Modeling the wireless radiance field (WRF) is fundamental to modern communication systems, enabling key tasks such as localization, sensing, and channel estimation. Traditional approaches, which rely on empirical formulas or physical simulations, often suffer from limited accuracy or require strong scene priors. Recent neural radiance field (NeRF)-based methods improve reconstruction fidelity through differentiable volumetric rendering, but their reliance on computationally expensive multilayer perceptron (MLP) queries hinders real-time deployment. To overcome these challenges, we introduce Gaussian splatting (GS) to the wireless domain, leveraging its efficiency in modeling optical radiance fields to enable compact and accurate WRF reconstruction. Specifically, we propose SwiftWRF, a deformable 2D Gaussian splatting framework that synthesizes WRF spectra at arbitrary positions under single-sided transceiver mobility. SwiftWRF employs CUDA-accelerated rasterization to render spectra at over 100 k FPS and uses the lightweight MLP to model the deformation of 2D Gaussians, effectively capturing mobility-induced WRF variations. In addition to novel spectrum synthesis, the efficacy of SwiftWRF is further underscored in its applications in angle-of-arrival (AoA) and received signal strength indicator (RSSI) prediction. Experiments conducted on both real-world and synthetic indoor scenes demonstrate that SwiftWRF can reconstruct WRF spectra up to 500x faster than existing state-of-the-art methods, while significantly enhancing its signal quality.

Large language models (LLMs) are trained on extensive historical corpora, but their ability to understand time and maintain temporal awareness of time-evolving factual knowledge remains limited. Previous studies often neglect the critical aspect of utilizing knowledge from various sources. To address this gap, we introduce EvolveBench, a comprehensive benchmark that evaluates temporal competence along five key dimensions: Cognition, which examines the ability to recall and contextualize historical facts. Awareness, which tests LLMs’ awareness of temporal misalignment between external inputs and the temporal context of a query. Trustworthiness, which assesses whether models can identify and appropriately refuse queries based on invalid timestamps. Understanding, which focuses on interpreting both explicit dates and implicit historical markers. Finally, reasoning evaluates the capacity to analyze temporal relationships and draw accurate inferences. Evaluating 15 widely used LLMs on EvolveBench shows that GPT-4o achieves the highest average EM score of 79.36, while the open-source Llama3.1-70B demonstrates notable strength in handling temporally misaligned contexts with an average score of 72.47. Despite these advances, all models still struggle with handling temporal misaligned context. Our code and dataset are available at https://github.com/zzysjtuiwct/EvolveBench.

Integrated sensing and communication (ISAC) is a very promising technology designed to provide both high rate communication capabilities and sensing capabilities. However, in Massive Multi User Multiple-Input Multiple-Output (Massive MU MIMO-ISAC) systems, the dense user access creates a serious multi-user interference (MUI) problem, leading to degradation of communication performance. To alleviate this problem, we propose a decentralized baseband processing (DBP) precoding method. We first model the MUI of dense user scenarios with minimizing Cramér-Rao bound (CRB) as an objective function. Hybrid precoding is an attractive ISAC technique, and hybrid precoding using Partially Connected Structures (PCS) can effectively reduce hardware cost and power consumption. We mitigate the MUI between dense users based on Thomlinson-Harashima Precoding (THP). We demonstrate the effectiveness of the proposed method through simulation experiments. Compared with the existing methods, it can effectively improve the communication data rates and energy efficiency in dense user access scenario, and reduce the hardware complexity of Massive MU MIMO-ISAC systems. The experimental results demonstrate the usefulness of our method for improving the MUI problem in ISAC systems for dense user access scenarios.

