The multi-probe anechoic chamber (MPAC) method is widely regarded as the most promising for 5G multiple-input multiple-output (MIMO) dynamic channel over-the-air (OTA) testing. However, the probes used in the testing system is varying to reproduce the channel in a laboratory environment, which leads to a dramatic increase in the complexity of probe selection. In this paper, a concept of context channel information is presented based on the variation of angles. And two corresponding probe selection algorithms are proposed to resolve the above problem. These algorithms map the context channel information to the variation of probe locations. Based on the proposed methods, reconstruction of two typical dynamic channel models are performed. Through different indicators like four-dimensional root-mean-square (4D - RMS) error of spatial correlation, four-dimensional power angular spectrum similarity percentage (4D - PSP) and execution time, the improvement in complexity and accuracy of our proposed methods relative to the snapshot-by-snapshot methods are discussed. The simulation results indicate that the proposed methods can accurately reconstruct the channel environment around the device-under-test (DUT) with much lower computational complexity.
As the primary source of energy consumption in communication networks, the power usage of 5G base station(BS) is a significant concern. The sleep mode (SM) of BS can be utilized to reduce mobile network consumption during periods of low traffic demand. However, current BS sleep technologies primarily rely on network-side participation, with insufficient involvement from user equipment. In this paper, inspired by the concept of user-initiated BS activation mentioned in 3GPP TR38.864 [1], we design a user-driven base station sleep and wake-up mechanism that considers user-generated wake-up signals to activate sleeping BSs. Analysis results indicate that this method can achieve significant energy savings.
Steganographic coding is a fundamental technique in adaptive steganography, and the introduction of polar codes has significantly enhanced its security. However, the performance of steganographic polar codes has primarily been evaluated through experiments, and their construction has remained some-what heuristic. This paper theoretically presents an optimal model for steganographic polar codes based on a analysis of their performance bounds, by examining the relationship between polar code decoding and steganographic coding based on polar codes. Although this model can only be constructed approximately, numerical simulations indicate that the proposed optimal construction offers improved security performance compared to classic codes.
This paper presents a computer-aided search scheme based on simulated annealing methodology for regular quasi-cyclic low-density parity-check (QC-LDPC) codes, ensuring a minimum girth of at least 8. The approach utilizes the simulated annealing algorithm, leveraging the correlation between cycles and trapping sets, and analyzes their positioning in the Tanner graph to iteratively reduce trapping sets. This process ultimately results in the generation of high-girth QC-LDPC codes. A significant advantage of our method is that it greatly reduces the search time compared to existing approaches. Furthermore, our method generates QC-LDPC codes that demonstrate a diminution in the number of 8-cycles, concurrently achieving a reduction in detrimental trapping sets. Simulation results reveal that our method generates QC-LDPC codes with excellent bit error rate performance.
Freshness of information is a critical issue for real-time Internet of Things (IoT) applications. Unmanned aerial vehicles (UAVs), due to their high flexibility and low cost, are widely deployed in time-sensitive IoT scenarios. In this article, we explore the trajectory planning problem in rechargeable-UAV-aided IoT Networks. To accurately measure the freshness of information, we introduce Age of Information (AoI) and Age of Synchronization (AoS). AoI is utilized to plan the UAVs' trajectories for accessing sensors (SNs) and reduce the average AoS of the system. UAVs can recharge at charging stations to maintain energy above a certain threshold. We formulate a Markov decision process with the objective of minimizing the average AoS under the energy constraint. To adjust the dynamic environment, we propose a multi-agent deep reinforcement learning method to jointly optimize the UAVs' flight trajectories and battery recharging. Simulation results demonstrate that the proposed algorithm can significantly reduce the total average AoS under the energy constraint.
Reed-Solomon (RS) codes are widely applied in storage systems. The main bottleneck of RS codes is that the high decoding complexity defined as the number of multiplications and additions. In this paper, we propose a fast decoding algorithm for RS codes that requires fewer number of multiplications. Our fast decoding algorithm is designed by a decomposition of Vandermonde matrices. We show that our decoding algorithm requires fewer number of multiplications than the existing best known algorithm when the number of parities $T$ is large. When the codeword length $N=16$, our decoding algorithm reduces the number of multiplications by 7% and 29% when $T=4$ and $T=7$, respectively, compared with the best known decoding algorithm.
