Hybrid non-orthogonal multiple access (H-NOMA), which combines the advantages of pure NOMA and conventional OMA, has emerged as a highly promising multiple access technology for future wireless networks. While recent studies have proposed various H-NOMA systems using different successive interference cancellation (SIC) methods, their analyses typically assume a fixed channel gain order between paired users. However, in practice, user pairing is often configured typically based on long-term network deployment requirements or statistical channel characteristics (e.g., geographic layout or average channel gain) rather than instantaneous channel states. This practical pairing strategy leads to random channel gain ordering, where the relative channel gains between paired users are inherently stochastic and time-varying. This aspect is critical and fundamentally affects system performance, yet remains understudied. To address this issue, this paper analyzes the performance of three H-NOMA schemes under such random channel gain ordering: (a) fixed-order SIC (FSIC) aided H-NOMA; (b) hybrid SIC with non-power adaptation (HSIC-NPA) aided H-NOMA; and (c) hybrid SIC with power adaptation (HSIC-PA) aided H-NOMA. For the opportunistic users seeking to maximize data rate, the closed-form expressions for the probability that each H-NOMA scheme underperforms conventional OMA are derived rigorously. Asymptotic analyses in the high SNR regime are also developed. Simulation results validate the theoretical derivations and demonstrate the performance of the H-NOMA schemes across different SNR scenarios, thereby offering foundational insights for deploying robust H-NOMA in next-generation wireless systems.

In vehicle-to-everything (V2X) scenarios, the high dynamic characteristics of V2X environments impose significant challenges on communication channel estimation, where the emerging integrated sensing and communication technology could serve as a vital tool for achieving accurate channel estimation. This paper leverages radar-sensed angle information to assist in communication channel estimation and proposes a deep unfolding-based radar-assisted channel estimation network (Radar-CEnet). Specifically, for the radar module, to address the challenges posed by insufficient data in imperfect arrays, we employ a model-agnostic meta-learning with a convolutional neural network (MAML-CNN) approach to achieve high-precision direction-of-arrival (DOA) estimation. Then, the angle information obtained by the radar module, as prior knowledge, is used for channel estimation. Building on this, we design a novel soft-thresholding shrinkage function and propose the Radar-CEnet algorithm to efficiently estimate the sparse channel. Finally, we rigorously prove the convergence of the Radar-CEnet algorithm and demonstrate that it achieves a lower estimation error. Experimental results show that the proposed Radar-CEnet outperforms existing traditional methods and deep learning-based approaches in channel estimation performance. At an SNR of 20dB, the proposed Radar-CEnet method reduces the NMSE from –23.75dB to –27.15dB compared to the learning-based iterative soft-thresholding method, achieving an estimation accuracy improvement of approximately 54%.

A novel near-field wideband integrated sensing and communication framework is proposed to address the prohibitively high pilot overhead challenge in extremely large-scale MIMO systems. Unlike conventional approaches that rely on exhaustive two-dimensional codebook search, a unified architecture leveraging true-time-delay-based rainbow beamforming with controllable distance-dependent beam squint is proposed to extend spatial coverage. Furthermore, the inter-antenna phase ambiguity is harnessed to introduce beam split phenomena, enabling simultaneous multi-angle and multi-distance sensing within a single pilot transmission. Based on this architecture, a two-stage low-complexity sensing protocol is carried out, where distance-ring identification via beam-split-enhanced rainbow beams is performed in the first stage using sub-array structures, followed by angle refinement in the second stage. To mitigate frequency-dependent beamwidth variations, a frequency-compensated joint reconstruction algorithm based on virtual grid mapping and sparse optimization is proposed. Additionally, an echo-aided velocity estimation method exploiting intra-symbol Doppler diversity across subcarriers is developed, eliminating the need for multiple pulse transmissions. Simulation results demonstrate that: 1) complete spatial coverage is achieved with only two OFDM symbols, representing over 98% overhead reduction compared to exhaustive search methods; 2) the proposed scheme achieves superior localization accuracy with root-mean-square errors below 0.001 in normalized angle domain and 0.01 in distance-ring domain at moderate SNR; 3) communication rates are improved by 7% to 15% compared to conventional near-field beam training approaches under identical pilot budgets.

Hybrid non-orthogonal multiple access (H-NOMA) technology, which combines the benefits of non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) through flexible resource allocation in a single transmission, has shown great potential for enhancing the performance of wireless communication systems. To further exploit the potential of H-NOMA, this paper proposes a novel design of H-NOMA which jointly incorporates hybrid successive interference cancellation (HSIC) and power adaptation (PA) in the NOMA transmission phase, by introducing a power adaptation factor . For a given power reducing coefficient β, which ensures that the energy consumption of the proposed scheme is lower than that of conventional OMA, the probability that the achievable rate of the proposed HSIC-PA aided H-NOMA scheme fails to outperform its OMA counterpart is derived in closed form. Besides, the impact of user pairing is considered. Furthermore, the asymptotic analysis shows that the aforementioned probability of the proposed H-NOMA scheme can approach zero in the high signal-to-noise ratio (SNR) regime without constraints on either users’ target rates or transmit power. By dynamically adjusting the transmission power of the opportunistic user and the decoding order of HSIC, signal interference between the legacy user and the opportunistic user can be effectively controlled, thereby improving the achievable rate and energy efficiency of the opportunistic user. This represents a significant improvement over conventional H-NOMA schemes, which require specific restrictive conditions to make the probability that their achievable rate underperforms OMA approach zero at high SNR, as shown in existing work. The above observation indicates that, with lower energy consumption, the proposed HSIC-PA aided H-NOMA can achieve a higher data rate than pure OMA with probability 1 at high SNR, leading to improved energy efficiency. Finally, numerical results are provided to verify the accuracy of the analysis and also to demonstrate the superior performance of the proposed H-NOMA scheme.

Integrated sensing and communication (ISAC) is a key technology for improving spectrum efficiency and enabling intelligent wireless networks, yet its deployment in short-packet transmission scenarios faces significant challenges such as finite block-length (FBL) effects, channel estimation uncertainty, and limited coverage. To address these issues, this paper investigates a short-packet ISAC system assisted by a hybrid simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and proposes a two-stage ISAC transmission paradigm. In Stage I, the hybrid STAR-RIS performs target direction-of-arrival estimation, and a closed-form expression of Cramér–Rao Bound (CRB) is derived to establish channel state information (CSI) uncertainty model based on CRB. Meanwhile, each user performs channel estimation locally and feeds results back to DFBS. In Stage II, the estimated CSI is utilized to jointly design resource allocation, and an optimization problem is formulated to maximize target illumination power under FBL and CSI uncertainty constraints. To tackle this strongly coupled non-convex problem, we develop a hierarchical solution strategy: the sensing duration is first determined via one-dimensional search, and then, an alternating optimization framework is employed to decouple the problem into DFBS beamforming and hybrid STAR-RIS coefficient optimization, where iterative algorithms based on semi-definite relaxation, semi-definite programming, and singular value decomposition are proposed to ultimately obtain a convergent optimal solution. Simulation results validate the fast convergence and superior performance of our proposed algorithm, reveal the inherent trade-off between the two stages under constrained resources, and demonstrate the importance of joint two-stage resource design assisted by hybrid STAR-RIS in enhancing short-packet ISAC system performance.

As the demand for ubiquitous connectivity and environmental awareness continues to rise in the 6G era, Integrated Sensing and Communication (ISAC) technology has become a key pathway to realizing high-precision sensing and intelligent awareness services. To explore the application potential of ISAC in passive target localization, we build a passive bistatic sensing system on a 26 GHz millimeter-wave communication platform and conduct measurement experiments in an open square scenario. The system estimates multipath propagation parameters from the received sensing channel and, combined with a power-weighted clustering method, extracts multipath features to jointly localize static scatterers and moving targets. Experimental results show that, without requiring any involvement of the target device, the system attains sub-meter passive localization accuracy. The study validates the feasibility and practical value of millimeter-wave ISAC for high-precision sensing and localization in complex environments.

This paper aims to reveal the potential of hybrid non-orthogonal multiple access (NOMA) in improving energy efficiency, which combines the advantages of NOMA and conventional orthogonal multiple access (OMA). In particular, a novel hybrid NOMA scheme is proposed, which can be implemented as an simple add-on to the legacy OMA network. Specifically, in the proposed hybrid NOMA scheme, a user can transmit signals by using not only its own allocated channel resource block as in OMA, but also sharing the channel of other users via NOMA. To release the potential of hybrid NOMA, a flexible successive interference cancellation (SIC) method is adopted. Rigorous analysis is provided, which indicates that even with less energy consumption, the proposed hybrid NOMA scheme offers a higher data rate than the conventional OMA scheme. The numerical results support the analysis and highlight the superior performance of HSIC in comparison to FSIC.

With the rapid advancement of intelligent railway systems, a high-quality train-ground communication system is crucial. However, ensuring reliable wireless communication in ultra-high-speed environments remains a significant challenge due to severe Doppler effects, frequent inter-cell handovers, and diverse QoS demands. Existing solutions, such as soft/hard handover schemes, lack the flexibility to adapt to dynamic conditions, leading to service interruptions and suboptimal performance. In this paper, we propose a dynamic resource allocation strategy based on dual base station coordination, utilizing millimeter-wave (mmWave) technology and real-time train position prediction. This approach dynamically optimizes user association and spectrum allocation to accommodate the rapid movement of trains, reducing service interruptions and ensuring continuous high-quality communication. Simulation results show that our algorithm improves system QoS satisfaction by 37.5%-68.8% and achieves spectrum utilization rates between 76% and 90%, outperforming the comparison schemes. These results validate the effectiveness of our approach in addressing high-speed mobility challenges.

