In this paper, we introduce a novel approach to user-centric association and feedback bit allocation for the downlink of a cell-free massive MIMO (CF-mMIMO) system, operating under limited feedback constraints. In CF-mMIMO systems employing frequency division duplexing, each access point (AP) relies on channel information provided by its associated user equipments (UEs) for beamforming design. Since the uplink control channel is typically shared among UEs, we take account of each AP's total feedback budget, which is distributed among its associated UEs. By employing the Saleh-Valenzuela multi-resolvable path channel model with different average path gains, we first identify necessary feedback information for each UE, along with an appropriate codebook structure. This structure facilitates adaptive quantization of multiple paths based on their dominance. We then formulate a joint optimization problem addressing user-centric UE-AP association and feedback bit allocation. To address this challenge, we analyze the impact of feedback bit allocation and derive our proposed scheme from the solution of an alternative optimization problem aimed at devising long-term policies, explicitly considering the effects of feedback bit allocation. Numerical results show that our proposed scheme effectively enhances the performance of conventional approaches in CF-mMIMO systems.

In next-generation non-terrestrial network environments, the increasing risk of detection by unauthorized observers has motivated extensive research on covert communication approaches that minimize the probability of detection. In particular, jamming-assisted cooperative covert communication has attracted significant attention as an effective approach to simultaneously ensure communication performance and security, leading to growing interest in cooperative architectures among heterogeneous platforms. This study investigates covert communication in Low Earth Orbit (LEO) satellite-unmanned aerial vehicle (UAV) cooperative networks, where the LEO satellite serves a legitimate user, while the UAV acts as a cooperative jammer to enhance covertness. A network that integrates a LEO satellite with wide service coverage and a UAV with high mobility offers flexible support for covert communication in diverse environments. However, the problem of optimally allocating power between the LEO satellite and UAV while satisfying the covert communication constraint inherently exhibits a non-convex structure, which commonly necessitates a discretized grid-search baseline over feasible candidate combinations. As the number of candidates increases, this approach suffers from rapidly increasing computational complexity. To address this computational burden, this study proposes a machine-learning (ML)-based power-allocation scheme. The proposed ML model leverages key channelrelated and covertness-related features to efficiently select an effective pair of power scaling factors, while significantly reduced computational complexity. Simulation results demonstrate that the proposed scheme achieves comparable average covert rate to that of the discretized grid-search baseline while requiring substantially lower computational complexity. These results further indicate that the proposed scheme enables low-latency and efficient power control in LEO satellite-UAV cooperative networks. Finally, future work will extend the proposed scheme to more complex multi-LEO satellite-UAV cooperative scenarios through joint optimization of additional system parameters.

Beam squint is a phenomenon in wideband wireless communication systems where the beam direction varies with frequency, resulting in beam misalignment across subcarriers. This frequency-dependent beam deviation becomes one of the major factors degrading beamforming performance in multicarrier systems. In particular, in Low Earth Orbit (LEO) satellite communication, the impact of beam squint is further exacerbated due to high mobility and large-scale antenna array characteristics. Furthermore, when physical layer security (PLS) is considered, beam misalignment can increase signal leakage toward untrusted users, thereby degrading secrecy performance. Therefore, in this paper, a machine learning (ML)-based secure beam selection scheme is proposed for codebook-based beamforming in orthogonal frequency division multiplexing (OFDM)-based millimeter-wave (mmWave) LEO satellite communication systems under beam squint conditions. The proposed scheme selects an appropriate beam from a predefined codebook by considering spatial information and subcarrier-dependent channel characteristics, while reducing beam search computational complexity using ML. Simulation results demonstrate that the proposed scheme reduces beam search computational complexity while achieving average sum secrecy rate performance comparable to that of brute-force codebook-based beam selection.

In this paper, we propose a tensor-based channel estimation framework for beam squint-aware low Earth orbit (LEO) downlink communication at a ground station. We first demonstrate that the downlink signals received at the ground station from multiple LEO satellites can be represented as a structured tensor using mode-n products. Based on this tensor representation, we formulate the channel estimation problem as a tensor decomposition problem to mitigate beam squint effects and inter-satellite interference in downlink communication. To solve this problem, we propose constrained versions of CANDECOMP/PARAFAC (CP) and Tucker decompositions. Specifically, our modified approach incorporates additional constraints, such as a power constraint, which are not considered in the standard CP and Tucker decompositions. Finally, we validate the proposed methods through numerical simulations. Results show that the Tucker-based method achieves lower computational error than the CP-based method, albeit with higher computational cost. Furthermore, the proposed methods incorporating power constraints demonstrate comparable convergence performance to conventional approaches while requiring fewer iterations.