Task-oriented communications, which focus on the effective execution of the task of interest rather than the recovery of the transmitted symbols, have great potentials in future Internet of Things (IoT) systems. In particular, the fundamental change in the design philosophy brings new opportunities for the physical-layer technique. In this paper, we consider a new joint channel estimation and classification (JCEC) receiver design for a multi-device edge inference system. By fully exploiting both the feature and the channel distributions, we establish a maximum $a$ posteriori (MAP) based JCEC formulation, which turns out to be a challenging non-convex problem. A majorization-minimization (MM) algorithm is proposed to solve the difficult problem, where only a closed-form expression needs to be calculated in each iteration. Simulations validate that our scheme outperforms the benchmark with decoupled channel estimation and MAP classifier on both a synthetic feature dataset and the widely used CIFAR-10 dataset.
Integrated sensing and communication (ISAC) has emerged as a promising technology for realizing beyond fifth-generation (B5G) and sixth-generation (6G) wireless networks due to its ability of significantly improving both communication and sensing performances. In this paper, we study the distribution on the performance Pareto boundary for a general ISAC scenario, a dual-functional BS simultaneously estimates the target response matrix while communicating with a user. First, we investigate the effect of randomness of random signals on sensing performance and use the lower bound of minimum mean square error (MMSE) to measure sensing performance. Then, taking the transmit signal's distribution as the optimization variable, we establish the optimization problem to realize the Pareto performance boundary. Subsequently, we show that the optimal distribution is the discrete distribution with finite mass points in single-input single-output (SISO) ISAC and discrete in amplitude and uniform in phase (DAUP) in multiple-input multiple-output (MIMO) ISAC. Finally, we give simulation results and provide insights into deterministic-randomness tradeoff (DRT) through analyzing changes of the distribution on the performance Pareto boundary.
With the rapid advancement of distributed systems technology, deep learning-based methods have become a common scheme to implement multiple data processing. This paper presents a novel multi-channel encoder-decoder architecture (called as (DCN)-C-2), which is explained by integrating generalized singular value decomposition (GSVD) with the principles of Hankel convolution framelet. Specifically, we employ the feature extraction capability of GSVD to perform data interactions by forward/backward propagation, where numerous inputs are designed using the common bases and the reliable performance is achieved by training shared right bases. The network intuitively shows the interactions of multiple data in propagation, which is suitable for a wide range of inverse problems. Finally, we demonstrate the superiority of (DCN)-C-2 over other fundamental networks through numerical experiments conducted on inverse problem tasks.
Deep joint source-channel coding (DeepJSCC) emerges as a novel technology in semantic communication, coin-ciding with the rising demand for edge devices in the Internet of Things (IoT). Consequently, the deployment of DeepJSCC on edge devices becomes a pivotal research direction in semantic communication. However, DeepJSCC confronts issues related to the fading of complex channels. Besides, implementing DeepJSCC on edge devices also poses challenges due to the constrained computing resources. In this paper, we propose a method named DeepJSCC with Ensemble learning (DeepJSCC-ES) to enhance its ability in resisting fading in the Rician channel. Furthermore, we present a solution by introducing a signal-to-noise ratio (SNR)-adaptive pruning algorithm called the DeepJSCC SNR-Adaptive Pruning method (DJSAP) to make the DeepJSCC network lightweight, reducing computational complexity and enhancing its suitability for edge devices. Our simulations show that the DeepJSCC-ES system significantly outperforms the baseline DeepJSCC, particularly excelling in low SNR conditions. Further, the parameter size of the pruned model using DJSAP is compressed by 93.37%, while the average structural similarity index metric (SSIM) for the images only decreases by 0.92% compared with the baseline DeepJSCC.