The deployment of space–air–ground integrated networks (SAGIN) is critical for addressing signal coverage challenges in high-speed railway (HSR) tunnel scenarios. However, when aerial access networks and ground base stations (BSs) simultaneously serve trains, co-frequency interference can severely degrade system performance, preventing the satisfaction of Quality of Service (QoS) requirements for flows. To address this issue, this article formulates an optimization problem aimed at maximizing the number of scheduled flows through transmission optimization for HSR tunnel communications in SAGIN. Subsequently, a link selection algorithm is proposed to identify valid transmitter-receiver associations by filtering potential link combinations based on the signal-to-interference-plus-noise ratio (SINR) threshold, ensuring that only feasible associations are selected to meet each flow’s QoS requirement. To improve the number of successfully scheduled flows, a graph theory-based transmission optimization (GTTO) algorithm is developed. This approach effectively avoids the simultaneous transmission of highly interfering links by introducing an interference factor and reorders the scheduling sequence of flows to prioritize those associated with links that have higher SINR. The proposed method is evaluated through simulations, demonstrating its ability to substantially improve the number of scheduled flows and system throughput under varying conditions, such as different train speeds, QoS requirements, airship altitudes, tunnel lengths, and the number of mobile relays (MRs). The results also demonstrate the superiority of the proposed method over conventional approaches, achieving reliable communication and enhanced performance across HSR tunnel scenarios.

With the rapid development of 6G communication and the wide deployment of high-speed rail (HSR), it becomes essential to enhance the utilization of HSR communication resources while ensuring the requirements of communication-sensitive users for high reliability and low latency. Meanwhile, the development of integrated sensing and communication (ISAC), brings more inspiration for smart HSR. In this background, we model an ISAC and hyper-reliable low-latency communication (HRLLC) system for UAV-assisted HSR. We formulate a mixed integer nonlinear programming problem (MINLP) with the objective of maximizing the fair sum rate while satisfying the minimum radar sensing requirement. To solve this problem of nonconvex and high coupling, we propose a hybrid particle swarm optimization-genetic algorithm (PSO-GA) that combines the fast convergence of PSO-only (PSO) and the strong global search ability of GA, with parameter-free penalty functions. Through careful design, PSO-GA dynamically balances the exploration and development capabilities. It achieves the best overall performance with a faster convergence speed than existing algorithms. An average improvement of 29%, 57%, and 42% has been achieved with different numbers of passengers, total transmission power, and number of resource blocks. This article supports the future development of intelligent HSR communication.

Secure communication is crucial in many emerging systems enabled by uncrewed aerial vehicle (UAV) communication networks. To protect legitimate communication in a chaotic UAV environment, where both eavesdropping and jamming become straightforward from multiple adversaries with line-of-sight signal propagation, a new reliable and integrated physical-layer security mechanism is proposed in this article for a massive multiple-input-multiple-output (MIMO) UAV system. Particularly, a physical-layer fingerprint, also called a tag, is first embedded into each message for authentication purpose. We then propose to reuse the tag additionally as a reference to encode each message to ensure secrecy for confidentiality enhancement at a low cost. Specifically, we create a new dual-reference symmetric tag generation mechanism by inputting an encoding-insensitive feature of plaintext along with the key into a hash function. At a legitimate receiver, an expected tag, reliable for decoding, can be symmetrically regenerated based on the received ciphertext, and authentication can be performed by comparing the regenerated reference tag to the received tag. However, an illegitimate receiver can only receive the fuzzy tag which can not be used to decode the received message. Additionally, we introduce artificial noise (AN) to degrade eavesdropping to further decrease message leakage. To verify the efficiency of our proposed tag-based encoding (TBE) scheme, we formulate two optimization problems, including ergodic sum secrecy rate maximization and authentication fail probability minimization. The power allocation solutions are derived by difference-of-convex (DC) programming and the Lagrange method, respectively. The simulation results demonstrate the superior performance of the proposed TBE approach compared to the prior AN-aided tag embedding scheme.

In the space-air-ground integrated network (SAGIN), high-speed railway (HSR) communication is expected to be enhanced by simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS). However, when both aerial platforms and ground base stations (BSs) provide services to HSR user equipments (UEs), severe signal interference can arise, leading to the system failing to meet the quality of service (QoS) requirements of the flows. In this paper, we introduce the optimization problem of transmission scheduling for STAR-RIS assisted train-to-ground communications in SAGIN. To address this issue, a phase optimization algorithm of passive STAR-RIS is proposed to improve the channel quality of HSR UEs. Furthermore, a coalition game algorithm is proposed to associate HSR UEs with the optimal links that minimize inter-flow interference. Finally, a QoS-aware flow scheduling algorithm is proposed to optimize the order of the flows for the selected links. Simulation results confirm that the proposed scheduling scheme effectively increases the number of completed flows and total transmitted bits for STAR-RIS assisted train-to-ground communications in SAGIN, outperforming traditional methods.

During the check node (CN) update, the elements of input message vectors are redundant for the output message vectors. Hence, in this paper, we exactly select from the input message vectors the elements, which really have contributions to the error-correction performance and constitute the minimum set for the CN update. With adoption of the forward and backward (FB) scheme, an adaptive minimum-set min-sum algorithm (AMSA) is proposed to reduce the computation complexity of the FB process. In order to concurrently update the output vectors belonging to the same CN, we present a parallel minimum-set min-sum algorithm (PMSA) with lower memory complexity than the AMSA. Compared with the min-sum algorithms, the two proposed minimum-set based algorithms introduce no error performance loss.

In this paper, we study the deep learning (DL) based channel extrapolation problem and conduct the over-the-air (OTA) antenna extrapolation and frequency channel interpolation test for the 3rd generation partnership project (3GPP) long-term evolution (LTE) time-division duplex (TDD)-like orthogonal frequency division multiplexing (OFDM) massive MIMO prototype. We first present measurement campaigns using universal software radio peripherals (USRP) at 3.5 GHz, where the base station (BS) is composed of a 64-element antenna array. A DL-based antenna extrapolation network is then designed to approximate the inner deterministic function among antennas from the attained channel data within the “training” pilots. We present an antenna selection network (ASN) that can select a limited number of antennas for the best extrapolation, which outperforms the uniform antenna selection in terms of channel reconstruction and signal detection. We also design a deep residual neural network for channel interpolation. The performance of the extrapolated channel is evaluated in terms of normalized mean squared error (NMSE) in comparison to the measured channels on all antenna ports or the full pilot-aided channels in all OFDM subcarriers. Experimental results show that ASN can reduce an average of 87.5% antenna ports and maintain channel estimation NMSE by$10^{-2}$when compared to 3GPP channel estimation protocols.

Hybrid non-orthogonal multiple access (NOMA), which organically combines pure NOMA and conventional OMA, has recently received significant attention to be a promising multiple access framework for future wireless communication networks. However, most of the literatures on hybrid NOMA only consider fixed order of successive interference cancellation (SIC), namely FSIC, for the NOMA transmission phase of hybrid NOMA, resulting in limited performance. Differently, this paper aims to reveal the potential of applying hybrid SIC (HSIC) to improve the energy efficiency of hybrid NOMA. Specifically, a HSIC aided hybrid NOMA scheme is proposed, which can be treated as a simple add-on to the legacy orthogonal multiple access (OMA) based network. The proposed scheme offers some users (termed “opportunistic users”) to have more chances to transmit by transparently sharing legacy users’ time slots. For a fair comparison, a power reducing coefficient$\beta $is introduced to ensure that the energy consumption of the proposed scheme is less than conventional OMA. Given$\beta $, the probability for the event that the achievable rate of the proposed HSIC aided hybrid NOMA scheme cannot outperform its OMA counterpart is obtained in closed-form, by considering impact of user pairing. Furthermore, asymptotic analysis shows that the aforementioned probability can approach zero in the high SNR regime under some given conditions, which are compositely determined by the users’ transmit powers, primary user’s target data rate and$\beta $, indicating that the energy efficiency of the proposed scheme is almost surely higher than that of OMA for these given conditions. Numerical results are presented to verify the analysis and also demonstrate the benefit of applying HSIC compared to FSIC.

A channel measurement system for accurate modeling of the millimeter-wave (mmWave) band air-to-ground (A2G) wireless channel is presented. The measurement system consists of a ground station on a small cart and an air station installed on a custom-made octocopter unmanned aerial vehicle (UAV). Channel sounders at 26 GHz mmWave band with a bandwidth of 1 GHz are mounted on the air station and the ground station. In contrast to the on-the-shelf drones, the custom-made UAV offers the advantages of greater payload capacity and extended flight endurance. The measurement campaign was conducted in a university campus scenario, providing valuable measurement data for further analyses of the A2G channel. To analyze multipath effects in the A2G channel, simulation result of a ray tracing (RT) model is used to identify the principal propagation effects in the actual measurements. Subsequently, the path-loss exponent and the standard deviation of shadowing are calculated based on the measurement. Furthermore, sparsity of the A2G mmWave channel is evaluated through analyses of the Gini index and the Rician$K$factor. By comparing with the measurement results in the different propagation scenarios presented in the related work, e.g., industrial Internet-of-Things (IIoT), vehicular urban, and laboratory room, unique characteristics of the A2G mmWave channel are discussed.

Emerging as an important enabling technology for smart factories, ultra-reliable low latency communications (URLLC) have attracted extensive attention from academia and industry. In this paper, we aim to improve the performance of downlink URLLC in a smart factory. We first construct the system model based on the 5G New Radio (NR) standard, which specifies the modulation scheme, resource block structure and achievable data rates under finite blocklength codes (FBC). Next, since it is challenging to fulfill all transmission requests with limited radio and power resources, we formulate the problem to maximize the network throughput while considering delay and reliability constraints. This is a mixed integer non-convex nonlinear problem that is difficult to solve directly. To be tractable, we decompose it into two sub-problems, and apply the alternating optimization to obtain a sub-optimal solution. Specifically, the flow scheduling sub-problem is transformed into a matching game (MG) and solved by a delayed acceptance-based algorithm. Also a local water-filling algorithm is utilized to solve the power allocation sub-problem. Simulation results reveal that our proposed scheme outperforms other benchmark schemes.