In this paper, we investigate a covert communication system with a full-duplex decode-and-forward (DF) relay and introduce a user-relaying scheme that maximizes the covert rate while ensuring the covertness requirement. In our system model, Alice (transmitter) sends regular data to Carol (regular user) and occasionally embeds covert data for Bob (covert user). Meanwhile, Willie (warden) monitors for covert transmissions. Carol assists Alice by acting as a full-duplex DF relay, decoding both data types via successive interference cancellation and relaying covert data using phase steering and power allocation to confuse Willie. Our proposed scheme adopts a novel approach in which the covert data received by Willie is perfectly canceled, optimizing Alice’s and Carol’s transmissions to maximize the covert rate while keeping Willie’s detection probability below a given threshold.

This paper proposes an efficient beamforming technique to mitigate the beam squint effect in wideband low Earth orbit (LEO) satellite communication systems. Based on the dynamic-subarray with fixed-true-time-delays (DS-FTTD) architecture, the proposed method optimizes the beamforming structure for multi-user transmission using statistical channel state information (CSI). Unlike the original DS-FTTD design, which assumes point-to-point communication and relies on instantaneous CSI, the proposed scheme is tailored for the practical constraints of LEO systems. Simulation results demonstrate that the proposed approach significantly improves beamforming gain and system throughput compared to conventional methods.

In this paper, we consider covert communication with multiple relays and an active warden who not only sends jamming signals but also aims to detect the covert transmission. In the relay system with the active warden, the most critical factor is the channel between the relay and the warden, as the warden leverages this channel to transmit jamming signals while trying to detect the presence of covert communication. To mitigate the impact of the active warden, we propose a relay selection scheme that selects the relay with the minimum channel gain to the warden. We analyze the performance of the proposed scheme and demonstrate how increasing the number of relays leads to performance improvements based on analytical results. Numerical results show that the analytical predictions closely match the simulations, and our proposed scheme effectively increases the covert rate while minimizing the threat posed by the active warden.

In nonorthogonal multiple access (NOMA), which is one of the technologies attracting attention due to its high frequency efficiency in wireless communication systems, the decoding order is considered a crucial factor influencing system performance. However, finding the optimal decoding order becomes challenging when there is wide service coverage and multiple users due to the increased computational complexity involved in the process. In this paper, we propose a machine learning (ML)-based NOMA in low earth orbit (LEO) satellite communication systems. Our proposed scheme aims to utilize ML to determine the optimal decoding order that satisfies the target signal-to-interference-plus-noise ratio (SINR) constraints with low computational complexity, particularly in systems where the satellite utilizes beamforming vector and NOMA to serve each user. The numerical results demonstrate that our proposed scheme achieves performance comparable to that of the optimal scheme for obtaining the optimal decoding order by using the iterative calculation.

This letter proposes machine learning-based non-orthogonal multiple access (NOMA) for a multiuser multiple-input single-output (MISO) broadcast channel (BC), where a transmitter selects and serves multiple users with a fixed rate using NOMA. Unlike a single-input single-output NOMA, where the optimal decoding order is determined by channel gains, the optimal decoding order for a MISO NOMA should be found with a brute force approach. In this case, the transmitter needs to check the validity of all decoding orders under a transmit power budget, so should calculate power allocations for all decoding order candidates, whose complexity is prohibitive as the number of users increases. Our proposed scheme uses a machine learning model to directly predict the optimal decoding order based on channel gains and cross-channel correlations. Once the decoding order is determined, we only need to calculate the power allocation for that decoding order, greatly reducing complexity. Our results show that our machine learning model performs well in finding the optimal decoding order and achieves performance comparable to the optimal scheme.

