterogeneous vehicular integrated sensing and communication (ISAC) system, where a dual-functional multi-antenna base station (BS) intends to communicate with a multi-antenna vehicular receiver (MR) and sense the surrounding environments simultaneously. The time-varying complex channel impulse responses (CIRs) of the sensing and communication channels are derived, respectively, in which the sensing and communication channels are correlated with shared clusters. The proposed models show great generality for the capability in covering both monostatic and bistatic sensing scenarios, and as well for considering both static clusters/targets and mobile clusters/targets. Important channel statistical characteristics, including time-varying spatial cross-correlation function (CCF) and temporal auto-correlation function (ACF), are derived and analyzed. Numerically results are provided to show the propagation characteristics of the proposed ISAC channel model. Finally, the proposed model is validated via the agreement between theoretical and simulated as well as measurement results.

In this paper, we propose a grouped-adaptive-connected (GAC) hybrid precoding structure, where the antennas are divided into groups and the connections between each group of antennas and each RF chain are dynamically controlled by RF switches. Compared with the fully-adaptive-connected (FAC) structure, the proposed GAC structure requires fewer RF switches. The problem of maximizing energy efficiency (EE) is transformed into an adaptive spectral efficiency (SE) maximization (ASEM) problem and a combinatorial optimization problem. For the ASEM problem, we propose an equivalent baseband channel design based hybrid precoding (EBCD-HP) algorithm. Then, utilizing the solution of the ASEM problem, we propose a cross-entropy based grouped-adaptive hybrid precoding (CE-GAHP) algorithm. Simulation results reveal the advantages of the proposed hybrid precoding scheme.

Visible light positioning (VLP) systems provide higher accuracy and lower costs compared to their radio-frequency counterparts. Angle-of-arrival (AOA) based VLP systems are stable and accurate. However, existing AOA VLP systems with a photodiodes (PD) array usually require an unrotated receiver, densely placed LEDs, or auxiliary devices such as accelerometers and gyroscopes. In this paper, we develop an approach to exploit LED clusters and a PD array to locate the receiver. The new approach increases the angle-difference-of-arrival (ADOA) positioning accuracy and circumvents the rotation estimation in AOA positioning. More importantly, the method only requires the solution of linear equation sets, which is computationally efficient. Simulations show that the proposed positioning method can achieve an average mean error below 0.1 m within a 3 m × 3 m area.

Channel estimation is one of the main challenges for implementing reconfigurable intelligent surface (RIS)-aided communication system with a large number of antennas at the base station (BS) because RIS is equipped with many passive reflective elements without active transmitting/receiving and signal processing abilities. In this paper, we focus on the channel estimation in a general RIS-aided multi-user mmWave communication system. Specifically, a novel two-phase channel estimation scheme consisting of on-line and off-line phases is proposed to estimate the BS-RIS channel, RIS-user channel, and BS-user channel, respectively. Inspired by the characteristics of few scatterers in mmWave communication systems, the antenna domain channels are converted into the virtual angular domain channels by using the discrete Fourier transform (DFT) matrix. Thus, the estimation problem can be formulated as a compressed sensing (CS) problem, and solved by using efficient vector approximate message passing (VAMP) algorithm with expectation-maximization (EM) to learn unknown parameters and obtain the estimates simultaneously. Simulation results show that, the above algorithm with two choices of prior distributions in the proposed mmWave RIS-aided multi-user system has fast convergence speed and desirable estimation performance.

Reconfigurable intelligent surface (RIS) consisting of a large number of programmable near-passive units has been a hot topic in wireless communications due to its capability in providing smart radio environments to enhance the communication performance. However, the existing research are mainly based on simplistic channel models, which will, in principle, lead to inaccurate analysis of the system performance. In this paper, we propose a general three-dimensional (3D) wideband non-stationary end-to-end channel model for RIS assisted multiple-input multiple-output (MIMO) communications, which takes into account the physical properties of RIS, such as unit numbers, unit sizes, array orientations and array configurations. By modeling the RIS by a virtual cluster, we describe the end-to-end channel by a superposition of virtual line-of-sight (V-LoS), single-bounced non-LoS (SB-NLoS), and double-bounced NLoS (DB-NLoS) components. We also derive an equivalent cascaded channel model and show the equivalence between end-to-end and cascaded modeling of RIS channels. Then, a sub-optimal solution with low complexity is used to derive the RIS reflection phases. The impact of physical properties of RIS, such as unit numbers, unit sizes, array orientations, array configurations and array relative locations, on channel statistical characteristics has been investigated and analyzed, the results demonstrate that the proposed model is helpful for characterizing the RIS-assisted communication channels.

