The channel nonlinearity and high-speed mobility in low-earth orbit (LEO) constellation communication are key bottlenecks that constrain the performance of waveform transmission. To address these challenges, we propose a universal continuous phase modulation (CPM) spread-spectrum communication method with high spectral efficiency, and a Doppler-insensitive signal detection mechanism. First, an enhanced dual-phase CPM spread-spectrum (DP-CPM-SS) waveform is investigated. By analyzing the principles and power spectrum characteristics of DP-CPM-SS, we correct the modulation index and design the optimalWiener filtering reception for CPM, improving the spectral efficiency and noise resilience. Subsequently, a CPM noncoherent detection integrating frequency estimation and phase pre-compensation is developed. The performance lower bound of the partial matched filter-fast fourier transform (PMF-FFT) frequency estimation algorithm is derived, revealing the relationship among the length and number of partial matched filters, the normalized frequency offset and the mean square error (MSE) of frequency estimation. Additionally, error probability performance and frequency offset adaptation range are analyzed. Numerical results show that the proposed method exhibits superior and stable performance under severe Doppler effects, which is a promising scheme for LEO constellation communication.

By focusing on the intrinsic meaning of information, semantic communication (SemCom) marks a fundamental paradigm shift from physical bit transmission to personalized semantic service. Considering the importance of personalized features related to the speaker in speech for source recovery and understanding, we propose a semantic-driven framework for personalized speech transmission, named PerSemCom, which combines speaker acoustic features with semantic information. Specifically, we first introduce an efficient semantic extraction mechanism to achieve the conversion from speech to text transcriptions, and design a semantic corrector coupled with multi-domain knowledge to mitigate the effects of wireless channel distortion. Building upon the reliable transcriptions at receiver, we further establish a speaker embedding vector knowledge base and achieve high-fidelity speech reconstruction through quantitative modeling of speaker-specific acoustic features. Extensive experimental results demonstrate that our proposed framework outperforms existing schemes in terms of subjective perception at harsh channel conditions. Complexity analysis and latency measurements also show competitive advantages in computational efficiency and real-time capabilities. Reconstructed personalized speech samples have been publicly available at https://kwtankw.github.io/PerSemCom/.

Unmanned aerial vehicle (UAV) wireless image transmission faces challenges of limited bandwidth and dynamic channel conditions. To address these issues, we propose a novel semantic communication architecture employing a dual-level semantic transmission strategy that decomposes original images into coarse-grained and fine-grained semantic information. Specifically, by integrating the efficient MobileViT network into a joint source-channel coding (JSCC) framework, we design a lightweight semantic encoder-decoder dedicated to coding the image details. To ensure reliable transmission, we introduce a decoupled control-data transmission (DCDT) mechanism that transmits the dual-level semantic information over independent channels, with a fusion module at the receiver integrating them to produce the reconstructed image. Experimental results demonstrate that the proposed system significantly outperforms the traditional method and standard JSCC method in both reconstruction quality and channel robustness, while achieving performance comparable to the heavyweight model with substantially reduced model complexity, thereby validating its deployment potential on resource-constrained UAVs.

Semantic communication (SemCom) is emerging as a key technology for efficient and robust data transmission in future networks. To improve its reliability, the integration of hybrid automatic repeat request (HARQ) has garnered increasing attention. However, current semantic HARQ methods often rely on redundant retransmissions and do not effectively utilize past semantic and channel information, leading to inefficient resource usage. In this paper, we propose a semantic communication framework that incorporates cross-training feedback integration. This system dynamically fuses semantic features and channel states from previous transmissions to optimize the current encoding process. At the transmitter, a fusion module utilizes the semantic representation from the previous decoding to refine the current transmission. At the receiver, the channel buffer and the semantic combiner work together to progressively integrate features, thereby improving the reliability of semantic decoding. Extensive experiments on image transmission under various channel conditions and bandwidth settings demonstrate that the proposed method achieves significant performance gains.