With the increasing demands from passengers for data-intensive services, millimeter-wave (mmWave) communication is considered as an effective technique to release the transmission pressure on high speed train (HST) networks. However, mmWave signals encounter severe losses when passing through the carriage, which decreases the quality of services on board. In this paper, we investigate an intelligent refracting surface (IRS)-assisted HST communication system. Herein, an IRS is deployed on the train window to dynamically reconfigure the propagation environment, and a hybrid time division multiple access-nonorthogonal multiple access scheme is leveraged for interference mitigation. We aim to maximize the overall throughput while taking into account the constraints imposed by base station beamforming, IRS discrete phase shifts and transmit power. To obtain a practical solution, we employ an alternating optimization method and propose a two-stage algorithm. In the first stage, the successive convex approximation method and branch and bound algorithm are leveraged for IRS phase shift design. In the second stage, the Lagrangian multiplier method is utilized for power allocation. Simulation results demonstrate the benefits of IRS adoption and power allocation for throughput improvement in mmWave HST networks.

As research on integrated sensing and communication (ISAC) progresses, it has been discovered that ISAC can be effectively utilized to enhance the security of wireless communications. Its sensing function can assist in both eavesdropping detection and physical-layer security techniques. In this article, our focus lies on addressing the security challenges associated with high-speed train-to-ground communication using ISAC technology. We explore a novel secure communication scheme. Specifically, we exploit the sensing capabilities of ISAC to detect eavesdropping at the receiving end and subsequently establish a signal blind zone at the location where eavesdropping occurs through beamforming and waveform optimization techniques. This approach ensures the achievement of secure wireless communication. Mathematically modeling the problem as an optimization problem, we derive a lower bound for simplification purposes. Subsequently, we employ an alternating optimization algorithm to iteratively find suboptimal solutions for the optimization variables. Through extensive simulation experiments and comparative analysis, we demonstrate that our proposed algorithm not only guarantees communication security but also outperforms existing algorithms in terms of efficiency.

The Industrial Internet of Things (IIoT) enables machines to communicate robustly. High reliability, high throughput, and low latency are the critical capabilities of IIoT, which have posed great challenges to existing wireless solutions for industrial applications. Due to the vast available bandwidth, the emerging millimeter-wave (mmWave) technology is promising to address this bottleneck. However, the propagation behaviors at such high frequencies in the harsh industrial environment have yet been well understood. In this work, extensive measurements have been conducted in a representative industrial application scenario using a 2-GHz wideband directional channel sounder in the 28-GHz mmWave band. By exploiting the measurement with excellent resolution, the multipath components’ (MPCs) delay-angular space is transformed onto the scatter points (SPs) in the propagation environment. An effective clustering algorithm is then proposed to cluster the SPs without prior knowledge and iterations. Through a geometrical optics analysis, the SP clusters are classified corresponding to the reflectors. By doing this, the cluster-generating reflectors are reduced to a quasi-deterministic (QD) channel model that ensures spatial consistency and MPCs’ stochastic dispersion. Finally, it is shown that the measurement data agrees well with the proposed QD model, indicating the high fidelity of the proposed model.

The cellular-vehicle-to-everything (C-V2X) communication network is constantly evolving and changing the way people travel. To realize connected automated vehicles, both precise positioning and reliable communications of vehicles and associated terminals are demanding. Since the orthogonal frequency division multiplexing (OFDM) scheme is vulnerable to Doppler spread under high-mobility, the orthogonal time frequency space (OTFS) modulation is proposed recently to tackle this challenge based on the sparsity and stability of the channel spreading function. To this end, this article proposes a waveform design for V2X based on OTFS modulation, named orthogonal time frequency code space modulated waveform (OTFCSMW). The waveform design is able to realize random access and positioning simultaneously. In detail, the transceiver design of OTFCSMW is introduced, where orthogonal spreading sequences are utilized to provide spreading gain and represent terminal identifications based on the proposed orthogonal spreading combinations. Then a joint time-of-arrival (ToA) estimation and channel estimation strategy is proposed. The ToAs of terminals are estimated based on the sparsity of taps in the channel spreading function, and remaining unknown channel parameters are estimated based on the minimum-mean-square-error (MMSE) principle. Finally, the equalization scheme for OTFCSMW based on MMSE principle is proposed. Simulation results demonstrate that OTFCSMW can realize similar positioning performance to OFDM and outperforms the orthogonal-spreading-based-OFDM-waveform (S-OFDMW) scheme on bit error rate (BER) in different V2X channel environments.

Ambient backscatter communication (AmBC) has emerged as a paradigm distinguished by its energy-efficient attributes and low-power dynamics, ideally suited to address the vast expanse of the Internet of Things (IoT). Unmanned aerial vehicles (UAVs) deployed with flexibility can effectively establish wireless connections for isolated IoT devices through AmBC. This paper delves into the exploration of secure transmission within a UAV-assisted AmBC network, particularly addressing the challenges posed by the presence of a passive eavesdropper. Specifically, a UAV is utilized as an aerial base station to offer services to an isolated ground user, an AmBC tag transmits its information to its associated receivers by leveraging the UAV’s radio frequency (RF) signals. Furthermore, a multi-antenna cooperative jammer is integrated within the system to intentionally interfere with the eavesdropper without affecting legitimate receivers. To characterize the secrecy performance, the expressions of secrecy outage probability of the air-ground link and backscatter link are both deduced leveraging a two-layer Gaussian-Chebyshev quadrature. Moreover, the asymptotic behaviors under the high signal-to-noise ratio (SNR) regime are also analyzed. Monte Carlo simulations are performed to validate the correctness and effectiveness of the analytical results.

Orthogonal time frequency space (OTFS) modulation is a promising technology for communications under high mobility in the sixth-generation (6G) communications system. To enable machine-type communications (MTC) with high mobility in 6G, researchers have considered designing multiple access (MA) technologies based on OTFS modulation. The reliability and connectivity of OTFS-MA are highly correlated with the characteristics of the channel spreading function. In the spectrum-limited MA system where the channel spreading function is not sparse and compact enough, the system device capacity of OTFS-MA schemes is limited. To this end, a grant-free MA scheme, named orthogonal time frequency code space modulation enabled multiple access (OTFCSMA) is proposed in this article. In general, orthogonal code domain resources are introduced into OTFCSMA to enhance device connectivity and transmission reliability of MA systems based on OTFS modulation. In detail, firstly, the characteristic of the realistic channel spreading function is described, especially the reduced sparsity and compactness of the channel in the spectrum-limited system. Secondly, the principles for designing OTFCSMA are described, including orthogonal spreading/despreading, data interleaving/deinterleaving, device identification, two-stage channel estimation, and data recovery strategies. Thirdly, the system device capacity and the system complexity of OTFCSMA are analyzed. Fourthly, a date-block-wise device connectivity scaling-up scheme for OTFCSMA is proposed, based on which the exponential system user capacity growth is realized. Finally, the performances of OTFCSMA on transmission reliability and device connectivity are demonstrated, and the gain brought by orthogonal spreading is analyzed.

Computation offloading tends to be an effective way for mitigating computing pressure of user equipments (UEs). By computation offloading, the task can be handled in network edge and/or cloud center to compensate insufficient resources and capabilities of UEs. In this study, we construct a three-tier cloud-edge integration network, where user tasks are offloaded to satellite based edge server and further to the remote ground cloud server via backhaul links. The optimization problem is modeled for minimizing system energy consumption and considers user association, power allocation, task scheduling, and bandwidth assignment jointly. By the proposed schemes based on relaxation transformation and fractional programming, four subproblems are transformed into corresponding convex optimization problems and solved respectively. In order to find the global optimal solutions, a joint iterative algorithm for three-tier computation offloading problem is designed. In numerical simulations, we compare different communication schemes and computation offloading schemes to present the rationality and superiority of the designed algorithm for reducing system energy consumption.

At present, in order to make use of sufficient spectrum resources to provide even better quality of service and with the development of millimeter wave (mm-wave) communications technology, high speed railway (HSR) communication systems have also taken mm-wave frequency band into consideration. However, since the train runs with high speed as well as the operating environment is complex and dynamic, which may cause the communication link blockage issue for a while. To solve the problem, we adopt the emerging innovative intelligent reflecting surface (IRS) technology to enhance the robustness of mm-wave HSR communication system by introducing a reflection link. Therefore, when the direct communication link is blocked in some case, the reflection link can ensure that the communication is not interrupted. In this paper, we focus on maximizing the number of flows meeting their QoS requirements. A robust IRS-assisted scheduling scheme is proposed under the constraints of half duplex transmission, transmit power, IRS phase shift and limited time slots. As the formulated problem is non-convex, it is difficult to solve directly. Therefore, we divide it into four subproblems, and utilize alternative optimization method to get a sub-optimal solution. The simulation results show that compared with the other three baseline schemes, the IRS-assisted transmission scheduling algorithm proposed in this paper can improve the system performance effectively.

Massive machine-type communication (mMTC) is an important scenario for 5G and future 6G networks, as it can provide massive connectivity for internet of things (IoT) devices. However, the large number of supported devices raises challenges to random access with limited spectrum resources. In this paper, we propose a dictionary learning based method for active user detection (AUD) in massive MIMO systems, which leverages the potential spatial channel characteristics of users. Our approach separates users into clusters and reuses the same pilot pool among different clusters, which greatly saves the pilot resource. To resolve collisions caused by the reuse of pilots, we propose a cluster-specific dictionary to differentiate multiple active users of different clusters. Numerical experiments demonstrate the improved performance of the proposed AUD algorithm in comparison to the existing methods.

With the rapid development of wireless communications, the industries and academia acknowledge that the millimeter-wave (mm-wave) frequency band is rich in spectral resources. This paper considers the mm-wave train-ground communication system with multiple mobile relays (MRs) in a high-speed rail (HSR) scenario. We use the deep reinforcement learning (DRL) method to solve the beam management problem to maximize the system throughput. First, the inter-beam interference in the MRs scene is modeled due to the effect of the inter-beam angle on the system performance. Second, the maximization problem of system throughput is constructed for the beam management at both the transmitter and receiver. And we proposed the dynamic joint beam management scheme for the base station and the MRs located on the train’s roof based on DRL. Finally, the performance evaluation shows that the proposed scheme displays low computational complexity in the online phase. Additionally, the system throughput performance is close to the ideal optimal beam tracking algorithm, which combines high performance and low complexity, proving the feasibility of using DRL theory for beam management in train-ground communication.

