Conventional fixed-orientation antenna (FOA) arrays offer limited degrees of freedom (DoF) for flexible beamforming such as null steering. To address this limitation, we propose a new rotatable antenna array (RAA) architecture in this paper, which enables three-dimensional (3D) rotational control of an antenna array to provide enhanced spatial flexibility for null steering. To characterize its performance, we aim to jointly optimize the 3D rotational angles of the RAA, to maximize the beam gain over a given desired direction, while nulling those over multiple interference directions under zero-forcing (ZF) beamforming. However, this problem is non-convex and challenging to tackle due to the highly nonlinear expression of the beam gain in terms of the rotational angles. To gain insights, we first examine several special cases including both isotropic and directional antenna radiation patterns, deriving the conditions under which full beam gain can be achieved over the desired direction while meeting the nulling constraints for interference directions. These conditions clearly indicate that compared with FOA arrays, RAAs can significantly relax the angular separation requirement for achieving effective null steering. For other general cases, we propose a sequential update algorithm, that iteratively refines the 3D rotational angles by discretizing the 3D angular search space. To avoid undesired local optimum, a Gibbs sampling (GS) procedure is also employed between two consecutive rounds of sequential update for solution exploration. Simulation results verify our analytical results and show superior null-steering performance of RAAs to FOA arrays.

Terahertz (THz) band unmanned aerial vehicle (UAV) links exploit ultra-wide spectra and high directivity to deliver multi-Gbps secure data for remote sensing and wireless backhaul, but their open three-dimensional flight paths increase vulnerability to covert detection and jamming. Altitude-dependent atmospheric loss, negligible in microwave or terrestrial THz studies, becomes critical in this band owing to triple selectivity, where propagation varies sharply with frequency, distance, and environment. In this paper, a spatially-aware transmission framework is proposed that jointly allocates spectrum and power according to node altitude and beam orientation to maximize jam-resilience covert throughput. Specifically, a three-dimensional propagation model incorporating altitude-dependent molecular absorption, weather loss, and turbulence is established, closed-form expressions for covert outage probability and throughput are derived, and the resulting nonconvex band-wise optimization is solved. Simulation results verify significant gains in covert throughput and jamming robustness and reveal that downward transmissions are more secure than upward counterparts, as their propagation path traverses denser and more absorptive air, whereas the upward path quickly rises into thinner layers that expose the signal to remote eavesdroppers. These analytical insights furnish a quantitative basis for altitude-aware spectrum planning and multilayer topology design in future space-air-ground integrated networks.

Terahertz communication is a pivotal candidate technology for future 6G networks. Deep learning (DL)-based transmission methods can utilize the real-time data to model channel statistics and device imperfections, presenting an effective manner to solve the modeling problems of non-ideality and channel in the terahertz band. However, there still exist several shortages hindering the development of DL-based terahertz communications, that is high system complexity, costly online retraining to fit changing non-ideality and channel conditions, and learned diagrams with high peak-to-average power ratio (PAPR). This paper proposes novel methods to address the above challenges. At first, a new regulated autoencoder (RAE) structure is proposed to fit changing conditions without online retraining. Secondly, a binary neural network (BNN) method is leveraged to reduce the receiver complexity and a lookup table based method to cut down the transmitter complexity. Lastly, a new maximum normalization method is proposed to reduce PAPR of the learned diagram. Extensive simulations are performed to verify the effectiveness of the proposed methods.

Intelligent reflecting surface (IRS) is composed of numerous passive reflecting elements and can be mounted on unmanned aerial vehicles (UAVs) to achieve six-dimensional (6D) movement by adjusting the UAV’s three-dimensional (3D) location and 3D orientation simultaneously. Hence, in this paper, we investigate a new UAV-enabled passive 6D movable antenna (6DMA) architecture by mounting an IRS on a UAV and address the associated joint deployment and beamforming optimization problem. In particular, we consider a passive 6DMA-aided multicast system with a multi-antenna base station (BS) and multiple remote users, aiming to jointly optimize the IRS’s location and 3D orientation, as well as its passive beamforming to maximize the minimum received signal-to-noise ratio (SNR) among all users under the practical angle-dependent signal reflection model. However, this optimization problem is challenging to be optimally solved due to the intricate relationship between the users’ SNRs and the IRS’s location and orientation. To tackle this challenge, we first focus on a simplified case with a single user, showing that one-dimensional (1D) orientation suffices to achieve the optimal performance. Next, we show that for any given IRS’s location, the optimal 1D orientation can be derived in closed form, based on which several useful insights are drawn. To solve the max-min SNR problem in the general multi-user case, we propose an alternating optimization (AO) algorithm by alternately optimizing the IRS’s beamforming and location/orientation via successive convex approximation (SCA) and hybrid coarse- and fine-grained search, respectively. To avoid undesirable local sub-optimal solutions, a Gibbs sampling (GS) method is proposed to generate new IRS locations and orientations for exploration in each AO iteration. Numerical results validate our theoretical analyses and demonstrate the superiority of our proposed AO algorithm with GS to conventional AO and other baseline deployment strategies with location or orientation optimization only.

Movable antenna (MA) has attracted increasing attention in wireless communications recently. As compared to conventional fixed-position antennas (FPAs), the geometry of MAs can be dynamically reconfigured, such that more flexible beamforming can be achieved for different purposes. In this paper, we investigate the application of MAs to wide-beam coverage, aiming to jointly optimize the MAs’ beamforming weights and positions within a line segment to maximize the minimum beam gain among all possible directions in a target region. However, the resulting optimization problem is non-convex and difficult to be optimally solved. To tackle this difficulty, we first derive a closed-form optimal solution to this problem in the special case with two MAs. While for the case with more than two MAs, an alternating optimization (AO) algorithm is proposed to obtain a high-quality suboptimal solution, where the MAs’ beamforming weights and positions are alternately optimized by applying the successive convex approximation (SCA) technique. To reduce computational complexity, we further propose a more efficient MA position optimization method by leveraging the frequency modulation continuous wave (FMCW) design. Specifically, we construct a spatial FMCW-based continuous phase profile for the entire line segment and then select an optimal set of MA positions to optimize the wide-beam coverage performance with their FMCW-based phase profiles, thus greatly simplifying the wide-beam design. Furthermore, we extend the proposed AO and FMCW-based algorithms for the linear MA array to the planar MA array. Numerical results show that both our proposed algorithms can significantly outperform conventional FPAs even with optimized beamforming weights.

Movable antennas (MAs) offer additional spatial degrees of freedom (DoFs) to enhance wireless communication performance through local antenna movement in a confined region. However, to achieve accurate and fast antenna movement, MA drivers entail non-negligible mechanical power consumption, rendering energy efficiency (EE) optimization more critical compared to conventional fixed-position antenna (FPA) systems. To address this problem, we develop in this paper a fundamental power consumption model for stepper motor-driven multi-MA systems by resorting to basic electric motor theory. Based on this model, we investigate an EE maximization problem for the downlink transmission from a multi-MA base station (BS) to multiple single-antenna users. In particular, we aim to jointly optimize the MAs’ positions and moving speeds as well as the BS’s transmit precoding matrix subject to collision-avoidance constraints during the multi-MA movements. However, this problem appears to be difficult to be solved optimally. To tackle this challenge, we first reveal that the collision-avoidance constraints can always be relaxed without loss of optimality by properly renumbering the MA indices. For the resulting relaxed problem, we first consider a simplified single-user setup and uncover a hidden monotonicity of the EE performance with respect to the MAs’ moving speeds. To solve the remaining optimization problem, we develop a two-layer optimization framework. In the inner layer, the Dinkelbach algorithm is employed to derive the optimal beamforming solution in a semi-closed form for any given MA positions. In the outer layer, a sequential update algorithm is proposed to iteratively refine the MA positions based on the optimal values obtained from the inner layer. Next, we proceed to the general multi-user case and propose an alternating optimization (AO) algorithm to obtain a high-quality suboptimal solution. Numerical results demonstrate that despite the additional mechanical power consumption, the proposed algorithms can outperform both conventional FPA systems and existing EE maximization algorithms that neglect mechanical power consumption.

As communications evolve into the terahertz (THz) band, the near-field region expands accordingly, providing additional distance-domain information that can be exploited for high-accuracy sensing. While the ultra-short THz wavelength offers high resolution, it in turn narrows the unambiguous estimation range and demands large-scale arrays for path loss compensation. Sparse arrays (SAs) are considered to reduce the cost of massive elements, but inevitably cause performance degradation. Moreover, near-field channels exhibit complex coupling among time, frequency, and angular parameters, complicating sensing information extraction. To address these challenges, we propose a coarse-to-fine velocity estimation algorithm, enabled by a tensor-based near-field channel decomposition, while balancing complexity through SAs. Simulation results show that the proposed scheme achieves near-CRB performance with an extended unambiguous speed range. Furthermore, analysis reveals that SAs significantly reduce the computational complexity, and the resulting performance loss can be effectively mitigated by moderately increasing the bandwidth or signal duration, enabling high-accuracy, low-complexity velocity estimation.

Terahertz (THz) integrated sensing and communication (ISAC) is a promising enabler for 6G networks, offering ultra-high data rates and environment-aware capabilities. However, realizing its full potential requires accurate construction of directional THz radio maps and environment map from sparse and noisy signal measurements, which is a highly ill-posed problem. While recent generative models, particularly conditional diffusion models, show promise in radio map construction, they fail to capture the physical characteristics of THz signal propagation, limiting generalization. To address this, we propose a THz propagation-informed diffusion model that jointly generates multi-directional radio maps and infers the environment map via an image-intersection strategy. Crucially, our model embeds two novel physics-guided loss functions: the intra-beam propagation-informed loss and inter-beam environmental consistency loss, which enforce geometric and semantic fidelity on THz propagation behaviors into the training process. Simulation results demonstrate superior performance over existing methods across varying sensor densities and environment complexities.

Movable antennas (MAs) have emerged as a promising technology to achieve high data rates in wireless communications by dynamically adjusting their positions to mitigate deep fading within a given region. However, to determine the optimal MA positions, full channel state information (CSI) is required for each position within the transmit/receive movement region, which leads to extremely high channel estimation overhead. To tackle this challenge, this paper proposes a new learning-based approach to predict the optimal positions of multiple transmit MAs in a multiple-input single-output (MISO) system without explicit CSI estimation. Specifically, we show that there exists a clear mapping between the optimal MA positions and the channel power gains from a subset of locations within the transmit region to the receiver. To acquire and leverage this mapping, we train a deep neural network (DNN) via offline supervised learning and then use the pre-trained DNN to determine the optimized MA positions in real-time data transmission, based on partial power measurements within the transmit region only. Numerical results demonstrate that the proposed DNN-based method achieves near-optimal performance and significantly outperforms conventional fixed-position antenna (FPA) systems.

Active intelligent reflecting surface (AIRS) is expected to further improve wireless communication performance compared to traditional passive IRS (PIRS) due to its additional signal amplification capability. To leverage the benefits of AIRSs at the network level, we investigate a new AIRS-user association problem in a general wireless network consisting of multiple base stations (BSs), users and distributed IRSs. Specifically, each AIRS assists in the communication from its associated BS to user, while randomly scattering and amplifying interference and noise to all users. We first derive the average signal-to-interference-plus-noise ratio (ASINR) achievable at each user for any given AIRS-user associations. We then analytically compare the ASINRs of multi-AIRS and multi-PIRS networks in terms of different metrics, gaining key insights into the advantages of AIRSs over PIRSs at the network level. Furthermore, we jointly optimize AIRS-user associations to maximize the minimum ASINR among all users and propose an efficient successive refinement algorithm to obtain a high-quality suboptimal solution. Numerical results validate our performance analysis and demonstrate the superiority of our proposed algorithm over other baseline schemes.

This paper considers channel estimation for extremely large-scale intelligent reflecting surface (XL-IRS)-assisted terahertz (THz) communication systems. Specifically, an XL-IRS is deployed close to users (UEs) to enhance communication performance between the base station (BS) and UE. With its large aperture, the XL-IRS has a Rayleigh distance of tens of meters. Therefore, the users are likely located in the near-field region of the XL-IRS, while the BS is in its far-field region. Consequently, a spherical wavefront propagation model should be considered to characterize the propagation property between the XL-IRS and the UE, while the planar wavefront propagation model is utilized in the BS-IRS link. By leveraging Khatri-Rao product and Kronecker product properties, we rephrase the channel estimation problem. In addition, we construct an orthogonal dictionary, which essentially modifies the well-known Discrete Fourier Transform (DFT) matrix. We further find that the considered channel can be block-sparsely represented by this dictionary. Hence, the channel estimation can be converted into a block sparse recovery problem, which can be efficiently solved by several off-the-shelf methods. The simulation results show that our proposed method achieves better estimation performance than the conventional polar-domain-based method.

Terahertz (THz) communication systems suffer severe blockage issues, which may significantly degrade the communication coverage and quality. Bending beams, capable of adjusting their propagation direction to bypass obstacles, have recently emerged as a promising solution to resolve this issue by engineering the propagation trajectory of the beam. However, traditional bending beam generation methods rely heavily on the specific geometric properties of the propagation trajectory and can only achieve sub-optimal performance. In this paper, we propose a new and general bending beamforming method by adopting the convex optimization techniques. In particular, we formulate the bending beamforming design as a max-min optimization problem, aiming to optimize the analog or digital transmit beamforming vector to maximize the minimum received signal power among all positions along the bending beam trajectory. However, the resulting problem is non-convex and difficult to be solved optimally. To tackle this difficulty, we apply the successive convex approximation (SCA) technique to obtain a high-quality suboptimal solution. Numerical results show that our proposed bending beamforming method outperforms the traditional method and shows robustness to the obstacle in the environment.

This paper considers a single input multiple output (SIMO) integrated sensing and communication (ISAC) system, where orthogonal frequency division multiplexing (OFDM) communication signals are multiplexed to detect target locations, including the range and angle parameters. The two-dimensional multiple signal classification (2D-MUSIC) algorithm is applied to process such signals with a format of multiple subcarriers related to large arrays. However, the standard 2D-MUSIC suffers from high computational complexity and its estimation accuracy is limited by the two-dimensional grid-based exhaustive search step size. To overcome these issues, we propose a two-step improved 2D-MUSIC (I2D-MUSIC) algorithm, which replaces the original grid-based exhaustive search and performs dung beetle optimization (DBO) algorithm to get a coarse estimation of the parameters. Subsequently, a stochastic gradient descent (SGD) based method is derived to obtain fine estimation of the range and angle parameters. Simulation results and hardware-based experiments demonstrate that the proposed algorithm significantly reduces computational complexity while maintaining comparable estimation accuracy to the standard 2D-MUSIC algorithm, and its accuracy is not constrained by the search step size.

The coming industrial internet of things (IIoT) era is anticipated to see the proliferations of goal-oriented applications in real-time wireless control systems. In such systems, a low processing latency is required to guarantee the timely transmission of control information. To achieve this goal, this paper proposes a new integrated sensing, computing, communication, and control$(\text{ISC}^{3})$architecture, where semantic communications are adopted to make sensible semantic inference (SI) for the control information over time. In particular, we introduce a new criterion, i.e., mutual information (MI), for control performance evaluation by drawing from the field of wireless communications. We calculate the MI by designing a semantic feature extractor (SFE) module at the transmitter (Tx) to identify the semantic correlation among its sensed control information over time, thereby adjusting the frequency of its control information transmission. Meanwhile, a semantic feature reconstructor (SFR) module is employed at the receiver (Rx) to predict the current control information based on its previously received information if there is no control information transmission from the Tx. Finally, on-site experimental results are provided, showing that our proposed scheme can significantly reduce the communication overhead while improving the control performance significantly.

Intelligent reflecting surface (IRS) and movable antenna (MA)/fluid antenna (FA) techniques have both received increasing attention in the realm of wireless communications due to their ability to reconfigure and improve wireless channel conditions. In this paper, we investigate the integration of MAs/FAs into an IRS-assisted wireless communication system. In particular, we consider the downlink transmission from a multi-MA base station (BS) to a single-antenna user with the aid of an IRS, aiming to maximize the user's received signal-to-noise ratio (SNR), by jointly optimizing the BS/IRS active/passive beamforming and the MAs' positions. Due to the similar capability of MAs and IRS for channel reconfiguration, we first conduct theoretical analyses of the performance gain of MAs over conventional fixed-position antennas (FPAs) under the line-of-sight (LoS) BS-IRS channel and derive the conditions under which the performance gain becomes more or less significant. Next, to solve the received SNR maximization problem, we propose an alternating optimization (AO) algorithm that decomposes it into two subproblems and solve them alternately. Numerical results are provided to validate our analytical results and evaluate the performance gains of MAs over FPAs under different setups.