Most existing deep learning-based path loss (PL) models rely on measurements in a given frequency range and specific scenarios, making it difficult to balance accuracy and generalization. The emergence of pre-trained language model (PLM) provides a solution to this challenge. In this paper, we integrate prior knowledge, engineering data, and environmental features into PLM for multi-modal feature extraction and fusion, and use the Low-Rank Adaptation (LoRA) for lightweight fine-tuning to achieve cross-modal knowledge transfer. Moreover, in our model, a residual structure is introduced to bridge the gap between the statistical model and the measurement data. The experimental results show that the proposed model achieves a prediction error [root mean square error (RMSE)] of 3.5 ± 0.2 dB compared to the 7.1 ± 1.5 dB for 3GPP statistical model prediction, and the pearson correlation coefficient of 0.88. When extrapolated to new scenarios, the proposed model has excellent few-shot performance.

Unmanned aerial vehicles (UAVs) are recognized as effective data collectors for wireless sensor networks. The Age of Information (AoI), a metric indicating data freshness, is crucial for decision making in time-sensitive applications. It can be significantly reduced by jointly optimizing UAV trajectories and communication scheduling of sensor nodes (SNs). However, rapid changes in the environment make it challenging to predesign UAV trajectories and communication scheduling decisions using traditional methods, especially when central controllers are absent and the numbers of UAVs and SNs vary. In this article, we propose hypernetwork-based QMIX (HyperQMIX), a population-invariant multiagent deep reinforcement learning (MADRL) algorithm capable of transferring policies across tasks with varying population sizes. First, we design neural network modules adaptable to varying input and output dimensions, facilitated by parameter generation through a hypernetwork. Then, HyperQMIX leverages these modules to process fluctuations in state and action dimensions. This approach ensures that the network structure remains consistent regardless of population sizes, thereby enhancing the algorithm’s scalability. Extensive simulations demonstrate that HyperQMIX significantly outperforms state-of-the-art algorithms in terms of learning efficiency and converged performance. Moreover, agents pretrained with HyperQMIX perform well in tasks of different population sizes without additional training. Fine-tuning these models achieves performance comparable to training from scratch.

The reciprocity of wireless channels is a prerequisite for physical layer secret key generation (SKG). However, inherent factors, such as noise, asynchronous observations, and hardware impairments, disrupt the ideal reciprocity in the channel state information (CSI) observed by the two legitimate parties. To address this issue, we propose a mutual information-driven autoencoder (MIAE) architecture to extract reciprocal channel features from the non-ideal channel observations of legitimate parties. MIAE is constructed with an AutoEncoder Network (AENet) and a mutual information neural estimator (MINE). Specifically, AENet employs a structure with dual encoders and a shared decoder. The two encoders, integrated with convolutional block attention modules (CBAMs), are designed to focus on reciprocal features within CSI observations and to compensate for temporal variations caused by asynchronous observations. The shared encoder and the correspondingly designed loss function further concentrate the autoencoder’s reciprocity enhancement capability into the encoders. MINE is integrated into the proposed MIAE to estimate the mutual information between the channel features of the two parties. This estimation is then used to formulate a mutual information loss, which guides the encoders to learn channel features that closely match the optimal distribution, thereby boosting the key generation rate. Furthermore, a complete SKG scheme is designed based on the proposed channel feature extractor, MIAE. Simulation results show that our proposed MIAE can extract channel features with strong reciprocity and thus achieve excellent SKG performance based on the extracted features. The generalization performance of the proposed MIAE architecture for communication scenarios of different scales has also been examined.