With the massive deployment of 5G commercial applications, the Internet of Everything promotes the transformation and upgrading of the society production mode. The Internet of Things (IoT) is supported by the massive Machine-Type Communications (mMTCs), which is one of the three major application scenarios of 5G. Recently, a novel spreading-based nonorthogonal multiple access (NOMA) scheme named tandem spreading multiple access (TSMA) has been proposed for grant-free random access in mMTC. However, TSMA only considers the case of single antenna, the connectivity expansion on spatial domain has not been considered. In this article, a multiantenna system scheme of TSMA (MIMO-TSMA) is proposed to scale up user connections. In this scheme, spectrum efficiency can be promoted by sharing the time–frequency resources in different beams. In the meantime, scheme against channel deep fading is considered in this work. The simulation results show that MIMO-TSMA can effectively take advantage of multiple-input–multiple-output and TSMA to enhance the mMTC system performance.

High-speed train (HST) communications are envisioned to provide diversified broadband services by integrating with 5G while the high mobility induces fast-fading channels and potentially degrades the system performance. To address this issue, we investigate an HST communication network empowered by intelligent reflecting surfaces (IRSs) with the multiple-input–multiple-output (MIMO) technology. Statistical channel state information (CSI) is exploited to mitigate the impact of the fast time-varying fading. The transceiver beamforming vectors and the IRS phase shift matrix are optimized to improve the system performance in terms of the outage probability and the ergodic capacity considering the channel uncertainty. First, we derive the analytical expression of the outage probability with a generalized Marcum$Q $-function. Then, we develop an alternating optimization algorithm to minimize the outage probability by capitalizing on the generalized eigenvalue–eigenvectors. Moreover, the ergodic capacity is deduced with statistical CSI and then optimized by analyzing the upper bound with Jensen’s approximation. Extensive simulations show that simulation results are consistent with the theoretical analysis, and the IRS-assisted system significantly outperforms the system without IRS in terms of the outage probability and the ergodic capacity.

In this article, we consider an unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) and relay communication network consisting of a base station (BS), a UAV, and multiple ground users. The UAV acts as both a wireless power transmission source and an uplink communication relay. Specifically, an entire transmission period of the considered system is divided into two stages. In the first stage, the UAV transfers the power to the ground users along a well-optimized flight trajectory and meanwhile, the users transmit data to the UAV using the harvested energy. Subsequently, in the second stage, the UAV flies to the vicinity of the BS and forwards the data to the BS. For the purpose of minimizing the energy consumed by the UAV, we jointly optimize the time durations of the two stages, the UAV’s transmit powers for WPT and data forwarding, as well as its flight trajectory, subject to the constraints of the Quality of Service (QoS), the information forwarding, the energy causality, and the mobility of the UAV. The involved optimization problem is nonconvex and highly intractable. To this end, we propose an efficient alternating algorithm to iteratively solve the two subproblems with respect to the time durations of the two stages and the UAV’s transmit powers and trajectory, respectively. The first subproblem has a closed-form optimal solution and the second subproblem is handled by addressing a surrogate convex problem based on the technique of successive convex approximation. Finally, the simulation results confirm the superiority of our proposed algorithm.

Recently, grant-free orthogonal time–frequency space-based tandem spreading multiple access (OTFS-TSMA) is proposed for machine-type communications (mMTCs) in smart railways environmental sensing. To achieve massive connections with scarce radio resources, OTFS-TSMA combines the advantages of OTFS and TSMA. It shows high connectivity and reliability under time–frequency-selective fading channels. Meanwhile, smart railways require over-horizon and all-weather environmental sensing based on mMTC, and the implementation of both would cost a lot in terrestrial networks. With the development of low-Earth orbit (LEO) satellites, enabling smart railways mMTC over LEO satellite is a potential diagram. However, in this scenario, due to the larger transmission delay and Doppler frequency shift, the time–frequency resource requirements of the OTFS modulation-based system increase significantly and are difficult to meet. To this end, OTFS-TSMA based on differential Doppler shift is proposed in this article. Specifically, in this article, the satellite-to-ground communication system model consisting of three sections is introduced, and the Doppler shift and differential Doppler shift characteristics of access points (APs) are investigated. Next, it is proven that designing OTFS-based multiple access schemes over the LEO satellite based on differential Doppler shift is not only resource-friendly but also has the advantages of service continuity and controllable multiuser interference. Then, the transceiver of differential-Doppler-shift-based OTFS-TSMA and its improved designs are proposed. Finally, the simulation results demonstrate that the proposed transceiver realizes high resource efficiency, collision resolution capability, and reliability for smart railways mMTC over the LEO satellite.

Voice user interface (VUI) brings high efficiency and convenience for the applications of Internet of Things (IoT), meanwhile, it can also cause increasingly serious security issues. The word-level voice liveness detection is proved to be the promising solution to thwart spoofing attacks. However, the complex acoustic feature, diversified attacks, and different interaction distance can severely affects the improvement of detection accuracy. To alleviate this issue, we develop a novel pop noise-based word-level voice liveness detection framework. First, a new voice frame selection method is proposed for determining optimal frames, including short time Fourier transform, low-frequency average energy computation, and sequencing. Then, the acoustic features of the selected frames are calculated by the Gammatone frequency cepstral coefficient (GFCC). Finally, based on these features, a newly built joint voice detector, fusing the self-attentional residual network (ResNet), and light gradient boosting machine (LightGBM), can achieve accurate voice classification. On the popular voice spoofing attack data sets, experimental results show that our proposal significantly outperforming the baseline and the state-of-the-arts models, and it is gender dependent. Moreover, our proposal has good generalization ability for far-field replay voice attack, speech synthesis and voice conversion attacks, and partial fake voice attack. Finally, its effectiveness is verified by the ablation study.

The increased throughput brought by MIMO technology relies on the knowledge of channel state information (CSI) acquired in the base station (BS). To make the CSI feedback overhead affordable for the evolution of MIMO technology (e.g., massive MIMO and ultra-massive MIMO), deep learning (DL) is introduced to deal with the CSI compression task. In traditional communication systems, the compressed CSI bits is treated equally and expected to be transmitted accurately over the noisy channel. While the errors occur due to the limited bandwidth or low signal-to-noise ratios (SNRs), the reconstruction performance of the CSI degrades drastically. As a branch of semantic communications, deep joint source-channel coding (DJSCC) scheme performs better than the separate source-channel coding (SSCC) scheme—the cornerstone of traditional communication systems—in the limited bandwidth and low SNRs. In this paper, we propose a DJSCC based framework for the CSI feedback task. In particular, the proposed method can simultaneously learn from the CSI source and the wireless channel. Instead of truncating CSI via Fourier transform in the delay domain in existing methods, we apply non-linear transform networks to compress the CSI. Furthermore, we adopt an SNR adaption mechanism to deal with wireless channel variations. The extensive experiments demonstrate the validity, adaptability, and generality of the proposed framework.

Edge computing is a commonly used paradigm for providing low-latency computation services by locally deploying computation and storage resources close to the user equipments (UEs). Since the computation resource demand of the offloaded tasks of a UE is naturally a random variable, it is possible that the real-time computation capacity demand of a resource-limited hosting virtual machine (VM) or edge computing server (ECS) is larger than its computation capacity, causing unexpected delay or delay-jitter to the services, which should be avoided if possible, for delay-sensitive applications. We consider an edge computing scenario wherein the transmission links are unmanageable and computation resource demands of VM servers are stochastic. We propose a novel Logistic function-based service reliability probability (SRP) estimation model without specifying the distributions of the resource demands. We study the average SRP maximization problem (ASRPMP) in a VM-based edge computing server (ECS) by jointly optimizing the service quality ratios (SQRs) and the computation resource allocations, and we propose an alternative optimization algorithm (AOA) by decomposing the problem into a resource allocation problem (RAP) and a service quality control problem (SQCP). Based on the derived analytical solutions of the two subproblems, we propose an effective and low-complexity heuristic AOA (HAOA) to solve the ASRPMP. The simulation results obtained from both synthetic Gaussian workload data and PlanetLab trace data demonstrate that, given the same target SQR or computation resource, the proposed method can achieve similar performance compared with the convex AOA (CAOA) method with much higher complexity, and can improve the reliability of the services compared with the baseline weighted allocation method (WAM) in both high and low SRP regimes.

Traffic control is regarded as a key issue to alleviate congestion in internet of vehicles (IoV) machine-type communications (MTC). Recently, many traffic control schemes have been studied, such as access class barring (ACB) scheme and back-off (BO) scheme. However, the dynamics of traffic and the heterogeneous requirements of different IoV applications are not considered in most existing studies, which is significant for the random access resource allocation. In this paper, we consider a hybrid scheme, combining the priority dynamic ACB (PDACB) scheme and BO scheme. The IoV devices are classified depending on different delay characteristics, where the delay-sensitive devices are classified as high priority. The target is to maximum the successful transmission of packets with the success rate constraint by adjusting the various ACB factors. Proximal policy optimization (PPO) algorithm as a unique deep reinforcement learning (DRL) method is utilized in this paper, which can obtain continuous action space and solve for the optimal ACB factors without estimating backlog of nodes. A quick convergence is achieved by designing sensible state space, action space and reward. The access capability of the PDACB traffic control scheme is verified by simulations.

With the development of wireless communication, people have put forward higher requirements for train-ground communications in the high-speed railway (HSR) scenarios. With the help of mobile relays (MRs) installed on the roof of the train, the application of Millimeter-Wave (mm-wave) communication which has rich spectrum resources to the train-ground communication system can realize high data rate, so as to meet users’ increasing demand for broad-band multimedia access. Also, full-duplex (FD) technology can theoretically double the spectral efficiency. In this paper, we formulate the user association and transmission scheduling problem in the mm-wave train-ground communication system with MR operating in the FD mode as a nonlinear programming problem. In order to maximize the system throughput and the number of users meeting quality of service (QoS) requirements, we propose an algorithm based on coalition game to solve the challenging NP-hard problem, and also prove the convergence and Nash-stable structure of the proposed algorithm. Extensive simulation results demonstrate that the proposed coalition game based algorithm can effectively improve the system throughput and meet the QoS requirements of as many users as possible, so that the communication system has a certain QoS awareness.