Existing works on machine learning (ML)-empowered wireless communication primarily focus on monolithic scenarios and single tasks. However, with the blooming growth of communication task classes coupled with various task requirements in future 6G systems, this working pattern is obviously unsustainable. Therefore, identifying a groundbreaking paradigm that enables a universal model to solve multiple tasks in the physical layer within diverse scenarios is crucial for future system evolution. This paper aims to fundamentally address the curse of ML model generalization across diverse scenarios and tasks by unleashing multi-modal feature integration capabilities in future systems. Given the universality of electromagnetic propagation theory, the communication process is determined by the scattering environment, which can be more comprehensively characterized by cross-modal perception, thus providing sufficient information for all communication tasks across varied environments. This fact motivates us to propose a transformative two-stage multi-modal pre-training and downstream task adaptation paradigm. In the pre-training stage, we introduce a multi-modal two-tower model and a corresponding contrastive learning method to integrate the explicit description of the scattering environment and implicit channel state information (CSI) into a universal representation, which encapsulates rich high-level knowledge and can be leveraged for all downstream tasks in different scenarios. Additionally, we present two specially designed model structures to enhance the interaction of communication modalities. In the second stage, based on the frozen pre-trained model, we propose a direct method and a pluggable method for flexible and low-cost task adaptation. Experimental results demonstrate that our proposed approach significantly outperforms benchmarks in both task performance and tuning parameter size for exemplary sub-tasks in unseen scenarios.

Generating faithful and fast responses is crucial in the knowledge-grounded dialogue.Retrieval Augmented Generation (RAG) strategies are effective but are inference inefficient, while previous Retrieval Free Generations (RFG) are more efficient but sacrifice faithfulness.To solve this faithfulness-efficiency trade-off dilemma, we propose a novel retrieval-free model training scheme named Retrieval Augmented to Retrieval Free Distillation (RA2FD) to build a retrieval-free model that achieves higher faithfulness than the previous RFG method while maintaining inference efficiency.The core idea of RA2FD is to use a teacher-student framework to distill the faithfulness capacity of a teacher, which is an oracle RAG model that generates multiple knowledge-infused responses.The student retrieval-free model learns how to generate faithful responses from these teacher labels through sequence-level distillation and contrastive learning.Experiment results show that RA2FD let the faithfulness performance of an RFG model surpass the previous SOTA RFG baseline on three knowledge-grounded dialogue datasets by an average of 33% and even matching an RAG model's performance while significantly improving inference efficiency.Our code is available at https:// github.com/zzysjtuiwct/RA2FD.

In video-based point cloud compression (V-PCC), the optimal allocation of the total bitrate between geometry and color is a challenging but rewarding problem. Existing bit allocation approaches leverage statistical models to describe the rate and distortion of geometry and color information as functions of V-PCC quantization steps. However, to obtain the parameters of statistical models, these methods need to perform pre-coding for input point clouds for multiple times, resulting in high computational complexity. Consequently, the capability of these methods for practical application is limited. To address this problem, we derive the rate and distortion models based on projected images produced in the V-PCC encoding process, transforming the expensive point cloud pre-coding process into an efficient image pre-coding process. By utilizing the image-based distortion and rate models, the bit allocation problem is further formulated as a constrained convex optimization problem. Experimental results demonstrate that the proposed method exhibits significantly lower time complexity and higher rate-distortion performance compared to the existing methods.

Multi-User Multiple-Input Multiple-Output (MU-MIMO) antenna arrays are considered a crucial technology for future wireless communication systems. However, precoding for MU-MIMO meets significant challenges. To tackle this issue, this paper introduces a novel star decentralized precoding algorithm, aiming to decentralize part of the precoded calculations from the central unit (CU) to the decentralized units (DUs) and reduce the computational complexity of the CU. Then, we apply the Zero Forcing Tomlinson-Harashima precoding (ZF-THP) algorithm to star decentralized baseband processing (DBP) for enhanced transmission rate, and this algorithm has the same performance as centralized precoding but with reduced CU complexity. Furthermore, we propose the star decentralized minimum mean square error THP (sDMMSE-THP) algorithm to enhance system performance further. Extensive simulation data validate the effectiveness of our proposed scheme.