By providing cost-efficient flexibility beyond wired control systems, real-time wireless control systems (WCSs) are pivotal in industrial internet of things (IIoT), which can enable massive emerging applications in IIoT, e.g., autonomous driving, remote medical, teleoperation, etc. To guarantee good control performance over wireless networks, ultra-reliable and low-latency communications (URLLCs) are required from communication perspective. However, this would result in frequent high-rate data transmission, leading to extremely high resource consumption or even network congestion, which impedes the application of WCSs in IIoT. This paper proposes a novel co-design method for communication and control in WCSs, which offers a new strategy to address the challenges introduced by URLLC. Specifically, we first formulate an optimization problem aiming to minimize the communication and control cost by jointly optimizing transmission trigger, communication power, and Lyapunov cost, which, however, is NP-hard. To address this challenge, we employ a two-stage strategy by first defining a new metric, i.e, state-to-error ratio (SER), as a trigger condition to evaluate control performance. Based on this metric, we analyze the relationship between SER and signal-to-noise-ratio (SNR) and show their hidden consistency in evaluating both communication and control performance, thus facilitating our communication and control co-design. Subsequently, we establish a closed-form relationship between the control convergence rate and communication reliability and thereby obtain the optimal transmit power to ensure the overall system performance. Finally, simulation results are provided to demonstrate the efficacy of our proposed method.

In this article, we consider the problem of joint transceiver design for millimeter-wave (mmWave)/terahertz (THz) multiuser MIMO integrated sensing and communication (ISAC) systems. Such a problem is formulated into a nonconvex optimization problem, with the objective of maximizing a weighted sum of communication users’ rates and the passive radar’s signal-to-clutter-and-noise ratio (SCNR). By exploring a low-dimensional subspace property of the optimal precoder, a low-dimensional subspace property-inspired block-coordinate-descent (LS-BCD)-based algorithm is proposed with remarkably reduced computational complexity. Our analysis reveals that the hybrid analog/digital beamforming structure can attain the same performance as that of a fully digital precoder, provided that the number of radio frequency (RF) chains is no less than the number of resolvable signal paths. Also, through expressing the precoder as a sum of a communication-precoder and a sensing-precoder, we develop an analytical solution to the joint transceiver design problem by generalizing the idea of block diagonalization (BD) to the ISAC system. Simulation results show that with a proper tradeoff parameter, the proposed methods can achieve a decent compromise between communication and sensing, where the performance of each communication/sensing task experiences only a mild performance loss as compared with the performance attained by optimizing exclusively for a single task.

Movable antenna (MA) and intelligent reflecting surface (IRS) are considered promising technologies for the next-generation wireless communication systems due to their shared capabilities of reconfiguring and improving wireless channel conditions. This, however, raises a fundamental question: Does the performance gain of MAs over conventional fixed-position antennas (FPAs) still exist in the presence of the IRS passive beamforming? To answer this question, we investigate in this paper an IRS-assisted multi-user multiple-input single-output (MISO) MA system, where a multi-MA base station (BS) transmits to multiple single-FPA users. We formulate a sum-rate maximization problem by jointly optimizing the active/passive beamforming of the BS/IRS and the MA positions within a one-dimensional transmit region, which is challenging to be optimally solved. To drive essential insights, we first study a simplified case with a single user. Then, we analyze the performance gain of MAs over FPAs in the light-of-sight (LoS) BS-IRS channel and derive the conditions under which this gain becomes more or less significant. In addition, we propose an alternating optimization (AO) algorithm to solve the signal-to-noise ratio (SNR) maximization problem in the single-user case by combining the block coordinate descent (BCD) method and the graph-based method. For the general multi-user case, our performance analysis unveils that the performance gain of MAs over FPAs diminishes with typical transmit precoding strategies at the BS under certain conditions. We also propose a high-quality suboptimal solution to the sum-rate maximization problem by applying the AO algorithm that combines the weighted minimum mean square error (WMMSE) algorithm, manifold optimization method and discrete sampling method. Numerical results validate our theoretical analyses and demonstrate that the performance gain of MAs over FPAs may be reduced if the IRS passive beamforming is optimized.

Despite narrow beams with strong anti-interception capabilities, terahertz communications still face eavesdropping risks in short-range indoor networks. This paper investigates physical-layer security of downlink terahertz communications for indoor three-dimensional (3D) networks comprised of a large number of access points (APs), users, human blockages, and eavesdroppers. We propose two different artificial noise (AN)-assisted terahertz secure transmission schemes, namely the full-AN (F-AN) scheme and partial-AN (P-AN) scheme, under the nearest line-of-sight association (NLA) strategy. The F-AN scheme involves full APs emitting AN to deteriorate the reception of eavesdroppers, and the P-AN scheme selects only those APs with blocked links to the typical user to emit AN based on the unique blocking feature of terahertz. We first obtain the expression for association probability. Then, we determine the eavesdropping region covered by the 3D beam on the ground. We derive the connection outage probability and secrecy outage probability for the two schemes by calculating the Laplace transform of aggregate interference. Our results provide interesting insights into how the secrecy performance is influenced by various system parameters, including the densities of APs and blockages. Moreover, we show that the P-AN scheme outperforms the F-AN scheme regarding the average number of perfect links per unit area.

Obstacle sensing is essential for terahertz (THz) communication since the subsequent beam management can avoid THz signals blocked by the obstacles. In parallel, radio environment, which can be manifested by channel knowledge such as the distribution of received signal strength (RSS), reveals signal propagation situation and the corresponding obstacle information. However, the awareness of the radio environment for obstacle sensing is challenging in practice, as the sparsely deployed THz sensors can acquire only little a priori knowledge with their RSS measurements. Therefore, we formulate in this paper a radio environment awareness problem, which for the first time considers a probability distribution of obstacle attributes. To solve such a problem, we propose a THz-based generative radio environment awareness framework, in which obstacle information is obtained directly from the aware radio environment. We also propose a novel generative model based on conditional generative adversarial network (CGAN), where U-net and the objective function of the problem are introduced to enable accurate awareness of RSS distribution. Simulation results show that the proposed framework can improve the awareness of the radio environment, and thus achieve superior sensing performance in terms of average precision regarding obstacles’ shape and location.

Intelligent reflecting surface (IRS) can be mounted on an unmanned aerial vehicle (UAV) to enhance the coverage performance of base stations (BSs) by leveraging the UAV’s flexible and controllable deployment. However, the existing works on UAV-mounted IRSs have mainly focused on their location optimization, which may not unleash their full potential in performance enhancement in light of the UAV’s capability of three-dimensional (3D) posture control. Hence, in this paper, we consider the UAV-mounted IRS-assisted wireless communication from a BS to a remote user, aiming to jointly optimize its location and 3D orientation to maximize the user’s received signal-to-noise ratio (SNR) under the practical angle-dependent signal reflection model. However, this optimization problem is challenging to be optimally solved due to the intricate relationship between the user’s SNR and the IRS’s location and orientation. To tackle this challenge, we first prove that one-dimensional (1D) orientation suffices to achieve the optimal performance, thereby significantly simplifying the optimization problem. Next, we show that for any given IRS’s location, the optimal 1D orientation can be derived in closed form, based on which several useful insights are drawn. Furthermore, in some special cases regarding the UAV/IRS’s altitude and the BS-user distance, we also derive the UAV’s optimal location in closed form. Numerical results validate our theoretical analyses and demonstrate the superiority of the joint location and orientation optimization for the UAV-mounted IRS to its location/orientation optimization only.

Fluid antennas (FAs) and mobile antennas (MAs) are innovative technologies in wireless communications that are able to proactively improve channel conditions by dynamically adjusting the transmit/receive antenna positions within a given spatial region. In this paper, we investigate an MA-enhanced multiple-input single-output (MISO) secure communication system, aiming to maximize the secrecy rate by jointly optimizing the positions of multiple MAs. Instead of continuously searching for the optimal MA positions as in prior works, we propose to discretize the transmit region into multiple sampling points, thereby converting the continuous antenna position optimization into a discrete sampling point selection problem. However, this point selection problem is combinatory and thus difficult to be optimally solved. To tackle this challenge, we ingeniously transform this combinatory problem into a recursive path selection problem in graph theory and propose a partial enumeration algorithm to obtain its optimal solution without the need for high-complexity exhaustive search. To further reduce the complexity, a linear-time sequential update algorithm is also proposed to obtain a high-quality suboptimal solution. Numerical results show that our proposed algorithms yield much higher secrecy rates as compared to the conventional FPA and other baseline schemes.

Fluid antennas (FAs) and movable antennas (MAs) have attracted increasing attention in wireless communications recently. As compared to the conventional fixed-position antennas (FPAs), their geometry can be dynamically reconfigured, such that more flexible beamforming can be achieved for signal coverage and/or interference nulling. In this paper, we investigate the use of MAs to achieve uniform coverage for multiple regions with arbitrary number and width in the spatial domain. In particular, we aim to jointly optimize the MAs’ weights and positions within a linear array to maximize the minimum beam gain over the desired spatial regions. However, the resulting problem is non-convex and difficult to be optimally solved. To tackle this difficulty, we propose an alternating optimization (AO) algorithm to obtain a high-quality suboptimal solution, where the MAs’ weights and positions are alternately optimized by applying successive convex approximation (SCA) technique. Numerical results show that our proposed MA-based beam coverage scheme can achieve much better performance than conventional FPAs.

Intelligent reflecting surface (IRS) is deemed as a promising technology to improve the spectral and energy efficiency of wireless communications cost-effectively. In this paper, we investi-gate a double-IRS aided wideband terahertz (THz) communication system and the beam-squint effects at the two IRSs over their double-reflection line-of-sight (LoS) link. To gain useful insights into such beam-squint effects, we first analyze the performance loss incurred by applying the conventional narrowband cooperative passive beamforming (CPB) at the two IRSs in the considered wideband system, which unveils that signal nulling may frequently occur over frequency in the case of large-size IRSs. To resolve this issue, we propose in this paper a new max-min CPB design, aiming to maximize the minimum end-to-end channel power gain over the frequency band. However, this problem is non-convex and difficult to be optimally solved. To tackle this difficulty, we propose to combine the alternating optimization (AO) and alternating direction method of multipliers (ADMM) algorithms to obtain a high-quality suboptimal solution. Numerical results show that the proposed max-min CPB design can achieve much better performance than the conventional narrowband CPB design.

Terahertz (THz) wireless communications have the potential to realize ultra-high-speed data transfer with miniaturized devices for unmanned aerial vehicle (UAV) communications. Existing THz UAV channel models assume a homogeneous medium along the propagation path. However, the atmospheric turbulence due to random airflow leads to temporal and spatial inhomogeneity of the communication medium, motivating the analysis and modeling of the influence of atmospheric turbulence on $\mathbf{T H z}$ wave propagation. In this paper, we statistically modeled the attenuation effect of turbulence on THz UAV channels. Specifically, the refractive index structure constant, as a critical statistical parameter characterizing the influence of turbulence on channel medium, is first investigated. Then, the scintillation characteristic and attenuation of the THz communications caused by atmospheric turbulence are modeled, where the scintillation effect is characterized by a Gamma-Gamma distribution, and the turbulence attenuation as a function of altitude and frequency is derived. Numerical simulations on the refractive index structure constant, scintillation, and attenuation in the THz band are presented to quantitatively analyze the influence of turbulence for THz UAV channels. It is discovered that THz turbulence can lead to at most 10 dB attenuation with frequency less than 1 THz and distance less than $10 \mathbf{k m}$.

One primary challenge hindering the development of Terahertz communication is the significant non-ideal device attributes including phase noise (PN) and in-phase/quadrature-phase (I1Q) imbalance that is hard to model offline. Deep learning (DL)-based models can learn and fit channels and non-ideal characteristics by the actual transmission data. This paper proposes two novel constellation shaping methods to limit the distribution of the constellation points in the DL-based modulation for Terahertz communication, in order to resist non-ideal effects in both the training and testing stages. Simulation results show that, without any extra compensation modules, the DL-based modulation and normalization can greatly mitigate non-ideal effects such as PN and I1Q imbalance. In addition to restricted constellation distribution, the proposed normalization and corresponding training methods can learn to have diagrams with smaller peak-to-average power ratio (PAPR), and thus can potentially support a higher average transmission power.

The Terahertz band, ranging from 0.1 THz to 10 THz, is envisioned as a key technology to realize ultra-high-speed communications in 6G and beyond wireless networks, attributed to its abundant bandwidth resource. Channel measurements and link-level analysis are still missing in a typical use case of THz picocells. In this paper, using a correlation-based time domain channel sounder, channel measurement campaigns are conducted on a university street at 220 GHz. Based on the measurement results, a full portrait of channel characteristics, including path loss, shadow fading, K-factor, delay and angular spreads, as well as cluster parameters, is calculated and analyzed. Comparison with existing 3GPP standard models shows weak multipath effects and strong sparsity in the THz picocell scenario. Moreover, small-scale fading is evaluated and fitted, where a Rician distribution among other competitors shows great fitting performance. Furthermore, considering realistic THz communication links, the ergodic capacity and outage probability are analyzed. Results have shown that more than 150 Gbps channel capacity can be achieved and reliable communication links can be guaranteed for coverage up to 40 m in the THz picocell. The results and analysis in this work offer guidance for effective system design for future THz picocell communications.

Aerial-ground interference has been deemed as the main challenge to realize the cellular-connected unmanned aerial vehicle (UAV) communications. Due to the strong line-of-sight (LoS)-dominant aerial-ground channels, UAVs could cause/suffer severe interference to/from a large number of co-channel base stations (BSs) in their uplink/downlink communications. In this paper, we propose a new cooperative interference cancellation (CIC) scheme with hybrid linear and nonlinear processing for the UAV's uplink communication to mitigate its strong interference to co-channel BSs, by leveraging the local cooperation between each co-channel BS and its adjacent BSs. In particular, the helping BSs quantize and forward their received signal from the UAV to the co-channel BS, which can combine these quantized signals with its own received signal to decode its served terrestrial user's message via either linear or nonlinear interference cancellation, which achieves the best rate performance of the UAV and terrestrial users under different conditions. To exploit their complementary benefits, we aim to select an optimal subset of co-channel BSs to perform the linear/nonlinear interference cancellation and optimize the UAV's transmit power over its assigned resource blocks (RBs) to maximize the weighted sum rate of the UAV and all co-channel BSs. As this problem is non-convex and difficult to be optimally solved, we propose an alternating optimization (AO) algorithm to obtain a high-quality suboptimal solution. Numerical results show that our proposed CIC scheme achieves better performance than other baseline CIC schemes.

By driving automation devices to achieve allocated tasks, real-time wireless feedback control systems can be treated as a key enabler for task-orientated applications in Industrial Internet of Things (IIoT). In such a system, timely information from wireless communication aspect is one of the most important components affecting control performance, e.g., control stability and tracking error, where the effect is determined by information update policy. In this article, we propose a novel information update scheduling policy for the aforementioned systems. Specifically, based on the existing research on the Age of Information (AoI) evaluating the timeliness of the information, we analyze the relationship between AoI and control performance, where we find that minimizing AoI is not always equivalent to maximizing control performance. Then, we adopt two metrics, i.e., Age of Stale Information (AoSI) and the Value of Information (VoI), to evaluate the timeliness of information update for stochastically stable control and VoI is for completely stable control, respectively. More importantly, we analyze the relationship between AoSI and VoI, where minimizing AoSI is a subset of maximizing VoI. Then, VoI is adopted as the unique metric for both the aforementioned systems. Finally, we propose an$\alpha $-wait policy for maximizing VoI. In such a policy, an optimal waiting time interval$\alpha $can be obtained to maximize control performance and meanwhile reduce communication resource consumption. The simulation results show that the proposed method can reduce more than 68% channel occupied probability from communication perspective and meanwhile reduce almost 20% mean-square-error (MSE) from control perspective compared with conventional methods.

This paper investigates the joint estimation of multi-targets’ position and velocity for a terahertz multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) system operating in the near field based on tensor decomposition. The waveforms transmitted from shared antennas carry communication messages and are orthogonal to each other in the frequency domain. The estimation of the position and velocity of multiple targets in the considered near-field scenario is challenging because it involves spherical wavefronts. A signal model based on spherical wavefronts enables higher resolution on spatial position, which, if properly designed, can be used to improve the estimation accuracy. In this paper, we propose a CANDE-COMP/PARAFAC (CP) decomposition-based near-field localization (CP-NFL) algorithm for the joint estimation of the position and velocity of multiple targets. In our proposed method, the received signal is expressed as a third-order tensor; based on its factor matrices we convert the original non-convex optimization problem into a convex one and solve it with CVX tools. Our analysis reveals that the uniqueness in CP decomposition can be guaranteed and the computational complexity of our proposed method is linear to the sum of the third powers of the number of sub-carriers, OFDM symbols, antennas, and targets. Numerical results show that our proposed method has a clear advantage over the existing method in terms of estimation accuracy and computational complexity.

Abstract Near-field technology is increasingly recognized due to its transformative potential in communication systems, establishing it as a critical enabler for sixth-generation (6G) telecommunication development. This paper presents a comprehensive survey of recent advancements in near-field technology research. First, we explore the near-field propagation fundamentals by detailing definitions, transmission characteristics, and performance analysis. Next, we investigate various near-field channel models—deterministic, stochastic, and electromagnetic information theory based models, and review the latest progress in near-field channel testing, highlighting practical performance and limitations. With evolving channel models, traditional mechanisms such as channel estimation, beamtraining, and codebook design require redesign and optimization to align with near-field propagation characteristics. We then introduce innovative beam designs enabled by near-field technologies, focusing on non-diffractive beams (such as Bessel and Airy) and orbital angular momentum (OAM) beams, addressing both hardware architectures and signal processing frameworks, showcasing their revolutionary potential in near-field communication systems. Additionally, we highlight progress in both engineering and standardization, covering the primary 6G spectrum allocation, enabling technologies for near-field propagation, and network deployment strategies. Finally, we conclude by identifying promising future research directions for near-field technology development that could significantly impact system design. This comprehensive review provides a detailed understanding of the current state and potential of near-field technologies.

