Next-generation wireless networks are progressing beyond conventional connectivity to incorporate emerging sensing and computing capabilities. This convergence gives rise to integrated systems that enable not only uninterrupted communication, but also environmental awareness, intelligent decision-making, and novel applications that take advantage of these combined features. At the same time, this integration brings substantial security challenges. As computing, sensing, and communication become more tightly intertwined, the overall complexity of the system increases, creating new vulnerabilities and expanding the attack surface. The widespread deployment of data-heavy artificial intelligence applications further amplifies concerns regarding data security and privacy. This paper presents a comprehensive survey of security and privacy threats, along with potential countermeasures, in integrated wireless systems. We first review physical-layer security techniques for communication networks, and then investigate the security and privacy implications of semantic and pragmatic communications and their associated cross-layer design methodologies. For sensing functionalities, we pinpoint security and privacy risks at the levels of signal sources, propagation channels, and sensing targets, and summarize state-of-the-art defense strategies for each. The growing computational requirements of these applications drive the need for distributed computing over the network, which introduces additional risks such as data leakage, weak authentication, and multiple points of failure. We subsequently discuss secure coded computing approaches that can help overcome several of these challenges. Finally, we introduce unified security frameworks tailored to integrated communication–sensing–computing architectures, offering an end-to-end perspective on protecting future wireless systems.

Pinching antennas (PAs), a new type of reconfigurable and flexible antenna structures, have recently attracted significant research interest due to their ability to create line-of-sight links and mitigate large-scale path loss. Owing to their potential benefits, integrating PAs into wireless powered mobile edge computing (MEC) systems is regarded as a viable solution to improve both the efficiency of the energy transfer and task offloading. Unlike prior studies that assume ideal continuous PA placement along waveguides, this paper investigates a practical discrete PA-assisted wireless powered MEC framework, where devices first harvest energy from PA-emitted radio-frequency signals and then adopt a partial offloading mode, allocating part of the harvested energy to local computing and the remainder to uplink offloading. The uplink phase considers both the time-division multiple access (TDMA) and non-orthogonal multiple access (NOMA), each examined under three levels of PA activation flexibility. For each configuration, we formulate a joint optimization problem to maximize the total computational bits and conduct a theoretical performance comparison between the TDMA and NOMA schemes. To address the resulting mixed-integer nonlinear problems, we develop a two-layer algorithm that combines closed-form solutions based on Karush–Kuhn–Tucker (KKT) conditions with a cross-entropy-based learning method. Numerical results validate the superiority of the proposed design in terms of the harvested energy and computation performance, revealing that TDMA and NOMA achieve comparable performance under coarser PA activation levels, whereas finer activation granularity enables TDMA to achieve superior computation performance over NOMA.

Integrating dense channel fingerprints into deep learning (DL) becomes a promising way to realize precise three-dimensional (3D) indoor localization. However, most existing methods are frequency-dependent, which limits the localization precision when operating in different frequency bands. To address this challenge, this paper proposes a masked Transformer encoder (MTE) model capable of using the channel state information (CSI) data of an arbitrary number of sub-channels (frequency bands) as input. The proposed MTE model can locate a UE using frequency-scalable CSI data, to realize resilient localization. We first introduce how to transform CSI data into sequential data suitable for Transformer-based models, with length of the sequence determined by the number of sub-channels. Based on this, an MTE model is designed to achieve resilient FP localization with frequency-scalability, i.e., capable of processing the CSI data of an arbitrary number of sub-channels. Next, we construct a 3D CSI FP dataset using ray-tracing (RT) simulations based on real-world indoor scenarios and versatile electromagnetic (EM) coefficients. The reliability of the dataset is verified by measurement data. Extensive experiments demonstrate that the MTE model outperforms many state-of-the-art baselines, classical time-series models, and alternative Transformer-based methods, especially under arbitrary sub-channel CSI data. Moreover, we demonstrate that the MTE model also offers many advantages in terms of training and storage costs through comparisons with conventional models.