This paper investigates the problem of activity detection and channel estimation in cooperative multi-cell massive access systems with temporally correlated activity, where all access points (APs) are connected to a central unit via fronthaul links. We propose to perform user-centric AP cooperation for computation burden alleviation and introduce a generalized sliding-window detection strategy for fully exploiting the temporal correlation in activity. By establishing the probabilistic model associated with the factor graph representation, we propose a scalable Dynamic Compressed Sensing-based Multiple Measurement Vector Generalized Approximate Message Passing (DCS-MMV-GAMP) algorithm from the perspective of Bayesian inference. Therein, the activity likelihood is refined by performing standard message passing among the activities in the spatial-temporal domain and GAMP is employed for efficient channel estimation. Furthermore, we develop two schemes of quantize-and-forward (QF) and detect-and-forward (DF) based on DCS-MMV-GAMP for the finite-fronthaul-capacity scenario, which are extensively evaluated under various system limits. Numerical results verify the significant superiority of the proposed approach over the benchmarks. Moreover, it is revealed that QF can usually realize superior performance when the antenna number is small, whereas DF shifts to be preferable with limited fronthaul capacity if the large-scale antenna arrays are equipped.

Integrating reconfigurable intelligent surface (RIS) into multi-access edge computing (MEC) systems to assist computation offloading from mobile devices (MDs) to edge servers has been increasingly considered. However, most resource allocation strategies in the current RIS-assisted MEC systems are based on perfect channel state information (CSI). Due to the passive characteristics of RIS and the large number of reflecting elements in the RIS, it is difficult to obtain the accurate CSI in RIS-assisted wireless systems. In this paper, we aim to minimize the expected energy consumption of MDs in a RIS-assisted multi-user MEC system with imperfect CSI and probabilistic latency constraints. The formulated problem is non-trivial to tackle due to the tightly coupled optimization variables, non-closed-form expression of the objective function, and the probabilistic latency constraints. To deal with the problem, we propose a constrained stochastic successive convex approximation (CSSCA) framework-based algorithm to approximate the problem into a sequence of convex surrogate problems and solve them iteratively. Extensive numerical results validate the superiority of introducing RIS into MEC systems and demonstrate that our proposed algorithm is effective when the CSI is imperfect.

This paper studies the user activity detection and channel estimation problem in a temporally-correlated massive access system where a very large number of users communicate with a base station sporadically and each user once activated can transmit with a large probability over multiple consecutive frames. We formulate the problem as a dynamic compressed sensing (DCS) problem to exploit both the sparsity and the temporal correlation of user activity. By leveraging the hybrid generalized approximate message passing (HyGAMP) framework, we design a computationally efficient algorithm, HyGAMP-DCS, to solve this problem. In contrast to only exploiting the historical estimations, the proposed algorithm performs bidirectional message passing between the neighboring frames for activity likelihood update to fully exploit the temporally-correlated user activities. Furthermore, we develop an expectation maximization HyGAMP-DCS (EM-HyGAMP-DCS) algorithm to adaptively learn the hyperparameters during the estimation procedure when the system statistics are unknown. In particular, we propose to utilize the analysis tool of state evolution to find the appropriate hyperparameter initialization of EM-HyGAMP-DCS. Simulation results demonstrate that our proposed algorithms can significantly improve the user activity detection accuracy and reduce the channel estimation error.

This paper studies the temporal-correlated massive access system where a large number of devices communicate with the base station sporadically and continue transmitting data in the adjacent frames in high probability when being active. By exploiting the sparsity and the temporal correlations of the user activities, the joint user activity detection and channel estimation (JUADCE) problem in multiple consecutive frames can be formulated as a dynamic compressed sensing (DCS) problem. Specifically, we formulate a probabilistic model that accounts the statistics of channels and characterizes the evolutions of the user activities by a steady Markov chain. The hybrid generalized approximate message passing (HyGAMP) framework is leveraged to develop a computationally efficient algorithm named HyGAMP-DCS to solve the JUADCE problem. The HyGAMP-DCS algorithm performs channel estimation in the GAMP part and soft user activity information update in the MP part, then exchanges intrinsic information between these two parts for performance enhancement. Simulation results demonstrate that the proposed algorithm can significantly outperform the conventional DCS-based algorithms and the GAMP algorithm which ignores the temporal correlations.