Most AI-based channel estimation methods with static environment assumptions suffer from performance degradation due to the distribution shift caused by the varying channel environment. As one of the solutions, transfer learning also faces the problem of catastrophic forgetting, where the model tents to fail in previous estimation tasks after learning from new ones. In this paper, we propose a continuous learning-based channel estimation (CLCE) scheme that integrates a series of subnetworks to preserve historical knowledge and achieve an ongoing process of self-improvement. To determine whether the wireless environment is previously unobserved, we first propose a distance-based unsupervised out-of-distribution (OOD) detection algorithm to perceive the distribution shift of the channel environment. The OOD detection algorithm is developed based on the representation of channel data in the latent space of a variational autoencoder (VAE), which is designed to infer the latent variable that implies the characteristics of the wireless environment. Then, a new channel estimation subnetwork is initiated with meta-learning to adapt to the dynamic channel environments with a small set of OOD channel data. Simulation results reveal that our proposed scheme can accurately detect unobserved channel environments without introducing additional detection network, and efficiently adapt to them with few online samples. Furthermore, the mean square error (MSE) result of the channel estimations across multiple environments demonstrates that CLCE effectively mitigates catastrophic forgetting and outperforms the competitors.

Mixed-numerology transmission still suffers from a large peak-to-average power ratio (PAPR) and the conventional PAPR reduction methods cannot be applied straightforwardly due to its multiple baseband processing units. In this paper, we develop a novel parallel PAPR reduction approach for the decentralized baseband processing architecture. By considering the in-band distortion minimization problem subject to the PAPR constraint, we find that this problem is separable in both the objective function and the constraints. Based on the “decomposition-coordination” mode of alternating direction method of multipliers (ADMM), original problem can be divided into several subproblems which can be easily solved in each subbands with its dedicated numerology. Because of the independence between each subbands, the proximate Jacobian method is also applied so that the subproblems can be updated parallelly and are suitable to the decentralized baseband processing architecture. Analysis corroborated by simulations demonstrate that the proposed approach is convergent. Numerical results illustrate that the proposed approach is less time-consuming than existing benchmark when the same PAPR reduction performance is achieved.

As a potential technique, unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) can provide flexible coverage and computing services for real-time applications such as emergency search, traffic control and disaster rescue. In this paper, we investigate a freshness sensitive multi-UAV assisted MEC system where tasks arrive stochastically. The system aims to minimize the age of information (AoI), subject to the constraints on computation offloading, trajectory control and communication resource allocation. Due to the dynamic environment and the coupling of variables, we develop a multi-agent reinforcement learning (MARL) scheme, in which a federated updating method is introduced. Through our scheme, smart mobile devices, UAVs and cloud center can collaborate to learn interactive policies. Simulation results validate that our scheme outperforms local computing, remote computing, and centralized solutions in terms of both the average AoI and convergence.

Secret-key generation (SKG) at physical layer is considered as a promising solution for lightweight key distribution. On the analysis of the secret-key capacity over multipath fading channels, existing studies generally neglect the channel observation method, which has a direct impact on the secret-key capacity. Moreover, there is a lack of concise and interpretable expressions for the secret-key capacity based on the channel state information. Motivated by the above, we analyze the performance of SKG based on MMSE estimation of channel impulse response over multipath fading channels. The general expression of secret-key capacity is derived. We find that the secret-key capacity depends on the estimation signal-to-noise ratios (SNRs) of channel-tap gains. Then, the condensed-parameter expressions of exact secret-key capacity and the expressions of asymptotic secret-key capacity at high SNR are derived in two specific PDP cases. We show that the bandwidth and the transmission SNR determine the upper bound of secret-key capacity over channels with different degrees of freedom for the flat PDP case. In addition, we find that the asymptotic secret-key capacities under these two PDPs follow a uniform form, which is a linear function of the channel estimation SNR (in dB). Finally, we simulate the channel probing process under different transmission and channel parameters and exploit the simulated channel estimates to estimate the secret-key capacity through numerical methods. The simulation results demonstrate the theoretical analysis and conclusions.