Integrated Sensing and Communications (ISAC) is a new usage scenario for 6G. To enable studying ISAC solutions, standardization bodies such as European Telecommunications Standards Institute (ETSI) and Third Generation Partnership Project (3GPP) require specified ISAC channel models. 3GPP has been studying changes to the existing TR 38.901 channel models to model radio-frequency (RF) signal interactions between the transmitter, sensing target, receiver, and surrounding environment. In parallel, ETSI has been working on additional features to further support channel modelling for the rich variety of ISAC use cases envisioned in 6G. In this paper, we present an outline of the latest developments from industry and further provide an evaluation framework for assessing the models.

Block-code based probabilistic amplitude shaping (BC-PAS) replaces the distribution matcher (DM) in the original PAS scheme by utilizing a block code decoder. In the original paper of BC-PAS, Matsumine etc. showed that BC-PAS achieves significantly reduced storage and computational complexity than DM-based PAS (DM-PAS) at the receiver. However, BC-PAS can only support spectral efficiency (SE) η within the range η≥m−1 when employing a 2m-ary pulse-amplitude modulation (2m-PAM), where m denotes the modulation order. To address this limitation, we propose a partial shaping on the BC-PAS scheme that supports SE η in the extended range ηm-PAM is used.

In this paper, we study employing movable components on both base station (BS) and intelligent reflecting surface (IRS) in a wideband terahertz (THz) multiple-input-single-output (MISO) system, where the BS is equipped with a movable antenna (MA) array and the IRS consists of movable subarrays. To alleviate double beam squint effect caused by the coupling of beam squint at the BS and IRS, we propose to maximize the minimal received power across a wide THz spectrum by delicately configuring the positions of MAs and IRS subarrays, which is highly challenging. By adopting majorization-minimization (MM) methodology, we develop an algorithm to tackle the aforementioned optimization. Numerical results demonstrate the effectiveness of our proposed algorithm and the benefit of utilizing movable components on the BS and IRS to mitigate double beam squint effect in wideband THz communications.

This article presents a novel concept termed integrated imaging and wireless power transfer (IWPT), wherein the integration of imaging and wireless power transfer functionalities is achieved on a unified hardware platform. IWPT leverages a transmitting array to efficiently illuminate a specific Region of Interest (ROI), enabling the extraction of ROI’s scattering coefficients while concurrently providing wireless power to nearby users. The integration of IWPT offers compelling advantages, including notable reductions in power consumption and spectrum utilization, pivotal for the optimization of future 6G wireless networks. As an initial investigation, we explore two antenna architectures: 1) a fully digital array and 2) a digital/analog hybrid array. Our goal is to characterize the fundamental tradeoff between imaging and wireless power transfer by optimizing the illumination signal. With imaging operating in the near-field, we formulate the illumination signal design as an optimization problem that minimizes the condition number of the equivalent channel. To address this optimization problem, we propose an semi-definite relaxation-based approach for the fully digital array and an alternating optimization algorithm for the hybrid array. Finally, numerical results verify the effectiveness of our proposed solutions and demonstrate the tradeoff between imaging and wireless power transfer.

Integrated sensing and communication (ISAC) systems may face a heavy computation burden since the sensory data needs to be further processed. This paper studies a novel system that integrates sensing, communication, and computation, aiming to provide services for different objectives efficiently. This system consists of a multi-antenna multi-functional base station (BS), an edge server, a target, and multiple single-antenna communication users. The BS needs to allocate the available resources to efficiently provide sensing, communication, and computation services. Due to the heavy service burden and limited power budget, the BS can partially offload the tasks to the nearby edge server instead of computing them locally. We consider the estimation of the target response matrix, a general problem in radar sensing, and utilize Cramér-Rao bound (CRB) as the corresponding performance metric. To tackle the non-convex optimization problem, we propose both semidefinite relaxation (SDR)-based alternating optimization and SDR-based successive convex approximation (SCA) algorithms to minimize the CRB of radar sensing while meeting the requirement of communication users and the need for task computing. Furthermore, we demonstrate that the optimal rank-one solutions of both the alternating and SCA algorithms can be directly obtained via the solver or further constructed even when dealing with multiple functionalities. Simulation results show that the proposed algorithms can provide higher target estimation performance than state-of-the-art benchmarks while satisfying the communication and computation constraints.