Unmanned aerial vehicle (UAV) communications exploiting millimeter wave (mmWave) can satisfy the increasing data rate demands for future wireless networks owing to the line-of-sight (LoS) dominated transmission and flexibility. In reality, the LoS link can be easily and severely blocked due to poor propagation environments such as tall buildings or trees. To this end, we introduce a reconfigurable intelligent surface (RIS), which passively reflects signals with programmable reflection coefficients, between the transceivers to enhance the communication quality. Specifically, in this paper we generalize a three-dimensional (3D) non-stationary wideband end-to-end channel model for RIS auxiliary UAV-to-ground mmWave multiple-input multiple-output (MIMO) communication systems. By modeling the RIS as a virtual cluster, we study thepower delivering capabilityof RIS as well as thefading characteristicof the proposed channel model. Important channel statistical properties are derived and thoroughly investigated, and the impact of RIS reflection phase configurations on these statistical properties is studied, which provides guidelines for the practical system design. The agreement between theoretical and simulated as well as measurement results validate the effectiveness of the proposed channel model.

Lognormal-Rician random variables (RV) can model large-scale and small-scale fading, line-of-sight and non-line-of-sight channels in wireless communications. However, the versatile stochastic tool is mathematically intractable for correlated cases. In this work, we derive closed-form expressions for the outage probabilities of diversity receptions over dual-branch lognormal-Rician fading channels with arbitrary correlation at high signal-to-noise ratio (SNR). The analytical results show that the lognormal RV and the Rician RV, respectively, contribute an exponential factor and a fractional factor to the outage probability expressions. Some important insights are revealed based on the elegant expressions. For example, negative correlation between the lognormal RVs can suppress the outage probability, and the phase of the complex correlation coefficient between the accompanying Gaussian RVs in the Rician channels can significantly influence the performance.

Abstract Intelligent reflecting surface (IRS) is a new concept originating from metamaterials, which can achieve beamforming through controllable passive reflecting. This device makes it possible to engineer the wireless communication environment, and has drawn increasing attention. However, the associated channel models in current literature are mainly borrowed from conventional wireless channel models directly, omitting the unique features of IRS. In this paper, a geometry‐based stochastic channel model for IRS‐assisted wireless communication system is employed. The model has certain accuracy and low computational complexity. In particular, it captures the correlations of subchannels associated with different IRS elements, which is typically not considered in current works. Based on this channel model and the derived channel spatial correlation functions (CFs), an iterative reflection coefficients configuration method is proposed exploiting statistical channel state information to maximise the ergodic channel capacity. The impacts of the IRS spatial positions as well as the number of the IRS elements on the ergodic channel capacity is investigated through simulations. It is found that to obtain a larger ergodic channel capacity, the IRS should be placed in the vicinity of either the transmitter side or the receiver side, which is a useful guideline for practical deployment.

Abstract Low earth orbit constellation satellite communication has the characteristics of low propagation delay, low path loss, low launch cost and wide range of applications. Due to the low orbital altitude and the short orbital period of low earth orbit satellites, the relative position between satellites and gateway stations changes fast. As a result, the links between gateway stations and satellites need to be switched continuously. Based on the minimum handover frequency algorithm, this paper considers the balance of satellite workloads, and proposes a load balanced satellite handover strategy. In the proposed handover strategy, a joint optimization algorithm is employed, and the power allocation of the satellite is optimized to improve the system capacity. In the multi‐satellite connection model, an adaptive power allocation algorithm is proposed to guarantee the service quality of the system. Simulation results demonstrate the efficiency of the proposed satellite handover strategy.