Unmanned aerial vehicles (UAVs) as mobile base stations are recognized as effective means for emergency communications. The performance of such systems depends on the movement of UAVs and scheduling of ground users (GUs). However, devising an efficient algorithm to jointly optimize UAV trajectories and user scheduling is still challenging, especially in real-time scenarios lacking central controllers. Multi-agent deep reinforcement learning (MADRL) provides a promising solution to this problem. Nevertheless, as the numbers of UAVs and GUs increase, existing MADRL algorithms encounter scalability and sample efficiency issues. In this paper, we develop a novel symmetry-augmented MADRL approach for learning scalable UAV trajectory design and user scheduling policies. The core idea is to utilize symmetries to reduce the multi-agent state-action space and enhance sample efficiency. Specifically, we design a family of neural networks to learn individual policies, namely entity permutation equivariant policy networks (EP2Nets). EP2Nets effectively leverage the permutation symmetry to reduce redundancy in the state-action space. Additionally, we achieve data augmentation by exploiting rotational and reflection symmetries, further boosting sample efficiency. Finally, a Symmetric QMIX (SymmQMIX) algorithm is proposed by integrating the EP2Net and data augmentation method into the QMIX algorithm. Simulation results indicate that SymmQMIX significantly outperforms QMIX and other symmetry-enhanced algorithms, achieving a 4.5-fold increase in converged performance and a 100-fold improvement in sample efficiency.

Non-orthogonal multiple access (NOMA) with mixed numerology is a promising technology that blends flexibility and high spectral efficiency. However, since NOMA enables multiplexing of the same time-frequency resources for different users and mixed-numerology allows superimposing sub-signals with different numerologies, high peak-to-average power ratio (PAPR) as well as power fluctuation problem in NOMA detection becomes cumbersome especially when applying PAPR reduction techniques. This study considers minimizing PAPR in mixed numerology NOMA systems using selected-mapping (SLM) method. The Riemann sequence is one of the simplest phase sequence that can be used to generate a set of signal copies for PAPR reduction. Our analysis reveals that as the amplitude variation caused by the Riemann sequence grows, the upper bound of PAPR for signal copies decreases correspondingly. Leveraging this insight, we introduce a new maximum-range Riemann (MRR)-based phase sequences, in which the amplitude factor can be adjusted to control the power fluctuations. Compared to previous works, our study delves deeper into the influence of phase sequence design of SLM on PAPR reduction performance. Simulations show that the proposed method offers significant performance advantages of PAPR reduction and bit error rate (BER) improvement even with consideration of power-amplifier.

Unmanned aerial vehicles as base stations (UAV-BSs) are recognized as effective means for tackling eruptive communication service requirements especially when terrestrial infrastructures are unavailable. Quality of service (QoS) received by ground terminals (GTs) highly depends on the spatial movement of UAV-BSs. In this paper, we investigate the cooperative trajectory design problem of multiple UAV-BSs towards fair throughput maximization of GTs. Considering the restriction of coverage and sensing, we first propose a heterogeneous-graph-based formulation of relations between GTs and UAV-BSs. Subsequently, we design a framework named graph vision and communication (GVis&Comm) to 1) let each UAV-BS efficiently manage time-varying local observations; 2) facilitate cooperation between UAV-BSs through explicit information exchange. To further reduce the overhead of over-the-air cooperation, we realize discretization of the message passing process among UAV-BSs while still enabling end-to-end training. By leveraging multi-agent reinforcement learning (MARL), UAV-BSs as agents learn a distributed trajectory design policy. Extensive numerical simulation shows that our framework on the one hand achieves remarkable efficiency in processing local observations of each UAV-BS, and on the other improves the overall network performance via close cooperation among UAV-BSs.

Machine learning (ML) has been widely used for efficient resource allocation (RA) in wireless networks. Although superb performance is achieved on small and simple networks, most existing ML-based approaches are confronted with difficulties when heterogeneity occurs and network size expands. In this paper, specifically focusing on power control/beamforming (PC/BF) in heterogeneous device-to-device (D2D) networks, we propose a novel unsupervised learning-based framework named heterogeneous interference graph neural network (HIGNN) to handle these challenges. First, we characterize diversified link features and interference relations with heterogeneous graphs. Then, HIGNN is proposed to empower each link to obtain its individual transmission scheme after limited information exchange with neighboring links. It is noteworthy that HIGNN is scalable to wireless networks of growing sizes with robust performance after trained on small-sized networks. Numerical results show that compared with state-of-the-art benchmarks, HIGNN achieves much higher execution efficiency while providing strong performance.