In this paper, authentication for mobile radio frequency identification (RFID) systems with low-cost tags is investigated. To this end, an adaptive modulus (AM) encryption algorithm is first proposed. To further enhance security without requiring additional storage for new key matrices, a self-updating encryption order (SUEO) algorithm is designed. Furthermore, a diagonal block local transpose key matrix (DBLTKM) encryption algorithm is presented, which effectively expands the feasible domain of the key space. Building upon these three algorithms, a novel joint AM-SUEO-DBLTKM encryption algorithm is constructed. Making full use of the strengths of the proposed joint algorithm, a two-way RFID authentication protocol, named AM-SUEO-DBLTKM-RFID, is proposed specifically for mobile RFID systems. In addition, the Burrows-Abadi-Needham (BAN) logic and security analysis indicate that the proposed AM-SUEO-DBLTKM-RFID protocol can effectively combat various typical attacks. Numerical results demonstrate that the proposed AM-SUEO-DBLTKM algorithm can save 99.59% of tag storage over traditional algorithms. Finally, the proposed AM-SUEO-DBLTKM-RFID protocol achieves both low computational complexity and low storage overhead, making it well-suited for deployment in resource-constrained, low-cost RFID tags.

Existing works mainly rely on the far-field planar-wave-based channel model to assess the performance of reconfigurable intelligent surface (RIS)-enabled wireless communication systems. However, when the transmitter and receiver are in near-field ranges, the investigation of the channel statistics based on the planar-wave-based model will result in relatively low computing accuracy. To tackle this challenge, we initially develop an analytical framework for sub-array partitioning. This framework divides the large-scale RIS array into multiple sub-arrays, effectively reducing modeling complexity while maintaining acceptable accuracy. Then, we develop a beam domain channel model based on the proposed sub-array partition framework for large-scale RIS-enabled unmanned aerial vehicle (UAV)-to-vehicle communication systems, which can be used to efficiently capture the sparse features of RIS-enabled UAV-to-vehicle channels in both near-field and far-field ranges. Furthermore, some important propagation characteristics of the proposed channel model, including the spatial cross-correlation functions (CCFs), temporal auto-correlation functions (ACFs), frequency correlation functions (FCFs), channel capacities, and path loss statistics with respect to the different physical features of the RIS array and non-stationary properties of the channel model are derived and analyzed. Finally, simulation results are provided to demonstrate that the proposed framework is helpful to achieve a good tradeoff between the modeling complexity and accuracy for investigating the channel propagation characteristics, and therefore providing highly-efficient communications in RIS-enabled air-ground wireless networks.

As a superior multicarrier technique utilizing chirp signals for high-mobility communications, affine frequency division multiplexing (AFDM) is envisioned to be a promising candidate for sixth-generation (6G) wireless networks. AFDM is based on the discrete affine Fourier transform (DAFT) with two adjustable parameters of the chirp signals, termed the pre-chirp and post-chirp parameters, respectively. Whilst the post-chirp parameter complies with stringent constraints to combat the time-frequency doubly selective channel fading, we show that the pre-chirp counterpart can be flexibly manipulated for an additional degree of freedom. Therefore, this paper proposes a novel AFDM scheme with the pre-chirp index modulation (PIM) philosophy (AFDM-PIM), which can implicitly convey extra information bits through dynamic pre-chirp parameter assignment, thus enhancing both spectral and energy efficiency. Specifically, we first demonstrate that the subcarrier orthogonality is still maintained by applying distinct pre-chirp parameters to various subcarriers in the AFDM modulation process. Inspired by this property, we allow each AFDM subcarrier to carry a unique pre-chirp signal according to the incoming bits. By such an arrangement, extra bits can be embedded into the index patterns of pre-chirp parameter assignment without additional energy consumption. We derive asymptotically tight upper bounds on the average bit error probability (BEP) of the proposed schemes with the maximum-likelihood detection, and validate that the proposed AFDM-PIM can achieve full diversity under doubly dispersive channels. Based on the derived result, we further propose an optimal pre-chirp alphabet design to enhance the bit error rate (BER) performance via intelligent optimization algorithms. Simulation results demonstrate that the proposed AFDM-PIM outperforms the classical benchmarks.