Filter bank multiple access (FBMA) without subbands orthogonality has been proposed as a new candidate waveform to better meet the requirements of future wireless communication systems and scenarios. It has the ability to process directly the complex symbols without any fancy preprocessing. Along with the usage of cyclic prefix (CP) and wide-banded subband design, CP-FBMA can further improve the peak-to-average power ratio and bit error rate performance while reducing the length of filters. However, the potential gain of removing the orthogonality constraint on the subband filters in the system has not been fully exploited from the perspective of waveform design, which inspires us to optimize the subband filters for CP-FBMA system to maximizing the achievable rate. Besides, we propose a joint optimization algorithm to optimize both the waveform and the covariance matrices iteratively. Furthermore, the joint optimization algorithm can meet the requirements of filter design in practical applications in which the available spectrum consists of several isolated bandwidth parts. Both general framework and detailed derivation of the algorithms are presented. Simulation results show that the algorithms converge after only a few iterations and can improve the sum rate dramatically while reducing the transmission delay of information symbols.

Spatial domain modulation employs spatial degree of freedom to convey information bits. Traditional spatial domain modulation schemes include spatial modulation, spatial shift keying (SSK), and variations of these two schemes. To fully make use of the spatial degree of freedom and improve the achievable data rate of traditional spatial domain modulation schemes, we propose a successive-coded SSK (SC-SSK) modulation scheme. The core of the proposed SC-SSK scheme is a multiple step coded modulation strategy, in which different information bits are mapped to the designed pattern in each step. The constellation of the transmit signals is designed. A QR decomposition based successive detection algorithm is proposed for this particular coded modulation strategy. The achievable rate, the error rate performance, and the computational complexity of the proposed SC-SSK scheme are analyzed in detail. Simulations are carried out to compare the proposed the SC-SSK scheme with the reference spatial domain modulation schemes, and reveal the advantages of the proposed SC-SSK scheme.

Intelligent reflecting surface (IRS), which is composed of a large number of low-cost passive elements, has the ability to reflect the incident signal independently with an adjustable phase and amplitude shifts, has been regarded as a key and emerging technology for achieving the cost-effectively spectrum and addressing energy issues in the next generation of wireless networks. In this paper, we propose a general wideband non-stationary channel model for IRS-assisted multiple-input multiple-output (MIMO) communication scenarios, which aims at capturing the underlying propagation characteristics of IRS-assisted communication systems. By properly adjusting the key system parameters, the proposed channel model can be used to describe various IRS-assisted communication scenarios. Furthermore, we separate the channel between the mobile transmitter (MT) and mobile receiver (MR) into the subchannel between the MT and IRS, the subchannel between the IRS and MR, and the subchannel between the MT and MR. The physical properties of each subchannel are investigated accordingly; then, we develop an equivalent channel model to study the key characteristics of the proposed IRS-assisted channel model, such as the time-varying spatial-temporal (ST) cross-correlation functions (CCFs), temporal auto-correlation functions (ACFs), and frequency CCFs. Finally, numerical results demonstrate that the proposed channel model is practical for describing the IRS-assisted MIMO wireless communication scenarios.

For efficiently investigating the statistical properties of wideband multiple-input multiple-output (MIMO) channels for vehicle-to-vehicle (V2V) communication scenarios, we propose a novel computationally efficient solution to estimate the parameters of the proposed channel model for different propagation delays in this paper. To be specific, we first introduce a Unitary transformation method to estimate the propagation delay of the proposed channel model for the first tap in the preliminary stage before the mobile transmitter (MT) and mobile receiver (MR) move. Then, we estimate the real-time angular parameters based on the estimated delay and moving time/directions/velocities of the MT and MR. Furthermore, we estimate the expressions of the real-time complex channel impulse responses (CIRs), which can be used to characterize the physical properties of the proposed channel model, by substituting the estimates of the time-varying AoD and AoA and model parameters into the complex CIRs. Numerical results of the channel characteristics fit the theory results very well, which validate that the proposed channel model is practical for characterizing the beyond fifth-generation (B5G) V2V communication systems.