This paper considers the massive connectivity problem in an asynchronous grant-free random access system, where a huge number of devices sporadically transmit data to a base station (BS) with imperfect synchronization. The goal is to design algorithms for joint user activity detection, delay detection, and channel estimation. By exploiting the sparsity on both user activity and delays, we formulate a hierarchical sparse signal recovery problem in both the single-antenna and the multiple-antenna scenarios. While traditional compressed sensing algorithms can be applied to these problems, they suffer high computational complexity and often require the perfect statistical information of channel and devices. This paper solves these problems by designing the Learned Approximate Message Passing (LAMP) network, which belongs to model-driven deep learning approaches and ensures efficient performance without tremendous training data. Particularly, in the multiple-antenna scenario, we design three different LAMP structures, namely, distributed, centralized and hybrid ones, to balance the performance and complexity. Simulation results demonstrate that the proposed LAMP networks can significantly outperform the conventional AMP method thanks to their ability of parameter learning. It is also shown that LAMP has robust performance to the maximal delay spread of the asynchronous users.

This paper considers massive connectivity in asynchronous systems, where a large number of devices sporadically send data to the base station (BS) with imperfect synchronization. Grant-free random access is considered and each device is assigned with a unique but not necessarily orthogonal pilot sequence for identification and channel estimation. The goal is to design algorithms for joint user activity detection, delay detection, and channel estimation. We first adopt a transmission model where a guard interval is inserted between pilot and data in order to eliminate the potential cross pilot-data interference between asynchronous devices. By exploiting the feature of the asynchronous massive connectivity, we formulate a sparse signal recovery problem with hierarchical sparsity on the user activity and the time delays. We propose the Learned approximate message passing (LAMP) network that combines deep learning in the AMP framework to solve the problem. This neural network benefits from parameter learning ability of deep learning and low computation complexity of the AMP algorithm. Simulation results demonstrate that the LAMP network can perform much better than the AMP algorithm with no prior knowledge of the system statistics. Its performance is also insensitive to the maximal delay spread of the asynchronous users.

Recently, many researches based on millimeter wave (mmWave) together with analog beamforming technology have been done to provide higher transmission throughput in vehicle-to-everything (V2X) communication. While hybrid beamforming technology, which can generate multi radio frequency (RF) chains with reduced hardware complexity, continues attracting attention. Hence, this paper focuses on a hybrid-beamforming-based roadside unit (RSU) beam resource scheduling problem in vehicle-to-infrastructure (V2I) communication and intends to improve transmission fairness. Then, this paper proposes an indicator Q to evaluate transmission fairness. Furthermore, a Power-allocated-based Beam resource Scheduling strategy (PBS) and a Time-allocated-based Beam resource Scheduling strategy (TBS) are designed for the multi-RF-chain based V2I communication to enhance transmission fairness. Simulation results prove that, the two proposed beam resource scheduling strategies can effectively improve the transmission fairness in V2I communication.

In 5G era, the base stations are capable of providing multiple services in various scenarios (e.g. vehicle network, Internet of Things), which provides fine opportunity to enhance spectrum efficiency. Base stations can flexibly utilize the idle frequency band for spatiotemporal low-demand services and guarantee services with high priority (e.g. urgent broadcasting), which construct a distributed architecture for spectrum allocation. In this paper, we provide a dynamic spectrum allocation scheme in base station, which can flexibly rearrange spectrum considering service priority, energy consumption and renting cost. We use Lyapunov optimization method to solve the problem. Moreover, we propose online Lyapunov optimization algorithm (OLOA) to figure out the optimal solution of penalty-and-drift function and show mathematical proofs on the performance of the algorithm. The simulation results show that the superiority and stability of our algorithm, which corroborates theoretical analysis.