Terahertz (THz) wireless communications have the potential to realize ultra-high-speed and secure data transfer with miniaturized devices for unmanned aerial vehicle (UAV) communications. The atmospheric turbulence due to random airflow leads to spatial inhomogeneity of the communication medium, which is yet missing in most existing studies, leading to additional propagation loss and even loss of spatial coherence (LoSC) in MIMO systems. In this paper, the attenuation and loss of spatial coherence for atmospheric turbulence are modeled in THz UAV MIMO channels. Specifically, the frequency- and altitude-dependency of the refractive index structure constant (RISC), as a critical statistical parameter characterizing the intensity of turbulence, is first investigated. Then, the LoSC, fading, and attenuation caused by atmospheric turbulence are modeled, where the turbulence-induced fading is modeled by a Gamma-Gamma distribution, and the turbulence attenuation as a function of altitude and frequency is derived. Numerical results show that the turbulence leads to at most 10 dB attenuation with frequency less than 1 THz and distance less than 10 km. Furthermore, when the distance is 10 km and the RISC is 10-9m-2/3, the loss of spatial coherence effect leads to 10 dB additional loss for a 1024 × 1024 ultra-massive MIMO system.

Intelligent reflecting surface (IRS) can be densely deployed in complex environments to create cascaded line-of-sight (LoS) links between base stations (BSs) and users, which significantly enhance the signal coverage for both wireless information transfer and wireless power transfer (WPT). In this paper, we consider the WPT from a multi-antenna BS to multiple energy users (EUs) by exploiting the signal beam routing via multi-IRS reflections. First, we present a baseline beam routing scheme with each IRS serving at most one EU, where the BS transmits wireless power to all EUs simultaneously while the signals to different EUs undergo disjoint sets of multi-IRS reflection paths. Under this setup, we aim to tackle the joint beam routing and resource allocation optimization problem by jointly optimizing the reflection paths for all EUs, the active/passive beamforming at the BS/each involved IRS, as well as the BS’s power allocation for different EUs to maximize the minimum received signal power among all EUs. Next, to further improve the WPT performance, we propose two new beam routing schemes, namely dynamic beam routing and subsurface-based beam routing, where each IRS can serve multiple EUs via different time slots and different subsurfaces, respectively. In particular, we prove that dynamic beam routing outperforms subsurface-based beam routing in terms of minimum harvested power among all EUs. In addition, we show that the optimal performance of dynamic beam routing is achieved by assigning all EUs with orthogonal time slots for WPT. A clique-based optimization approach is also proposed to solve the joint beam routing and resource allocation problems for the baseline beam routing and proposed dynamic beam routing schemes. Numerical results are finally presented, which demonstrate the superior performance of the proposed dynamic beam routing scheme to the baseline scheme.

The codebook-based analog beamforming is appealing for future terahertz (THz) communications since it can generate high-gain directional beams with low-cost phase shifters via low-complexity beam training. However, conventional beamforming codebook design based on array response vectors for narrowband communications may suffer from severe performance loss in wideband systems due to the “beam squint” effect over frequency. To tackle this issue, we propose in this paper a new codebook design method for analog beamforming in wideband THz systems. In particular, to characterize the analog beamforming performance in wideband systems, we propose a new metric termed wideband beam gain, which is given by the minimum beamforming gain over the entire frequency band given a target angle. Based on this metric, a wideband analog beamforming codebook design problem is formulated for optimally balancing the beamforming gains in both the spatial and frequency domains, and the performance loss of conventional narrowband beamforming in wideband systems is analyzed. To solve the new wideband beamforming codebook design problem, we divide the spatial domain into orthogonal angular zones each associated with one beam, thereby decoupling the codebook design into a zone division sub-problem and a set of beamforming optimization sub-problems each for one zone. For the zone division sub-problem, we propose a bisection method to obtain the optimal boundaries for separating adjacent zones. While for each of the per-zone-based beamforming optimization sub-problems, we further propose an efficient augmented Lagrange method (ALM) to solve it. Numerical results demonstrate the performance superiority of our proposed codebook design for wideband analog beamforming to the narrowband beamforming codebook and also validate our performance analysis.

The ultra-wide Terahertz (THz) band with jointly high-speed transmission and precise sensing has come into vision to realize integrated sensing and communication (ISAC) for emerging immersive applications. However, THz networks face a coverage bottleneck. Narrow beams are exploited to compensate for the limited signal power and path loss. But they bring in beam misalignment that degrades link connectivity and affects the THz network coverage, characterized by coverage probability. ISAC-THz networks can benefit from the sensing-aided beam alignment to improve the coverage probability. But there exists a trade-off between sensing assistance and its cost, that requires efficient resource allocation. This paper provides time-frequency resource allocation for sensing signal mapping schemes that maximize the coverage probability of the ISAC-THz networks with reduced sensing costs. Results show the effectiveness of the scheme in reducing the sensing cost with near-ideal coverage. We reveal design insights into the sensing signal insertion and preferable THz transmission band selection that achieves the desired coverage with the least sensing overhead. Wider coverage requires more sensing resources, which are more allocated to bandwidth for accurate long-range sensing. The high angular resolution of narrow beams helps reduce the sensing cost, sparing resources in the time domain for velocity estimation.

Due to the ultra-high transmission rate and resolution, integrated sensing and communications (ISAC) is promising to be achieved in terahertz (THz) frequency, which is treated as one of the most important technologies to enabled the development of autonomous systems in the coming sixth generation cellular communications (6G). In this paper, we investigate orthogonal time frequency space (OTFS) waveform-based ISAC in THz multi-input multi-output (MIMO) communications. To deal with the extremely high complexity in the traditional methods, we propose a tensor decomposition based THz channel estimation method in OTFS for ISAC with significantly low algorithm complexity. Then, the real-time requirement for the decision making based on ISAC in autonomous systems can be guaranteed. Specifically, we consider the THz downlink transmission from a base station (BS) to a mobile station (MS), where both of them are equipped with large-scale antenna arrays. OTFS modulation waveform is adopted for synchronous communication and sensing. Considering the sparse THz channel and the multidimension of the received multichannel signals, we propose an OTFS channel parameter estimation method based on CANDECOMP/PARAFAC (CP) decomposition, where the received signal is expressed as a third-order tensor. The tensor has a form of a low-rank CP decomposition, and satisfies the uniqueness of CP decomposition. Then, the estimated value of the channel parameter estimation can be obtained by the factor matrix obtained with CP decomposition. In addition, we analyze the algorithm complexity of the proposed method. Simulation results show the performance of the proposed method.

Integrated sensing and communication (ISAC) in terahertz (THz) networks for delay-sensitive services is regarded as a key enabler for future 6G networks, with ISAC helping to aid beam alignment and improve communication reliability in THz networks. Nevertheless, in ISAC systems, there exists resource and performance tradeoffs amongst sensing, communication and computation. For instance, while better sensing accuracy adds information to aid communication beam alignment, it reduces the temporal resources available for communication. Therefore, in this paper we present a joint sensing, communication and computation model, with a non-uniform frame structure allowing the sensing and communication time to be flexibly adjusted. Based on our model, we formulate an average communication outage probability minimization problem, which optimizes the time and carrier frequency allocation schemes, given constrained resources. We then derive in closed-form the available communication outage probability subject to a given delay threshold, decoupling the computation sub-problem from the original problem. Based on this result, we reformulate the original problem into a joint resource allocation problem between sensing and communication, and solve it by applying the Hungarian algorithm. Numerical results show that our strategy outperforms existing baselines in the average communication outage probability performance.

Edge devices in existing systems mainly rely on cloud-based services to conduct the semantic understanding, which brings in unnecessary communication overhead and slower response. This paper proposes a novel semantic communication framework for joint semantic transmission and understanding at the network edge, wherein the structured semantic information such as the user intent and slot values that can be understood by machines are extracted directly from the received features without recovering the explicit user input. To achieve this target, we propose to use the slot name as the initial information to generate slot values by a lightweight recurrent neural network. An additional attention mechanism is adopted to integrate the semantic information in the slot value generation process and a hybrid loss is proposed to train the intent prediction and the slot value generation in parallel. Experimental results show that, compared to the conventional separate bit transmission and se-mantic understanding technologies, the proposed system achieves a much better performance under low SNR channels, and greatly reduces the computing overhead at the receiver end and thus lowers the threshold for the local semantic understanding at the edge.

Terahertz (THz) communication is expected to be one of the core enabling technologies for future systems. Due to the poor scattering and severe reflection loss of THz waves, the line-of-sight (LoS) communication is considered as a leading feature in THz multiple-input–multiple-output (MIMO) systems. To realize LoS communication, beam training is a promising scheme to find the beamforming vectors without leveraging explicit channel state information (CSI). In this context, a crucial issue for THz MIMO is how to design the beam codewords for realizing any expected radiation pattern during the training. In particular, the narrow beams can be realized by array response vectors whereas the wide-beam design is still an open problem. In this article, we propose two high-quality algorithms, namely, successive convex approximation (SCA)-based auxiliary target pursuit (SCA-ATP) and the sum of symmetrical array response vectors (S-SARVs), for offline design and real-time design, respectively. Numerical results show that SCA-ATP yields the best performance in terms of the beam-pattern error (BPE) compared with benchmarks, and S-SARV can achieve a close performance to SCA-ATP with low computational complexity.

Terahertz (THz) communications are envisioned as a key technology of next-generation wireless systems due to its ultra-broad bandwidth. One step forward, THz integrated sensing and communication (ISAC) system can realize both unprecedented data rates and millimeter-level accurate sensing. However, THz ISAC meets stringent challenges on waveform and receiver design to fully exploit the peculiarities of THz channel and transceivers. In this work, a sensing integrated discrete Fourier transform spread orthogonal frequency division multiplexing (SI-DFT-s-OFDM) system is proposed for THz ISAC, which can provide lower peak-to-average power ratio than OFDM and is adaptive to flexible delay spread of the THz channel. Without compromising communication capabilities, the proposed SI-DFT-s-OFDM realizes millimeter-level range estimation and decimeter-per-second-level velocity estimation accuracy. In addition, the bit error rate (BER) performance is improved by 5 dB gain at the 10°3 BER level compared with OFDM. At the receiver, a deep learning based ISAC receiver with two neural networks is developed to recover transmitted data and estimate target range and velocity, while mitigating the imperfections and non-linearities of THz systems. Extensive simulation results demonstrate that the proposed deep learning methods can realize mutually enhanced performance for communication and sensing, and is robust against Doppler effects, phase noise and multi-target estimation.

The combination of cell-free massive multiple-input multiple-output (CF-mMIMO) and reconfigurable intelligent surface (RIS) is envisioned as a promising paradigm to improve network capacity and enhance coverage capability. However, to reap full benefits of RIS-aided CF-mMIMO, the main challenge is to efficiently design cooperative beamforming (CBF) at base stations (BSs), RISs, and users. Firstly, we investigate the fractional programing to convert the weighted sum-rate (WSR) maximization problem into a tractable optimization problem. Then, the alternating optimization framework is employed to decompose the transformed problem into a sequence of subproblems, i.e., hybrid BF (HBF) at BSs, passive BF at RISs, and combining at users. In particular, the alternating direction method of multipliers algorithm is utilized to solve the HBF subproblem at BSs. Concretely, the analog BF design with unit-modulus constraints is solved by the manifold optimization (MO) while we obtain a closed-form solution to the digital BF design that is essentially a convex least-square problem. Additionally, the passive BF at RISs and the analog combining at users are designed by primal-dual subgradient and MO methods. Moreover, considering heavy communication costs in conventional CF-mMIMO systems, we propose a partially-connected CF-mMIMO (P-CF-mMIMO) framework to decrease the number of connections among BSs and users. To better compromise WSR performance and network costs, we formulate the BS selection problem in the P-CF-mMIMO system as a binary integer quadratic programming (BIQP) problem, and develop a relaxed linear approximation algorithm to handle this BIQP problem. Finally, numerical results demonstrate superiorities of our proposed algorithms over baseline counterparts.

Terahertz (THz) integrated sensing and communication (ISAC) is a promising interdisciplinary technology that realizes simultaneously transmitting Terabit-per-second (Tbps) and millimeter-level accurate environment or human activity sensing. However, both communication performance and sensing accuracy are influenced by the Doppler effects, which are especially severe in the THz band. Moreover, peak-to-average power ratio (PAPR) degrades the THz power amplifier (PA) efficiency. In this paper, a discrete Fourier transform spread orthogonal time frequency space (DFT-s-OTFS) system with superimposed pilots is proposed to improve the robustness to Doppler effects and reduce PAPR for THz ISAC. Then, a two-phase sensing parameter estimation algorithm is developed to integrate sensing functionality into the DFT-s-OTFS waveform. Meanwhile, a low-complexity iterative channel estimation and data detection method with a conjugate gradient based equalizer is proposed to recover the data symbols of DFT-s-OTFS. The proposed DFT-s-OTFS waveform can improve the PA efficiency by 10% on average compared to OTFS. Simulation results demonstrate that the proposed two-phase sensing estimation algorithm for THz DFT-s-OTFS systems is able to realize millimeter-level range estimation accuracy and decimeter-per-second-level velocity estimation accuracy. Moreover, the effectiveness of the iterative method for data detection aided by superimposed pilots in DFT-s-OTFS systems is validated by the simulations and the bit error rate performance is not degraded by the Doppler effects.

Image compression and bit transmission are con-ducted separately in most existing methods for image trans-mission, leading to possible transmission failure or a waste of communication resource for a time-varying channel condition. This paper proposes a neural network-based image transmission system trained by generative adversarial networks (GANs) aiming to achieve robust transmission. Specifically, the deep semantic of an input image is extracted and represented as bit streams at the transmitter, and the receiver reconstructs the original image based on possible bit error and the same background knowledge as the transmitter. Experimental results show that the proposed robust transmission system trained by GAN can adapt to the current communication condition, and achieve a high-quality reconstruction even with a high transmission error rate and a smaller transmission data size than engineered codecs such as JPEG.

Array-of-subarrays (AoSA) is an appealing architecture in terahertz (THz) communications since the analog beamformers on sub arrays can provide beam gain to combat severe propagation loss, by low-cost phase shifters. However, the traditional beamforming scheme for AoSA, i.e., each subarray serves an exclusive user, cannot cope with the effect of beam split in THz wideband communications. In this paper, we propose a novel concept, i.e., beam split aggregation and multiplexing (BSAM), to support wideband THz communication with AoSA architecture. Specifically, we first characterize the direction of beam split and then derive the maximum bandwidth of a subband that will not cause beam split. Finally, based on the above results, we propose a criterion to plan the subbands and design the analog beamformers for BSAM.

Terahertz (THz) communications are expected to provide ultra-high transmission rate to enable connected automatic guided vehicles (C-AGV) in future smart factories for the industrial internet of things (IIoT). However, beam alignment is extremely challenging due to its narrow beam-width and the fast mobility of C-AGV. To deal with this issue, we propose a novel communication-aware motion control scheduling method for THz beam alignment in this paper. Specifically, the accuracy state-feedback of the C-GAV’s motion control is very high, which has the potential to provide the necessary information for beam alignment, e.g., the location of the receiver. However, the feedback needs to be carefully designed not to increase the control consumption and meanwhile to maintain the beam alignment for the required transmission data rate. Thus, this paper adopts transmission data rate as the activation criterion for motion control of C-AGV, where the feedback is synchronous with the motion control activation. Based on that, the beam alignment maintaining the required transmission data rate can be maintained. Meanwhile, the control consumption can be significantly reduced, and control stability can be guaranteed. Finally, we obtain a closed-form expression for the threshold of the transmission data rate. Simulation results show remarkable performance gain of the proposed method.

The sum-rate maximization for intelligent reflecting surfaces (IRS)-aided multi-user MIMO is a recent open problem. The challenge lies in the coefficient designs for reflecting phase shifts (at the IRS) and precoder/decoders (at the BS/users). By imposing two additional constraints, i.e., 1) each IRS only serves one user, 2) no interference exists between users, this paper proposes a novel space-orthogonal scheme for multiple IRSs- aided multi-user MIMO in millimeter wave (mmWave) and terahertz (THz) communications. Based on a new zero-interference criterion, we can successively find high-quality solutions for the IRSs' phase shifts and precoder/decoders one by one. Specifically, we first propose a null-space singular value decomposition (SVD) approach to determine a part of the precoder/decoders. Then, two solutions are developed for IRSs’ phase shifts, namely, the segment matching (SM) and the phase iterative evolution (PIE) solutions. Finally, the remanent part of the precoder/decoders are calculated by SVD with water-filling under the zero-interference constraint. Numerical results demonstrate the effectiveness and superiority of our proposed scheme.