Considering mobile terminals with time-varying velocities and randomly moving clusters, a novel multi-mobility non-stationary wideband multiple-input multiple-output (MIMO) channel model for future intelligent vehicle-to-everything (V2X) communications is proposed. To describe the non-stationarity of multi-mobility V2X channels, the proposed model employs a time-varying acceleration model and a random walk process to describe the motion of the communication terminals and that of the scattering clusters, respectively. The evolution of the model parameters over time and the stochastic characteristics of the phase shift caused by the time-varying Doppler frequency are derived. The proposed model is sufficiently general and suitable for characterizing various V2X communication scenarios. Under two-dimensional (2D) non-isotropic scattering scenarios, the important channel statistical properties of the proposed model are derived and thoroughly investigated. The impact of the random walk process of the clusters and the velocity variations of the communication terminals on these statistical properties is studied. The simulation results verify that the proposed model is useful for characterizing V2X channels.

With the growing demand for high accuracy indoor localization, the fifth generation (5G) wireless communication technology based localization attracts increasing attention. Aiming at the indoor localization with a single base station, this paper proposes a 5G signal based localization method by employing the estimates of the angle of arrival (AoA) and the time of flight (ToF). The proposed method takes advantage of the large bandwidth and subcarrier spacing of 5G signals to improve localization accuracy. First, the base station carries out frequency synchronization and channel estimation to obtain the channel state information (CSI) matrix. Second, the CSI matrix is utilized for joint estimation of the AoA and ToF for each path. Finally, the corresponding geometric localization method is applied. Simulation results show that this method can achieve a centimeter-level localization accuracy.

Traditional asymptotic analysis techniques fail for lognormal random variables (RV) because a lognormal probability density function cannot be quantified by Taylor series at the origin, and the moment-generating function of a lognormal RV does not yield a unified form. In this work, we derive two closed-form asymptotic expressions for the multivariate cumulative distribution function (CDF) of L correlated lognormal RVs. Among these two asymptotic expressions, one has an elegant form but low converging speed, while the other has a fast converging speed but involves Gaussian Q-functions. Furthermore, we derive asymptotically tight upper and lower bounds for the multivariate CDF to evaluate the error of the asymptotic approximation. The new analytical results are shown to be effective in evaluating the outage probability of selection combining (SC) over correlated lognormal fading channels when Monte Carlo simulation is expensive. More importantly, the elegant asymptotic expressions show that little outage performance improvement can be introduced to the SC system by adding more antennas for some cases.

Millimetre wave (mmWave) signal is promising for the challenge of bandwidth shortage and canmotivate the research on large‐scale antenna arrays. In this paper, the authorsinvestigate the hybrid precoding design that combines with a radio frequency(RF) precoder and a digital precoder for multi‐user multiple‐input single‐output(MU‐MISO) systems with large‐scale antenna arrays. In practical large‐scaleantenna array systems, the small antenna spacing and the properties of ascattering environment can create a correlation between channel coefficients forseparated receive units. Such correlation prejudices the performance ofmulti‐user systems. This study proposes two approaches to reduce the channelcorrelation by the hybrid precoding structure. The first approach selects somevectors with the smallest influence with each other from all array responsevectors to design an angular‐based RF precoder. The second approach optimisesthe singular values characteristic of the virtual channel matrix before digitalprocessing for the RF precoder. Then, the MU‐MISO baseband precoding isimplemented by reduced RF chains. Numerical results show that they havedifferent performance depending on the environment. The first can achieve betterperformance when the channel correlation is not too high, while the second ismore effective when the channel realisation is terribly ill‐conditioned, especially for higher signal‐noise ratio (SNR).

Deep learning (DL) based autoencoder is a promising architecture to implement end-to-end communication systems. One fundamental problem of such systems is how to increase the transmission rate. Two new schemes are proposed to address the limited data rate issue: adaptive transmission scheme and generalized data representation (GDR) scheme. In the first scheme, an adaptive transmission is designed to select the transmission vectors for maximizing the data rate under different channel conditions. The block error rate (BLER) of the first scheme is 80% lower than that of the conventional one-hot vector scheme. This implies that higher data rate can be achieved by the adaptive transmission scheme. In the second scheme, the GDR replaces the conventional one-hot representation. The GDR scheme can achieve higher data rate than the conventional one-hot vector scheme with comparable BLER performance. For example, when the vector size is eight, the proposed GDR scheme can double the date rate of the one-hot vector scheme. Besides, the joint scheme of the two proposed schemes can create further benefits. The effect of signal-to-noise ratio (SNR) is analyzed for these DL-based communication systems. Numerical results show that training the autoencoder using data set with various SNR values can attain robust BLER performance under different channel conditions.