Reconfigurable intelligent surfaces (RISs) can establish favorable wireless environments to combat the severe attenuation and blockages in millimeter-wave (mmWave) bands. However, to achieve the optimal enhancement of performance, the instantaneous channel state information (CSI) needs to be estimated at the cost of a large overhead that scales with the number of RIS elements and the number of users. In this paper, we design a quasi-static broad coverage at the RIS with the reduced overhead based on the statistical CSI. We propose a design framework to synthesize the power pattern reflected by the RIS that meets the customized requirements of broad coverage. For the communication of broadcast channels, we generalize the broad coverage of the single transmit stream to the scenario of multiple streams. Moreover, we employ the quasi-static broad coverage for a multiuser orthogonal frequency division multiplexing access (OFDMA) system, and derive the analytical expression of the downlink rate, which is proved to increase logarithmically with the power gain reflected by the RIS. By taking into account the overhead of channel estimation, the proposed quasi-static broad coverage even outperforms the design method that optimizes the RIS phases using the instantaneous CSI. Numerical simulations are conducted to verify these observations.

In the past few years, satellite communications have greatly affected our daily lives, and the integrated terrestrial-satellite network can combine the advantages of satellite and base stations (BSs) to provide wider coverage and lower cost. Because the resources of terrestrial-satellite network are limited, how to allocate resources of terrestrial-satellite network through effective methods has become a major challenge. This paper proposes a framework for resource allocation of terrestrial-satellite network based on non-orthogonal multiple access (NOMA). Then, a deployment method of local cache pools is given to achieve lower time delay and maximize energy efficiency in terrestrial-satellite network. In the proposed framework, we adopt a multi-agent deep deterministic policy gradient (MADDPG) method to obtain the maximum energy efficiency by user association, power control, and cache design. The MADDPG algorithm is divided into two stages, users and BSs are set as agents to complete the optimization problem in the framework. Finally, the simulation results show that the proposed method has better optimized performance compared with the traditional single-agent deep reinforcement learning algorithm and can efficiently solve the problems of resource allocation and cache design in the integrated terrestrial-satellite network.

Orthogonal time frequency space (OTFS) modulation is an emerging technology to tackle time-frequency (TF) selective channel in high mobility scenarios. In OTFS, resource is multiplexed in the delay-Doppler (DD) domain. Based on the potential sparsity, separability, stability and compactness of the channel spreading function, OTFS is able to realize lower complexity of channel estimation, higher diversity and higher reliability compared with orthogonal frequency division multiplexing (OFDM). However, the channel spreading function for practical communication systems is rarely considered in the current OTFS-related literature. High-speed railway (HSR) is a typical high mobility scenario with trains travelling at over 200km/h, which has the potential to employ OTFS. To this end, the HSR channel spreading function is characterized and the performance of OTFS in HSR is evaluated based on the realistic channel measurement in this article. Firstly, the HSR channel in TF domain is measured based on the long term evolution railway (LTE-R) network. Then, the characteristics of the channel spreading function are analyzed. In particular, the impact of time domain channel fading on the spreading function is investigated. The characteristics of the measured spreading function are analyzed with the proposed metrics in railway viaduct and tunnel scenarios. Based on the above analysis, an algorithm for generating the channel spreading function is proposed. Feasibility of the proposed DD domain channel generation algorithm is verified through comparing metrics of which to those of the measured channel. By simulating the bit error rate (BER) and mean square channel estimation error performances of OTFS modulation in the practical band-limited systems, it is shown that the impacts of Doppler shift, delay, SFFT and time domain channel fading need be considered for the application of OTFS modulation, in contrast to the state-of-art DD domain channel generation scheme based on tap delay link (TDL) model. For example, compared to OTFS modulation under the ideal channel spreading function, OTFS modulation requires a signal gain greater than 5 dB under the practical channel spreading function affected by above factors, to achieve the same BER less than 10−2 under parameters defined in simulation.

Signal detectors are quite important to attain the diversity of doubly-dispersive wireless channels. Detectors based on message-passing (MP) of factor graphs have been regarded as the way to achieve the near-optimal performance for OTFS. In this paper, by deriving the pattern of the multipath vectorized channel matrix of the orthogonal time frequency space (OTFS) system, it is shown that short girth (i.e. girth-4) may exist in the Tanner graphs, which will degrade the performance of MP detectors, especially with high modulation orders. By introducing interleavers at the transmitter and receiver, the vectorized channel matrix turns out to be a sparse upper block Heisenberg matrix, whose structure is beneficial for the computation of matrix QR decomposition (QRD). Successive interference canceling (SIC) detectors based on QRD and sorted QRD are constructed to eliminate the cross-symbol interference and improve the reliability of the symbol-level channel. Simulation results show that for 4QAM, the QRD-based SIC detectors can achieve about 4dB gain at 10−2 over the non-SIC detectors, while the sorted QRD-based SIC detectors can bring an additional 2dB at 10−3, which is only 1dB gap from the MP. For 16QAM, the sorted SIC detectors show superior BER performance than the MP method, and for 64QAM, the MP detector reaches the error floor while SIC detectors show their excellent performance in all configurations.

Reconfigurable Intelligent Surface (RIS) has grown rapidly due to its performance improvement for wireless networks, and the integration of unmanned aerial vehicle (UAV) and RIS has obtained widespread attention. In this paper, the downlink of non-orthogonal multiple access (NOMA) UAV networks equipped with RIS is considered. The objective is to optimize the UAV trajectory with RIS phase shift to maximize the system capacity under the UAV energy consumption constraint. By deep reinforcement learning, a capacity maximization scheme under energy consumption constraints based on double deep Q-Network (DDQN) is proposed. The joint optimization of UAV trajectory with RIS phase shift design is achieved by DDQN algorithm. From the numerical results, the proposed optimization scheme can increase the system capacity of the RIS-UAV-assisted NOMA networks.

Semantic communication is a newly emerged communication paradigm that exploits deep learning (DL) models to realize communication processes like source coding and channel coding. Recent advances have demonstrated that DL-based joint source-channel coding (DeepJSCC) can achieve exciting data compression and noise-resiliency performances for wireless image transmission tasks, especially in environments with low channel signal-to-noises (SNRs). However, existing DeepJSCC-based semantic communication frameworks still cannot achieve adaptive code rates for different channel SNRs and image contents, which reduces its flexibility and bandwidth efficiency. In this paper, we propose a predictive and adaptive deep coding (PADC) framework for realizing flexible code rate optimization with a given target transmission quality requirement. PADC is realized by a variable code length enabled DeepJSCC (DeepJSCC-V) model for realizing flexible code length adjustment, an Oracle Network (OraNet) model for predicting peak-signal-to-noise (PSNR) value for an image transmission task according to its contents, channel signal to noise ratio (SNR) and the compression ratio (CR) value, and a CR optimizer aims at finding the minimal data-level or instance-level CR with a PSNR quality constraint. By using the above three modules, PADC can transmit the image data with minimal CR, which greatly increases bandwidth efficiency. Simulation results demonstrate that the proposed DeepJSCC-V model can achieve similar PSNR performances compared with the state-of-the-art Attention-based DeepJSCC (ADJSCC) model, and the proposed OraNet model is able to predict high-quality PSNR values with an average error lower than 0.5dB. Results also demonstrate that the proposed PADC can use nearly minimal bandwidth consumption for wireless image transmission tasks with different channel SNR and image contents, at the same time guaranteeing the PSNR constraint for each image data.

In the next-generation wireless communication systems, learning-based dynamic spectrum access strategy at the medium access control layer and physical layer shows its powerful capability of achieving optimal resources allocation, and it has become a hot research topic for the harmonious coexistence of heterogeneous wireless networks. In this paper, we propose a multiple access control method to achieve high network throughput by combining deep reinforcement learning and memory module. In specific, we introduce the bidirectional gated recurrent unit (Bi-GRU) in deep Q-learning (DQL) to utilize the information of varying environment observation at each time-step. Furthermore, we apply the method in a freeway scenario with real-world datasets, where the DQL node contends the same wireless channel with other nodes. Evaluated results demonstrate that the proposed approach learns an optimal policy without using complex mechanism or prior. Moreover, we consider realistic cases involving saturated or unsaturated uplink traffic flows of nodes on a freeway segment, and the on-line training strategies of the DQL node near the roadside facilities. The experimental results show that the proposed scheme leads to the highest throughput in all cases compared with the competing approaches.

With the massive deployment of 5G commercial, the interconnection of all things promotes the transformation and upgrading of the social production mode. The Internet of Things (IoT) is supported by the massive machine-type communications (mMTC), which is one of the three major application scenarios of 5G. Recently, a novel spreading based non-orthogonal multiple access (NOMA) scheme named tandem spreading multiple access (TSMA) has been proposed for grant-free random access in mMTC. However, TSMA only considers the case of single antenna. In this article, a multi-antenna system scheme of TSMA with MIMO (MIMO-TSMA) is proposed to scale up user connections. In this scheme, spectrum efficiency can be promoted by sharing the non-orthogonal resources in different beams. The simulation results show that MIMO-TSMA can effectively take advantage of MIMO and TSMA to enhance the mMTC system performance.