Spatial modulation (SM) transmits additional information bits by the selection of antennas. Generalized spatial modulation (GSM), as an advanced type of SM, can be divided into diversity and multiplexing (MUX) schemes according to the symbols carried on the selected antennas are identical or different. Recently, reconfigurable intelligent surface (RIS) assisted SM exhibits better reception performance compared to conventional SM. To overcome the limitations of SM, this paper combines GSM with RIS and proposes the RIS-aided receive generalized spatial modulation (RIS-RGSM) scheme. The RIS-RGSM diversity scheme is realized via a simple improvement based on the state-of-the-art scheme. To further increase the transmission rate, a novel RIS-RGSM MUX scheme is proposed, where the reflection phase shifts and on/off states of RIS elements are configured to achieve bit mapping. The theoretical bit error rate (BER) of the proposed scheme is derived and agrees well with the simulation results. Numerical simulations show that the RIS-RGSM MUX scheme has better BER performance than the diversity scheme. The proposed scheme can significantly increase the transmission rate and maintain good performance compared to the existing scheme under a limited number of antennas.

Integrated sensing and communication (ISAC) is a very promising 6G technology and confirmed by International Telecommunication Union (ITU) as one of six typical usage scenario of the 6th generation (6G) network. To evaluate the performance of potential ISAC solutions and demonstrate the technical superiority, an accurate channel model to characterize both sensing targets and background environment is critical towards both standardization and commercial deployment. In this paper, a novel approach to model the scattering environment, which is one of the current open issues in the standardization community, is proposed. Specifically, a virtual receiver with virtual departure angle and delay is introduced to reuse the methodology of the existing communication channel modeling for Base station (BS) mono-static sensing mode. Implementations of the proposed model are carried out through ray tracing simulations to demonstrate the feasibility and the accuracy.

Cross-link interference (CLI) is a prevalent type of interference in dynamic time division duplex (TDD) systems. Mitigating CLI is crucial for effective system design in contemporary wireless communication systems. Proper mitigation of CLI can enhance transmission and reception robustness, improve spectral efficiency, and overall throughput. Consequently, there is a need for innovative methods and approaches to address CLI mitigation. Therefore, this paper proposes and evaluates a novel beam nulling method based on block diagonalization (BD). The method is introduced and evaluated using numerical results. Analysis and numerical results demonstrate the superior performance of the proposed method in dynamic TDD scenarios.

Affine frequency division multiplexing (AFDM), tailored as a novel multicarrier technique utilizing chirp signals for high-mobility communications, exhibits marked advantages compared to traditional orthogonal frequency division multiplexing (OFDM). AFDM is based on the discrete affine Fourier transform (DAFT) with two modifiable parameters of the chirp signals, termed as the pre-chirp parameter and post-chirp parameter, respectively. These parameters can be fine-tuned to avoid overlapping channel paths with different delays or Doppler shifts, leading to performance enhancement especially for doubly dispersive channel. In this paper, we propose a novel AFDM structure with the pre-chirp index modulation (PIM) philosophy (AFDM-PIM), which can embed additional information bits into the prechirp parameter design for both spectral and energy efficiency enhancement. Specifically, we first demonstrate that the application of distinct pre-chirp parameters to various subcarriers in the AFDM modulation process maintains the orthogonality among these subcarriers. Then, different prechirp parameters are flexibly assigned to each AFDM subcarrier according to the incoming bits. By such arrangement, aside from classical phase/amplitude modulation, extra binary bits can be implicitly conveyed by the indices of selected prechirping parameters realizations without additional energy consumption. At the receiver, both a maximum likelihood (ML) detector and a reduced-complexity ML-minimum mean square error (ML-MMSE) detector are employed to recover the information bits. It has been shown via simulations that the proposed AFDM-PIM exhibits superior bit error rate (BER) performance compared to classical AFDM, OFDM and IM aided OFDM algorithms.

