In a recent article, Bai et al. (2022) investigated the impact of outdated channel state information (CSI) on covert communication in wireless greedy relay networks. In this commentary, we identify a critical mathematical flaw in their derivation of the cumulative distribution function (CDF) of the outdated channel gain. The flaw fundamentally undermines the paper’s main results and conclusions regarding the impact of outdated CSI in such networks. To prevent future studies from building upon this incorrect foundation, we also provide a correction for the flaw and suggestions for future investigations into this problem. Beyond correcting a specific error, this commentary serves as a warning on the potential pitfalls in probabilistic modeling under outdated CSI.

This paper investigates the use of an unmanned aerial vehicle (UAV) to assist covert communication between a low-Earth orbit (LEO) satellite and a ground user under the surveillance of a passive warden. The UAV simultaneously serves its own ground network and acts as a friendly jammer to enhance the covertness of satellite transmissions. We derive a closed-form lower bound on the warden's average minimum detection error probability which is then used to define the covert constraint. Building on this, we formulate an optimization problem to jointly design the UAV's 3D placement, its power allocation, and the satellite's transmit power to maximize the system's covert rate. To solve the resulting non-convex problem, we propose an algorithm based on the block coordinate descent (BCD) and successive convex approximation (SCA) techniques, and further develop a Dinkelbach's algorithm for a special case. Numerical results validate the tightness of the derived bound and demonstrate the effectiveness of the proposed algorithms in configuring optimal system parameters.

This article investigates the physical layer security of free-space optical communication from a low Earth orbit satellite to a high-altitude platform (HAP), in the presence of an eavesdropping HAP. Existing works typically assume identical channel statistics for both legitimate and eavesdropping HAPs, while favoring the legitimate one by presuming it captures a larger fraction of the optical beam power. In this work, we consider a more realistic model in which pointing errors cause random beam displacements relative to the legitimate and eavesdropping HAPs, directly determining their received power. Assuming the satellite has access only to statistical channel state information of both HAPs, we derive closed-form expressions for connection outage probability (COP) and secrecy outage probability (SOP) to characterize the reliability and secrecy performance of the system. Through detailed analysis of COP and SOP, we reveal a fundamental reliability-secrecy tradeoff in beam divergence angle (BDA). We then formulate an optimization problem that jointly optimizes BDA and wiretap code rates to maximize the system's secrecy throughput under both reliability and secrecy constraints. To solve this problem, we propose an algorithm providing the optimal solution, and also a low-complexity algorithm that yields the optimal solution in most practical scenarios. Numerical results demonstrate that the systems designed by the proposed algorithms achieve significantly improved secrecy throughput compared to conventional systems without the BDA optimization.

Existing studies on physical layer security (PLS) for uplink communications often assume a pseudo-random sequence (PRS)-based cooperative jamming, which enables the base station to perfectly cancel the jamming signal. However, the assumption of the pre-shared PRS leads to practical challenges, e.g., the key sharing problem, especially in Internet of Things (IoT) systems. To resolve the issues, this work proposes a novel PLS scheme utilizing the unique feature of fluid antenna (FA) systems, i.e., full exploitation of spatial degrees of freedom. In particular, for uplink IoT systems, we introduce an FA-aided cooperative jamming scheme in which all single FA devices except for a legitimate device play the role of jammers to protect the communication of the legitimate parties. To this end, we propose an FA port selection strategy to maximize the received signal-to-interference-plus-noise ratio of the legitimate parties and derive analytic expressions of the outage probability (OP) and the secrecy outage probability (SOP). In addition, in the interference-limited scenario, we develop more insightful closed-form expressions of OP and SOP, which allow one to capture important behaviors of the designed system. Then, we formulate an optimization problem that aims to maximize secrecy throughput by jointly optimizing the jamming power, transmission rate, and secrecy rate. To tackle the non-convexity of the problem, we develop a technique in which the minorization-maximization and the alternating optimization techniques are judiciously combined. Performance evaluations demonstrate that the proposed scheme can outperform the PRS-based scheme in practical IoT scenarios.

This work investigates system-level performance of uplink cell-free massive MIMO (CF-mMIMO) networks with the local minimum-mean-squared-error (L-MMSE) combining which is known to offer significant performance improvements over conjugate beamforming combining. Despite its advantages, the system-level performance of uplink CF-mMIMO networks with L-MMSE has been rarely studied due to its mathematical intractability. The work analytically conducts the performance evaluation using stochastic geometry, which enables us to derive closed-form expressions for some performance metrics, i.e., coverage probability and potential throughput, in both interference-limited and noise-limited scenarios. The derived expressions explicitly elucidate how the system parameters influence the system-level performance. Specifically, it is clearly demonstrated that for a given total number of antennas within a CF-mMIMO network, deploying as many access points with a single antenna as possible is optimal for both coverage probability and potential throughput in the noise-limited scenario, whereas the antenna distribution has no impact on the performance metrics in the interference-limited scenario. Moreover, this work reveals that in the noise-limited scenario, the potential throughput increases linearly with user density, while in the interference-limited scenario, it grows but eventually converges to a constant value. Comprehensive Monte-Carlo simulations validate our analytic derivations and insightful findings from the analysis.