Recently, intelligent reflecting surface (IRS) has become a research focus for its capability of controlling the radio propagation environments. Compared to the conventional terrestrial IRS, aerial IRS (AIRS) exploiting unmanned aerial vehicle (UAV)/high-altitude platform (HAP) can provide better deployment flexibility. To this end, a three-dimensional (3D) one-cylinder model is first developed for AIRS-assisted multiple-input multiple-output (MIMO) narrowband channels. In order to change the wireless channel with AIRS and create a favorable propagation environment, we propose a novel method of designing the phase-shifts for the IRS elements. Based on the model, channel impulse response (CIR), space-time correlation function, and channel capacity are derived and thoroughly investigated. A key observation in this paper is that multipath and Doppler effects in radio propagation environments can be effectively mitigated via adjusting the phase-shifts of IRS. More specifically, for the special propagation environments in the absence of any scatterers, it is found that the effects of multipath fading can be completely eliminated by IRSs. While for the general propagation environments with multiple scatterers, a small number of IRS elements can also significantly reduce the Doppler spread and the deep fades of the channels. Based on the numerical investigation of channel correlations, it is shown that channel non-stationarity is not introduced into the time domain when the phase shift of IRS is linear related to the time. Moreover, the channel capacity can also be improved by the proposed methods. Finally, the model with non-ideal IRSs is considered and it is found that using non-ideal IRSs results in poor performances compared with using ideal IRSs. These conclusions will provide a fundamental support for developing intelligent and controllable propagation environments of the future sixth-generation (6G) wireless networks.

Wireless communications are vulnerable to cyber attackers, which now have the flexibility to choose their type of attack. In this paper, combined with intelligent reflect surface (IRS), we jointly optimize base station beamforming and IRS reflected beamforming to counter smart attackers, thereby improving system security. Considering that attackers can flexibly choose their attack methods, such as jamming or eavesdropping, we make the base station intelligent by using reinforcement learning, which can predict the attack methods of attackers and choose whether to add artificial noise into the transmitted signals. At the same time, the interaction between the base station and the smart attackers are established as a non-cooperative game, the Nash equilibrium of the game is derived. Based on this, the base station anti-smart attackers strategy based on Deep Q-learning (DQN) is proposed, which can restrain the attack of the attacker to improve the security of the system. It can be verified from the simulation results that the proposed anti-smart attackers strategy can effectively enhance the secrecy rate of the wireless communication system, resist the attacker’s attack, and intelligently transmit artificial noise to improve system security.

Multi-access mobile edge computing (MEC) is envisioned as a key enabling technology to support compute-intensive and delay-sensitive applications in railway Internet of Things (RIoT) networks. However, the time-varying channel variations in RIoT scenarios make it challenging to achieve efficient resource allocation. The emerging deep reinforcement learning (DRL) is able to respond to the above-mentioned challenge. In this paper, with the aim of reducing the total computational cost (weighted sum of consumed energy and delay), we investigate the dynamic resource management issue of joint subcarrier assignment, offloading ratio, power allocation and computation resource allocation in multi-access MEC assisted RIoT networks. To address this intractable mixed integer nonlinear programming issue, we put forward a hybrid DRL (HDRL) scheme, which is an integration of deep double Q-learning (DDQN) and deep deterministic policy gradient (DDPG). The HDRL algorithm is capable of learning the advisable strategies for actions including discrete-continuous hybrid variables. In HDRL algorithm, DDQN plays the role of making subcarrier assignment decision, and DDPG plays the role of making offloading ratio, power allocation as well as computation resource allocation decisions. Numerical results demonstrate that HDRL scheme can yield much less computational cost than the existing baselines for multi-access MEC assisted RIoT networks. In addition, the HDRL scheme is close to the near-optimal performance with comparatively low execution time.

This paper investigates a long-term average total energy cost minimization problem via resource management, including admission control, power allocation, and Energy Sharing (ES) of renewable energy in Heterogeneous Cellular Networks powered by Grid-connected Smart Micro Grids (GSMG-HCNs). In GSMG-HCNs, both renewable and grid energy power the base stations. Unlike existing works, we consider the cost of both renewable and grid energy and formulate the power line loss process caused by ES into our model. To solve the proposed problem, we transform it into a real-time issue by the Lyapunov technique. The proposed Cost-Aware Online Resource Management (CAORM) algorithm decouples the real-time issue into two sub-problems, one of which is linear and the other is addressed based on the successive convex approximation approach. We theoretically prove the asymptotic optimality of the CAORM algorithm and a tradeoff between the average total energy cost and the average queue length. Simulation results reveal that the CAORM algorithm outperforms benchmarks in reducing total energy cost and can make appropriate decisions according to different unit costs of renewable energy. Besides, the designed distance-related ES loss rate can help obtain better solutions with lower ES losses.

Massive internet of things (mIoT) could play an important role in the future smart high-speed railway (HSR), where grant-free multiple access technologies are required. Recently, tandem spreading multiple access (TSMA) has been raised for mIoT without mobility which achieves high connectivity and reliability. Meanwhile, orthogonal time frequency space (OTFS) modulation shows its potential to combat high mobility in point-to-point communication systems. To this end, in this article, we jointly design OTFS and TSMA, and propose OTFS-TSMA for HSR mIoT. The principle of OTFS-TSMA transceiver is described, where OTFS and TSMA are improved respectively. Especially, two-dimension cyclic shift of DD domain elements in OTFS is transformed into cyclic shift of Doppler elements, segments, symbols and chips by the proposed novel resource allocation and interleaving schemes. Data recovery approaches of the four categories of cyclic shift are given, thus massive user interference is mitigated. Simulation results illustrate that both high user connectivity and transmission reliability in HSR massive IoT can be achieved by OTFS-TSMA.

In massive machine-type communications (mMTC), the conflict between millions of potential access devices and limited channel freedom leads to a sharp decrease in spectral efficiency. The sparse nature of mMTC provides a solution by using compressive sensing (CS) to perform multiuser detection (MUD) but suffers conflict between the high computation complexity and low latency requirements. In this paper, we propose a novel Dual-network for joint channel estimation and data recovery. The proposed Dual-Net utilizes the sparse consistency between the channel vector and data matrix of all users. Experimental results show that the proposed Dual-Net outperforms existing CS algorithms and general neural networks in computation complexity and accuracy, which means reduced access delay and more supported devices.

Different from the traditional terrestrial intelligent reflecting surface (IRS), aerial IRS (AIRS) can provide some unique advantages, such as flexible deployment and wider-view signal reflection. In this paper, a three-dimensional (3D) single cylinder simulation channel model is proposed for AIRS-aided multiple-input multiple-output (MIMO) communication systems, where the considered propagation scenario consists of a fixed base station (BS) and a mobile station (MS). Based on the model, the channel impulse response (CIR), spreading function, and channel capacity are derived. Then, some heuristic algorithms are proposed to obtain the phase shifts of the IRS elements. It is found that multipath fading and Doppler effects stemming from the movement of MS can be effectively mitigated via adjusting the tunable phase shifts of the IRS elements. Moreover, the channel capacity of the system could also be improved by the proposed schemes. These findings can be used to lay a foundation for developing intelligent and controllable propagation environments.

In this article, we study a robust beamforming design for multiuser multiple-input–multiple-output secrecy networks with simultaneous wireless information and power transfer (SWIPT). In this system, an access point, multiple Internet-of-Things (IoT) devices under the nonlinear energy harvesting (EH) model with a help of one cooperative jammer (CJ). We employ artificial noise (AN) generation to facilitate efficient wireless energy transfer and secure transmission. To achieve EH fairness, we aim to maximize the minimum harvested energy among users subject to secrecy rate constraint and total transmit power constraint in the presence of channel estimation errors. By incorporating a norm-bounded channel uncertainty model, the original robust problem is transformed into a two-layer optimization problem, where the inner layer problem is reformulated as semidefinite programming (SDP) and the outer layer problem is solved by a one-dimensional (1-D) line search algorithm. In addition, in order to reduce computational complexity, we propose an algorithm based on sequential parametric convex approximation (SPCA). Finally, simulation results show that the proposed SPCA method achieves the same performance as the two-layer algorithm with much lower complexity.

In Vehicle-to-Everything (V2X) networks, where all vehicles and infrastructures are interconnected for information sharing, data dissemination is increasingly playing a significant role in superior and pluralistic communication services. To empower the efficiency of data dissemination, in this paper, we propose a novel unmanned aerial vehicle (UAV)-enabled scheduling protocol consisting of a proactive caching policy and a file sharing strategy in V2X networks. In the proactive caching process, we deploy UAVs as flying base stations (BSs) with caching capability, where we propose a UAV dynamic trajectory scheduling (DTS) algorithm to optimize the caching duration. Whereas in the file sharing strategy, based on the previous vehicular caching status, we provide a framework of file sharing cycle for data dissemination scheduling and employ a channel prediction algorithm to alleviate communication overhead. Moreover, we propose a relay ordering algorithm to effectively improve the file sharing process. Simulation results demonstrate that, our proposed scheduling protocol can enhance the efficiency of data dissemination and achieve an improved network performance in terms of caching process, system throughput, and file sharing latency in V2X networks.

Massive machine-type communications (mMTC) is a new key feature of 5G cellular and is expected to be further improved in future evolutions of the cellular standards. Data collection from machine-type communication devices (MTCDs), which can be achieved by various approaches, is important to operation of mMTC networks. This work studies data collection for mMTC networks enabled by unmanned aerial vehicle (UAV) stations moving in the air. Consider the limitation in battery lifetime at both the MTCDs and the UAV station, the UAV trajectory design problem is investigated from an energy efficiency perspective. In a generalized model where the target MTCDs are grouped into multiple clusters, the UAV station travels across the clusters and collect data from each cluster while hovering above the cluster. The corresponding MTCD clustering strategy, UAV hovering strategy and UAV flying strategy all have impacts on the energy consumption of the system, which results in a strongly coupled energy minimization problem that is difficult to solve. The sub-problems obtained through decomposition are decoupled in the proposed solution approach. Clustering of the MTCDs is done by a greedy learning clustering (GLC) algorithm. A novel modeling technique based on the idea of artificial energy map (AEM) is proposed to find the optimal hovering position within a cluster. The flying strategy that minimizes the energy consumption is equivalently transformed into a classic travelling salesman problem that is readily solved by the genetic algorithm (GA). Through alternating iterative optimization of the clustering and hovering strategies, the communication energy consumption and the UAV hovering energy consumption are monotonically decreasing until convergence.