Poor coverage quality is a common cause of poor wireless communication network quality, which seriously affects the user experience in mobile communication. Currently, the front line mainly adopts a manual trial-and-error method, which has problems such as low efficiency and high human cost. How to use artificial intelligence algorithms to quickly and accurately identify and solve the problem of poor coverage quality based on existing data is one of the important research directions in the field of wireless networks. The data of wireless networks is essentially spatio-temporal data, but most of the existing methods are based on time-domain and space-domain data for analysis and modeling, and the information mining in the spatio domain is not sufficient. In the spatio domain, the distribution of base stations is not uniform in Euclidean space, which increases the difficulty of spatio-temporal modeling. In view of the natural advantages of graph mining technology for modeling and processing unstructured data, this paper proposes a model named Spatio-Temporal Graph Message Propagation (STGMP) based on graph technology. This method uses spatio-temporal graphs to represent the historical states of related service cells, proposes a processing layer that combines the time and spatio domains, and maps the actual problem to a multi-classification task, thereby achieving the identification of the causes of poor coverage quality. This paper also conducts experiments on real data sets, and the results show that the proposed method STGMP is very effective.

Integrated sensing and communication (ISAC) is recognized as one of key enabling technologies for the Meta-verse. To enhance both communication data rate and sensing accuracy, the exploitation of millimeter wave (mmWave) and terahertz (THz) frequencies becomes mandatory due to huge amount of spectrum resources. To combat the severe path-loss at mmWave/THz band, large-scale antenna arrays are usually employed to form directional beams. However, the ultra-broad bandwidth induces undesirable beam squint (BS) effects, where the beams from different subcarriers point to diverse angles, leading to the communication performance loss. Fortunately, this BS effect can be leveraged to facilitate the multi-angle super-resolution sensing with minimal beam sweeping overhead. Against this background, we propose a novel multicarrier waveform design methodology for the BS-assisted ISAC systems, which optimizes the frequency resource allocation between sensing and communications to reach a good dual-functional performance trade-off. To mitigate mutual interference, both functions are assigned non-overlapping subcarriers. The subcarrier assignment design is formulated as a mixed integer programming problem to maximize the communication throughput while ensuring the required sensing range and resolution, which involves high complexity to get an exact solution. To this end, we propose a two-stage iterative update algorithm to obtain a quasi-optimal solution with low computational complexity. Numerical results demonstrate that our proposed methodology achieves high-rate communication and high-resolution sensing simultaneously with relatively low overhead.

The fragmented architecture, cloud-based infrastructure, and functionally virtualized network elements within the 5 G core network have significantly surged the volume and diversity of alarms generated on cloud network service platforms that it supports. Given the inherently cross-layered nature of failure scenarios on these platforms, identifying the root causes presents a significant challenge. Alarm association rule mining has become an effective means to address the problems of alarm correlation and root cause localization. In this paper, an explainable alarm association rule mining approach based on knowledge graph, referred to as ARK-G, is proposed. Initially, a cloud-network cross-layer alarm association knowledge graph (CA2KG) is constructed. Subsequently, the knowledge embedding based graph convolutional network is employed to perform knowledge graph embedding on CA2KG. This embedding is then utilized to enhance the RNNLogic algorithm, thereby facilitating cross-layer alarm association rule mining with interpretable paths. Finally, a weighted rule tree is derived from a subset of CA2KG and the generated explainable rules, enabling the deduction of the root alarm. Experimental results demonstrate that the proposed ARK-G approach for association rule mining yields a higher hit rate compared to the baseline model, which provides valuable assistance in the faults analysis of 5 G cloud-network platforms.

Integrated sensing and communication (ISAC) is expected to be a promising technology in the sixth-generation (6G) wireless networks for its ability to alleviate resources shortage and excessive hardware expenses. One typical representative for ISAC waveforms is the orthogonal frequency division multiplexing (OFDM) waveform, which divides the time-frequency resources into orthogonal resource elements (REs). In order to satisfy their diverse design requirements and mitigate mutual interference, the communication and sensing subsystems can be assigned with different REs, which necessitates effective allocation strategies of different resources across time and frequency domains. In this article, a cross-domain multicarrier waveform design method-ology is proposed, which optimizes the RE assignment and power allocation strategies for the OFDM-based ISAC system. Specifically, for sensing performance enhancement, the unit cells of the ambiguity function (AF) of the sensing components are spe-cially shaped to achieve a “locally” perfect auto-correlation (AC) property within a predefined region of interest (RoI) in the Delay-Doppler domain. Afterwards, the irrelevant cells outside the RoI, which can determine the sensing power allocation strategy, are optimized alternatively with the communication power allocation strategy to maximize the throughput for the communication purpose. Numerical results demonstrate the superiority of the cross-domain multicarrier waveform design, which also provides useful guidelines for parameter settings of the proposed OFDM-based ISAC system.