This paper considers low-complexity hybrid analog-digital precoding for millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems with finite alphabet inputs. We analyze the mutual information with finite alphabet inputs in the extreme signal-to-noise ratio (SNR) region. At low SNR, we find out that it is optimal to transmit signals through its strongest sub-channel, and thus formulate the analog precoding problem as a single-variable optimization problem. An iterative coordinate descent algorithm is developed to obtain the near-optimal hybrid precoder. At high SNR, the mutual information degrades when the Gaussian scheme is directly applied into the practical systems with finite alphabet inputs. Therefore, a modified fully-digital precoder is proposed to improve the mutual information. Numerical results validate that the proposed schemes achieve a good trade-off between mutual information and computational complexity.

Abstract In the linear minimum mean square error (LMMSE) estimation for orthogonal frequency division multiplexing (OFDM) systems, the channel correlation function (CCF) is required. Some methods have been proposed to calculate the CCF. Instead of providing a novel method to obtain the CCF, a scheme is developed for the estimator to select among different CCFs. In this paper, an enhanced LMMSE estimation is proposed that is able to select the best‐matched CCF within a candidate set. To this end, a parameter comparison scheme is proposed, in which the possible channel statistics for the LMMSE estimation can be evaluated using the sampled noise MSE. Analytical expressions are thus derived to indicate the accuracy of the proposed scheme. Furthermore, fuzzy bound is provided as the performance metric, which reflects the resolution of the parameter comparison scheme. As an example of application, the enhanced LMMSE method is used with the block pilot pattern in the OFDM systems, and the possible CCF candidates for typical scenarios are presented. The complexity of the estimator is also analyzed and a simplified parameter comparison algorithm is proposed to reduce the complexity. Finally, the theoretical analysis and performance comparison are demonstrated by simulation experiments.

Devices in the Internet of Things (IoT) are usually limited in computing resources and energy capacity, which means that encryption schemes with higher complexity are not suitable for them to ensure secure communication. As a promising solution to this problem, physical layer key generation suggests that shared secret keys can be generated from noisy wireless channel measurements to enhance the security of wireless communications. In this article, we propose a key generation scheme with extremely low implementation complexity, which allows physical layer key generation to be implemented on IoT nodes. First, we preprocess the channel measurements with simple moving average filtering before quantization to improve channel reciprocity. Next, a bidirectional difference quantization scheme is proposed to realize reliable quantization of channel measurements, which is ingenious in that the quantization process does not depend on quantization thresholds, and thus, the mismatched key bits caused by measurements close to quantization thresholds can be effectively avoided. Then, we propose an improved Cascade protocol to achieve lightweight and efficient information reconciliation. The simulation results show that our scheme can well balance the reliability and efficiency of key generation, and has excellent performance in terms of implementation complexity and key randomness.

The heterogeneous cloud radio access network (H‐CRAN) is considered a promising solution to expand the coverage and capacity required by fifth‐generation (5G) networks. UAV, also known as wireless aerial platforms, can be employed to improve both the network coverage and capacity. In this paper, we integrate small drone cells into a H‐CRAN. However, new complications and challenges, including 3D drone deployment, user association, admission control, and power allocation, emerge. In order to address these issues, we formulate the problem by maximizing the network throughput through jointly optimizing UAV 3D positions, user association, admission control, and power allocation in H‐CRAN networks. However, the formulated problem is a mixed integer nonlinear problem (MINLP), which is NP‐hard. In this regard, we propose an algorithm that combines the genetic convex optimization algorithm (GCOA) and particle swarm optimization (PSO) approach to obtain an accurate solution. Simulation results validate the feasibility of our proposed algorithm, and it outperforms the traditional genetic and K‐means algorithms.