To enhance coverage and improve service continuity, satellite-terrestrial integrated radio access networks (STIRANs) have been seen as an essential trend in the development of 6G. However, there is still lack of theoretical analysis on its coverage performance when low-earth-orbit (LEO) satellites are considered. To fill this gap, we establish a system model to characterize a typical scenario where LEO satellites and terrestrial base stations (BSs) are both deployed, which features the orbit model of satellites, a deployment hole of BSs centered at a typical user and practical satellite antenna radiation pattern. With stochastic geometry (SG), the downlink coverage probability is analyzed under the setting where LEO satellites and BSs occupy distinct frequency band. Based on the distribution of the nearest serving distance, the expression of coverage probability is derived. Via extensive simulation, the correctness of theoretical derivations is verified and the influence of network design parameters is demonstrated, including satellite's density and orbit polar angle. Moreover, the performance improvement brought by STIRANs over LEO satellite network and terrestrial network is highlighted.
In Integrated Sensing and Communications (ISAC) systems assisted by Intelligent Reflecting Surfaces (IRS), precise channel estimation is crucial for optimal system performance and maximizing the potential benefits. The estimation problem, however, becomes intricate due to complex channel coupling and specific environmental conditions. This paper addresses these challenges by formulating channel estimation as a denoising problem and proposing a novel diffusion-based framework. This approach mitigates the impact of ill-posedness, which is typically problematic for general neural network (NN)-based techniques. By modeling the joint distribution and sampling from the learned posterior distribution, we accurately recover the channel coefficients from noisy pilot-based observations. Simulation results demonstrate significant performance improvements of the proposed method compared to both least-squares and NN-based benchmarks. Moreover, thanks to the carefully designed architecture and residual prediction strategy, the number of sampling steps in the diffusion reverse process is greatly reduced, enabling real-time CSI acquisition.
In this paper, we study the multistatic cooperative sensing assisted secure transmission via intelligent reflecting surface (IRS). To be specific, a multistatic cooperative sensing scheme is proposed to obtain the angle of arrival and the localization of the eavesdropping target to achieve the beam alignment accurately. Our goal is to maximize the sum secrecy rate by jointly optimizing the association variables of base station (BS) and users, the BS beamforming and the IRS phase shifts, subject to the requirement of target sensing. Due to the coupling of variables and nonconvex objective function, we decompose it into three subproblems and develop an alternative algorithm to solve them iteratively. The association variables are first obtained by successive convex approximation. Then, the BS transmit beamforming can be derived via the semidefinite relaxation. Finally, an alternating direction method of multiplier is adopted for the IRS phase-shift design. Simulation results indicate that the multistatic cooperative sensing via IRS can enhance the sensing performance and guarantee the security.
Recently developed semantic communication facilitates an intelligent and minimalist approach to meet the growing demands for future sixth-generation (6G) communication systems. However, since the constraints of backpropagation during neural network training, the predominant approach of most extant studies are based on differentiable channel inputs, which is difficult to match with existing digital communication systems effectively. Meanwhile, the research on modulation techniques considering constellation optimization for digital semantic communication is still in the early stage. To deal with these issues, we propose a novel deep joint source-channel coding (DJSCC)-based digital semantic communication with considering constellation optimization, which is referred to as DJSCC-C. Firstly, the digital semantic communication system is introduced. Secondly, the DJSCC-C system is proposed for image transmission, which is trained with a newly-constructed loss function, aiming to improve the transmission rate in the digital semantic communication system as well as optimize the joint source-channel coding capability. Finally, simulation results are provided to verify the superiority of the proposed scheme for future design of digital semantic communication systems.
Cell-free and device-to-device (D2D) heterogeneous networks can effectively enhance spectral efficiency. However, spectrum reuse among different transmission links leads to severe interference to limit the growth of network capacity. In this paper, we propose a power control algorithm based on graph attention network for interference coordination in cell-free and D2D heterogeneous networks. Specifically, a heterogeneous graph is utilized to model the power control problem, where desirable transmissions and interference are represented as edges of different types. The edge-featured attention mechanism is applied over the constructed graph to capture the relationships among various edges. Simulation results show that our proposed algorithm can improve network capacity and exhibit generalization performance with respect to network scales.