In this article, we propose a three-dimensional (3D) multi-input multi-output (MIMO) channel model for air-to-ground (A2G) communications in unmanned aerial vehicles (UAV) environments, where the UAV transmitter and ground receiver are in motion in the air and on the ground, respectively. A novel angular estimation algorithm is proposed to estimate the real-time azimuth angle of departure (AAoD), elevation angle of departure (EAoD), azimuth angle of arrival (AAoA), and elevation angle of arrival (EAoA) based on the non-stationary nature of the channel model. In the model, we investigate the time-varying spatial cross-correlation functions (CCFs) and temporal auto-correlation functions (ACFs) with respect to the different moving directions and velocities of the UAV transmitter and ground receiver. Furthermore, we derive and study the Doppler power spectral densities (PSDs) and power delay profiles (PDPs) of the proposed channel model. Numerical results show that characteristics of the proposed channel model are very close to those of practical measurements, which provide a new and practical approach to evaluate the performance of next generation UAV-MIMO communication systems.

In intensity-modulation and direct-detection (IM/DD) multiple-input and multiple-output (MIMO) visible light communication (VLC) systems, spatial subchannels are usually correlated, and spatial modulation is a good choice to achieve the advantages of MIMO technology. Peak-to-average power ratio (PAPR) is a key issue in VLCs due to the limited linear dynamic range of light emitting diodes (LEDs). Single-carrier communication systems have a lower PAPR than orthogonal frequency division multiplexing (OFDM) communication systems. However, it is challenging to design a single-carrier spatial modulation for high-rate transmissions because of the time domain intersymbol interference. This paper develops an optical spatial modulation (OSM) scheme based on bipolar pulse amplitude modulation (PAM) and spatial elements for high-rate indoor VLC systems. Multiple data streams can be transmitted simultaneously in the proposed scheme. Based on the transmit strategy, we develop a low-complexity receiver algorithm that achieves better bit-error rate performance than reference schemes, and the proposed OSM scheme has a much lower PAPR than OFDM based OSM schemes. When the spatial subchannels are highly correlated, a spatial area division strategy is applied, and the receiver algorithm is investigated. The symbol-error rate expression of the proposed OSM scheme is derived, and the computational complexity is analyzed.

Deep neural network (DNN) has a wide range of applications in various fields, including solving sparse inverse problems. In this paper, we propose a novel network called the Stein's unbiased risk estimate based-trainable iterative thresholding algorithm (SURE-TISTA) for sparse signal recovery problems. Without prior information, SURE-TISTA outperforms TISTA, an algorithm based on the minimum mean squared error (MMSE) estimator. SURE-TISTA also shows a great robustness in many cases including large-scale and large-variance problems. Meanwhile, SURE-TISTA uses fewer learnable variables to achieve similar performance as learned approximate message passing (LAMP), which has more learnable parameters. Without any error measure estimator, SURE-TISTA achieves a near MMSE-based performance. Our numerical results indicate that SURE-TISTA is superior to TISTA and other traditional algorithms in many aspects, which can be promising in image denoising.

Deep learning (DL) based autoencoder is a potential architecture to implement end-to-end communication systems. In this paper, we first give a brief introduction to the autoencoder-represented communication system. Then, we propose a novel generalized data representation (GDR) to improve the data rate of DL-based communication systems. Finally, simulation results show that the proposed GDR scheme has lower training complexity, comparable block error rate performance and higher channel capacity than the conventional one-hot vector scheme. Furthermore, we investigate the effect of signal-to-noise ratio (SNR) in DL-based communication systems and show that training at high SNR can produce a good training convergence performance for the autoencoder.