Terahertz (THz) integrated sensing and communication (ISAC) is a promising interdisciplinary technology that realizes simultaneously transmitting Terabit-per-second (Tbps) and millimeter-level accurate environment or human activity sensing. However, both communication performance and sensing accuracy are influenced by the Doppler effects and peak-to-average power ratio (PAPR), which are especially severe in the THz band. In this paper, a discrete Fourier transform spread orthogonal time frequency space (DFT-s-OTFS) system for THz ISAC is proposed with a two-stage sensing parameter estimation algorithm. The proposed sensing algorithm can realize millimeter-level range estimation accuracy and decimeter-per-second velocity estimation accuracy. Moreover, the proposed DFT-s-OTFS can improve the power amplifier efficiency by 10% on average compared to OTFS and enhance the sensing accuracy by one order of magnitude and the bit error rate performance by two orders of magnitude in high-mobility scenarios in contrast with orthogonal frequency division multiplexing (OFDM) and discrete Fourier transform spread OFDM (DFT-s-OFDM).

In Industrial Internet of Things (IIoT), the conventional event-triggered control method requires sensors continuously monitoring control states in periodical time. This would consume a huge amount of energy resource, which impedes the application of such conventional methods since the sensors are powered by batteries in most cases. To deal with this issue, this article proposes a new state-to-noise-ratio (SNR)-based transmission scheduling method from a wireless communication perspective. Specifically, we first adopt an additive white Gaussian noise (AWGN) channel model to represent the event-triggered control model, by which we can convert the original event-triggered control problem into a wireless transmission scheduling problem from the sensor to the controller. Based on that, the mutual information is proposed to measure the value of the control states. More importantly, a new control SNR is defined based on mutual information. Using control SNR as the transmission activating criterion, the perfect state observation assumption and sensor’s monitoring with periodical time in traditional event-triggered control are no longer needed. Furthermore, we provide an analytical expression of dynamic SNR thresholds to determine whether the transmission should be activated. Finally, we prove the mean-square stability of our proposed method. Numerical comparisons are provided to demonstrate the effectiveness of our proposed method.

As promising candidate frequency bands, millimeter wave (mmWave) and terahertz (THz) communications can provide ultrahigh transmission rate to enable vehicle-to-everything (V2X) networks for connected autonomous vehicles (CAVs). However, beam alignment is extremely challenging in mmWave/THz communications due to its narrow beam width and fast mobility of CAV. In this article, we propose a new joint communication and control algorithm for beam alignment, where the mutual positive effect of communications and motion control of CAV on each other is discussed. Specifically, we first provide a framework to show the interaction between motion control of CAV and beam alignment of transmission from base station (BS) to CAV. Then, we analyze the effect of CAV control on beam alignment in communications, where a theorem is obtained to show the closed-form expression of their relationship. Finally, we discuss the CAV control design affected by beam alignment. Simulation results show remarkable performance of the proposed method.

By providing ultra-high transmission data rate, millimeter wave (mmWave) and terahertz (THz) communications are promising to enable backhaul data transmission in cellular connected unmanned aerial vehicle (UAV) networks. In such networks, with little or no human assistance, connected autonomous UAVs (CA-UAV) can build air-ground networks and achieve seamless wide-area coverage. With the usage of high frequency (i.e., mmWave/THz), radio/radar sensing function is expected to be achieved in wireless networks, which can be used to track UAV for beam tracking in mmWave/THz communications and motion control of UAV. However, it is extremely difficult to jointly design sensing, communication, and motion control since they have been developing in relatively parallel with limited intersections. In this paper, we propose a new integrated scheduling method of sensing, communication, and control for mmWave/THz communications in UAV networks to enable data transmission of the backhaul from UAV to the ground base station (BS). In the proposed method, we first analyze the interactions among sensing, communication, and motion control, where sensing and motion control are strongly coupled to form the sensing-control pattern. Then, we provide a new definition from motion control perspective, i.e., state-to-noise-ratio, which links the relationship between sensing-control pattern activation and data rate determined by beam alignment in mmWave/THz communications. Finally, a closed-form expression is obtained for data rate triggered sensing-control pattern activation design, where both data rate requirement in mmWave/THz communications and motion control performance of UAV are guaranteed. Simulation results show remarkable performance of the proposed method.

Multiple-input multiple-output (MIMO) and intelligent reflecting surface (IRS) are two appealing technologies in millimeter-wave (mmWave) and terahertz (THz) communications. The challenge of combining these two technologies lies in joint design for active beamforming (at the base-station (BS)/users) and passive beamforming (at the IRSs). In this paper, we consider a multi-IRS-aided multi-user MIMO scenario and propose a novel space-orthogonal scheme by applying zero-forcing techniques. Specifically, we first propose a multi-IRS-based zero-interference criterion, under which multi-user interference can be eliminated regardless of the IRS’s phase shifts. Based on this criterion, we decompose the precoder/decoder matrix into a product of two matrices, with one of them devised for interference cancellation and the other one of them devised for achievable rate maximization. Next, an approximate space-orthogonal technique referred to as partial zero-forcing (IRS-PZF) is proposed for proposed for devising the former matrix whose objective is to cancel the multi-user interference; while two efficient phase-shift schemes are proposed for the IRS passive beamforming, namely, water-filling segment matching (WSM) and phase iterative evolution (PIE), which balance between performance and complexity. Finally, we calculate the latter matrix of the precoder/decoder by applying the singular value decomposition (SVD) for the effective BS-user channels, so as to maximize the users’ achievable rates. Numerical results demonstrate the effectiveness and superiority of our proposed scheme compared with the benchmarks.

Benefiting from tens of GHz of bandwidth, terahertz (THz) communication has become a promising technology for future 6G network. To deal with the serious propagation loss of THz signals, massive multiple-input multiple-output (MIMO) with hybrid precoding is utilized to generate directional beams with high array gains. However, the standard hybrid precoding architecture based on frequency-independent phase-shifters cannot cope with the beam split effect in THz massive MIMO caused by the large bandwidth and the large number of antennas, where the beams split into different physical directions at different frequencies. The beam split effect will result in a serious array gain loss across the entire bandwidth, which has not been well investigated in THz massive MIMO. In this paper, we first quantify the seriousness of the beam split effect in THz massive MIMO by analyzing the array gain loss it causes. Then, we propose a new precoding architecture called delay-phase precoding (DPP) to mitigate this effect. Specifically, the proposed DPP introduces a time delay network composed of a small number of time delay elements between radio-frequency chains and phase-shifters in the standard hybrid precoding architecture. Unlikefrequency-independentphase shifts, the time delay network introduced in the DPP can realizefrequency-dependentphase shifts, which can be designed to generate frequency-dependent beams towards the target physical direction across the entire bandwidth. Due to the joint control of delay and phase, the proposed DPP can alleviate the array gain loss caused by the beam split effect. Furthermore, we propose a hardware structure by using true-time-delayers to realize frequency-dependent phase shifts for realizing the concept of DPP. A corresponding precoding algorithm is proposed to realize the precoding design. Theoretical analysis and simulations show that the proposed DPP can mitigate the beam split effect and achieve near-optimal rate with higher energy efficiency.

Despite the narrowbeam nature of Terahertz (THz) communications, the physical layer security in the THz band is challenging when eavesdroppers are inside the beam radiation sector. In this paper, a deep neural network (DNN)-based self-interference cancellation (SIC)-free receiver artificial noise (AN) assisted mechanism is proposed to address the in-beam security challenge, by considering randomly distributed eavesdroppers in THz secure communications. By exploiting the different temporal broadening effects of the AN signals at distinct distances, the SIC can be saved with a proper signal detection design rather than using conventional high-complexity cancellation techniques. To combat the non-linearity and non-convexity of the optimization problem, the system parameters including carrier frequency, power of transmission signal and AN signal power, and frame time are designed by an efficient deep neural network (DNN) algorithm to minimize the secrecy outage probability. Numerical results demonstrate that the maximum secrecy rate of our proposed DNN-powered SIC-free receiver AN scheme is up to 3.3 bps/Hz over 10 m transmission when the eavesdropper is in close proximity. Moreover, the secrecy outage probability is less than 0.5% when the eavesdropper density is 0.001 per square meter, which is approximately 33% lower than that of conventional transmitter AN schemes.

Terahertz (THz) communication is considered as an attractive way to overcome the bandwidth bottleneck and satisfy the ever-increasing capacity demand in the future. Due to the high directivity and propagation loss of THz waves, a massive MIMO system using beamforming is envisioned as a promising technology in THz communication to realize high-gain and directional transmission. However, pilots, which are the fundamentals for many beamforming schemes, are challenging to be accurately detected in the THz band owing to the severe propagation loss. In this paper, a unified 3D beam training and tracking procedure is proposed to effectively realize the beamforming in THz communications, by considering the line-of-sight (LoS) propagation. In particular, a novel quadruple-uniform planar array (QUPA) architecture is analyzed to enlarge the signal coverage, increase the beam gain, and reduce the beam squint loss. Then, a new 3D grid-based (GB) beam training is developed with low complexity, including the design of the 3D codebook and training protocol. Finally, a simple yet effective grid-based hybrid (GBH) beam tracking is investigated to support THz beamforming in an efficient manner. The communication framework based on this procedure can dynamically trigger beam training/tracking depending on the real-time quality of service. Numerical results are presented to demonstrate the superiority of our proposed beam training and tracking over the benchmark methods.

Terahertz (THz) communication is capable of providing ultra-wide bandwidth and high data rates. Therefore attracts widespread attention to its applications in next-generation networks. Highly directional antennas are used to compensate for the THz propagation loss, which also incurs beam management challenges. Specifically, caused by node mobility and blockage, frequent beam reselections and beam misalignment greatly degrade THz network performance in terms of reliability and spatial throughput. In this paper, using stochastic geometry, we fill the current research gap in system-level theoretical models for the analysis of beam misalignment and network spatial throughput by considering the effects of beamwidth, mobility, blockage, and molecular absorption. Our analyses show that an increase in nodes density or user mobility often results in severe beam misalignment, which in turn requires more signaling overhead and degrades THz network reliability and throughput. Although using wider beams reduces this impact, it increases THz network sensitivity to molecular absorption. To maximize spatial throughput, optimal beamwidth needs to be adjusted according to communication demand priority and network status. Our work provides useful insights into beamwidth adaptation according to parameters trade-off that helps THz network achieve higher reliability and throughput in different applications.

In this paper, we propose to apply intelligent reflecting surface (IRS) to the physical-layer service integration (PHY-SI) system, where a single-antenna access point (AP) integrates two sorts of service messages, i.e., multicast message and confidential message, via superposition coding to serve multiple single-antenna users. Our goal is to optimize the power allocation (for transmitting different messages) at the AP and the passive beamforming at the IRS to maximize the achievable secrecy rate region. To this end, we formulate this problem as a bi-objective optimization problem. To tackle the non-convexity of this problem, we propose a Charnes-Cooper transformation (CCT)-based algorithm to obtain its high-quality suboptimal solutions, thereby approximately characterizing the secrecy rate region. Numerical results demonstrate the advantages of leveraging IRS in improving the performance of PHY-SI.

Terahertz (THz) communications are envisioned as a key technology of next-generation wireless systems due to its ultra-broad bandwidth. By integrating communication and sensing, the the THz joint communication and sensing (JCS) system can realize both unprecedented data rates and ubiquitously accurate sensing. However, THz JCS meets stringent challenges due to the peculiarities of THz channel and devices. In this work, a sensing integrated discrete Fourier transform spread orthogonal frequency division multiplexing (SI-DFT-s-OFDM) system is proposed for THz JCS. The proposed system is able to provide lower peak-to-average power ratio than OFDM. Without compromising communication capabilities, SI-DFT-s-OFDM can realize millimeter-level range estimation accuracy and improve the velocity estimation accuracy by 10 times than OFDM. Finally, SI-DFT-s-OFDM achieves 18% improvement of data rate over DFT-s-OFDM.

Recently researchers have been attracted by the joint communication and sensing (JCS) techniques, due to their diverse benefits in communication-sensing applications. By sharing the spectrum and hardware components, the advantages of JCS systems include enhanced spectrum efficiency and reduced device costs. Following the trend of scaling up the carrier frequencies for 5G and beyond, Terahertz (THz) band is envisioned as a promising chunk of spectrum featuring multi-GHz bandwidth windows. However, despite the great promise, the waveform design for THz JCS is still not explored enough. In this work, the design guidelines for THz JCS waveform are presented. With the help of these guidelines, a non-uniform multi-wideband orthogonal frequency division multiplexing (NMW-OFDM) THz system is proposed to overcome the limitations of OFDM based JCS. The proposed NMW-OFDM system is able to realize sub-millimeter-level accuracy of the range estimation by using a multi-stage sensing algorithm, which is three orders of magnitude improvement compared to the OFDM-based JCS system. In addition, the accuracy of velocity estimation can be enhanced by 10 times within a short frame time. Finally, the system can achieve unprecedented communication data rate of 100 Gbps without sacrificing the maximum detectable distance.

The proliferation of Industrial Internet of Things (IIoT) applications has significantly boosted the need for fresh information to guarantee good real-time performance. To characterize the freshness of information in time-critical systems, the concept of the age of information (AoI) has been proposed. Based on minimizing AoI, most research has designed information update policies from the perspective of the source to keep data fresh. In this work, we find that actively discarding some packets at the receiver can reduce AoI as well, which motivates us to develop this idea. By introducing a packet dropping threshold at the receiver, we formulate an AoI minimization problem. Then, we propose a heuristic algorithm to solve this problem. Finally, simulation results demonstrate that the proposed approach has priority on decreasing the average AoI.

Fueled by ubiquitous connectivity, packets are expected to be timely updated to the controller of interest in real-time control systems. Recently, the Age of Information (AoI) becomes a popular metric to quantify the packet importance, which improves the efficiency of communication resource utilization. However, it is analyzed only from the information freshness perspective, losing sight of considering feature data. In light of this, we first establish a feature-oriented teleoperation framework to quantify the packet importance, derived from the information bottleneck principle. Under this framework, a packet management method is proposed to increase the average feature quantity of the receiver. Finally, we build a prototype to deploy the proposed method, and the results show superiority in reducing both the communication traffic and the control error.

Abstract The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

Large-scale millimeter-wave (mm-Wave) integrated phased array is the key technology to enable broadband 5G and satellite communications. This paper details the design considerations, challenges and trade-offs of mm-Wave integrated phased arrays based on bulk CMOS and multi-layer hybrid PCB technologies. Both technologies attain high yield and low cost for mass production. Important beamforming building blocks are addressed and compared in detail. Demonstrators of integrated phased arrays are presented from circuit to board levels. The 1024- and 4096-element integrated phased arrays achieve the EIRP of 72.5 and 84.0 dBm respectively. Finally, relevant phased-array transceivers and antennas from the recent literature are discussed.

In industrial internet of things (IIoT), ultra-reliable and low-latency communication (URLLC) is proposed to guarantee the requirement of real-time wireless control systems in worst case, so as to maintain the system working in all cases. However, it is extremely challenging to maintain URLLC throughout the whole control process due to the scarcity of wireless resource. This paper develops an autonomous device-to-device (D2D) communication scheme by jointly considering reliability in URLLC and control requirement. In the proposed scheme, we consider the actual control requirement, i.e., control convergence rate, into communication design, where we find that it can be converted into a constraint on communication reliability. Then, the communication reliability constraint comes from control aspect, instead of URLLC, which leads to that the system does not need to guarantee worst case in URLLC. Second, the sensors autonomously decide whether to be activated with optimal probabilities to participate in the control process, which can maintain the communication reliability requirement with significantly less resource consumption. Simulation results show remarkable performance gain of our method. For instance, compared with fixed activation probability 40% only considering URLLC, the average power consumption of the proposed method can be reduced by at most about 100%.

This paper considers a scenario in which an access point (AP) is equipped with a server of finite computing power, and serves multiple resource-hungry users by charging users a price. This price helps to regulate users' behavior in offloading jobs to the AP. However, existing works on pricing are based on abstract concave utility functions, giving no dependence on physical layer parameters. To that end, we first introduce a novel utility function, which measures the cost reduction by offloading as compared with executing jobs locally. Based on this utility function we then formulate two offloading games, with one maximizing individuals interest and the other maximizing the overall systems interest. We analyze the structural property of the games and admit in closed-form the Nash Equilibrium and the Social Equilibrium for the homogeneous user case, respectively. The proposed expressions are functions of user parameters such as the weights of time and energy, the distance from the AP, thus constituting an advancement over prior economic works that have considered only abstract functions. Finally, we propose an optimal price-based scheme, with which we prove that the interactive decision-making process with self-interested users converges to a Nash Equilibrium point equal to the Social Equilibrium point.

The Terahertz (THz) band is envisioned as a promising technique to support bandwidth-hungry and secure applications. Although the significant path loss and strong directivity make THz communications secure naturally, the information security is still imperfect at near regions along the beam propagation path. In this paper, a novel distance-adaptive absorption peak modulation (DA-APM) is developed for THz covert communications, by exploring the unique spectrum features of frequency-dependent molecular absorption. Although high-attenuation molecular absorption is unfavored for communications, the main principle to enhance covertness or equivalently, minimize the eavesdroppable distance, is dynamically modulating signals under the molecular absorption peaks in the THz spectrum, where the eavesdroppable distance is defined as the threshold distance within which an eavesdropper can wiretap the transmission. Furthermore, an optimization framework is proposed to minimize the eavesdroppable distance, to which the sub-optimal solutions are derived for the multi-wideband waveform by controlling carrier frequencies, power allocation, and rate distribution on each sub-band. Extensive numerical results show that the THz-spectrum-based DA-APM approach can reduce the eavesdroppable distance by 60% compared with random spectrum selection methods, which significantly reduce the insecure area and enhance the covertness of THz wireless transmission.