To compute a summation (S) of independent classical data streams from multiple servers over a quantum multiple access channel (QMAC), every server must directly send its quantum system containing multiple qudits to the user. Previous work Sigma-QMAC in [1] considers that the servers are entangled. In this work, the maximum rate, defined as the maximum number of dits of the sum computed per qudit transmitted, is derived, and a quantum coding scheme is designed to achieve it. In practice, a server is not always directly connected to the user, necessitating a relay between them for connectivity. Motivated by this, we consider a two-layer quantum MAC (2QMAC) model, where a user is connected to a layer of mirrors, and one of the mirrors (acting as a relay) is connected to a layer of servers. Quantum systems within the same layer are entangled, whereas quantum systems across different layers are independent. In Sigma-2QMAC, deriving the maximum rates of two layers simultaneously is challenging since the rates of the two layers affect each other. To address this, we first derive the maximum rate of the first layer by neglecting the second layer. Second, to relate the rates of the two layers, we derive the maximum rate of the second layer for a given feasible rate (less than the maximum rate) of the first layer. In addition, a joint coding scheme based on the N-sum box abstraction is designed to achieve the maximum rate of the second layer.
In this paper, we propose a resource allocation scheme with clustered interference alignment (IA) in Ultra-Dense Networks (UDNs). Our goal is to maximize the sum spectral efficiency (SE) of users according to the network distribution and provide additional subchannels for redundant users as well as ensure fairness of the primary users, while other works mainly consider the Degrees of Freedom (DoFs) demand of users, ignore how well one DoF can achieve and treat IA cluster as the object of subchannel allocation. Our method takes the coherence bandwidth as the minimum resource where precoding and equalizing of IA become effective in all resource blocks. We construct a weighted graph at first to cluster users with their corresponding small base stations into disjoint and different sizes of groups. Second phase, we utilize graph coloring to allocate primary subchannels to groups of users to achieve fairness. Then we propose a redundant user extraction scheme to allow users free of interference to join in primary subchannels of other cluster to increase sum SE of the network. Simulation results indicate in a certain transmit signal to noise ratio (transmit SNR) interval, the proposed solution has obvious advantages over existing scheme, promising for further research.
Semantic communication aims to transmit the underlying semantic information of a signal from the sender to the receiver, where the key requirement is to ensure that the receiver reconstructs a signal semantically (almost) equivalent to the source. Conventional image communication methods lack effective mechanisms for preserving both semantic coherence and reconstruction quality, especially for low-rate scenarios. In this paper, we propose an alternative method, namely Instance-Consistent Image Communication (ICIC), for semantic segmentation-based low-rate image communication. Our method leverages segmented instances and detected captions as intermediate vehicles to transmit semantic information from the sender to the receiver. Once transmitted, the receiver is capable of utilizing the vehicles to regenerate images with off-the-shelf diffusion models. Compared to state-of-the-art methods, experimental results on the COCO dataset illustrate that our method obtains higher semantic accuracy and higher image quality at extremely low rates.
This study investigates a simultaneous tasks offloading and communications (STOC) strategy in mobile edge computing (MEC) networks, facilitated by the integration of simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and unmanned aerial vehicles (UAVs). Diverging from conventional MEC approaches, the newly proposed scheme simultaneously accounts for the computational and communicative capacities of MEC networks, rendering it a more pragmatic solution in practice. An optimization problem is devised to maximize the weighted sum of the minimum offloaded task data and communication data, while ensuring the quality of service (QoS) constraints for STOC through joint design of time scheduling, resource allocation, active and passive beamforming, alongside with the UAV trajectory planning. A novel alternating optimization method is proposed to solve this non-convex problem with strong couplings among variables. We provide sufficient numerical results to validate the effectiveness of the proposed STOC scheme.
Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) can be assembled in the air-ground integrated sensing and communication (ISAC) to further enhance the performance. However, the near-field effect should be further considered with higher carrier frequency and increasing number of STAR-RIS elements. In this paper, we propose a STAR-RIS enabled air-ground near-field ISAC scheme, where the unmanned aerial vehicle (UAV) is deployed as a mobile base station and the semi-passive architecture is adopted to alleviate the severe path loss. We maximize the weighted sum rate to guarantee both the communication and sensing by modifying the beamforming vectors, the reflection/transmission matrices and the hovering location of UAV to match the near-field effect. To address this challenge, we first divide it into three subproblems, which are recast into convex ones by the semidefinite relaxation and successive convex approximation. Finally, we develop an alternate algorithm to iteratively solve them. Simulation results are shown to demonstrate the superiority and validity of the proposed scheme.