Spatial modulation is a promising transmission scheme for massive multiple‐input multiple‐output (MIMO) systems to improve energy efficiency. In this study, considering the low‐complexity hybrid structure, the authors propose a joint spatial modulation and beamforming scheme for a hybrid massive MIMO system in both single user and multi‐user scenarios. Specifically, a design criterion of the channel control parameter is provided to reduce the high correlation among different channel coefficients in the hybrid massive MIMO system. In addition, with the statistical channel state information acquired at the base station, the optimum analogue beamforming vectors are designed with the goal of maximising the signal to leakage and noise ratio in the multi‐user scenario. Furthermore, the upper bound, the lower bound as well as the closed form approximation of achievable spectral efficiency of the proposed scheme are derived for both single user and multi‐user scenarios. Numerical simulation results demonstrate that the proposed scheme outperforms the conventional MIMO scheme.

A new multi-carrier multiple access scheme based on filter bank multi-carrier (FBMC) without sub-band orthogonality, referred to as imperfect reconstructed filter bank multiple access (iPR-FBMA), has been recently proposed. While it entitles substantial performance improvement on the peak-to-average power ratio and bit error rate compared to the conventional orthogonal frequency division multiplexing (OFDM) scheme, its achievable information rate, the ultimate performance limit of this non-orthogonal scheme, has not been fully unveiled. As a consequence, whether the introduction of non-orthogonality to FBMA is really worthy from information perspective remains an open issue. In this paper, we present a comparative study on the ergodic achievable rate and region of iPR-FBMA with that of two orthogonal multiple access schemes based on OFDM and OFDM with offset QAM (OFDM/OQAM), respectively, in uplink multiple access with flat fading channels. We prove that the achievable rate of iPR-FBMA is largely determined by the number of non-zero singular values of its transmission matrix, which is directly related to the means of subbands allocation, the oversampling factor and the numerical precision. Numerical results show that iPR-FBMA with critical sampling has even lower sum rate compared to OFDM and OFDM/OQAM although it is non-orthogonal. However, with oversampling, iPR-FBMA could get much larger rate region than the orthogonal schemes, especially for imbalanced power constraints and interleaved subband allocation, and it could achieve the entire channel capacity region if the numerical precision could be fully guaranteed.

This paper presents 3-D vehicle massive multiple-input multiple-output (MIMO) antenna array model for vehicle-to-vehicle (V2V) communication environments. A spherical wavefront is assumed in the proposed model instead of the plane wavefront assumption used in the conventional MIMO channel model. Using the proposed V2V channel model, we first derive the closed-form expressions for the joint and marginal probability density functions of the angle of departure at the transmitter and angle of arrival at the receiver in the azimuth and elevation planes. We additionally analyze the time and frequency cross-correlation functions for different propagation paths. In the proposed model, we derive the expression of the Doppler spectrum due to the relative motion between the mobile transmitter and mobile receiver. The results show that the proposed 3-D channel model is in close agreement with previously reported results, thereby validating the generalization of the proposed model.

Universal filtered multi-carrier (UFMC) is a potential waveform technology, which can efficiently combat different carrier frequency offsets (CFOs) of multiple users. First, adaptive modulation and power allocation are applied for each subcarrier to meet a preset bit error rate (BER) requirement by ignoring CFOs. Then, we prove that different CFOs will cause different interference variances to adjacent users, which results in performance degradation in UFMC systems. To reduce the interference caused by CFOs and improve achievable rate, a novel adaptive filter configuration algorithm is proposed to adaptively design the parameters of the finite impulse response filters. Specifically, the proposed algorithm is available for the UFMC systems, where the user equipments are allocated with different bandwidths. Finally, simulation results show that the proposed adaptive filter configuration algorithm can dramatically eliminate the interference caused by different CFOs, and achieve better BER performance and a higher achievable rate than the conventional scheme.