Cell-free massive multiple-input multiple-output (CF-mMIMO) networks are known to enhance both spectral and energy efficiencies over traditional cellular networks by utilizing distributed access points (APs) to cooperatively serve users. The performance of uplink CF-mMIMO networks depends heavily on the combining scheme and the capacity of fronthaul links connecting APs to a central processing unit. This study introduces a generalized local minimum mean-square error (L-MMSE) combining scheme and investigates the performance of uplink CF-mMIMO with variable-resolution analog-to-digital converters (ADCs). The proposed combining scheme dynamically adjusts the AP's contribution to the desired user, incorporating the user's signal-to-interference-plus-noise ratio into the combining vector as a scaling factor, thus encompassing existing L-MMSE combining schemes as special cases. For performance evaluations, we propose a tight lower bound on achievable rates of CF-mMIMO networks tailored to finite-capacity fronthaul links, showing that the proposed bound is more accurate than the widely used UatF bound which assumes strong channel hardening. Leveraging the proposed bound, we develop an optimization algorithm using the majorization-minimization and Dinkelbach techniques to jointly optimize the bit allocation and large-scale fading decoding coefficients. Furthermore, we propose a low-complexity heuristic algorithm for the bit allocation which significantly reduces the design complexity with minimal performance degradation. Extensive performance evaluations demonstrate that the proposed design rules for CF-mMIMO networks with variable-resolution ADCs substantially outperform those with uniform bit allocation. Remarkably, with an average of just 4 bits per AP per user, the proposed design rules achieve performance comparable to the ideal case (i.e., infinite-capacity fronthaul links). In addition, the generalized L-MMSE combining scheme further enhances uplink CF-mMIMO performance, offering substantial improvements under practical constraints.

Previous studies on uplink physical-layer security (PLS) typically rely on pseudo-random sequences (PRSs) pre-shared between legitimate parties to enable perfect cancellation of jamming signals at the receiver. However, in practical scenarios, the use of pre-shared PRSs may be infeasible due to the associated computational overhead and/or the risk of tracking by intelligent eavesdroppers (Eves). To address these challenges, this work proposes a PLS framework for uplink orthogonal frequency-division multiple access (OFDMA) or single-carrier FDMA (SC-FDMA) networks that leverages fluid antennas (FAs) at user equipments and cooperative jamming under the assumption that no pre-shared key is available. The proposed FA port selection strategy not only effectively mitigates the impact of jamming interference but also enhances the quality of the legitimate link. We derive closed-form expressions for the cumulative distribution functions of the received signal-to-interference-plus-noise ratios at the base station and Eve. Based on these, an exact secrecy outage probability (SOP) expression in integral form is obtained. To further improve analytical tractability and provide clearer insights, a tight approximate closed-form SOP expression is developed for the interference-limited case. Numerical results validate the accuracy of the derived expressions and demonstrate the substantial secrecy performance gains of the proposed framework under the unavailable pre-shared PRS assumption.

Traditional deep learning-based joint source-channel coding (DeepJSCC) frameworks typically require separate models for different code rates and channel conditions, and remain incompatible with practical digital communication systems due to their analog transmission nature. To address these limitations, we propose a unified Rate-Adaptive Quantization-based DeepJSCC (RAQJSCC) framework that enables channel-aware rate adaptation within a single model while maintaining compatibility with conventional digital communication systems. The proposed framework achieves rate adaptivity through a rate-adaptive quantization (RAQ) mechanism that enables flexible codebook adaptation within a single model, supporting variable-rate operation without retraining. To improve robustness under vector quantization-based digital transmission, we design a cosine similarity-based codebook reordering (CSCR) mechanism that aligns quantized representations with modulation schemes and reduces the impact of index errors. Furthermore, we incorporate an adaptive modulation and coding (AMC) unit that jointly selects modulation orders and effective codebook sizes based on channel conditions, enabling dynamic link adaptation across diverse SNR regimes. Experimental results demonstrate that the proposed RAQJSCC framework outperforms existing DeepJSCC and separation-based methods in terms of reconstruction quality over a wide range of SNRs, while effectively mitigating the cliff effect. These results highlight the potential of RAQJSCC as a practical and scalable solution for next-generation wireless communication systems.