Abstract Collaborative transmission, comprising multiple devices owned by a single user, is progressively evolving into an essential strategy to meet the stringent demands of burgeoning collaborative scenarios in 5G‐advanced and 6G networks. This paper proposes three novel use cases for device collaboration, namely data duplication, data splitting and wireless backup, to address these requirements. To provide dynamic and efficient collaboration, both non‐transparent mode via the medium access control layer collaboration and transparent mode via the physical layer collaboration are proposed. The paper further introduces a comprehensive design framework including protocol stack design, user equipment capability reporting, user equipment pairing, scheduling mechanism and transmission mechanism for different collaborative use cases with different collaborative modes. Evaluation outcomes reveal that the recommended methods could decrease the resources consumed for data duplication while increasing the user perceived throughput for data duplication and data splitting. The proposed methods also augment transmission reliability for both data duplication and wireless backup.

Orthogonal frequency division multiplexing (OFDM) has been widely recognized as the representative waveform for 5G wireless networks, which can directly support sensing/positioning with existing infrastructure. To guarantee superior sensing/positioning accuracy while supporting high-speed communication simultaneously, the dual functions tend to be assigned with different resource elements (REs) due to their diverse design requirements. This motivates optimization of resource allocation/waveform design across time, frequency, power and delay-Doppler domains. Therefore, this article proposes two cross-domain waveform optimization strategies for effective convergence of OFDM-based communication and sensing/positioning, following communication- and sensing-centric criteria, respectively. For the communication-centric design, to maximize the achievable data rate, a fraction of REs are optimally allocated for communication according to prior knowledge of the communication channel. The remaining REs are then employed for sensing/positioning, where the sidelobe level and peak-to-average power ratio are suppressed by optimizing its power-frequency and phase-frequency characteristics for sensing performance improvement. For the sensing-centric design, a ‘locally’ perfect auto-correlation property is ensured for accurate sensing and positioning by adjusting the unit cells of the ambiguity function within its region of interest (RoI). Afterwards, the irrelevant cells beyond RoI, which can readily determine the sensing power allocation, are optimized with the communication power allocation to enhance the achievable data rate. Numerical results demonstrate the superiority of the proposed waveform designs.

With the emergence of communication services with stringent requirements such as autonomous driving or on-flight Internet, the sixth-generation (6G) wireless network is envisaged to become an enabling technology for future transportation systems. In this paper, two ways of interactions between 6G networks and transportation are extensively investigated. On one hand, the new usage scenarios and capabilities of 6G over existing cellular networks are firstly highlighted. Then, its potential in seamless and ubiquitous connectivity across the heterogeneous space-air-ground transportation systems is demonstrated, where railways, airplanes, high-altitude platforms and satellites are investigated. On the other hand, we reveal that the introduction of 6G guarantees a more intelligent, efficient and secure transportation system. Specifically, technical analysis on how 6G can empower future transportation is provided, based on the latest research and standardization progresses in localization, integrated sensing and communications, and security. The technical challenges and insights for a road ahead are also summarized for possible inspirations on 6G enabled advanced transportation.

A reactive jamming attack, which performs spec-trum jamming only during legal signal transmission based on the knowledge of sensor behaviors, poses a significant threat to wireless sensing networks (WSNs). In this paper, a novel deceiving approach is proposed for defending reactive jamming in dynamic WSNs. Specifically, when the maximum transmission power is given, we first formulate the anti-jamming process as an optimization problem to maximize the average received power while eliminating the effects of the jamming attack. Then the interaction between reactive jamming and legitimate sensors is modeled with the Markov decision process (MDP). Finally, a deep Q network (DQN) based jamming deceiving method is proposed to solve the formulated optimization problem. Simulation results show that the proposed anti-jamming scheme can converge quickly and is superior to the classical counterparts in terms of the mean of received signal power.