Massive machine-type communications (mMTC) is a key enabling technology for Internet of Things (IoT) services in 5G and beyond. Efficient data collection from massive machine-type communication devices (MTCDs) performing sensing tasks is an important part of the service. In this paper, we consider an unmanned aerial vehicle (UAV) being deployed to facilitate data collection from MTCDs. Taking into account the limited energy of battery-powered MTCDs, the UAV trajectory is optimized to improve the energy efficiency of data collection. By fixing the starting and ending points of the UAV trajectory, a globally optimal (GO) trajectory can be obtained, based on the assumption that the UAV's serving radius and access capacity (number of served MTCDs) are unlimited. Interestingly, it is shown that the optimal trajectory always exists as long as the UAV flying height is greater than its service radius multiplied by a constant. However, the increase of the UAV flying height deteriorates the channel, leading to reduced efficiency of energy consumption. Alternatively, a greedy dynamic (GD) trajectory optimization scheme with limited UAV service radius and access capacity is then investigated, resulting in the optimal service location of the UAV being at a lower flying height, and the energy consumption for accomplishing the data collection task being reduced. Specifically, the UAV sorts the MTCDs within its serving radius based on the distance and selects its closest serving MTCD set. In a serving MTCD set, there is an optimal hovering location that maximizes the data collection efficiency. The UAV dynamically adjusts its service set and the optimal data collection location when MTCDs finish their data transmission and exit the service set. The process continues until all the MTCDs are served and the UAV arrives at the ending point of the trajectory. Simulation results show that both the GO and GD algorithm can improve the efficiency of overall energy consumption. In particular, the online dynamic trajectory optimization scheme is less restrictive and achieves higher efficiency.

Differential spatial modulation (DSM) is a novel multiple-input-multiple-output (MIMO) transmission technology that uses the transmit antenna index matrices to carry part of the information in non-coherent communication scenarios. However, when high-order modulation should be used for spectral efficiency considerations, the conventional use of constant modulus Phase Shift Keying (PSK) constellations in DSM would result in significant performance loss, whereas using non-constant modulus constellations such as Quadrature Amplitude Modulation (QAM) in DSM is very challenging because the peak signal power may grow without bound through the differential iterative processing. In this work, a generalized transmission scheme that uses non-constant modulus constellations for DSM signaling is investigated. By normalizing the power of all symbols in the previous transmit matrix when performing differential transmission, the Precoding Normalized DSM (PN-DSM) scheme which can adopt non-constant modulus constellations is proposed. In addition to the legacy QAM, another two non-constant modulus constellations in the literature, Amplitude Phase Shift Keying (APSK) and star-QAM, are considered for PN-DSM transmissions. It is revealed by numerical studies that, compared with the legacy QAM, APSK and star-QAM are more robust to the error propagation issue of the proposed PN-DSM scheme. In addition, because of the increased minimum distance in the constellation, the star-QAM slightly outperforms the APSK in terms of the average error rate performance.

In this work, we study the buffer-aided relaying mechanism in a UAV-enabled mobile relaying system assisting the terrestrial communications. Optimal UAV trajectory design against a randomly located eavesdropper is investigated from the physical layer (PHY) security perspective considering the wireless channel dynamics as the UAV relay moves in the air. Specifically, we maximize the sum secrecy rate by optimizing the discrete trajectory anchor points based on the information causality and UAV mobility constraints. To make the non- convex problem tractable, the increments of the trajectory anchor points are optimized instead through an iterative updating procedure, and successive convex approximation technique is applied for progressive optimization. The convergence of the proposed iterative optimization technique is proved by introducing additional rate bound constraints and employing the squeeze principle. Simulation results show that the proposed optimal trajectory finding algorithm is effective and fast converging. Simulation results also reveal that the distribution of the eavesdropper location has a significant impact on the PHY security performance.

Pro-active pushing is a promising emerging communication technology to improve the resource efficiency and quality of service provisioning in mobile networks. This paper characterizes users' requests with request delay information (RDI), and proposes to use the cache-hit ratio (CHR) as the metric of the system performance in pushing strategy design. Different from conventional instant on-demand services, the pro-active pushing system can merge different users' requests for the same file at different time instants. The base station can therefore push files more efficiently by employing multicasting technologies. However, it is revealed that if one of the users' channel condition is significantly poor, it is a better choice to ignore the user in the pushing system from the overall CHR performance perspective. In the two-user one-file scenario, we derive analytical expressions of the CHR to help the BS determine the optimal pushing rate, which is shown to be a two-value-selection. In particular, for two users with uniformly distributed RDIs, the decision space is specifically characterized through analysis and calculation.

In this paper, we study a multi-user multiple-inputmultiple- output secrecy simultaneous wireless information and power transfer (SWIPT) channel which consists of one transmitter, one cooperative jammer (CJ), multiple energy receivers (potential eavesdroppers, ERs), and multiple co-located receivers (CRs). We exploit the dual of artificial noise (AN) generation for facilitating efficient wireless energy transfer and secure transmission. Our aim is to maximize the minimum harvested energy among ERs and CRs subject to secrecy rate constraints for each CR and total transmit power constraint. By incorporating norm-bounded channel uncertainty model, we propose a iterative algorithm based on sequential parametric convex approximation to find a near-optimal solution. Finally, simulation results are presented to validate the performance of the proposed algorithm outperforms that of the conventional AN-aided scheme and CJaided scheme.

Wireless energy transfer over radio frequency has been recognized as a promising alternative solution to powering the low power low complexity wireless equipments in future cellular networks. In this work, simultaneous wireless information and power transfer (SWIPT) operation for secure multi-user multipleinput multiple-output (MIMO) broadcast system is investigated with imperfect channel state information at the transmitter. The corresponding robust secure beamforming problem is studied, where the transmit power is to be minimized subject to the secrecy rate outage probability constraint for legitimate information users, and the harvested energy outage probability constraint for energy harvesting receivers. The original problem is shown to be non-convex due to the presence of the probabilistic constraints. These outage constraints are then transformed into deterministic forms by using the Bernstein-type inequalities. Based on successive convex approximation (SCA), a low-complexity approach, which reformulates the original problem as second order cone programming (SOCP), is proposed. Simulation results show that the proposed scheme outperforms the conventional method with lower complexity.

In this paper, an energy harvesting scheme for a multi-user multiple-input-multiple-output (MIMO) secrecy channel with artificial noise (AN) transmission is investigated. Joint optimization of the transmit beamforming matrix, the AN covariance matrix, and the power splitting ratio is conducted to minimize the transmit power under the target secrecy rate, the total transmit power, and the harvested energy constraints. The original problem is shown to be non-convex, which is tackled by a two-layer decomposition approach. The inner layer problem is solved through semi-definite relaxation, and the outer problem is shown to be a single-variable optimization that can be solved by one-dimensional (1-D) line search. To reduce computational complexity, a sequential parametric convex approximation (SPCA) method is proposed to find a near-optimal solution. Furthermore, tightness of the relaxation for the 1-D search method is validated by showing that the optimal solution of the relaxed problem is rank-one. Simulation results demonstrate that the proposed SPCA method achieves the same performance as the scheme based on 1-D search method but with much lower complexity.

In this paper, an energy harvesting scheme for a multi-user multiple-input-multiple-output secrecy channel with artificial noise (AN) transmission is investigated. Joint optimization of the transmit beamforming matrix, the AN covariance matrix, and the power splitting ratio is conducted to minimize the transmit power under the target secrecy rate, the total transmit power, and the harvested energy constraints. The original problem is shown to be non-convex, which is tackled by a two-layer decomposition approach. The inner layer problem is solved through semi-definite relaxation, and the outer problem, on the other hand, is shown to be a single-variable optimization that can be solved by 1-D line search. To reduce computational complexity, a sequential parametric convex approximation method is proposed to find a near-optimal solution. This paper is then extended to the imperfect channel state information case with norm-bounded channel errors. Furthermore, tightness of the relaxation for the proposed schemes is validated by showing that the optimal solution of the relaxed problem is rank-one. Simulation results demonstrate that the proposed SPCA method achieves the same performance as the scheme based on 1-D but with much lower complexity.

To keep the hardware costs of future communications systems manageable, the use of low-cost hardware components is desirable. This is particularly true for the emerging massive multiple-input multiple-output (MIMO) systems which equip base stations (BSs) with a large number of antenna elements. However, low-cost transceiver designs will further accentuate the hardware impairments, which are present in any practical communication system. In this paper, we investigate the impact of hardware impairments on the secrecy performance of downlink massive MIMO systems in the presence of a passive multiple-antenna eavesdropper. Thereby, for the BS and the legitimate users, the joint effects of multiplicative phase noise, additive distortion noise, and amplified receiver noise are taken into account, whereas the eavesdropper is assumed to employ ideal hardware. We derive a lower bound for the ergodic secrecy rate of a given user when matched filter data precoding and artificial noise (AN) transmission are employed at the BS. Based on the derived analytical expression, we investigate the impact of the various system parameters on the secrecy rate and optimize both the pilot sets used for uplink training and the AN precoding. Our analytical and simulation results reveal that: 1) the additive distortion noise at the BS may be beneficial for the secrecy performance, especially if the power assigned for AN emission is not sufficient; 2) all other hardware impairments have a negative impact on the secrecy performance; 3) despite their susceptibility to pilot interference in the presence of phase noise, so-called spatially orthogonal pilot sequences are preferable unless the phase noise is very strong; and 4) the proposed generalized null-space AN precoding method can efficiently mitigate the negative effects of phase noise.

A non-cooperative game approach is employed to optimize the energy efficiency (EE) for multi-user cognitive radio over multi-input-multi-output (MIMO) interference channels (ICs). Both the per-secondary-user (SU) power constraints and the total interference threshold are taken into consideration in the problem formulation. Although optimizing EE for the formulated multi-constraint fractional problem is non-convex and multi-objective, we show that it can be reformulated as an equivalent multi-objective unconstrained non-fractional problem. A distributed iterative EE optimization algorithm (DIEEOA) for multi-user cognitive radio over MIMO ICs is proposed to achieve the Nash Equilibrium of the non-cooperative game. Effectiveness of the algorithm is validated through computer simulation, and system parameters' impact on the EE is discussed.