Correlation across transmit antennas in multiple- input single-output (MISO) broadcast channel has been studied to be detrimental generally. The current downlink beamforming schemes have not considered correlation enough such that they are not suitable for the broadcast systems with high correlation among transmit antennas. We propose two novel user grouping precoders to improve the sum rate performance for the case of ill-conditioned channel and the case of small transmit antennas (i.e., the number of transmit antennas is less than that of users). The first proposed precoder partitions all the users into small groups step by step based on the condition number. Downlink beamforming is then done in a small size for the optimal channel submatrix with smaller condition number. Inter-group interference from the subsequent allocated groups is nulled out in the null spaces of the previous allocated groups. The second precoder regularizes the number of users in the first group to improve the sum rate further. Simulation results verify that our schemes outperform the current beamforming schemes for the case of high transmit correlation. Moreover, the proposed precoders allow to be applied for the case of small antennas.

Imperfect reconstructed filter bank multiple access (FBMA) for uplink transmission has been proposed recently which gets rid of real orthogonality in current filter bank multicarrier schemes. While it eases the filter design task and achieves better bit error rate (BER) performance than orthogonal frequency division multiple access (OFDMA), it still suffers from high peak-to-average power ratio (PAPR) and high computational complexity. In this paper we propose an improved version of existing imperfect reconstructed FBMA system. The basic idea is to replace the group of narrow-banded subbands of one user by a wide-banded subband. We will show that this simple modification not only reduces the transceiver complexity, but also substantially improves the PAPR and BER performance without any fancy preprocessing. Another contribution of this work is to propose an enhanced symbol detection algorithm by exploiting the released design freedom from PR constraint through polyphase component selection. Simulations show that by using the proposed detection algorithm, the BER performance of our proposed CP-FBMA approaches to that of single carrier frequency division multiple access (SC-FDMA) and could be even better at high signal-to-noise ratio region. Those results suggest the proposed FBMA using wide-banded subands is a good candidate new waveform for future generation communication systems.

In this paper, we present a three-dimensional (3D) geometric channel model for vehicle-to-vehicle (V2V) communication environments. We adopt a two-cylinder model to describe moving vehicles as well as semi-ellipsoid model to depict stationary roadside scenarios, where the received signal is constructed as a sum of the line-of-sight (LoS), single-, and double-bounced rays. Accordingly, the proposed channel model is sufficient for depicting a variety of V2V scenarios, such as macro-, micro-, and picocells. From the reference model, we analyze the proposed channel statistics such as space correlation functions (CFs) and frequency CFs are studied for different channel parameters. Additionally, we perform the Doppler frequency due to the relative motion between the mobile transmitter (MT) and mobile receiver (MR). The results demonstrate that the proposed model can fit those of the previous channel models and the measurement results for V2V scenarios very well, which validate the accuracy of the proposed 3D channel model.

With the development of vehicle-to-vehicle (V2V) communications, the interference among different V2V communication groups will become the limitation for high-rate transmissions. Two intergroup interference coordination schemes are proposed for the V2V communication. The first proposed scheme is a Doppler-shift-based frequency-domain interference alignment scheme. Through pre- and postprocessing, we guarantee that one V2V group is free from interference, and the other V2V group is partially interfered. The second proposed scheme is a frequency-domain precoding scheme. Through frequency-domain precoding, the intergroup interference between the two V2V groups can be mitigated completely.

Filter bank multi-carrier (FBMC) entitles many advantages over orthogonal frequency division multiplexing (OFDM) and is considered to be a more competitive waveform in the future generation cellular communications. In current FBMC, the prototype filter is deliberately designed to meet the perfect reconstruction (PR) constraint to establish subband orthogonality in real domain, which may not be optimal from communication perspective. In this paper, we challenge the necessity of PR constraint by proposing a new FBMC framework, which directly accepts non-orthogonal transmission. The resulting imperfect reconstruction FBMC (iPR-FBMC) has several advantages over its PR FBMC counterpart: 1) the constraint on the prototype filter is relaxed; 2) more importantly, the prototype filter can now be optimized with new goal of improving the detection performance rather than having to meet the PR condition; and 3) it allows for more flexible subband management in multi-user scenario. We will show how those advantages can be exploited. Simulations show that with moderate increase in computational complexity, the proposed iPR-FBMC with optimized prototype filter has superior bit error rate (BER) performance to existing FBMC with PR constraint and even outperforms OFDM, especially in highly frequency selective channels. The findings may shed light into potential research on non-orthogonal FBMC without PR constraint.

