Affine Frequency Division Multiplexing (AFDM) is an emerging multicarrier modulation technique well-suited for high-mobility scenarios in next-generation wireless systems. By achieving delay-Doppler orthogonality in a twisted time-frequency domain, AFDM offers robust and efficient communication while enabling integrated sensing and communication through precise environmental parameter estimation. However, a major drawback of AFDM is its high peak-to-average power ratio (PAPR), which can cause power amplifier saturation and degrade system reliability. To address this issue, we propose an autoencoder-based PAPR reduction scheme leveraging convolutional neural networks, which effectively lowers the PAPR while maintaining satisfactory bit error rate (BER) performance. Simulation results under various modulation schemes and linear time-varying channels demonstrate that the proposed approach outperforms conventional methods in both PAPR reduction and BER performance.

In this article, we propose a movable antenna (MA)-enabled frequency-hopping (FH) multiple-input-multiple-output (MIMO) radar system and investigate its sensing resolution. Specifically, we derive the expression of the ambiguity function and analyze the relationship between its main lobe width and the transmit antenna positions. In particular, the optimal antenna distribution to achieve the minimum main lobe width in the angular domain is characterized. We discover that this minimum width is related to the antenna size, the antenna number, and the target angle. Meanwhile, we present lower bounds of the ambiguity function in the Doppler and delay domains, and show that the impact of the antenna size on the radar performance in these two domains is very different from that in the angular domain. Moreover, the performance enhancement brought by MAs exhibits a certain tradeoff between the main lobe width and the side lobe peak levels. Therefore, we propose to balance between minimizing the side lobe levels and narrowing the main lobe of the ambiguity function by optimizing the antenna positions. To achieve this goal, we propose a low-complexity algorithm based on the Rosen’s gradient projection method, and show that its performance is very close to the baseline. Simulation results are presented to validate the theoretical analysis on the properties of the ambiguity function, and demonstrate that MAs can reduce the main lobe width and suppress the side lobe levels of the ambiguity function, thereby enhancing radar performance.

Due to the capability of reshaping wireless transmission environments, intelligent reflecting surface (IRS) has emerged as a promising solution to address the blockage issue in millimeter wave (mmWave) communication systems. However, to reduce the channel estimation overhead and in the meantime harvest the beamforming gain brought by the large-scale antennas and reflecting elements, efficient beam training methods are indispensable. In this paper, we develop a deep learning (DL) aided two-stage beam training scheme for an IRS-assisted mmWave system. In the first stage, we recognize the effective channel paths for both direct link and cascaded link via multibeam scanning, where the different sparse properties of these two links are exploited. In the second stage, a deep neural network (DNN)-based beam synthesizer is developed to generate an optimized reflecting vector and hybrid precoder, based on the recognized multiple channel paths obtained in the first stage. Simulation results are presented to demonstrate the superiority of the proposed scheme over the state-of-the-arts.

The orthogonal time frequency space (OTFS), a recently introduced two-dimensional modulation in the delayDoppler domain, holds great promise for high-mobility communications. OTFS-based millimeter-wave systems are gaining attraction due to their ability to offer a larger bandwidth and increased robustness against Doppler spread. However, the presence of phase noise (PHN) from high-frequency oscillators can lead to severe inter-carrier interference in OTFS. In this paper, we present a new approach that addresses the challenge of PHN in millimeter-wave OTFS systems by introducing a joint design of data detection and PHN estimation, which are performed iteratively to facilitate the exchange of the posterior information between the two components. Specifically, a lowcomplexity expectation propagation algorithm is developed for data detection, where the posterior information is updated in an inverse-free way based on the minorization-maximization method. As for the PHN estimation, a belief propagation algorithm is applied for message passing on a factor graph. Simulation results demonstrate the superior performance of our proposed design, showcasing faster convergence and substantial gains compared to existing OTFS detectors. Moreover, our approach also exhibits robustness against PHN, highlighting its effectiveness in practical scenarios.

In this paper, we propose a movable antenna (MA)-enabled frequency-hopping (FH) multiple-input multiple-output (MIMO) radar system and analyze the properties of its radar ambiguity function. Specifically, we derive the expression of the ambiguity function and analyze the relationship between its main lobe width and the transmit antenna positions. In particular, the optimal antenna distribution to achieve the minimum main lobe width is revealed and we discover that this minimum width is related to the ratio of antenna dimension to wavelength, the number of antennas, and the target angle. However, to achieve this minimum width, there is inevitable performance loss in the side lobes. Therefore, we propose to balance between minimizing the side lobe levels and narrowing the main lobe of the ambiguity function by optimizing the antenna positions. To achieve this goal, we propose a low-complexity algorithm based on the Rosen’s gradient projection method (RGPM), and we show that its performance is very close to that of the genetic algorithm (GA). Simulation results are presented to validate the theoretical analysis on the properties of the ambiguity function, and demonstrate the advantages of MAs in improving the radar performance.