The programmable metasurface (MTS), which can enhance the signal quality by flexibly manipulating the electromagnetic (EM) responses of reflected waves, has emerged as a promising technology for multiple-input multiple-output (MIMO) transmission due to its superior energy efficiency and cost-effective hardware implementations. In this paper, we consider a multi-user MIMO (MU-MIMO) system equipped with a MTS-based multi-feed transmitter, where an array of metallic elements is split into several subarrays (SAs), each irradiated by a unique radio-frequency (RF) feed. Additionally, each user is allocated with one unique RF-feed-SA pair for data services. Then we develop a novel index modulation (IM) scheme based on this array-of-SA (AoSA) structure of the MTS, named as MTS-SA -IM. More specifically, the allocation strategy of the SAs to different users, termed as SA allocation pattern (SAP), is flexibly controlled by the information bits. In other words, aside from classical amplitude/phase modulation bits, additional binary bits, referred to as index bits, can be embedded into the indices of the allocated SAs without extra power consumption, leading to enhancement of both spectrum and energy efficiencies. Furthermore, we propose a distributed mapping rule design between the index bits and SAPs, which guarantees that the index bits at each user are exclusively dependent on the index of its own allocated SA. By constructing a binary-tree structure, a general form of the proposed distributed mapping rule is generated recursively, which can be extended to the cases of arbitrary number of users. Simulation results demonstrate that the proposed MTS is capable of achieving desirable performance gain over its classical counterpart.

Integrated sensing and communication (ISAC) has been recognized as a critical technology as well as an usage scenario for the 6th generation (6G) wireless networks as reflected by the reports by impactful standardization bodies. The main idea of ISAC is to exploit communication signals to detect, monitor or image surrounding environment and support sensing, target detection or localization. As reflected by the broad consensus, communication networks will be used to support beyond communication use cases, such as target detection and indoor security surveillance and monitoring. Specifically, nonvisible light based monitoring approaches are needed in dark environments or environments without enough cameras. In this paper, an ISAC based indoor breath monitoring scheme is proposed and tested with prototypes under both line-of-sight (LOS) and non line-of-sight (NLOS) environments. In the tests, 5th generation (5G) base stations are used as transceivers and the frame structures of the transmitted signals are based on 5G standards. Test results demonstrate the feasibility and accuracy of the proposed approach.

Integrated sensing and communication (ISAC) is envisaged to play a prominent role in the next generation wireless networks. Developing generic and accurate channel models for ISAC to characterize signal propagation is critical towards its successful research, standardization and deployment. In this paper, a unified approach to model an ISAC channel is proposed, which is intended to be applicable for both communication and sensing purposes. To generate the channel coefficients for sensing channels, differences from traditional communication channels including pathloss, line of sight probability, the Doppler effect and the fast fading effect are analyzed systematically. Implementations of the proposed model are carried out through simulations to demonstrate the feasibility and the accuracy.

In this paper, we develop a unified dynamic intelligent reflecting surface (IRS) beamforming framework to boost the sum computation rate of an IRS-aided mobile edge computing (MEC) system, where each device follows a binary offloading policy. Specifically, the task of each device has to be either executed locally or offloaded to MEC servers as a whole with the aid of given number of IRS beamforming vectors available. By flexibly controlling the number of times for IRS reconfiguring phase-shifts, the system can achieve a balance between the performance and associated signalling overhead. We aim to maximize the sum computation rate by jointly optimizing the computational mode selection for each device, offloading time allocation, and IRS beamforming vectors across time. Since the resulting optimization problem is non-convex and NP-hard, there are generally no standard methods to solve it optimally. To tackle this problem, we first propose a penalty-based successive convex approximation algorithm, where all the associated variables in the inner-layer iterations are optimized simultaneously and the obtained solution is guaranteed to be locally optimal. Then, we further derive the offloading activation condition for each device by deeply exploiting the intrinsic structure of the original optimization problem. According to the offloading activation condition, a low-complexity algorithm based on the successive refinement method is proposed to obtain high-quality suboptimal solutions, which are more appealing for practical systems with a large number of devices and IRS elements. Moreover, the optimal condition for the proposed low-complexity algorithm is revealed. The effectiveness of the proposed algorithms is demonstrated through numerical examples. In addition, the results illustrate the practical significance of the IRS in MEC systems for achieving coverage extension and supporting multiple energy-limited devices for task offloading, and also unveil the fundamental performance-cost tradeoff embedded in the proposed dynamic IRS beamforming framework.