Considering the diversified communication requirements in the Internet-of-Things (IoT) networks, this article proposes a nonorthogonal multiple access (NOMA) scheme which pairs two types of typical users, i.e., delay-sensitive user (DSU) and security-required user (SRU) to share the same nonorthogonal communication resources. We focus on the investigation of the secure transmission for the SRU while guaranteeing the communication requirements of the DSUs at the high priority. Specifically, the delay requirement of the DSUs and the security demand of the SRU are guaranteed by using short-packet communications and the maximal ratio transmission (MRT) scheme, respectively. Since the packets cannot always be detected correctly in the short-packet communications, a novel power allocation strategy is designed sophisticatedly to avoid the outage performance floor at the SRU caused by the short-packet communications. A set of closed-form expressions of the connection outage probability (COP), secrecy outage probability, and effective secrecy throughput (EST) of the SRU are derived over Nakagami-m channels in the proposed NOMA scheme and also the benchmark OMA scheme. Besides, we provide the security- reliability tradeoff (SRT) and security-efficiency tradeoff (SET) results for obtaining more insights into system performance. Results demonstrate the superiority of the proposed NOMA scheme over the benchmark OMA scheme in terms of the COP, EST, SRT, and SET when the transmitter is equipped with multiple antennas.

Multiaccess edge computing (MEC) is regarded as a promising solution to overcome the limit on the computation capacity of mobile devices. This article investigates an energy-efficient unmanned aerial vehicle (UAV)-enabled MEC framework incorporating nonorthogonal multiple access (NOMA), where multiple UAVs are deployed as edge servers to provide computation assistance to terrestrial users and NOMA is adopted to reduce the energy consumption of task offloading. A utility is formed to mathematically evaluate the weighted energy cost of the system. Due to the coupling of parameters, the minimization of utility is a highly nonconvex problem and therefore, the problem is decomposed into two more tractable subproblems, i.e., the optimal allocation of radio and computation resources given UAV trajectories, and the trajectory planning based on given resource allocation schemes. These two problems are converted to convex ones via successive convex approximation (SCA) and quadratic approximation, respectively. Then, an efficient iterative algorithm is proposed where these two subproblems are alternately solved to gradually approach the optimal resource management of the proposed system. Sufficient numerical results show that our proposed strategy has a remarkable advantage over existing systems in terms of energy efficiency.

Different from conventional proper Gaussian signalling (PGS), whose achievable rate depends on the input signals' covariances only, the capacity of channels with improper Gaussian signalling (IGS) is a function with the signals' covariances and pseudo‐covariances. Thus, the additional degree of freedom provided by pseudo‐covariance is available to improve the achievable rate. In this study, the authors investigate the achievable rate region of two‐users X‐interference channel (IC) in the condition of applying IGS. By treating the interference as additive Gaussian noise, the authors analyze the mathematical expression of capacity based on Shannon's theorem. Specifically, for the two‐users simple input, simple output (SISO)‐IC, they propose two optimal signalling schemes to achieve the Pareto boundary in situations of employing PGS and IGS, respectively. In these optimal signalling schemes, the transparent geometric model which takes less computational complexity is applied to calculate the optimal transmission parameters for the Pareto boundary. Numerical simulation results show that the proposed IGS strategies can achieve larger achievable rate region and a greater sum rate. Finally, the authors give an in‐depth discussion about the structure of the Pareto boundary, which is characterised by the degree of impropriety measured by the covariance and the pseudo‐covariance of signals.

Exploiting unmanned aerial vehicles (UAVs) as flying base stations (BSs) to assist the terrestrial cellular networks is promising in 5G and beyond. Despite the inherent potentials, one challenging problem is how to optimally deploy multiple UAVs to achieve on-demand coverage for ground user equipment (UE). In this article, we model the deployment problem as minimizing the number of UAVs and maximizing the load balance among them, which is subject to two main constraints, i.e., UAVs should form a robust backbone network and they should keep connected with the fixed BSs. To solve this optimization problem with low complexity, we decompose the problem into two subproblems and propose a hybrid algorithm to solve them stepwise. First, a centralized greedy search algorithm is used to heuristically obtain the minimum number of UAVs and their suboptimal positions in a discontinuous space. Then, a distributed motion algorithm is adopted which enables each UAV to autonomously control its motion toward the optimal position in a continuous space. The proposed algorithm is applicable to various scenarios where UAVs are deployed alone or with fixed BSs regardless of the UE distribution. Extensive simulations validate the proposed algorithm.