Tag anticollision is critical to the performance of many radio frequency identification systems in industrial applications. Tree-based schemes are popular arbitration algorithms for tag collision, as they are scalable and easy to implement. In these schemes, collided tags are recursively divided into two groups until each group contains only one tag. However, we have determined that previous pure tree-based schemes do not fully utilize the information that can be collected; they allow only the responding tags to be involved in the splitting process. In this paper, we propose a novel pure tree-based method that we named parallel splitting with retrieve (PSR), which allows both the nonresponding and unidentified tags to also be involved in the splitting process. PSR consists of two mechanisms: parallel splitting (PS) and retrieve. The PS mechanism can reduce the total number of tag collisions and the retrieve mechanism can reduce the extra idle slots caused by PS. In comparison with conventional pure tree-based schemes, the proposed PSR algorithm achieves higher efficiency without the need to estimate the number of tags; simulation results indicate an efficiency level of 0.41. The PSR algorithm also has similar performance to state-of-the-art algorithms that do not need to estimate the number of tags, but it is simpler to implement and is thus preferred for resource constrained systems.

In vehicular ad hoc networks (VANETs), multi-hop wireless broadcast has been considered a promising technology to support safety-related applications that have strict quality-of-service (QoS) requirements such as low latency, high reliability, scalability, etc. However, in the urban transportation environment, the efficiency of multi-hop broadcast is critically challenged by complex road structure, severe channel contention, message redundancy, etc. In this paper, we propose an urban multi-hop broadcast protocol (UMBP) to disseminate emergency messages. To lower emergency message transmission delay and reduce message redundancy, UMBP includes a novel forwarding node selection scheme that utilizes iterative partition, mini-slot, and black-burst to quickly select remote neighboring nodes, and a single forwarding node is successfully chosen by the asynchronous contention among them. Then, bidirectional broadcast, multi-directional broadcast, and directional broadcast are designed according to the positions of the emergency message senders. Specifically, at the first hop, bidirectional broadcast or multi-directional broadcast conducts the forwarding node selection scheme in different directions simultaneously, and a single forwarding node is successfully chosen in each direction. Then, directional broadcast is adopted at each hop in the message propagation direction until the emergency message reaches an intersection area where multi-directional broadcast is performed again, which finally enables the emergency message to cover the target area seamlessly. Analysis and simulation results show that the proposed UMBP significantly improves the performance of multi-hop broadcast in terms of one-hop delay, message propagation speed, and message reception rate.

The problem of joint downlink cell association (CA) and wireless backhaul bandwidth allocation (WBBA) in two-tier cellular heterogeneous networks (HetNets) is investigated. Large-scale antenna array is implemented at the macro base station (BS), while the small cells within the macro cell range are single-antenna BSs and they rely on over-the-air links to the macro BS for backhauling. A sum logarithmic user rate maximization problem is studied under the wireless backhaul constraints. Duplex and spectrum sharing with co-channel reverse time-division duplex (TDD) and dynamic soft frequency reuse is considered for interference management in the two-tier HetNet employing large-scale antenna arrays at the macro BS and wireless backhauling for small cells. Two in-band WBBA scenarios, namely, unified bandwidth allocation and per-small-cell bandwidth allocation, are investigated for joint CA-WBBA in the HetNet. A two-level hierarchical decomposition method for relaxed optimization is employed to solve the mixed-integer nonlinear program (MINLP). Solutions based on the General Algorithm Modeling System (GAMS) optimization solver and fast heuristics are also proposed for cell association in the per-small-cell WBBA scenario. It is shown that when all small cells have to use in-band wireless backhaul, the system load has more impact on both the sum logarithmic rate and per-user rate performance than the number of small cells deployed within the macro cell range. The proposed joint CA-WBBA algorithms have an optimal load approximately equal to the size of the large-scale antenna array at the macro BS. The cell range expansion (CRE) strategy, which is an efficient cell association scheme for HetNets with ideal backhauling, is shown to be inefficient when in-band wireless backhauling for small cells comes into play.

The compressed spectrum sensing problem for cognitive radio (CR) spectrum sharing in device-to-device (D2D) communications is investigated, with an emphasis on collaborative sensing strategies for multi-user CR-based D2D networks. Because the D2D users are assumed to be geographically close to each other, the same spectral occupancy is expected, which can be exploited in the sensing algorithm design. We first investigate a single user compressed spectrum sensing algorithm, where the successive fast iterative shrinkage-thresholding algorithm (FISTA) is employed. The successive FISTA-based single-user sensing algorithm is then used to develop two collaborative compressed spectrum sensing schemes, namely Equal-Gain Combining (EGC) and Census-Weighted Detection Results Combining (CWDRC). It is demonstrated that both algorithms are effective in low signal-to-noise ratio (SNR) conditions. We further show that the census-based CWDRC algorithm significantly reduces system overhead compared to other collaborative sensing strategies with only a small degradation in the probability of detection. Thus, CWDRC is attractive for collaborative spectrum sensing in CR-based D2D systems.

Queue-aware transmission scheduling for cooperative wireless communications with sub-fading-block scheduling to better balance load and capacity in low mobility environments is investigated. The scheduling problem for joint cooperation scheduling and resource allocation is formulated as a constrained nonlinear integer optimization problem over an integer convex set based on a source buffer queueing analysis. It is shown that with queue-aware scheduling, the state transition matrix of the source buffer queue has a highly dynamic form. As a result, the objective function of the optimization problem does not have an analytic form in general. The constrained discrete Rosenbrock search algorithm, which is a gradient-free directed discrete search algorithm, is employed to solve the nonlinear integer problem. The output of the directed integer search algorithm is used for queue-aware transmission scheduling for the cooperative system. Numerical results are presented which show that, for cooperative transmission scheduling, the Rosenbrock search based queue-aware algorithm significantly outperforms the equal partitioning, random partitioning, and gradient-based algorithms under quasi-static channel assumptions. Under practical system conditions with unsaturated traffic, the proposed queue-aware scheduling scheme achieves the true optima, and maintains a large stability region for the buffer queue, over a wide range of channel and traffic conditions. It is also shown that when fading channel dynamics are taken into consideration, the performance of the proposed queue-aware scheduling algorithm significantly outperforms fixed relaying and fixed direct transmission channel-aware scheduling strategies.

Security issues of terrestrial free-space optical (FSO) communications over strong atmospheric turbulence are investigated. Based on a subcarrier intensity modulated FSO system model, we first study the scintillation reciprocity of the system from a coherence time perspective. A private secret key-based cryptosystem with key management is introduced to enhance FSO security, and a key agreement approach is proposed based on statistics of the random atmospheric turbulence-induced fading channel measurements. Secret key rate of the key agreement scheme is studied for the strong turbulence channel characterized by the K-distribution. Practical key agreement protocol is designed based on channel identification.

In this paper, we investigate the design of a practical information-theoretically secure secret key agreement protocol for a cooperative wireless network employing standard modulation. Assuming relay selection has been completed, the key agreement problem is studied in a three-node cooperative wireless communication system over block-fading channels. Passive attacks from an eavesdropper collocated with the relay are considered. We derive upper and lower bounds on the secret key rate of this cooperative wireless system. The difference between the bounds is shown to be small for practical communication scenarios, which indicates they are tight. We then propose a practical secret key agreement protocol for this system with both the communicants and the honest relay participating in the public discussion. The tradeoff between security and protocol efficiency is considered in the joint design of advantage distillation, information reconciliation, and privacy amplification. The protocol parameters are optimized to achieve the tight bound on the secret key rate.

We study practical information-theoretically secure secret key agreement protocol design for wireless cooperative communication networks. By assuming the relay selection process is finished in advance, we study the key agreement problem for a classical three-node cooperative wireless communication system over independent additive white Gaussian noise (AWGN) channels. Passive attacks from an eavesdropper collocated with the relay are assumed. We derive upper and lower bounds on the secret key rate under the assumed cooperative wireless system model and find that they are close, which indicates the tightness of the bounds. We then propose an efficient practical secret key agreement protocol with both Bob and the honest relay participating in the public discussion. A compromise between security and efficiency is achieved by the joint advantage distillation and privacy amplification.

We study a single-source single-destination cooperative wireless communication system with multiple relays operating in a modified decode-and-forward mode. Nakagami-m fading with additive white Gaussian noise is assumed for all inter-node channels. The bursty data packet arrival is modeled by a Markov-modulated Poisson process (MMPP). A packet feedback model is proposed to characterize packet loss in the wireless channel. An approximation to the steady state distribution of the proposed generalized discrete time M/G/1-type queue at the source is obtained by state truncation. Packet level performance of four transmission modes adopting different modulation and coding schemes is analyzed under a variety of channel and traffic conditions using the truncated queueing analysis. The network power is used as a criterion to find boundary curves of adaptive modulation and coding (AMC) scheduling for the cooperative wireless system. Two source node transmission protocols, namely the transmit-every-clock-tick (TREC) and the LAZY protocols, are examined for both the AMC scheduling and channel utilization analysis.

The generalized method of moments (GMM) is introduced in the framework of estimating the Nakagami-m fading parameter. This GMM approach provides a systematic procedure for finding the moment-based m parameter estimators. Using the multivariate delta method, we present a derivation for the asymptotic variance of the GMM Nakagami m parameter estimators. Monte Carlo simulation results show that the GMM approach can lead to estimators outperforming existing moment-based m parameter estimators over a wide range of channel conditions. It is shown that the asymptotic performance of these GMM estimators are close to that of the maximum-likelihood based estimator. The proposed method can be easily applied to both noiseless and noisy environments.

The generalized method of moments (GMM) is introduced in the framework of estimating the Nakagami-m fading parameter. This GMM approach provides a systematic procedure for finding the moment-based m parameter estimators. Using the multivariate delta method, we demonstrate a derivation for the asymptotic variance of the GMM Nakagami m parameter estimators. Monte Carlo simulation results show that the GMM approach can lead to estimators outperforming existing moment-based m parameter estimators over a wide range of channel conditions. It is shown that the asymptotic performance of these GMM estimators are close to that of the maximum-likelihood based estimation. The proposed method can be easily applied to both noiseless and noisy environments.