In this paper, we combine regular amplitude phase-shift keying (RAPSK) constellation with Gray mapping for link-level performance improvement. Under the criteria of the bit-interleaved coded modulation (BICM) mutual information (MI), we search for optimal Gray-mapped RAPSK (Gray-RAPSK) constellation with a given size. We design the labeling for Gray-RAPSK constellation based on bit-level MI. Simulation results show that shaping gains more than 0.5 dB are obtained for the 256-ary Gray-RAPSK constellation at the block error rate (BLER) of 10−2in the high spectral efficiency region. In addition, simulation results show that the error floor of low-density parity-check (LDPC) coding combined with Gray-RAPSK constellation is lower than that with quadrature amplitude modulation (QAM) constellation.

Terahertz has an emphatic effect on increasing data rates, supporting ultra-dense connections, and realizing low-latency transmission. In this paper, we investigate the peculiarity of the terahertz spectrum, study the propagation characteristics of terahertz technology, and discuss its channel modeling feature. Given the unique spectral characteristics of terahertz bands, physical layer waveforms, modulation, coding schemes are designed accordingly to reduce the peak-to-average power ratio (PAPR), increase the spectral flexibility, meet the backward compatibility and improve the system performance. The baseband signal processing of terahertz signals is analyzed and discussed in this paper, inspiring the design of future terahertz communication systems.

In this paper we consider a passive radar system which doesn't rely on the reception of reference signals from direct paths. A blind channel estimation based method is derived by exploring the relationship among the transmitted signal, the channel response and the received signal. With the help of this novel method, multiple incoming targets illuminated by a non-cooperative transmitter can be detected by a distributed radar system with at least two receivers. Numerical results demonstrate the feasibility and accuracy of the proposed method and some characteristics of the detector, namely, the performance as a function of the signal to noise ratio (SNR) and the number of radar receivers.

Terrestrial broadcasting signals have been recognized as promising candidates for positioning and navigation needs. However, in some terrestrial broadcasting networks, which are usually single frequency networks, the transmitter confusion problem poses a huge obstacle to almost all positioning algorithms. Without knowing the transmitter identities, measurements can't be associated to transmitters coordinates properly, thus leaving receiver coordinates unable to be estimated. Meanwhile, as the exact transmission delay is usually hard to estimate, the time difference of arrival (TDoA) based positioning is widely used. In this paper, a novel TDoA based localization method is proposed to realize positioning in single frequency networks. With the proposed method, the receiver can estimate its own position when transmitter identities are missing from the signals. Simulation results verify the feasibility and accuracy of the method and discussions regarding its properties are given.

Broadcasting signals are considered as promising candidates for positioning and navigation needs thanks to its many advantages. However, most broadcasting networks are single frequency networks (SFN), in which the transmitter confusion problem poses a huge obstacle. Since all transmitters in a SFN broadcast the same signal simultaneously, the receiver cannot obtain the transmitter identities, thus can no longer determine its position. In this paper, a novel angle of arrival (AoA) based position method is proposed to realize positioning in SFN. The proposed method can overcome the transmitter confusion problem without modifications on the broadcasting sequences. Simulation results confirm that the proposed method can realize positioning in SFN with high accuracy.

The visible light communication (VLC) and wireless LAN (WLAN) heterogeneous network is considered as a highly competitive candidate for the next generation indoor wireless communication. VLC can provide high data rate as downlink mechanism while WLAN supports reliable connectivity and covers uplink needs. In such heterogeneous systems, vertical handover (VHO) is critical for the system reliability and performance. This paper proposes an improved design of the dwell timer used in VHO process by introducing a dynamic dwell timer based on prediction. Simulation results show that the proposed design outperforms benchmark designs by avoiding unwise handovers while achieving a longer connection time and higher average data rate. Simulations also prove that the proposed design is applicable to different environments and user behaviors.