We propose an evolutionary belief propagation (EBP) decoder for quantum error correction, which incorporates trainable weights into the BP algorithm and optimizes them via the differential evolution algorithm. This approach enables end-to-end optimization of the EBP combined with ordered statistics decoding (OSD). Experimental results on surface codes and quantum low-density parity-check codes show that EBP+OSD achieves better decoding performance and lower computational complexity than BP+OSD, particularly under strict low latency constraints (within 5 BP iterations).

Symmetric block-wise concatenated Bose-Chaudhuri-Hocquenghem (SBC-BCH) codes are known to provide strong error-correcting performance with an iterative hard-decision decoding (IHDD). This work shows that some properties of SBC-BCH codes allow one to readily increase the error-correcting capability of constituent BCH codes by one, i.e., from t to t + 1, which greatly improves the error-rate performance of SBC-BCH codes. To this end, we propose a new decoding algorithm that utilizes structural features of SBC-BCH codes in conjunction with an extension of the Berlekamp-Massey (BM) algorithm, namely the one-step-ahead (OSA) BM algorithm. This combination enables us to reduce the computational complexity and combat the inherent decoding ambiguity associated with the OSA-BM algorithm. Furthermore, we develop an analytical framework to evaluate the average number of candidate codewords returned by the OSA-BM algorithm and derive an upper bound on the probability of ambiguity. Then, we propose a decoding algorithm, called OSA-aided IHDD and conduct extensive performance evaluations and comparisons for error-correcting systems employing SBC-BCH codes with the proposed decoding algorithm. The performance evaluations show that the proposed OSA-aided IHDD considerably improves the error-rate performance in both the waterfall and error-floor regions.

There have been extensive studies on performance analysis and/or system design for the uplink cell-free massive multiple-input multiple-output (CF-mMIMO) based on the Use-and-then-Forget (UatF) bound. The tightness of the UatF bound is, however, ensured in channel environments where the channel hardening manifests. It has been often reported that the channel hardening may not always be strongly observed in some channel environments. In such cases, the UatF bound seriously underestimates rates for users experiencing weak channel hardening. To address the issues associated with the UatF bound, we develop a new lower bound offering a more accurate estimate of the achievable rate for uplink in CF-mMIMO networks regardless of the extent of channel hardening. The claim is substantiated by applying the proposed bound to the joint optimization of power control and large-scale fading decoding (LSFD) coefficients for the design of CF-mMIMO networks on spatially correlated Rician fading channels. To facilitate the optimization, we also develop an approximation of the proposed bound in a more computationally amenable form. We conduct extensive performance evaluations and comparisons for CF-mMIMO networks designed with the proposed and UatF bounds. The comparisons highlight that the design based on the proposed bound leads to considerable performance improvement in the fairness. In addition, for the first time, we reveal with the new lower bound that an widely held belief based on the UatF bound is incorrect.

Surface codes have gained widespread popularity as quantum error-correction codes due to their unique structural feature that encoders and decoders can be implemented by leveraging interactions between physically neighboring qubits. The implementational advantage of surface codes has driven extensive research into decoding algorithms for surface codes, aimed at improving error-rate performance and/or reducing decoding complexity. In this paper, we propose a low-complexity postprocessing algorithm that effectively resolves failures of a syndrome-based belief-propagation (SB-BP) decoder for surface codes. In particular, we carefully analyze the topological structure of surface codes and present a method for selecting reliable qubits, enabling efficient recovery operator identification through a simple syndrome matching technique with a lookup table. Furthermore, we present an efficient way to construct lookup tables tailored to the proposed postprocessing algorithm. Numerical results demonstrate that the proposed postprocessing algorithm requires substantially reduced complexity as compared to that of a competing postprocessing algorithm, the ordered statistics decoding in the depolarizing error model, while providing the same logical error-rate (LER) performance. It is also demonstrated that the combination of SB-BP decoding and the proposed postprocessing algorithm outperforms minimum weight perfect matching, a more computationally expensive decoding algorithm, in terms of LER and threshold at drastically reduced complexity.

In this work, we propose an improved automorphism ensemble (AE) decoder for polar codes. With successive cancellation (SC) variant automorphisms, multiple decoding paths in the AE decoder produce their estimates of the transmitted codeword. In the proposed scheme, the decoding results from the multiple paths are cleverly utilized to generate a new channel output with which an additional decoding path is established. Performance evaluations clearly demonstrate that the proposed decoder achieves significantly improved error-rate performance as compared to the existing AE decoder for polar codes.