Intelligent reflecting surface (IRS) has emerged as an appealing solution to enhance wireless communication performance by reconfiguring the wireless propagation environment. In this paper, we propose to apply IRS to the physical-layer service integration (PHY-SI) system, where a single-antenna access point (AP) integrates two sorts of service messages, i.e., multicast message and confidential message, via superposition coding to serve multiple single-antenna users. Our goal is to optimize the power allocation (for transmitting different messages) at the AP and the passive beamforming at the IRS to maximize the achievable secrecy rate region. To this end, we formulate this problem as a bi-objective optimization problem, which is shown equivalent to a secrecy rate maximization problem subject to the constraints on the quality of multicast service. Due to the non-convexity of this problem, we propose two customized algorithms to obtain its high-quality suboptimal solutions, thereby approximately characterizing the secrecy rate region. The resulting performance gap with the globally optimal solution is analyzed. Furthermore, we provide theoretical analysis to unveil the impact of IRS beamforming on the performance of PHY-SI. Numerical results demonstrate the advantages of leveraging IRS in improving the performance of PHY-SI and also validate our theoretical analysis.

Although with narrow-beam transmissions, physical layer security of Terahertz (THz) communications faces great challenges with the presence of eavesdroppers in close proximity inside the Terahertz beam sector. This paper proposes a novel self-interference cancellation (SIC)-free receiver artificial noise (AN) assisted mechanism to address this challenge for THz secure communications. By exploiting the different temporal broadening effects of the artificial noise signals at the receiver side versus the eavesdropper side, the high-complexity SIC part can be mitigated with a proper receiver design. Moreover, the system parameters including power allocation, carrier frequency, transmit power, symbol time, pulse waveform type, and receiver parameter that maximize the secrecy rate of the THz communication system are solved, based on an efficient deep neural network (DNN) algorithm. Numerical results demonstrate that our proposed SIC-free receiver artificial noise scheme achieves 4 bps/Hz secrecy rate, with substantially lower hardware complexity than SIC-based receiver AN systems and reduced computational complexity than exhaustive search.

Intelligent reflecting surface (IRS) is envisioned as a promising technology to broaden signal coverage and enhance transmission in terahertz (THz) communications. Due to the passivity of IRS, the channel measurement can not be achieved by traditional pilot manner and the subsequent cooperative transmission design remains an open problem. This paper investigates the channel estimation and transmission solutions for massive multiple input multiple output (MIMO) IRS-assisted THz system. The channel estimation is realized by beam training and the quantization error is analyzed for evaluating performance. In addition, a novel hierarchical search codebook design is proposed as a low-complexity basis of beam training. Based on above foundations, we propose a cooperative channel estimation procedure to tactfully acquire the channel knowledge. Finally, by leveraging obtained channel information, the designs of IRS and transceivers are directly provided in closed form without reconstructing the full channel matrix or additional optimization. Simulation and numerical results are presented to illustrate the minimum signal to noise ratio (SNR) required for beam training and the efficacy of the proposed transmission solutions.

In real-time feedback control systems, control performance, e.g., control cost and tracking error, is significantly affected by information freshness, which in turn relies heavily on the design of the feedback update policy. In this paper, we study the update policy for real-time feedback control systems. We first discuss the relationship between the age of information (AOI) and control performance, where AOI represents the level of "dissatisfaction" for information staleness. We find that minimizing AOI is not always equivalent to optimizing the control performance. Then, a new metric, called the value of information (VOI), is proposed to evaluate the timeliness of system update by linking AOI to the decay rate of the control system. By maximizing VOI, we design a new update policy, called the α - wait, which has superiorities in improving both control performance and communication cost. Finally, simulation results verify our method.

In real-time feedback control systems, the freshness of the packet is crucial to control performance, where packet management is vital to keep data fresh. Recently, age of information (AOI) has been used to measure the freshness of the update information, where minimizing AOI becomes popular in system designs. In this paper, we find that minimizing AOI is not always equivalent to maximizing the control performance. In particular, we define a metric, called the age of stale information (AOSI), to link the instability of the control system to AOI. By minimizing AOSI, we can maximize the control performance, and also reduce the communication cost.

In this paper, we consider the problem of channel estimation for uplink multiuser massive MIMO systems, where, in order to significantly reduce the hardware cost and power consumption, one-bit analog-to-digital converters (ADCs) are used at the base station (BS) to quantize the received signal. We first extend the conventional Bussgang linear minimum mean square error (BLMMSE) estimator to the general nonzero threshold case. We then study the problem of one-bit quantization design, aiming at minimizing the mean squared error of the generalized BLMMSE estimator. A set partition scheme is proposed to devise the quantization thresholds. The rationale behind the proposed scheme is to divide each antenna's received samples into a number of disjoint subsets according to their pairwise correlation and assign diverse thresholds to those highly correlated data samples. In addition to the set partition scheme, a gradient descent scheme is developed to search for optimal quantization thresholds. The proposed schemes only require the statistical information of the received signals to devise the quantization thresholds, which can be calculated in advance before the training process begins. Simulation results show that the generalized BLMMSE estimator can achieve a significant performance improvement over the conventional Bussgang LMMSE estimator.

Ultra-reliable low-latency communication (URLLC) is critical to wireless control systems. In this paper, we propose a communication-control co-design method to deal with stringent reliability requirements. In particular, we adopt relay-assisted method to improve communication reliability. More importantly, packetized predictive control (PPC) is adopted to solve the problem of relay system, where the data transmission rate of the relay link is twice as that of the direct link, and this leads to the packet loss probability increasing. Furthermore, we explore the tradeoff between energy consumption of relay and packet length of PPC to maximize reliability. Simulation results verify the performance of the proposed method.

We consider the problem of channel estimation for uplink massive multiple-input multiple-output systems, where one-bit analog-to-digital converters (ADCs) are used at the base station (BS) to quantize the received signal. In this paper, we study the problem of one-bit quantizer design when a Bussgang linear minimum mean square error (BLMMSE) estimator is used for channel estimation. We first extend the conventional Bussgang LMMSE estimator \cite{LiTao17} to the general nonzero threshold case. We then analyze the estimation performance of the generalized Bussgang LMMSE estimator and investigate the design of quantization thresholds. A gradient descent scheme is developed to search for optimal quantization thresholds. Simulation results show that, with carefully devised quantization thresholds, the generalized Bussgang LMMSE estimator can achieve a substantial performance improvement over the conventional Bussgang LMMSE estimator.

Ultra-reliable and low-latency communication (URLLC) is promising to enable real-time wireless control systems for tactile internet. In such a system, it is difficult to maintain extremely high quality-of-service (QoS) in URLLC for real-time control. In this paper, we develop a probability-based device-to-device (D2D) scheme to deal with this issue, where communication and control are jointly considered. In our scheme, the transmitters autonomously decide whether to be active to participate in the control process of the receiver based on a certain probability, which can significant reduce the interacting communication latency between them, lower the transmission power consumption, and improve communication reliability. Compared with traditional D2D transmission method, simulation results show remarkable performance gain of our method.

This paper exploits the potential of millimeter wave (mmWave) system, where large-scale antenna arrays are allowed to implement in small physical dimension. We investigate a novel hybrid beamforming design for joint data and artificial noise (AN) precoding and power fraction selection in massive multi-input multi-output (MIMO) system. We aim at the secrecy rate maximization problem with respect to hybrid precoders design. The challenge of this problem lies in its non-convexity. To address this issue, we decouple the design for analog and digital precoders. We conduct analog precoder to maximize corresponding channel gain. For digital data precoder design, we first remove the non-convex codebook constraint and propose an iterative algorithm for optimal equivalent digital precoder design. Then, reconsidering the constraint, we conduct the digital data precoder to approach to the optimal design. Next, aiming to maximize AN power aligned at the eavesdropper, AN precoder design is optimally derived in closed form. Finally, we get power fraction by one-dimensional (1-D) search. Simulation results indicate that our proposed AN- aided hybrid precoding scheme achieves better secrecy performance compared with existing hybrid precoding schemes.

Covert communication, aiming at concealing the existence of data transmission from an eavesdropper, is attracting increasing concerns for communication security. With the trend of moving to higher carrier frequencies, Terahertz (THz) band communication, i.e., wavelength at 0.03-3mm, is envisioned as a promising technique to support bandwidth-hungry applications, as well as improve physical layer security due to its naturally strong directivity and high path loss. Narrow-beam transmission widely adopted in THz communications can effectively prevent from eavesdropping outside the beam sector. However, the challenge still remains when an eavesdropper resides inside the beam sector. To ensure the covertness under such condition, in this paper, a distance-adaptive absorption peak hopping (DA-APH) modulation scheme is proposed for THz covert communications, which takes advantages of distance- and frequency-selective spectral windows, and the frequency-hopping mechanism over the THz spectrum. In particular, the pulse waveform model with polarization is developed to guarantee the reliability of transmission and covertness from eavesdropping. Furthermore, a distance-adaptive carrier frequency selection scheme is designed to choose optimal hopping frequencies at molecular absorption peaks in the THz band.

As one of the most important communication scenarios in the comingfifth generation (5G) cellular networks, ultra-reliable and low-latency communication (URLLC) is promising to enable real-time wireless control systems. However, one of the biggest challenges is that how to integrate URLLC and control performance together to maximize the overall system performance. In this paper, we investigate the resource allocation for URLLC uplink in real-time wireless control systems. Specifically, we first discuss the relationship between communication and control performance. Based on that, we convert the hybrid co-design problem into a regular wireless resource allocation problem. Then, we propose an iteration algorithm to obtain the optimal wireless resource allocation. Simulation results indicate the performance of our method.

Packetized predictive control (PPC) is an effective solution to ensure the robustness of the control system over unreliable wireless links. However, conventional wireless transmission methods in PPC suffer from either high wireless resource consumption or poor performance of real-time control due to the separately design of the two parts. To deal with the issue, we propose a communication-control co-design approach to achieve good trade-off between real-time control performance and communication energy efficiency. Our results demonstrate the advantages of the communication-control co-design.

Massive machine-type communication (mMTC) has been identified as one of the three generic 5G services, with the aim of providing connectivity to a large number of devices. The concurrent massive access may lead to congestions due to limited access resources. Group paging (GP) has emerged as one of the promising solutions to alleviate network congestion by controlling access load. However, the performance of GP deteriorates drastically with the number of devices per paging group. This paper explores GP with pre-backoff strategy for a general mMTC scenario in which devices are allowed to have diverse access success probability (ASP) requirements, and proposes an ASP requirement guaranteed GP scheme with specific pre-backoff times (GPSP), with the aim of maximizing the total access rate. To fully adapt to mMTC applications, an efficient heuristic algorithm is designed. Numerical results demonstrate that the proposed GPSP scheme can effectively improve system performance in terms of average ASP, average access delay, and the average number of preamble transmissions.

Co-channel interference is one of the most important issue in cache-enabled device-to-device (D2D) wireless networks. In this paper, we propose a D2D link scheduling algorithm to manage the interference, called Max-SIR scheduling algorithm. It consists of two steps, i.e., link scheduling and link removal. In the algorithm, D2D links are scheduled with the maximum signal-to-interference ratios (SIRs) considering the co-channel interference in a cell. Simulation results show that the proposed algorithm outperforms the existing ones in terms of the system throughput and the number of scheduled D2D links.

This paper investigates the optimal secure beam steering design of millimeter wave (mmWave) communications, where an Alice-Bob pair wishes to communicate in secret in the presence of Eve, with each node equipped with large-scale antenna arrays. Owing to the reduced peak-to-average power ratio and hardware cost, beam steering design emerges as an attractive technique in mmWave communications recently. However, from the physical layer perspective, the beam steering design subject to security requirement has not been investigated yet. In this paper, we consider a secrecy rate maximization problem with respect to beam steering design, i.e., analog beam selection of radio frequency (RF) chains and power allocation over the selected RF chains, which turns out to be an intractable mixed integer nonlinear optimization problem. To tackle it, we first determine a set of optimal analog beam candidates, based on which the considered multi-input multi-output (MIMO) wiretap channel is decoupled into a sequence of parallel single-input single-output (SISO) wiretap channels. Then, it is shown that the optimal power allocation over the parallel wiretap channels can be derived in a semi-closed-form. Numerical results illustrate that the proposed design offer better secrecy performance than traditional beam steering design in the presence of wiretapping as long as the channel has more than two propagation paths.

Grant-free non-orthogonal multiple access (NOMA) has recently gained significant attention for reducing signaling overhead in machine-type communications (MTC). In this context, compressed sensing (CS) has been identified as a good candidate for joint activity and data detection due to the inherent sparsity nature of user activity. This paper augments activity and data detection for frame based multi-user uplink scenarios where users are (in)active for the duration of a frame, namely frame-wise joint sparsity model. Firstly, we formulate the block CS (BCS)-based sparse signal recovery framework, by fully extracting and exploiting the underlying frame-wise joint sparsity of the user activity. Then, to make explicit use of the block sparsity inherent in the equivalent block-sparse model and consider that the user sparsity level should be unknown for multiuser detection, two enhanced BCS- based greedy algorithms are developed, i.e., threshold aided block sparsity adaptive subspace pursuit (TA-BSASP) and cross validation aided block sparsity adaptive subspace pursuit (CVA- BSASP). Specifically, the proposed TA-BSASP algorithm can approach the oracle least squares (LS) performance, by reasonably setting the threshold based on the AWGN noise floor. And the proposed CVA-BSASP algorithm is a highly practical algorithm design that does not require any prior knowledge, by adopting the statistical and machine learning mechanism cross validation (CV) to determine the stopping condition of the algorithm. Superior performance of the proposed algorithms is demonstrated by numerical experiments.

In this paper, we consider cache-enabled device-to- device (D2D) wireless networks and propose a content-centric link scheduling method, called CTLinQ, to maximize the number of overall D2D links that can be simultaneously activated. Then, interference among different D2D links can be reduced and the quality-of-service (QoS) of each activated link can be guaranteed. Simulation results show that the proposed method outperforms the existing ones in terms of power consumption, the number of activated links, and overall system throughput in particular in high signal-to- interference-plus-noise ratio (SINR) region.

This paper considers a new frequency-domain artificial noise (AN)-aided transmit design for service integration in a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system. In this system, two sorts of service messages are combined and provided simultaneously at each frequency subchannel: one multicast message intended for all receivers and one confidential message intended for only one receiver. The confidential message is kept perfectly secure from all the other receivers. To characterize the tradeoff between the secrecy rate and multicast rate, our goal is to maximize the weighted sum of the two rates over all subchannels by jointly designing the input covariances for the multicast message, confidential message and AN at each subcarrier. This problem is nonconvex by nature and challenging to solve. To make it tractable, we recast this weighted sum rate maximization (WSRM) problem into a primal decomposable form, which is amenable to alternating optimization (AO). By this means, we can obtain a locally optimal solution to the WSRM problem. Numerical results are finally presented to show the efficacy of our proposed method and reveal some insights into our considered scheme.

In device-to-device (D2D) networks, co-channel interference is one of the main reasons that causes high power consumption, which further reduces the life time of mobile devices. In this paper, we adopt the superposition coding between macro and D2D users, which is expected to effectively eliminate the co-channel interference. In particular, we develop two power allocation methods to minimize the power consumption in cooperative and non-cooperative modes, respectively. Then, we use mode selection to obtain the minimum overall power consumption of the whole system. In power allocation, we model the average power consumption as a function of channel gain, power allocation factor, transmission rate, and noise power. Then, we obtain the close-form solution. Our results indicate that the proposed method outperforms the conventional non-cooperative methods in terms of power consumption, outage probability, and energy efficiency.

In this paper, we consider the channel estimation and multi-user detection problems in fog radio access networks (F-RANs). Based on block coordinate descent algorithm, we propose two methods to solve a mixed ℓ2,1-regularization functional which exploits both the sparsity of user activities and the spatial sparsity of user signals in F-RAN. Both of our methods split the computation and corresponding data into multiple units of a cluster and solve the problem in a distributed manner. Hence they can be deployed flexibly at the distributed logical edges as well as the cloud baseband unit pool in F-RAN. The differences between the two methods are that the first one operates in a serial manner and is guaranteed to converge, while the second one works in parallel and under empirical guidance. Deployment details are also provided. Numerical results demonstrate the effectiveness of the proposed methods.

Massive machine-type communication (mMTC) has been identified as one of the three generic 5G services, with the aim of providing connectivity to a large number of devices. The concurrent massive access in mMTC may lead to congestion due to limited access resources. Group paging (GP) is emerging as one of the promising solutions to alleviate network congestion by controlling access load. However, the performance of GP deteriorates drastically with the number of devices per paging group. This paper explores GP with a pre-backoff strategy for a general mMTC scenario in which devices are allowed to have diverse access success probability (ASP) requirements, and proposes a differentiated service-aware GP scheme with specific pre-backoff times (GPSP), with the aim of maximizing the total access rate while guaranteeing the ASP requirements for individual devices. An optimal solution to the GPSP problem is derived to provide a performance upper bound. As low-complexity algorithms are of key importance for mMTC applications, an efficient heuristic algorithm is further designed. Numerical results demonstrate that the proposed GPSP scheme can effectively improve the system performance in terms of average ASP, average access delay, and the average number of preamble transmissions.