Semantic communication is a promising framework for the next generation communication systems, which generally adopts deep learning based joint source and channel coding and has been verified to offer superior efficacy. A key ingredient in augmenting the reliability of this framework is the incorporation of hybrid automatic repeat request (HARQ) techniques. However, existing semantic HARQ architectures, such as fixed-length HARQ or chase combining HARQ (CC-HARQ), utilize predefined retransmission code lengths, lacking the flexibility to adjust to different channel signal-to-noise ratio (SNR) conditions. To address this issue, this paper develops an adaptive HARQ scheme by leveraging the double deep Q-network (DDQN) to determine the retransmission code lengths. Specifically, we first propose a basic model which consists of an image reconstruction module and a performance estimation module. The performance estimation module replaces the conventional error detection method like cyclic redundancy check (CRC) to estimate the structural similarity index measure (SSIM) of the reconstructed image at the receiver. Building on this basic model, our proposed HARQ scheme works by feeding back an NACK signal and an appropriate code length determined by the proposed DDQN algorithm to the semantic transmitter for the next transmission, if the estimated SSIM performance of the previous transmission does not exceed a predefined threshold. Experimental results demonstrate that our HARQ scheme is able to achieve the same SSIM performance as the existing semantic HARQ schemes, but with significantly reduced communication cost.

Traditional millimeter-wave beam selection or prediction algorithms typically rely on beam scanning measurements at the transceivers, incurring substantial training overhead and exhibiting limited adaptability in diverse environments. Recent efforts have aimed to mitigate these challenges by incorporating sensing information, thereby reducing or eliminating the need for extensive beam training. However, existing works predominantly concentrate on exploiting a single sensing modality and often overlook the potential benefits of utilizing historical sensing information. In this paper, we introduce an intelligent beam prediction framework that leverages a deep integration of multimodal sensing data, encompassing GPS, camera, radar, and LiDAR data. The design proposed involves the application of customized deep neural networks to extract features from camera, radar, and LiDAR data. These extracted features, combined with user position and selected beam index, are concatenated to form an aggregated feature vector at each time instance. Subsequently, a time series of these concatenated feature vectors is utilized to exploit temporal correlation for beam prediction through a dedicated long short-term memory network module. Numerical simulations confirm the effectiveness of the proposed design and its superiority over several considered state-of-the-art baselines.

Cooperative localization is a promising technique in wireless networks, and neighbor selection (NS) is essential to limit the degree of cooperation and reduce the amount of data to be exchanged. However, the existing NS algorithms may suffer from major performance loss when applied to networks with limited resources (e.g., bandwidth, time and energy). In this paper, we establish a general optimization framework for the NS problem to minimize the localization error under strict resource constraints. Based on the squared position error bound (SPEB) criterion, we formulate two distributed NS problems under implicit and explicit energy constraints, respectively, to balance the energy consumption of the network, where implicit energy constraints mean that specific energy profiles of the nodes’ neighbors are unavailable while explicit energy constraints mean the opposite. Moreover, we propose to jointly optimize the NS and power allocation in the explicit case to further improve the localization performance. The resulting problems are challenging to solve due to the nonlinear objective functions and discrete optimization variables. We first transform them into more tractable forms and then develop novel algorithms based on the penalty dual decomposition method to solve the transformed problems efficiently. Simulation results show that the proposed algorithms can significantly outperform benchmark algorithms. In particular, the proposed algorithm almost achieves the performance lower bound in the implicit case.

Recently, reconfigurable intelligent surface (RIS) have been introduced not only to overcome communication blockages due to obstacles but also for high-precision localization of users in GPS denied environments, e.g., indoors, woods, and underground tunnels, etc. This paper studies the cooperative localization problem in an RIS-aided milimeter wave (mmWave) system, where the RIS is deployed to assist the localization of two users and the two users further cooperate to improve their localization performance. We first build the system model based on the uniform planar array (UPA) response of RIS. Then, the Fisher information matrix (FIM) and the Cramér-Rao lower bound (CRLB) for estimating the absolute user equipment (UE) position are derived. An efficient block coordinate descent (BCD)-based reflect beamforming design algorithm is proposed to minimize the CRLB. Finally, numerical results are presented to show that user cooperation can provide additional localization performance gain as compared to the case without cooperation and centimeter-level positioning accuracy can be achieved by utilizing a large number of reflecting elements and exploiting user cooperation.

Consider a multi-cluster cooperative relay network, where each relay cluster (consists of a certain number of amplify-and-forward (AF) relays) forwards the signal from its associated user equipment (UE) to the base station (BS). With the goal of providing fairness among the UEs and reducing the costs of full relay cooperation, we study the joint design of relay clustering and beamforming to maximize the minimum signal-to-interference-and-noise ratio (SINR) under per relay power constraints. This max-min SINR problem with relay clustering is formulated as a mixed-integer programming (MIP) problem, which is generally NP-hard. To tackle this problem, we propose a block coordinate descent (BCD) based algorithm based on the property that the constraints are separable among the optimization variables, i.e., the clustering matrix, the relay cooperative beamforming vector, and the receive beamforming vectors at the BS. Specifically, these variables are optimized iteratively in an alternating fashion, one at each time with others being fixed and we show that each subproblem can be efficiently and optimally solved. Simulation results demonstrate the effectiveness of the proposed algorithm as compared with the benchmark schemes.