Dynamic malicious jamming of spectrum throughout the entire communication band is a major issue confronted by tactical communications. The conventional spectrum access scheme based on the central control node fails to satisfy the demand of tactical communications; it has a number of drawbacks including low battlefield survival rates, high computational complexity, large interactive communication overhead and slow reaction to dynamic jamming. In this paper, we propose a distributed multi-agent spectrum access strategy without the interactive communication overhead to evade dynamic jamming. Furthermore, A simplified Q reinforcement learning algorithm is applied to alleviate collisions of spectrum among nodes. Under the proposed strategy, all nodes can predict and evade dynamic jamming as well as avoid collisions of spectrum usage with other nodes. Simulation results verify the collision-avoidance learning algorithm and indicate that the proposed strategy outperforms the random spectrum access strategy.

The problem of quantization for Log-likelihood ratios in the presence of a practical automatic-gain-control (AGC) is elaborated in this paper. A median based quantization approach is proposed, in which the median of the absolute value of loglikelihood ratios (LLRs) is set as the quantized midpoint. This approach can diminish the mutual information loss caused by quantization. When applied to bit-interleaved coded modulation scheme, the proposed quantizer proves to be robust to different transmission schemes in both AWGN and rayleigh channels. The implementation issue of median estimate using subset averaged median estimator is also covered in details.

In this paper, we address physical layer security in multiple-input single-output (MISO) wiretap channels in the presence of a passive eavesdropper. Artificial noise (AN) is used to provide masked beamforming for degrading the eavesdropper's channel. Existing works based on Gaussian input assumption may lead to substantial secrecy rate loss when considering practical finite-alphabet input. In order to maximize ergodic secrecy rate, a power allocation scheme between the information-bearing signal and AN is proposed. Despite the fact that there is no closed- form expression of mutual information under the constraint of finite-alphabet input, we exploit the relationship between mutual information and minimum mean square error (MMSE) to transform the original problem into a zero-searching problem, which can be solved via gradient search algorithm. Numerical simulations demonstrate that the proposed scheme offers a very good approximation to the optimal performance.

In this paper, we investigate the secrecy performance for a multiple-input multiple-output (MIMO) wiretap channel in the presence of a multiantenna eavesdropper. In particular, the legitimate transmitter uses transmit antenna selection (TAS) to transmit on a single antenna with the largest signal-to-noise ratio (SNR) while both the legitimate receiver and the eavesdropper adopt maximal ratio combining (MRC) for reception. We derive exact closed-form expressions for the probabilities of achieving positive secrecy rate and secrecy outage in the case of imperfect feedback due to feedback delay and/or feedback error. Furthermore, we derive the asymptotic secrecy outage probability at high SNR, which accurately reveals the secrecy diversity loss due to imperfect feedback. Simulation results are provided to verify our analytical results and illustrate the impact of imperfect feedback on the secrecy performance of such a wiretap system.

This study addresses the problem of carrier frequency offset (CFO) tracking in co‐operative space‐frequency block‐coded orthogonal frequency division multiplexing (OFDM) systems with multiple CFOs. Considering that the inserted pilot tones are decayed by data subcarriers in the presence of multiple CFOs, a novel recursive residual CFO tracking (R‐RCFOTr) algorithm is proposed. This method first removes CFO‐induced inter‐carrier interference from data subcarriers, and then updates the residual CFO (RCFO) estimation of each OFDM block recursively. When used in conjunction with a multiple CFOs estimator, the proposed R‐RCFOTr can effectively mitigate the impacts from the multiple RCFOs with affordable complexity. Finally, simulation results are provided to validate the effectiveness of our proposed R‐RCFOTr algorithm, which has performance close to that of perfect CFO estimation at moderate and high signal‐to‐noise ratio, and significantly outperforms conventional CFO tracking algorithm for large CFOs.