Service placement is a significant issue in mobile edge computing (MEC) system. Many works have proposed efficient offline approaches for service placement problems in MEC system. However, because of the randomness and uncertainty of mobile networks, it is impractical for these approaches to be implemented. Facing these uncertainty, we propose an online service placement scheme for MEC system without knowing service demand and network states in advance. In order to maximize the long-term accumulated reward obtained by service placement with limited resource constraint, we analyse this problem by a combinatorial multi-armed bandit (MAB) framework. In addition, because we simultaneously consider the service placement and resource allocation among services, it can be formulated as a multiple choice knapsack problem (MCKP) in each time slot. To solve this long-term reward maximization problem, we first propose a combinatorial upper bound confidence(CUCB)-based online service placement and resource allocation scheme. Then, we analyse the performance of this algorithm theoretically. Finally, simulation results show the efficiency of the algorithm.

Mobile-edge computing (MEC) which employs cloud computing platforms at the network edge is an emerging paradigm for 5G networks. Users can be provided with lower latency and energy consumption by offloading computation tasks to the edge cloud. However, it is hard for MEC to guarantee low latency when large amount of users share the limited spectrum resource to offload computation tasks or download computation results because of high transmission delay. Full-duplex (FD) communication that allows simultaneous transmission and reception of signals over the same frequency band, is a promising solution to the shortage of spectrum resource. In this paper, we investigate a novel multi-user FD-MEC system involving both offloading and downloading processes. With the help of the FD capable BS, two half-duplex (HD) users can form a FD pair which can share the same time slots and frequency band for uplink and downlink transmission. To minimize the completion time of all users in the system, we formulate a joint optimization problem of time, power and user pairing scheme, which is a mixed-integer nonlinear programing (MINLP). This problem is further divided into two layers. For the inner layer problem, we obtain the optimal solution by a bisection method. While for the outer layer problem, we use concave-convex procedure (CCCP) to transform it into a tractable form and obtain a stationary point. Finally, numerical results show that with our proposed resource allocation scheme, the overall latency can be significantly reduced by introducing FD to MEC system.

Recently, sharing perception sensor information among vehicles sets a higher transmission demand for vehicular communication. Facing the challenges, millimeter wave (mmWave) has the potential to realize multi-Gbps throughput transmissions. However, in conventional methods, beam sweeping is utilized in beam alignment, which is inefficient in high mobility scenarios. To overcome the drawback, this paper introduces vehicular position information into beam alignment in vehicle-to-vehicle (V2V) communication. Considering localization errors, in order to avoid beam misalignment, two beamwidth optimization methods are proposed to maximize the average throughput among the nearby region of receiver's estimated position. Simulation results suggest that the proposed schemes provide appreciable performance improvements as compared to the traditional beam sweeping scheme.

Multimedia Broadcast Multicast Service (MBMS) will be expected to be added into 5G system in the coming 5G release of 3GPP. TV White Space (TVWS) are expected to be effectively used in MBMS mode to solve the problem of spectrum shortage. TV White Space (TVWS) can be organized to provide great help by auction to 5G service providers (SPs) in various scenarios, e.g. mobile wireless communication, Internet of Things (IOT) and Vehicular Network. We investigate imperfect information dynamic Stackelberg Game to allocate the idle TVWS spectrum resources using auction scheme, and, accordingly, the service transaction platform is constructed. First, Hidden Markov Model (HMM) is used to predict the service volume required by Service Providers (SPs). Then, the Nash Equilibrium is achieved by Service Providers, who make strategies based on the forecasting results. Next, Broadcast Operator (BO) allocates the idle spectrum by auction. The simulation results show that the predicted prices are close to the actual transaction prices and the profits of broadcasting operators and services providers are increased simultaneously. This provides solution for the unified regulation of TVWS, incenting the usage of TVWS and providing the feasible scheme that broadcasting can provide the services in 5G.