With the development of the Internet of vehicles and 5G, there emerge more and more challenging application scenarios with fast time-varying channels and high mobility nodes, such as high speed trains environment and vehicle-to-infrastructure (V2I) communication in highway. To support the reliable vehicular communication and mobile edge computing (MEC), it is important to obtain the future channel state information (CSI), which can help optimize system transmission scheme. In this paper, we propose an efficient blind CSI prediction model, called BCPMN. We first reshape the sampled signal into a specific 2-dimensional matrix. Then we propose a learning framework contains of convolutional neural network (CNN), long short-term memory (LSTM) network and fully connected layers. To validate the proposed model, we conduct extensive experiment in three modulation modes. The results show that the BCPMN achieves highly accurate signal-to-noise ratio (SNR) prediction in the fast changing channel model with different modulation modes. In particular, the proposed model can obtain better performance than other methods, and can achieve better performance than other methods without the payload cost of pilot.

Quick response (QR) codes are widely used in many fields. Various QR code recognition approaches have been proposed to improve the accuracy of decoding QR code. However, the recognition of distorted QR codes with one missing position detection pattern (PDP) remains a problem. In this study, based on the vector relationship and the structural features, the authors introduce a new method for decoding distorted QR code with one missing PDP. Three methods, Zxing, Halcon, and the newly proposed method, are used to test the decoding capability. For QR codes with one missing PDP, the experimental results show that the proposed method could meet the recognition angle range as much as 110°, while Zxing fails to recognise, and the angle of decoding for Halcon is 90°. Especially, the proposed method is available at an extremely harsh luminance and contrast environment, e.g. both phases as a 60% discount, when the decoding angle of Halcon is only 35°, while the proposed one better than 2.7 times of it. Besides, the proposed method is more robust to decode the QR codes with a missing PDP under different backgrounds and noisy images.

Cooperative localization can compensate weaknesses of traditional localization techniques which do not operate well in harsh environment. However, cooperative localization signals increase the interference power for communication. In this work, we seek to joint localization and transmission power in order to maximize the transmission rate of secondary user under the power budget and primary users' outage constraints. At the same time, we consider the trade-off between localization error and localization interference when formulating the above problem in cooperative localization. The proposed optimization problem is nonconvex and highly coupled, which is challenging to solve. To simplify the problem, we introduce some auxiliary variables to the original optimal problem and apply a algorithm based on concave-convex procedure (CCCP). The simulation results demonstrate the advantages of location-aware network based on cooperative localization.

Hyperspectral image (HSI) denoising has become an important research topic in the research community due to its significance improvements in many applications (e.g. classification). In this paper, we introduce a Monte Carlo non-local means (MCNLM) method for noise reduction of the HSI. Each band of the HSI is processed by the MCNLM, which is a randomized algorithm suitable for large-scale patch-based image (e.g. HSI) filtering. More specifically, the MCNLM is achieved by randomly choosing a fraction of the similarity weights to obtain an approximated result. Compared to the classical non-local means (NLM), the MCNLM consumes less time while achieves comparable performance. Experimental results on the real hyperspectral data set demonstrate the promising performance of the MCNLM for HSI denoising.

The main problem of the integrated waveform design for communication and navigation is timing. The timing accuracy requirement in navigation systems is much higher than that in communication systems. In order to solve this problem, a flexible design of waveform and its tracking algorithm is proposed in this paper. The proposed waveform is composed of PN sequences and subcarriers with different rates. The proposed tracking algorithm is based on the combination of tracking results of all the sequences and subcarriers. In addition, the performance of timing accuracy and multipath mitigation of proposed waveform is analyzed. Simulation results show that the proposed waveform has enhanced timing and multipath mitigation performance which are better than that of traditional spread spectrum communication waveform.

In this paper, a novel joint secure resource allocation optimization is proposed for full-duplex (FD) relay networks with cooperative jamming (CJ) in the presence of multiple source-destination (SD) pairs and an eavesdropper. We first derive the expression of the secrecy capacity for a single FD relay link with CJ. Then the joint power allocation and relay subchannel assignment (JPARA) optimization is proposed to maximize the sum secrecy capacity of the network. The proposed optimization is evaluated by numerical results, which prove that significant performance gain can be achieved by full-duplex relays when the self-interference is well suppressed. Besides, the cooperative jamming scheme is shown to improve the throughput effectively in the FD mode, while higher gap tends to be achieved by CJ in the HD mode.