In this study, the authors propose a cyclic prefix (CP) based imperfect reconstruction (IPR) filter bank multicarrier (FBMC) system in the uplink of multiuser networks, known as filter bank multiple access (FBMA). The prototype filter in the system is a common low‐pass filter without any perfect reconstruction (PR) or nearly perfect reconstruction (NPR) constraint. CP appended input signal format is applied to avoid inter‐symbol interference and facilitate the equalisation task. The received signal is jointly processed by multi‐subband equalisation. Besides, the input data symbols can be complex‐valued due to IPR design. Some properties and achievable data rate of the proposed system are discussed. Simulations illustrate that the proposed system with uniformly/non‐uniformly designed filter bank has a similar achievable data rate to orthogonal frequency division multiple access (OFDMA). Bit error rate performance of the proposed system is better than OFDMA and an error floor always exists when applying conventional PR FBMC to multi‐user networks. In addition, OFDMA is more sensitive to carrier frequency offset than FBMA because of full synchronisation requirement. Therefore, it suggests that the proposed CP based IPR FBMA system works well without PR/NPR conditions and more extensive development is feasible for the filter banks in the foreseeable future.

In indoor optical wireless communication systems with intensity modulation (IM) and direct detection (DD), diffuse links could be established through reflecting. For such a case, line-of-sight does not exist between the transmitter and receiver, and the channel delay spread will be large. The dispersion of the channel might cause inter-symbol interference. Also, the transmitted signal must be nonnegative. Thus most modulation schemes for radio communications cannot be used directly for such a system. This paper develops a new multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) IM/DD systems: real and imaginary coefficients separation (RIS) system. Two transmit data streams require only one inverse fast Fourier transform (IFFT) block, resulting in a low computational complexity. Compared with traditional OFDM based schemes, the RIS scheme achieves a better BER performance and requires fewer IFFT blocks at the transmitter. The transmitter architecture and receiver algorithm are analyzed in detail. BER performance and simulation results are provided.

Full-duplex transmission can be easily implemented on a visible light communication (VLC) link. In this paper, we investigate the performance of a VLC network with full-duplex optical links. We propose two contention protocols, named U-ALOHA and FD-CSMA, to utilize the full-duplex capability effectively. Their performances in terms of channel utilization and network throughput are analyzed and simulated and compared with a protocol with half-duplex links. The results show that the proposed protocols can effectively exploit the full-duplex capability. U-ALOHA achieves high channel utilization on the downlink channel while FD-CSMA has good performance on both downlink and uplink channels. Both of them outperform the protocol with half-duplex links when the traffic load is sufficiently high.

In visible light communication (VLC) systems with intensity modulation (IM) and direct detection (DD), the modulation bandwidth of light emitting diode (LED) is limited, and dispersion of the channel might cause inter-symbol interference. Also, the transmitted signal must be nonnegative. Thus most modulation schemes for radio communications cannot be used directly for such a system. This paper develops a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) VLC system that overcomes the modulation bandwidth limitation of LED and satisfies the requirements of the transmitted signal for IM/DD. Also, two transmit data streams require only one inverse discrete Fourier transform (IDFT) block, resulting in a low computational complexity. The transmitter architecture, receive algorithm, bit error rate performance, channel capacity analysis, and simulation results are provided.

This article studies adaptive modulation for QR decomposition and successive interference cancellation receivers in multiple‐input multiple‐output systems over slow‐ and fast‐fading channels. In slowly fading channels, adaptive modulation schemes with finite‐rate feedback under the constraints of a constant total transmit power, discrete‐rate and a target bit‐error‐rate (BER) are proposed. Specifically, the authors first develop adaptive modulation with modulation order feedback only. For power allocation and modulation order feedback, the authors establish an optimisation scheme that can be solved easily and analyse the effect of power‐level quantisation on the achievable data rates. For the fast fading channel case, the authors analyse the probability distribution of the ‘ R ’ matrix and derive a scheme that only requires feedback of the statistical information of the channel for adaptive modulation with optimal bit loading to guarantee that the BER target is met.