To perform reliable information processing in quantum computers, quantum error correction (QEC) codes are essential for the detection and correction of errors in the qubits. Among QEC codes, topological QEC codes are designed to interact between the neighboring qubits, which is a promising property for easing the implementation requirements. In addition, the locality to the qubits provides unusual tolerance to local errors. Recently, various decoding algorithms based on machine learning have been proposed to improve the decoding performance and latency of QEC codes. In this work, we propose a new decoding algorithm for surface codes, i.e., a type of topological codes, by using convolutional neural networks (CNNs) tailored for the topological lattice structure of the surface codes. In particular, the proposed algorithm takes advantage of the syndrome pattern, which is represented as a part of a rectangular lattice given to the CNN as its input. The remaining part of the rectangular lattice is filled with a carefully selected incoherent value for better logical error rate performance. In addition, we introduce how to optimize the hyperparameters in the CNN, according to the lattice structure of a given surface code. This reduces the overall decoding complexity and makes the CNN-based decoder computationally more suitable for implementation. The numerical results show that the proposed decoding algorithm effectively improves the decoding performance in terms of logical error rate as compared to the existing algorithms on various quantum error models.

Block-wise concatenated Bose-Chaudhuri-Hocquenghem (BC-BCH) codes are shown to have excellent error-correcting capability under hard-decision decoding. The variant of BC-BCH codes, namely symmetric BC-BCH (SBC-BCH) codes, is later introduced to improve the error-correcting performance of BC-BCH codes. In this work, we propose a new hard-decision algorithm, IHDD-GRAND, which aims to further enhance the error-rate of SBC-BCH codes. The proposed algorithm uses GRAND as an auxiliary decoder for the IHDD, and it can be efficiently implemented by utilizing the error-confinement property of SBC-BCH codes. It will be shown that the proposed algorithm significantly improves the error-correcting performance of SBC-BCH codes.

This study explores an uplink multi-users covert communication system where certain users cooperate to hide messages from a covert user. A recent work investigated the multi-users covert communication system in which all the users are located at an equal distance from the legitimate receiver and a warden. In contrast, our work extends the existing work to an unequal distance scenario, where users are at unequal distances from the legitimate receiver. In particular, we establish that an on-off scheme, previously proven to be optimal in the equal distance scenario, remains optimal in the unequal distance scenario. The optimality of the on-off scheme enables us to derive expressions of the minimum detection error probability and outage probability in closed-forms. In addition, the closed-form expressions allow us to turn the parameter optimization into a simple one-dimensional search problem. Finally, the theoretical results developed in this work are validated by comparing extensive performance evaluations utilizing the theoretical results and Monte Carlo simulations.

In this work, we study a covert communication scheme in which some users are opportunistically selected to emit interference signals for the purpose of hiding the communication of a covert user. This work reveals interesting facts that the channel correlation is beneficial to the throughput of the covert communication but detrimental to the energy efficiency, which has never been discussed before. The study is conducted in a generic setup where the channels between pairs of entities in the scheme are correlated. For the setup, we discover that the optimal power profile of the interference signals from the selected users turns out to be the equal power transmission at their maximum transmit power level. In addition, we optimize system parameters of the scheme for maximizing throughput and energy efficiency utilizing $Q$ -learning, which however is plagued with long learning time and large storage space when the dimension of state gets large and/or a fine resolution of reward function value is necessary. To resolve the technical challenge, we propose a scalable $Q$ -learning which recursively narrows down the discretization level of the continuous state in an iterative fashion. To confirm the results in this work, the system parameters are evaluated with theoretical results for independent channels and compared with the ones from the proposed scalable $Q$ -learning.

This paper shows that performance evaluations and system designs using the use-and-then-forget (UatF) bound are inappropriate when cell-free massive multiple-input multiple-output (CF-mMIMO) systems are not in the environment with the channel hardening. This work also reveals that the pilot contamination further impairs the channel hardening and makes the issue with the UatF bound more exacerbated. Motivated by the technical problem, we introduce a new bound on the achievable rate of downlink CF-mMIMO systems. It will be demonstrated that the proposed bound allows one to design a better CF-mMIMO system via extensive performance comparisons between designed systems based on the proposed bound and the UatF bound.

This work studies a covert communication scheme for an uplink multi-user scenario in which some users are opportunistically selected to help a covert user. In particular, the selected users emit interfering signals via an orthogonal resource dedicated to the covert user together with signals for their own communications using orthogonal resources allocated to the selected users, which helps the covert user hide the presence of the covert communication. For the covert communication scheme, we carry out extensive analysis and find system parameters in closed forms. The analytic derivation for the system parameters allows one to find the optimal combination of system parameters by performing a simple one-dimensional search. In addition, the analytic results elucidate relations among the system parameters. In particular, it will be proved that the optimal strategy for the non-covert users is an on-off scheme with equal transmit power. The theoretical results derived in this work are confirmed by comparing them with numerical results obtained with exhaustive searches. Finally, we demonstrate that the results of work can be utilized in versatile ways by demonstrating a design of covert communication with energy efficiency into account.