This paper investigates and quantifies the advantages of a Full-Duplex (FD) transmitter/receiver pair in improving the secrecy rate of the system. We consider a linear precoder design for a multiple-input multiple-output Gaussian wiretap channel, which comprises two legitimate nodes, i.e., Alice and Bob, operating in FD mode and exchanging confidential messages in the presence of a passive eavesdropper. Using the sum secrecy degrees of freedoms (sum SDoFs) as metric, we formulate an optimization problem with respect to Alice's and Bob's precoding matrices. In order to solve this problem, we first propose a cooperative secrecy transmission scheme, whose feasible set is sufficient to achieve the maximum sum SDoF. Based on that feasible set, we then determine in closed form the maximum achievable sum SDoF and also provide a method for constructing the precoding matrix pair, which achieves the maximum sum SDoF. The latter pair would be near-optimal in terms of the achievable secrecy sum rate in the high signal-to-noise ratio (SNR) regime. By providing the maximum achievable sum SDoF as a function of the number of antennas, one could select the optimal system parameters to further maximize the achievable sum SDoF. We use simulations to evaluate the performance of the proposed precoding matrices in realistic channel scenarios and at various levels of the SNR.

Grant-free non-orthogonal multiple access has recently gained significant attention for reducing signaling overhead in machine-type communications. In this context, compressed sensing (CS) has been identified as a good candidate for joint activity and data detection due to the inherent sparsity nature of user activity. This paper augments activity and data detection for frame-based multi-user uplink scenarios where users are (in)-active for the duration of a frame, namely, the frame-wise joint sparsity model. First, we formulate the block CS (BCS)-based sparse signal recovery framework, by fully extracting and exploiting the underlying frame-wise joint sparsity of the user activity. Then, to make explicit use of the block sparsity inherent in the equivalent block-sparse model and considering the user sparsity level to be unknown for multiuser detection, two enhanced BCS-based greedy algorithms are developed, i.e., threshold aided block sparsity adaptive subspace pursuit (TA-BSASP) and cross-validation aided block sparsity adaptive subspace pursuit (CVA-BSASP). Specifically, the proposed TA-BSASP algorithm can approach the oracle least squares (LS) performance by reasonably setting the threshold based on the additive white Gaussian noise floor. Moreover, the proposed CVA-BSASP algorithm is a highly practical algorithm design that adopts the statistical and machine learning mechanism cross-validation to determine the stopping condition of the algorithm and this does not require prior knowledge. Furthermore, the convergence and the computational complexity of the proposed algorithms are derived and the superior performance of the proposed algorithms is demonstrated by numerical experiments.

This paper considers the channel estimation (CE) and multi-user detection (MUD) problems in cloud radio access network (C-RAN). By taking into account of the sparsity of user activities in C-RAN, we solve the CE and MUD problems with compressed sensing to greatly reduce the large pilot overhead. A mixed ℓ2,1-regularization penalty functional is proposed to exploit the inherent sparsity existing in both the user activities and remote radio heads with which active users are associated. An iteratively re-weighted strategy is adopted to further enhance the estimation accuracy, and empirical and theoretical guidelines are also provided to assist in choosing tuning parameters. To speed up the optimization procedure, three low-complexity methods under different computing setups are proposed to provide differentiated services. With a centralized setting at the baseband unit pool, we propose a sequential method based on block coordinate descent (BCD). With a modern distributed computing setup, we propose two parallel methods based on alternating direction method of multipliers (ADMM) and hybrid BCD (HBCD), respectively. Specifically, the ADMM is guaranteed to converge but has a high computational complexity, while the HBCD has low complexity but works under empirical guidance. Numerical results are provided to verify the effectiveness of the proposed functional and methods.

Ultra-reliability and low-latency will be two topics of most concern in the wireless communication to realize the industrial automation during the next few decades. In this paper, we consider a uplink transmission model where massive machine-type-communication (MTC) devices aim to transmit their generated data to the BS under a strict Quality-of-Service (QoS) constraint on reliability and latency. Guaranteeing the strict constraints on transmission reliability and latency is the most significant thing we consider during the data transmission process. The achievable rate based on finite blocklength channel coding is adopted to characterized the decoding reliability at BS. We see the delay-sensitive area spectral efficiency (DASE) as a performance metric, and our goal is to find a optimal resource allocation policy to maximize the DASE while guaranteeing the strict QoS constraint on reliability. Numerical results show that the optimization strategy proposed in this paper can improve the DASE performance to a great extent than the optimization strategy which minimizes the total bandwidths allocated to devices.

Recently network slicing has been identified as a promising network architectural technology for the next generation mobile cellular networks (5G) to address the challenges stemming from a wide range of applications. Especially, for machine type communication (MTC) application, it is widely recognized that traditional cellular network architecture is not adequate to meet the requirements in terms of massive connectivity and low latency. For exploiting network slicing, data aggregation (DA) can be adopted to effectively address the massive connectivity and latency requirement. In this paper, we propose an efficient network slicing data aggregation (NSDA) scheme for MTC applications. Different from conventional DA scheme where data aggregation is performed based on device locations, we perform DA according to latency requirement for MTC devices (MTCDs), with aim to exploit the benefits of network slicing and thus improve network access capacity and decrease access latency. We formulate the DA problem as a 0-1 Linear Programming and propose an efficient two-step algorithm to aggregate the MTC data for accessing a specific network slice. We examine the performance of our proposed NSDA in typical MTC scenarios via simulations. Numerical results reveal that NSDA significantly outperforms traditional MTC access schemes (without network slicing) in terms of network capacity, access congestion degree, latency, etc.

Since it is challenging to position the noncooperative receiver (Rx), especially when the backward frequency band of the Rx cannot be obtained by the anchors. In this paper, we propose a novel noncooperative Rx positioning method using full-duplex relay technique, where the anchors act as full-duplex relays for the Rx. It can trigger the close-loop-power-control (CLPC) between the transmitter (Tx) and the Rx, which contains the information of the Rx's location. By measuring the received signal from the Tx, the anchors can estimate the location of the Rx. Simulation results demonstrate the performance of the proposed Rx positioning method, and the root-mean-square-error (RMSE) can reach about 30%, which is similar to the conventional Tx positioning using received-signal-strength (RSS).

In underlay heterogeneous networks (HetNets), the distance between a macro base station (MBS) and a macro user (MU) is crucial for a small-cell based station (SBS) to control the interference to the MU and achieve the coexistence. To obtain the distance between the MBS and the MU, the SBS needs a backhaul link from the macro system, such that the macro system is able to transmit the information of the distance to the SBS through the backhaul link. However, there may not exist any backhaul link from the macro system to the SBS in practical situations. Thus, it is challenging for the SBS to obtain the distance. To deal with this issue, we propose a median based (MB) estimator for the SBS to obtain the distance between the MBS and the MU without any backhaul link. Numerical results show that the estimation error of the MB estimator can be as small as $4\%$.

This paper considers a cellular multiple-input single- output (MISO) system overheard by multiple eavesdroppers, in the presence of one pair of single- antenna device-to-device (D2D) nodes working as an underlay. The D2D nodes are permitted to access the cellular channel for their own communications. We assume that the channel state information (CSI) on all links is imperfect, and more specifically, the CSI error follows an arbitrary distribution with only the first and second moments available at the transmitter. Our goal is to design the covariances of confidential message and artificial noise, as well as the transmit power at the D2D transmitter, such that the total consumed power is minimized subject to a sequence of worst-case outage constraints on the received signal- to-interference-plus-noise ratio (SINR) at each receiver. The worst-case outage constraints are imposed to satisfy SINR outage requirement under arbitrarily distributed CSI uncertainty. The resulting problem is challenging to solve due to its inherently complex structure. However, we reveal its hidden convexity by carrying out a duality-based reformulation. Then it is proved that the proposed method always yield a single- stream beamforming solution. The complexity analysis of our proposed method is also presented. Finally, the efficacy of the proposed design is demonstrated by simulations.

Listen-before-talk (LBT) is enforced in the regions such as European Union and Japan to harmonize coexistence of cellular and incumbent systems in unlicensed spectrum. In this paper, we study how to exploit LBT strategies for frame based equipment (FBE) in unlicensed spectrum. We consider two optimization problems: Throughput optimal stopping and nominal throughput optimal stopping. We discover that the throughput optimal transmission strategy for FBE in unlicensed spectrum is to transmit whenever the channel is clear. In contrast, we find that the nominal throughput optimal transmission strategy is less aggressive: The FBE does not transmit until it finds that the channel is clear and the channel quality exceeds an optimized threshold.

This paper considers a two-receiver multiple-input multiple-output (MIMO) Gaussian broadcast channel model with integrated services. Specifically, two sorts of service messages are combined and served simultaneously: one multicast message intended for both receivers and one confidential message intended for only one receiver and kept perfectly secure from the other receiver. Our goal is to jointly design the transmit covariances of the multicast message and confidential message, such that the secrecy capacity region is maximized. This maximization problem is a biobjective optimization problem, but can be converted into a general scalar optimization problem via our proposed method of scalarization. Nonetheless, the equivalent scalar problem is nonconvex by nature. To circumvent the nonconvex issue, a provably convergent difference-of-concave (DC) approach is introduced to solve it in an iterative fashion. In view of the high computational complexity of the DC approach, a power splitting method is also devised for fast implementation of service integration. The security performance and computational efficiency of our proposed algorithms are finally demonstrated by numerical results.

This paper considers the channel estimation (CE) and multi-user detection (MUD) problems in cloud radio access network (C-RAN). Assuming that active users are sparse in the network, we solve CE and MUD problems with compressed sensing (CS) technology to greatly reduce the long identification pilot overhead. A mixed ℓ2.1-regularization functional for extended sparse group-sparsity recovery is proposed to exploit the inherently sparse property existing both in user activities and remote radio heads (RRHs) that active users are attached to. Empirical and theoretical guidelines are provided to help choosing tuning parameters which have critical effect on the performance of the penalty functional. To speed up the processing procedure, based on alternating direction method of multipliers and variable splitting strategy, an efficient algorithm is formulated which is guaranteed to be convergent. Numerical results are provided to illustrate the effectiveness of the proposed functional and efficient algorithm.

This paper considers a cellular multiple-input multiple-output multiple-eavesdropper (MIMOME) channel, with a pair of single-antenna device-to-device (D2D) nodes working as an underlay. A novel eavesdropping scenario is studied in this paper, where the eavesdroppers intend to simultaneously overhear the cellular communication and the D2D communication. Our goal is to jointly optimize the covariance of confidential message and artificial noise, as well as the transmit power at the D2D transmitter, such that the sum secrecy rate is maximized, while satisfying the quality of service constraint on the D2D communication. This sum secrecy rate maximization (SSRM) problem is non-convex by nature. To handle it, an equivalent reformulation of this SSRM problem is introduced, wherein the resulting problem becomes primal decomposable and thus can be iteratively solved using an alternating optimization (AO) algorithm. Also, we prove that the AO algorithm is bound to converge to a stationary point of the primal SSRM problem. Furthermore, we extend the SSRM problem to a more general case with multiple pairs of D2D nodes. Again, the resulting problem is shown to be solvable by the AO algorithm, with provable convergence to the stationary point. Finally, numerical results are presented to verify the efficacy of our proposed method.

Consider a cellular multiple-input single-output (MISO) channel, in the presence of multiple eavesdroppers (Eves) and one pair of single-antenna device-to-device (D2D) nodes working as an underlay. A novel eavesdropping scenario, termed as selective eavesdropping, is studied in this paper, where Eves arbitrarily select one target from the cellular receiver and the D2D receiver to overhear, but their selection is unknown to any other nodes. Since Eves' two sorts of selection would lead to two different secrecy rates, we define the achievable secrecy rate as the smaller one of the two rates. With imperfect channel state information on all links, our interest lies in the robust design of transmit covariances at the cellular transmitter, such that the worst-case achievable secrecy rate is maximized. This worst-case secrecy rate maximization problem is nonconvex by nature. To deal with it, we develop a convex approximation to seek a computationally efficient lower bound. In particular, the solution can be efficiently computed by successively solving a sequence of convex optimization problems. Then it is proved that the obtained lower bound is attainable by simply utilizing single-stream beamforming. Numerical results are finally presented to demonstrate the efficacy of our proposed methods.

In this paper, we consider wireless caching in device-to-device (D2D) cooperative networks and propose a new definition,energy-consumption-ratio (ECR), to measure the energy efficiency that caching schemes can achieve. Based on this definition,we formulate an optimal energy-efficient caching problem and develop a caching scheme, named SBRC-2S. Simulation results show that the proposed scheme outperforms the existing ones and approaches the optimal performance bounds in terms of energy efficiency.

This paper considers an energy-efficient transmit design in a multiple-input single-output (MISO) wiretap channel. In particular, a transmitter sends one confidential message to a legitimate receiver, which must be kept perfectly secure from multiple external single-antenna eavesdroppers. Assuming statistical eavesdroppers' channel state information (ECSI) at the transmitter, we aim to design the transmit beamformer, such that the outage secrecy energy efficiency (SEE) is maximized, subject to the outage-constrained secrecy rate and transmit power constraints. The resultant problem is intractable to solve even after introducing a semidefinite relaxation (SDR) reformulation. To handle it, an equivalent parametric reformulation, based on the fractional programming and difference-of-concave programming theories, is proposed to recast this problem as a convex problem. By this means, the maximum outage SEE can be found in an iterative fashion. Moreover, we also give an approach to constructing a rank-one covariance matrix from our proposed method, implying the feasibility of transmit beamforming to achieve the obtained SEE performance. Numerical results are presented to show the effectiveness of our proposed method.

This paper considers a cellular multiple-input single- output (MISO) system overheard by multiple eavesdroppers, in the presence of one pair of device- to-device (D2D) nodes working as an underlay to the cellular network. A novel eavesdropping scenario is studied in this paper, where the eavesdroppers intend to simultaneously overhear the cellular communication as well as the D2D communication. Assuming imperfect channel state information (CSI) at the transmitter, our goal is to design the input covariance matrix of confidential message such that the worst-case sum secrecy rate is maximized, while satisfying the quality of service (QoS) requirement in the D2D communication. Although this worst-case sum secrecy rate maximization (SSRM) problem is non-convex, we show that it can be handled by solving a sequence of semidefinite programming (SDP) problems. Moreover, we give complexity analysis of our proposed optimization method and prove that transmit beamforming is an optimal strategy for the confidential message transmission. Numerical results are presented to verify the efficacy of our proposed method.

This paper studies the throughput maximization problem for a three-node relay channel with direct link and non-ideal circuit power. The relay operates in a half- duplex manner, and the decode-and-forward (DF) relaying is adopted. Considering the extra power consumption by the circuits, the optimal power allocations over infinite time horizon are investigated. First, two special scenarios, i.e., the direct link transmission (only use the direct link to transmit) and the relay assisted transmission (the source and the relay transmit with equal probability), are studied. By solving two non-convex optimization problems, the solutions show that the source and the relay transmit with certain probability, which is determined by the average power budgets, circuit power consumptions, and channel gains. Then, based on the above results, the optimal power allocation for the original throughput maximization problem is investigated, which is shown to be a mixed transmission scheme between the direct link transmission and the relay assisted transmission.

In cognitive radio networks, the channel gain between primary transceivers, namely, primary channel gain, is crucial for a cognitive transmitter (CT) to control the transmit power and realize spectrum sharing. To obtain the primary channel gain, a backhaul between the primary system and the CT is needed. However, the backhaul is usually unavailable in practice. To deal with this issue, the CT is enabled to sense primary signals and estimate the primary channel gain in this paper. In particular, two estimators, namely, a high-complexity maximum likelihood (ML) estimator and a low-complexity median based (MB) estimator are proposed. Numerical results show that the ML estimator outperforms the MB estimator in terms of the accuracy if the signal to noise ratio (SNR) of the received primary signals at the CT is no smaller than 4 dB. Otherwise, the MB estimator is superior to the ML estimator from the aspects of both the computational complexity and accuracy.

Non-orthogonal multiple access (NOMA) is an emerging research topic in the future fifth generation wireless communication networks, which is expected to support massive connectivity for massive machine-type communications (mMTC). Due to the sporadic communication nature of mMTC, the grant-free transmission methodology is highly expected in uplink NOMA systems, to drastically reduce the transmission latency and signaling overhead. Exploiting the inherent sparsity nature of user activity, compressive sensing (CS) techniques have been applied for efficient multi-user detection in the uplink grant-free NOMA. In this paper, we propose a prior-information-aided adaptive subspace pursuit (PIA-ASP) algorithm to improve the multi-user detection performance. In this algorithm, a parameter evaluating the quality of the prior-information support set is introduced, in order to exploit the intrinsically temporal correlation of active user support sets in several continuous time slots adaptively. Then, to mitigate the incorrect estimation effect of the prior support quality information, a robust PIA-ASP algorithm is further proposed, which adaptively exploits the prior support based on the corresponding support quality information in a conservative way. It is noted that both of the two proposed algorithms do not require the knowledge of the user sparsity level, while most of the state-of-the-art CS-based multi-user detection algorithms usually need. Moreover, for the two proposed algorithms, the upper bound of the signal detection error and the computational complexity is derived. Simulation results demonstrate that the two proposed algorithms are capable of achieving much better performance than that of the existing CS-based multi-user detection algorithms with a similar computational complexity.