Real-time road traffic information brings great convenience for drivers. Various road information acquisitions are enabled by recent mobile crowdsensing paradigm. However, the accuracy of information can not be guaranteed, and appropriate incentive mechanism is still unavailable. In this paper, we study the problem of extracting the actual road traffic information according to the reports from an amount of unknown contributors. To obtain the accurate road traffic result with high probability, we establish a reputation system to evaluate the reliability of each contributor, which takes both location and time deviation factors into account. We also design an incentive mechanism to elicit the truthful report of each qualified contributor. Furthermore, we improve the existing answer inference methods and derive the correct result in an efficient way. Extensive simulations are carried out to evaluate the proposed algorithms.

Crowd sensing systems enable a wide range of data collection, where the data are usually tagged with private locations. How to incentivize users to participate in such systems while preserving location-privacy is coming up as a critical issue. To this end, we consider location-privacy protection when motivating users to sense data instead of viewing them separately. Without loss of generality,$k$-anonymity is utilized to reduce the risk of location-privacy disclosure. Specifically, we propose a location aggregation method to cluster users into groups for$k$-anonymity preserving, and meanwhile mitigating the incurred information loss. After that, an incentive mechanism is carefully designed to select efficient users and calculate rational compensations based on clustered groups obtained in location aggregation, where the influences of both the information loss and$k$-anonymity in location-privacy preserving are captured into group values and sensing costs. Through theoretical analysis and extensive performances evaluated on real and synthetic data, we find out that the incentive payment increases sharply with more stringent privacy protection and the information loss can be further mitigated compared with conventional methods.

Single Carrier-Frequency Division Multiple Access (SC-FDM) and Tone Reservation (TR) are used independently to reduce Peak to Average Power Ratio (PAPR) in wireless communications. In this paper, we jointly consider TR and SC-FDM to achieve much lower PAPR. We first present the PAPR of SC-FDM system with different DFT/IDFT size, and then theoretically analyze the PAPR gain of TR in SC-FDM. By considering the impact of TR on transmission power, we propose a novel metric, the effective signal power, to measure the performance of TR in SC-FDM. Afterwards, the TR optimization problem is formulated and solved by TR gradient algorithm. Finally, the performance of TR with SC-FDM is evaluated.

Cooperative diversity is a transmission technique, where multiple terminals forms a virtual antenna array that realizes spatial diversity gain in a distributed fashion. The concept of cooperation has already been introduced to MAC layer to design MAC protocol. However, it's much different with that at physical layer. In this paper, we present a new MAC protocol based on IEEE 802.11, called C-MAC, that can support the basic building block of cooperative system. That is, in C-MAC, source would invite a relay node into data transmission if there exits an available one. During data transmission, source sends the signal to destination at first. The relay node will retransmit the overheard information to the destination at the second time slot. The destination combines two signals from source and helper, thus creating spatial diversity and robustness against channel fading. The C-MAC is backward compatible with legacy IEEE 802.11 system. The performance of C-MAC mainly depends on the physical layer's performance as it just provides the support for cooperation at MAC layer. If the physical layer works well, C-MAC would outperform IEEE 802.11 considering packet error rate. We also do the simulation using ns-2 with assumptive physical parameters. The result shows C-MAC would outperform 802.11 if packet error rate is a little high, and C-MAC would lead to some unfairness to nodes without relay.

In this paper, we propose a novel type-II hybrid ARQ scheme using low density parity check (LDPC) codes. The proposed approach combines low hardware overhead with good decoding performance by using an efficient decoder operating at a much higher rate with a much smaller size parity check matrix. Our scheme makes parts of the previously received data gradually improved until successful decoding of the entire original codeword. We also present a novel efficient framework for constructing rate-compatible LDPC codes applied in our hybrid ARQ scheme. The progressive edge growth (PEG) construction method with zigzag pattern results in linear-time encoding.