We consider the problem of downlink channel estimation for millimeter wave (mmWave) MIMO-OFDM systems, where both the base station (BS) and the mobile station (MS) employ large antenna arrays for directional precoding/beamforming. Hybrid analog and digital beamforming structures are employed in order to offer a compromise between hardware complexity and system performance. Different from most existing studies that are concerned with narrowband channels, we consider estimation of wideband mmWave channels with frequency selectivity, which is more appropriate for mmWave MIMO-OFDM systems. By exploiting the sparse scattering nature of mmWave channels, we propose a CANDECOMP/PARAFAC (CP) decomposition-based method for channel parameter estimation (including angles of arrival/departure, time delays, and fading coefficients). In our proposed method, the received signal at the MS is expressed as a third-order tensor. We show that the tensor has the form of a low-rank CP, and the channel parameters can be estimated from the associated factor matrices. Our analysis reveals that the uniqueness of the CP decomposition can be guaranteed even when the size of the tensor is small. Hence the proposed method has the potential to achieve substantial training overhead reduction. We also develop Cramér-Rao bound (CRB) results for channel parameters and compare our proposed method with a compressed sensing-based method. Simulation results show that the proposed method attains mean square errors that are very close to their associated CRBs and present a clear advantage over the compressed sensing-based method.

This paper considers a scenario in which an Alice-Bob pair wishes to communicate in secret in the presence of an active Eve, who is capable of jamming as well as eavesdropping and operates in full-duplex (FD) mode. As countermeasure, Bob operates in FD mode, using a subset of its antennas to receive, and the remaining antennas to transmit jamming noise. Alice and Bob employ linear precoding, and all nodes use Gaussian code books. In that context, our goal is to maximize the achievable secrecy degrees of freedom (S.D.o.F.) of the system. We provide the optimal receive/transmit antennas allocation at Bob, based on which we determine in closed form the maximum achievable S.D.o.F. We also provide a method for constructing the precoding matrices of Alice and Bob, based on which the maximum S.D.o.F. can be achieved. We further investigate the adverse scenario in which Eve knows Bob's transmission strategy and optimizes its transmit/receive antennas allocation in order to minimize the achievable S.D.o.F. For that case, we find the worst case achievable S.D.o.F. Numerical results validate the theoretical findings and demonstrate the performance of the proposed method.

This paper studies the throughput maximization problem for a three-node relay channel with non-ideal circuit power. In particular, the relay operates in a half-duplex manner, and the decode-and-forward (DF) relaying scheme is adopted. Considering the extra power consumption by the circuits, the optimal power allocation to maximize the throughput of the considered system over an infinite time horizon is investigated. First, two special scenarios, i.e., the direct link transmission (only use the direct link to transmit) and the relay assisted transmission (the source and the relay transmit with equal probability) are studied, and the corresponding optimal power allocations are obtained. By transforming two non-convex problems into quasi-concave ones, the closed-form solutions show that the source and the relay transmit with certain probability, which is determined by the average power budgets, circuit power consumptions, and channel gains. Next, based on the above-mentioned results, the optimal power allocation for both the cases with and without direct link is derived, which is shown to be a mixed transmission scheme between the direct link transmission and the relay assisted transmission.

In this paper, a new iteratively reweighted least squares method is proposed for recovery of block-sparse signals with unknown cluster patterns. In many practical applications, sparse signals have block-sparse structures with nonzero coefficients occurring in clusters, while the prior information of the cluster pattern is usually unavailable. To address this issue, we propose an element-overlapping log-sum functional to encourage the sparseness and the cluster pattern simultaneously. The algorithm is developed by iteratively minimizing a convex surrogate function that majorizes the original objective function, which results in an iteratively reweighted process that alternates between estimating the sparse signal and refining the weights of the surrogate function. Convergence of the iterations to a local minimum of the penalty function is also guaranteed. Numerical results are provided to illustrate the effectiveness of the proposed method.

In this paper, we propose a new estimation method to obtain the cross-channel gain, which avoids the severe interference to the primary receiver (PR) in existing relay-assisted estimation methods. In our method, we let the cognitive transmitter add a time delay when it conducts the full-duplex amplify-and-forward relaying. This forces the time-difference-of-arrival (TDOA) between the direct and relay signals to be large enough rather than randomly large or small. Then we develop our estimation method only in the large TDOA case and precisely control the interference to the PR. Simulation results indicate that the proposed method can significantly reduce the interference to the PR.

This paper considers secrecy communication from a signal processing point of view, and studies the maximal achievable secrecy degrees of freedoms (S.D.o.F.) of a helper-assisted Gaussian wiretap channel, consisting of a source, a legitimate receiver, an eavesdropper and an external helper. Each terminal is equipped with multiple antennas. We first propose a cooperative secrecy transmission scheme, and show that it achieves the maximal secrecy degrees of freedom. We then propose a heuristic method, through which, we solve analytically the optimization problem associated with the proposed cooperative secrecy transmission scheme. By this way, we obtain the maximal achievable S.D.o.F. and also the precoding matrices which achieve the maximal S.D.o.F. in closed-form.

This paper considers an optimal artificial noise (AN)-aided transmit design for multi-user MISO systems in the eyes of service integration. Specifically, two sorts of services are combined and served simultaneously: one multicast message intended for all receivers and one confidential message intended for only one receiver. The confidential message is kept perfectly secure from all the unauthorized receivers. This paper considers a general case of imperfect channel state information (CSI), aiming at a joint and robust design of the input covariances for the multicast message, confidential message and AN, such that the worst-case secrecy rate region is maximized subject to the sum power constraint. To this end, we reveal its hidden convexity and transform the original worst-case robust secrecy rate maximization (SRM) problem into a sequence of semidefinite programming. Numerical results are presented to show the efficacy of our proposed method.

In frequency duplex division systems, it is very challenging to position the receiver (Rx) that belongs to the third-party system since the anchors usually do not know which frequency band the Rx uses for transmission. As a result, they can only use the received signal from the third-party transmitter (Tx) to position the Rx. In this paper, we propose a relay-based positioning method to estimate the location of the third-party Rx. In our method, we let each anchor alternatively act as a full-duplex amplify-and-forward (AF) relay for the Rx, which artificially creates a relay path. By estimating the time-difference-of-arrival (TDOA) between the direct and relay paths, the anchors can estimate the Rx location based on the received signal from the third-party Tx. Simulation results indicate the effectiveness of the proposed method.

In frequency division duplex (FDD) two-tier networks, the probing technique has recently been introduced into the cross-channel gain estimation, which requires the tier-two user to act as a relay for the tier-one user. Then the tier-two user can autonomously estimate the cross-channel gain. However, the improper location of the tier-two user, i.e., the relay, may cause severe interference to the tier-one user. In this paper, we analyze the impacts of the probing on the tier-one user and find two location regions, in which the probing does not cause interference. Based on that, we develop a detection method to let the tier-two user autonomously identify its located region. Then the interference caused by the probing can be avoided. Simulation results demonstrate that the proposed method has about 90% correct detection probability.

This paper considers an artificial noise (AN)-aided transmit design for multi-user MISO systems in the eyes of service integration. Specifically, we combine two sorts of services, and serve them simultaneously: one multicast message intended for all receivers and one confidential message intended for only one authorized receiver. The confidential message is kept perfectly secure from all the unauthorized receivers. Assuming imperfect channel state information (CSI) of unauthorized receivers at the transmitter, our goal is to jointly design the input covariances of the multicast message, confidential message and AN such that the outage secrecy rate is maximized for a given outage probability, while keeping the outage probability of multicast message for each user below a certain threshold. Due to the intrinsical complexity of this problem, a safe and convex albeit suboptimal reformulation, based on two advanced convex restriction approaches, is applied to generate a tractable approximation for this problem. By this means, a computationally efficient lower bound on the outage secrecy rate can be determined. We also prove the feasibility of beamforming to achieve the obtained secrecy rate. Numerical results are presented to verify the efficacy of our proposed method.

This paper considers an energy-efficient transmit design in a three-node MISO wiretap channel in the eyes of service integration. Specifically, we combine two sorts of services, and serve them simultaneously: one multicast message intended for both receivers and one confidential message intended for only one authorized receiver. The confidential message must be kept perfectly secure from the unauthorized receiver. Our goal is to jointly design the input covariance matrices of the multicast message and confidential message such that the secrecy energy efficiency (SEE) is maximized, subject to the multicast rate, secrecy rate and total transmit power constraints. Due to the nonconvexity of this problem, an equivalent parametric reformulation, based on the fractional programming theory, is proposed to recast this problem as a sequence of semidefinite programs. By this means, the maximum SEE can be found via a root search algorithm. Moreover, we also give an approach to constructing a rank-one optimal covariance matrix of the confidential message from our proposed algorithm, which implies the feasibility of transmit beamforming to achieve the maximum SEE. Numerical results are finally presented to verify the efficacy of our proposed method.

In cognitive radio networks, the location information of the primary receiver is critical for underlay spectrum sharing. However, positioning the primary receiver in frequency division duplex (FDD) systems is very challenging. In this paper, we propose a novel method to position the primary receiver using the full-duplex amplified-and-forward (AF) relay technique. Simulation results indicate that the proposed method can obtain the same level of estimation error compared with the conventional received signal strength (RSS)-based transmitter positioning methods.

In two-phase cooperative multicast communications, the unbalanced outage probabilities of the cell-center and cell-edge users are the main reason that caps the energy-efficiency of the system. In this paper, we consider the unbalanced outage probability and propose a probability-based relay selection and power control method to improve the energy-efficiency, in which each user can autonomously decide whether to participate in the relay transmission. In particular, we obtain the optimal solution that can minimize the user power consumption. In addition, since our method works in a distributed manner, it does not require any feedback either between the BS and users, or among the users. This saves the extra energy consumption caused by the feedback. Simulation results demonstrate that the proposed method can reduce the user energy consumption up to 54%.

This letter considers secrecy communication from an information-theoretic perspective, and studies the secrecy capacity of a multi-input multi-output (MIMO) Gaussian wiretap channel with a source (Alice), an eavesdropper (Eve) and a Full-Duplex (FD) legitimate receiver (Bob). Bob can allocate part of his antennas to transmit jamming signals to impair Eve’s channel. Our goal is to identify the secrecy capacity behavior in the high signal-to-noise ratio (SNR) regimes, i.e., the maximal achievable secure degrees of freedom (S.D.o.F). Such S.D.o.F maximization is generally difficult to solve since it requires to face a nonlinear fractional problem. To deal with this issue, we first propose a cooperative secrecy transmission scheme, and prove its optimality in the sense of achieving the maximal S.D.o.F.. By studying this proposed transmission scheme, we obtain the maximal achievable S.D.o.F. in closed form for any given antenna allocation at Bob. Based on this closed-form result, we further analytically derive the optimal antenna allocation at Bob. To the best of our knowledge, this is the first time that the benefit brought by using the FD jamming Bob has been quantified.

This letter considers a two-receiver multiple-input single-output Gaussian broadcast channel model with integrated services. Specifically, two sorts of service messages are combined and served simultaneously: one multicast message intended for both receivers and one confidential message intended for only one receiver. The confidential message is kept perfectly secure from the unauthorized receiver. Our goal is to jointly design the input covariances for the multicast message and confidential message, such that the secrecy capacity region is maximized. This secrecy capacity region maximization (SCRM) problem is a nonconvex vector maximization problem. To deal with this issue, we reformulate the SCRM problem into a provably equivalent scalar optimization problem and propose a searching method to find its overall Pareto optimal points. Further, for implementation efficiency, transmit beamforming is proved to be Pareto optimal. However, since the two service messages are coupled in our optimization problem, it is difficult to deduce closed-form expressions of the Pareto optimal beamformers. A suboptimal transmit design is accordingly proposed to analytically obtain beamformers for both service messages. Numerical results illustrate that the performance gap between the Pareto optimal design and our proposal is negligible.

This letter considers a two-receiver multiple-input multiple-output Gaussian broadcast channel model with integrated services. Specifically, we combine two sorts of service messages, and serve them simultaneously: One multicast message intended for both receivers and one confidential message intended for only one receiver. The confidential message is kept perfectly secure from the unauthorized receiver. DueAN54-B6010-A001 to the coupling of service messages, it is intractable to seek capacity-achieving transmit covariance matrices. Accordingly, we propose a suboptimal precoding scheme based on the generalized singular value decomposition (GSVD). The GSVD produces several virtual orthogonal subchannels between the transmitter and the receivers. Subchannel allocation and power allocation between multicast message and confidential message are jointly optimized to maximize the secrecy rate in this letter, subject to the quality of multicast service constraints. Since this problem is inherently complex, a difference-of-concave algorithm, together with an exhaustive search, is exploited to handle the power allocation and subchannel allocation, respectively. Numerical results are presented to illustrate the efficacy of our proposed strategies.

In cognitive radio networks, the signal-to-noise ratio (SNR) of the primary receiver (PR), called primary-link SNR, is critical for underlay spectrum sharing. However, it is very difficult for the cognitive user (CU) to obtain the primary-link SNR. In this letter, we propose a new method to let the CU autonomously estimate the primary-link SNR, where the full-duplex relay technique is used. Then, the CU can conduct the underlay spectrum sharing more efficiently. Simulation results indicate the effectiveness of the proposed method.

We consider the problem of peak-to-average power ratio (PAPR) reduction in orthogonal frequency-division multiplexing (OFDM) based massive multiple-input multiple-output (MIMO) downlink systems. Specifically, given a set of symbol vectors to be transmitted to K users, the problem is to find an OFDM-modulated signal that has a low PAPR and meanwhile enables multiuser interference (MUI) cancellation. Unlike previous works that tackled the problem using convex optimization, we take a Bayesian approach and develop an efficient PAPR reduction method by exploiting the redundant degrees of freedom of the transmit array. The sought-after signal is treated as a random vector with a hierarchical truncated Gaussian mixture prior, which has the potential to encourage a low PAPR signal with most of its samples concentrated on the boundaries. A variational expectation-maximization (EM) strategy is developed to obtain estimates of the hyperparameters associated with the prior model, along with the signal. In addition, the generalized approximate message passing (GAMP) is embedded into the variational EM framework, which results in a significant reduction in computational complexity of the proposed algorithm. Simulation results show our proposed algorithm achieves a substantial performance improvement over existing methods in terms of both the PAPR reduction and computational complexity.

In this paper, we propose a probability-based relay selection and power control method in two-phase cooperative multicast communications to minimize the user power consumption for any given multicast data rate. Our method gives each user the ability to autonomously decide whether to participate in the relay transmission based on an active probability. In particular, we develop an optimal algorithm to calculate the optimal active probability and relay power by balancing the outage reduction efficiency. We also develop a sub-optimal algorithm by balancing the outage probability of the cell-center and the cell-edge users. Simulation results demonstrate that the proposed optimal and sub-optimal algorithms can reduce the user power consumption up to about 54% and 40%, respectively, compared with the conventional algorithm that requires all users to participate in the relay transmission.

We consider the problem of uplink channel estimation for millimeter wave (mmWave) systems, where the base station (BS) and mobile stations (MSs) are equipped with large antenna arrays to provide sufficient beamforming gain for outdoor wireless communications. Hybrid analog and digital beamforming structures are employed by both the BS and the MS due to hardware constraints. We propose a layered pilot transmission scheme and a CANDECOMP/PARAFAC (CP) decomposition-based method for joint estimation of the channels from multiple users (i.e., MSs) to the BS. The proposed method exploits the intrinsic low-rank structure of the multiway data collected from multiple modes, where the low-rank structure is a result of the sparse scattering nature of the mmWave channel. The uniqueness of the CP decomposition is studied, and the sufficient conditions for essential uniqueness are obtained. The conditions shed light on the design of the beamforming matrix, the combining matrix, and the pilot sequences, and meanwhile provide general guidelines for choosing system parameters. Our analysis reveals that our proposed method can achieve a substantial training overhead reduction by leveraging the low-rank structure of the received signal. Simulation results show that the proposed method presents a clear advantage over a compressed sensing-based method in terms of both estimation accuracy and computational complexity.

In wireless positioning, estimating the location of a receiver is very challenging since the receiver does not transmit signals. The difficulty is to obtain the distance between the anchors and the silent receiver. To deal with the issue, this paper proposes to use the full-duplex relay technique to estimate the anchor-receiver distance. Then, we can obtain the location of the silent receiver. With the proposed method, the location-aware applications are not limited to the conventional transmitter positioning. Instead, they can be extended to the receiver positioning. Simulation results demonstrate the performance of the proposed method.

We investigate the secrecy capacity of Gaussian wiretap channel with a source, an external helper, an eavesdropper and a single-antenna legitimate receiver where the former three terminals are equipped with Na, Njand Neantennas, respectively. Generally, an analytical form of the secrecy capacity in this scenario is difficult to obtain. Instead, we recast the original nonconvex secrecy rate maximization (SRM) problem into a sequence of convex optimization problems. In doing so, the secrecy capacity can be obtained using a combination of convex optimization and a one-dimensional search. On the other hand, to gain more insight into how the secrecy capacity behaves, we study the secure degrees of freedom (s.d.o.f.) and quantify its connection with system parameters, where our result proves that the s.d.o.f. equal to 1 can be achieved if and only if Nea+Nj-1. As a by-product, we give a suboptimal but closed-form solution to the original SRM problem for the scenario where Nea+Nj-1.Numerical results are presented to validate the theoretical findings and illustrate the efficacy of the proposed schemes.

In this paper, we study the problem of sparse signal recovery when partial but partly erroneous prior knowledge of the signal's support is available. Based on the conventional sparse Bayesian learning framework, we propose an improved hierarchical prior model. The proposed modeling constitutes a three-layer hierarchical form. The first two layers, similar to the conventional sparse Bayesian learning, place a Gaussian-inverse-Gamma prior on the signal, while the third layer is newly added, with a prior placed on the parameters {bi}, where {bi} are parameters characterizing the sparsity-controlling hyperparameters {αi}. Such a modeling enables to automatically learn the true support from partly erroneous information through learning the values of the parameters {bi}. A variational Bayesian inference algorithm is developed based on the proposed prior model. Numerical results are provided to illustrate the performance of the proposed algorithm.

In this paper, we consider a massive MIMO wiretap system where the transmitter, the receiver and the eavesdropper are equipped with a large number of antennas. Being different from the previous works using asymptotic random matrix theory, our analysis relies on the concentration measure of non-asymptotic random matrix theory which allows us to obtain tight bounds for secrecy capacity of massive MIMO system with finite antenna number. The analytical and simulation results reveal the following, in the massive MIMO system employing equal power allocation at each transmit antenna: 1) the secrecy capacity falls within a bounds with a probability growing exponentially with the number of transmit antenna, while the ergodic secrecy capacity falls within a deterministic bounds; 2) the gap between the upper and the lower bound on secrecy rate is proportional to the square root of the SNR at legitimate receiver and the SNR at eavesdropper, respectively; 3) when the entry of legitimate channel matrix and eavesdropping channel matrix satisfies Gaussian distribution, the gap between the upper and the lower bound on secrecy rate is a linear reciprocal function of the number of transmit antennas.

This paper studies the link selection protocol for the relay network consisting of N pairs of sources and destinations, and one half-duplex decode-and- forwarding (DF) relay equipped with data buffers. An optimal link selection scheme is proposed to maximize the average throughput of the relay network. Using the Karush-Kuhn-Tucker (KKT) conditions, the decision function for the optimal link selection is also obtained for Rayleigh fading channels. Simulation are given to verify the proposed scheme. The results show that the proposed scheme achieves not only significantly higher data rate than the classic max-min scheme without data buffers at the relay, but also the existing buffer- aided link selection schemes such as the max-max scheme. This making the proposed scheme attractive in practice.

Cognitive radio (CR) technology has been developed to solve the spectrum-underutilization problem. In CR networks, the CR users have opportunities to access the licensed spectrum bands assigned to the primary users (PUs). Since the PUs have priorities to use the bands, the CR users are not allowed to generate unacceptable interference to them. In this paper, we investigate power allocation schemes for source and relay nodes in CR with direct and relay link. We show that there is a significant benefit to the system rate by jointly distributing the transmitting power. Three schemes are considered, where particularly the scheme based on analogue network coding shows significant superior performance to other two classic schemes. The optimum power allocation is derived analytically. The proposed schemes are verified by the simulations which also show the impact of different positions of relay node and the primary user locations on power allocation solutions.

We study the transmit design at relay nodes for secure amplify-and-forward (AF) networks. Two joint cooperative relaying and jamming schemes, namely Secrecy Rate Maximization Scheme and Null-Space Jamming Scheme, are proposed. In the first scheme, optimal relaying weight vector and optimal covariance matrix associated with the artificial noise (AN) are obtained, which involves doing a one-dimensional search and solving a sequence of semidefinite programs(SDPs). In the second scheme, which is suboptimal but computationally much cheaper, AN is designed to decrease the rate at the eavesdropper while the relaying weight vector is determined to increase the rate at the destination. In addition, Power Allocation based on secrecy rate maximization provides a balance between these two goals. Numerical results show that the proposed Secrecy Rate Maximization Scheme outperforms the existing cooperative relaying without jamming scheme. Especially, when the power is large enough, the secrecy rate achieved by the proposed schemes approaches the maximal achievable rate for the no-eavesdropper case.

In this paper, we investigate the two-phase cooperative multicast communication, where a base station broadcasts the same information to a group of users in the first phase and then some successful users act as relay to help the failed users in the second phase. To maximize the energy efficiency of the cooperative multicast communication, we propose a probability-based relay selection and power control method. With our method, each successful user may autonomously decide whether to conduct the relay for the failed users and also select the proper transmission power for the relay transmission. In particular, the proposed method does not require any handshake either between base station and users, or among users. Simulation results demonstrate that our method obtains almost the same performance as the optimal relay selection and power control method with exhaustive search.

In this paper, we consider the transmit design for multi-input multi-output (MIMO) wiretap channel including a malicious jammer. We first transform the system model into the traditional three-node wiretap channel by whitening the interference at the legitimate user. Additionally, the eavesdropper channel state information (ECSI) may be fully or statistically known, even unknown to the transmitter. Hence, some strategies are proposed in terms of different levels of ECSI available to the transmitter in our paper. For the case of unknown ECSI, a target rate for the legitimate user is first specified. And then an inverse water-filling algorithm is put forward to find the optimal power allocation for each information symbol, with a stepwise search being used to adjust the spatial dimension allocated to artificial noise (AN) such that the target rate is achievable. As for the case of statistical ECSI, several simulated channels are randomly generated according to the distribution of ECSI. We show that the ergodic secrecy capacity can be approximated as the average secrecy capacity of these simulated channels. Through maximizing this average secrecy capacity, we can obtain a feasible power and spatial dimension allocation scheme by using one dimension search. Finally, numerical results reveal the effectiveness and computational efficiency of our algorithms.

We consider a physical layer security problem where there is a source, an external helper, a legitimate receiver, and an eavesdropper, each equipped with one antenna. We assume that an additional out-of-band link from the source to the helper is available to improve the transmission security rate. A two-stage cooperative scheme is proposed. The proposed scheme consists of an information sharing stage and a cooperative transmission stage. Specifically, in the information sharing stage the source informs the helper of the signal to be transmitted, while in the cooperative transmission stage the source and the helper cooperate to transmit the signal to the legitimate receiver. Under this framework, we determine the optimal weights associated with this scheme and examine the secrecy capacity of the helper-assisted wiretap channel. The optimal weight design problem is generally nonconvex. To deal with this issue, an algorithm involving a one-dimensional search is developed. On the other hand, an analytical lower bound on the secrecy capacity is derived. Based on this lower bound, we further analyze the sufficient and necessary condition to ensure a positive secrecy capacity and derive the maximal achievable secure degrees of freedom, which are shown to be exactly the same as those of the multi-input single-output (MISO) wiretap channel.

This paper exploits co-channel interference (CCI) to secure the multi-antenna wiretap IFC consisting of two source-destination-eavesdropper triples, where each source-destination link is wiretapped by an external eavesdropper. To this end, we first propose a cooperative secrecy beamforming scheme, which is proved to be sufficient and necessary to achieve the secure degrees of freedom (S.D.o.F.) pair (1,1). By investigating the feasibility of the proposed beamforming scheme, we obtain the sufficient and necessary condition and also the beamforming vectors in closed-form to achieve the S.D.o.F pair (1,1). To the best of our knowledge, this is the first time that the benefit brought by CCI has been quantified.

This paper studies robust transmit design to maximize the worst-case secrecy rate in AF networks under both total and individual relay power constraints. Channel state information (CSI) in the network is assumed to be perfectly known except for that associated with the eavesdroppers whose imperfection is modeled as deterministic bounded errors. To use the power at the relay nodes more efficiently, a joint cooperative relaying and jamming scheme is considered. Through some matrix manipulations, we recast the original nonconvex optimization problem as a sequence of semidefinite programs (SDPs), which enables us to obtain the optimal relay weights and the optimal covariance matrix of the jamming signal. Numerical results are presented to show the efficacy of the proposed scheme.

We study the secrecy capacity of Gaussian wiretap channel aided by an external jammer/helper. Both the transmitter and the intended receiver are equipped with a single antenna, while the eavesdropper and the jammer are equipped with$M$and$N$antennas, respectively. Generally, an analytical form of the secrecy capacity in this scenario is difficult to obtain. Instead, we consider a null-space jamming scheme which totally nulls out the jamming signal at the legitimate receiver, and derive lower and upper bounds on its maximal achievable secrecy rate${R_N}$. The relationship between the average secrecy capacity${\bar C_N}$of Gaussian wiretap channel and the average secrecy rate${\bar R_N}$achieved by the null-space jamming scheme is investigated, and we prove that${\bar R_N} \leq {\bar C_N} \leq {\bar R_{N + 1}}$. Based on this inequality and the derived lower and upper bounds on${R_N}$, the upper and lower bounds on the average secrecy capacity of Gaussian wiretap channel aided by an external jammer can be obtained, where our result shows that when$N > M$, the average secrecy capacity increases linearly with the total transmit power; while when$N \leq M - 1$, there exists a performance ceiling on it.

This paper considers the security of transmission in buffer-aided decode-and-forward cooperative wireless networks. An eavesdropper which can intercept the data transmission from both the source and relay nodes is considered to threaten the security of transmission. Finite size data buffers are assumed to be available at every relay in order to avoid having to select concurrently the best source-to-relay and relay-to-destination links. A new max-ratio relay selection policy is proposed to optimize the secrecy transmission by considering all the possible source-to-relay and relay-to-destination links and selecting the relay having the link which maximizes the signal to eavesdropper channel gain ratio. Two cases are considered in terms of knowledge of the eavesdropper channel strengths: exact and average gains, respectively. Closed-form expressions for the secrecy outage probability for both cases are obtained, which are verified by simulations. The proposed max-ratio relay selection scheme is shown to outperform one based on a max-min-ratio relay scheme.

In this paper, we develop a new proactive estimation method to obtain the cross-channel gain from cognitive transmitter to primary receiver without any backhaul between cognitive and primary users. In conventional proactive methods, the jamming signal is used for probing, which introduces the extra interference to primary receivers. In our method, the relayed primary signal is used for probing, which instead assists the primary transmission. Simulation results demonstrate that the proposed method with 2% estimation errors can obtain up to about 72% throughput improvement introduced by the cross-channel gain.

This paper considers the problem of reconstructing sparse or compressible signals from one-bit quantized measurements. We study a new method that uses a log-sum penalty function, also referred to as the Gaussian entropy, for sparse signal recovery. Additionally, in the proposed method, the sigmoid function is introduced to quantify the consistency between the measured one-bit quantized data and the reconstructed signal. A fast iterative algorithm is developed by iteratively minimizing a convex surrogate function that bounds the original objective function. This leads to an iterative reweighted process that alternates between estimating the sparse signal and refining the weights of the surrogate function. Connections between the proposed algorithm and other existing methods are discussed. Numerical results are provided to illustrate the effectiveness of the proposed algorithm.

In this paper, we propose a buffer-aided relaying protocol for a multiuser relay network consisting of N sources, a relay with a buffer, and a destination. All nodes are equipped with a single antenna and operated in a decode-and-forward half-duplex mode. We assume the direct source-destination links do not exist. Different from the conventional multiuser relaying that the half-duplex relay always alternates between reception and transmission in successive time slots, the proposed protocol enables the relay to choose link to transmit based on the channel state information (CSI). The optimal link selection is derived from throughput maximization under the buffer stability constraint. We solve this optimization problem by relaxing the discrete constraints and applying the Karush-Kuhn-Tucker (KKT) conditions. The threshold of the link selection strategy is obtained under Rayleigh fading channel conditions. The numerical results show that the buffer-aided multiuser relay network with the optimal link selection strategy achieves significant throughput gains compared with conventional multiuser relaying that use the best user selection scheme (BUS) with fixed transmission schedule.

We consider the problem of interference alignment (IA) for the K-user constant multiple-input multiple-output interference channel (K-user MIMO IFC) over correlated channels with imperfect channel state information (CSI). Recent performance evaluations show that most of the existing IA algorithms suffer serious sum rate degradations when the available CSI is imperfect. To deal with this issue, an uplink-downlink (UL-DL) Average-Mean-Square-Error(AMSE) duality is firstly established for the K-user MIMO IFC. Based on this duality, a robust IA algorithm is developed. Numerical results show that the proposed algorithm not only achieves better sum rate performance than other existing algorithms, but can also accommodate to the case when the perfect CSI is not available.

In this paper, we will propose a novel method to estimate the cross channel gain between cognitive transmitter to primary receiver as well as the primary channel gain between primary transceivers, where the cognitive user is acting as a relay to proactively trigger the primary link adaptation. But, this kind of estimator may obtain two possible estimations for each channel, which may confuse the cognitive user. Thus, we will further develop a selection method to pick the estimation with less errors. Simulation results show that the proposed method can effectively improve the estimation performance.

We consider the problem of transmitting multiple spatially distributed correlated sources to a common destination (e.g. a fusion center or an access point) in wireless sensor networks (WSNs). The correlated data from multiple sensors are jointly transmitted to the destination via orthogonal channels. We assume that the channel between each sensor and the receiver is multiple-input multiple-output (MIMO), with each sensor and the receiver equipped with multiple transmit/receive antennas. In this framework, we study the problem of joint linear precoder design for all sensors by assuming the knowledge of the instantaneous channel state information (CSI), aiming at maximizing the mutual information between the sources and the received signals at the destination. We propose a Gauss-Seidel iterative approach which successively optimizes the precoding matrix associated with each sensor, while fixing the other precoding matrices. Numerical results show that the proposed algorithm that takes into account the spatial correlation across sensors can achieve higher capacity than conventional methods that neglect the spatial correlation.

In cognitive radio systems, the channel gains between primary users (PUs) and cognitive users (CUs) and that between PUs are critical for the coexistence of CUs and PUs. In this paper, we propose a proactive channel gain estimation approach by using the received primary signal for probing, which obtains both kinds of channel gains without information exchange between CUs and PUs. In average, the probing in our proactive approach does not introduce interference to PUs while conventional ones usually do. Simulation results show that the relative estimation errors of the proposed approach are below 0.02 with a proper CU location, where the channels suffer path loss and shadowing, and their gains range from about -120 dB to about -50 dB.

In this paper, a joint source and relay optimization problem is studied for a multiuser multiple-input multiple-output (MIMO) relay system. Assuming that the channel state information (CSI) at the source and relay is available, two amplify and forward (AF) relaying schemes are proposed under the criterion of maximizing the sum-rate. First, a scheme which iteratively searches the optimal source and relay matrices by deriving the partial derivatives of the sum-rate and applying the gradient search algorithm is proposed. Next, in order to reduce the computational complexity, an alternating method utilizing the equivalent channel method is developed. This method also resorts to a so called maximum-signal-leakage-and-noise-ratio (SLNR) that can suppress the co-channel interference (CCI) and noise at the users effectively. Theoretical analysis and Monte Carlo simulation illustrate the performance of the both schemes.

Differential Frequency Hopping (DFH) is a novel technique for the frequency-hopping system in HF (High Frequency) bands. The frequency transition function (FTF) and the signal detection methods are the key issues for DFH system. Symbol-by-symbol detection and sequence detection are adopted in DFH system. With these two detection methods, the symbol error rate (SER) performances of DFH system are analyzed over a Rayleigh-fading channel with multi-tone jamming (MTJ) and additive white Gaussian noise (AWGN). Simulations validate the analyses. The results of analyses and simulations prove that the DFH system with sequence detection can achieve better anti-jam (AJ) performance than that of conventional frequency-hopping (FH) system.

This paper presents a type of frequency-hopping system in HF (High Frequency) bands, which is called Differential Frequency Hopping (DFH) system. The frequency transition function (FTF) and the methods of signal detection are the key technologies for DFH system. The error-rate performances of DFH with Partial Band Jamming (PBJ) and additive white Gaussian noise (AWGN) over a Rayleigh-fading channel by using product-combining receiver (PCR) are studied analytically, the performances are validated by simulation results, performance comparisons among the various receivers show that the anti-jam (AJ) performance of DFH system with PCR outperforms that with linear-combining receiver (LCR).

Differential frequency hopping (DFH) is a novel technique for the frequency-hopping system in HF (high frequency) bands. The frequency transition function (FTF) and the signal detection methods are the key issues for DFH system. A novel non-coherent detection scheme is proposed in asynchronous multi-user environments and additive white Gaussian noise (AWGN) channels. The multi-user performance of DFH with this novel receiver is studied analytically and validated by simulation results. By comparing of synchronous multi-user system and asynchronous multi-user system with conventional non-coherent detection receiver, it is shown that the performance of asynchronous multi-user system outperform that of synchronous multi-user system, and the novel detection scheme can achieve better performance than the conventional non-coherent detection scheme in asynchronous multi-user environments

A type of frequency-hopping system in HF (high frequency) bands, differential frequency hopping (DFH) system, is presented in this paper. The frequency transition function (FTF) and the methods of signal detection are the key issues for DFH system. Symbol-by-symbol detection and sequence detection are adopted in DFH system. With these two detection methods, the symbol error rate (SER) performances of DFH system are analyzed over a Rayleigh-fading channel with partial band noise jamming (PBNJ) and additive white Gaussian noise (AWGN). Simulations validate the analyses. The results of analyses and simulations prove that the DFH system with sequence detection can achieve better anti-jam (AJ) performance than that of conventional frequency-hopping (FH) system.