In this paper, we propose a beamforming design with a two-layer time delay network (TDN) to mitigate the beam squint effect in terahertz (THz) communication systems. We first explain our proposed two-layer TDN that exploits both the true-time-delayers (TTDs) and the fixed true-time-delayers (FTTDs) in separate layers. In our design, the first layer is a TTD layer, where a single TTD is used for each RF chain, while the second layer is an FTTD layer, which comprises FTTDs connecting the first layer and the transmit antennas. We then optimize our proposed design to maximize the achievable rate using our proposed iterative delay beamforming algorithm. In numerical results, we show that our proposed beamforming design outperforms the conventional energy-efficient beamforming design by adding only a single TTD per RF chain.

본 논문에서는 셀프리 다중안테나 네트워크에서 신호 대 간섭 및 잡음비(signal-to-interference plus noise ratio, SINR) 기반의 전력할당 기법을 제안한다. 각 사용자는 네트워크에서 미리 설계된 임계값을 기반으로 사용자 중심으로 클러스터를 구성한다. 클러스터에서 각 Access point(AP)는 서비스할 사용자들의 채널 이득에 비례하도록 최초 전력을 할당한다. 다음으로 사용자들의 SINR 정보를 기반으로 상위 SINR 사용자 그룹과 하위 SINR 사용자를 결정한다. 이를 바탕으로 상위 SINR 그룹 사용자의 전력을 줄이고, 이를 하위 SINR 사용자에게 추가로 최종 전력을 할당하여, 하위 성능 사용자의 주파수 효율을 증가시킨다. 시뮬레이션 결과를 통해 제안한 전력할당 기법이 기존 기법의 주파수 효율을 개선함을 보인다.

Wireless local area networks (WLANs) have recently evolved into technologies featuring extremely high throughput and ultra-high reliability. As WLANs are predominantly utilized in Internet of Things (IoT) and Wi-Fi-enabled sensor applications powered by coin cell batteries, these high-efficiency, high-performance technologies often cause significant battery depletion. The introduction of the trigger frame-based uplink transmission method, designed to enhance network throughput, lacks adequate security measures, enabling attackers to manipulate trigger frames. Devices receiving such frames must respond immediately; however, if a device receives a fake trigger frame, it fails to enter sleep mode, continuously sending response signals and thereby increasing power consumption. This problem is specifically acute in next-generation devices that support multi-link operation (MLO), capable of simultaneous transmission and reception across multiple links, rendering them more susceptible to battery draining attacks than conventional single-link devices. To address this, this paper introduces a Secure Triggering Frame-Based Dynamic Power Saving Mechanism (STF-DPSM) specifically designed for multi-link environments. Experimental results indicate that even in a multi-link environment with only two links, the STF-DPSM improves energy efficiency by an average of approximately 55.69% over conventional methods and reduces delay times by an average of approximately 44.7% compared with methods that consistently utilize encryption/decryption and integrity checks.

In this paper, we propose the wireless energy transfer and data collection system for multi-unmanned aerial vehicles (UAVs). This system enables multiple UAVs to transfer energy to and collect data from internet of things (IoT) devices, and to recharge their batteries at a central charging station when their batteries are low. In this paper, we propose a multi- UAV path algorithm based on distributed multi-agent deep Q-network (MADQN) to minimize the overall age-of-information (AoI) of IoT devices. The numerical results demonstrate that our proposed algorithm outperforms the benchmark scheme in terms of AoI enables to prolong data collection by continuously charging the battery of both IoT devices and UAVs.

In this paper, we propose random beam-based non-orthogonal multiple access (NOMA) for massive multiple-input multiple-output (MIMO) low earth orbit (LEO) satellite communication systems that operate with frequency-division duplexing (FDD). Our system model consists of a massive-antenna satellite and multiple single-antenna users within its coverage area. The satellite selectively serves a subset of users based on a target signal-to-interference-plus-noise power ratio (SINR). In the random beam-based NOMA, the satellite utilizes random beams, where each beam can support multiple users using NOMA. To facilitate user selection and power allocation, each user provides several scalar values obtained from statistical channel state information (CSI) as feedback to the satellite. This allows us to reduce the computational complexity of beamforming design and minimize the feedback overhead for channel acquisition. We propose two random beam-based NOMA schemes with varying complexities and feedback overheads. We optimize these schemes by solving joint user selection and power allocation problems. The numerical results demonstrate that our proposed schemes outperform conventional random beamforming, specifically orthogonal multiple access (OMA) at each beam, by supporting a greater number of users.

본 논문은 많은 수의 기기들과 복수의 재배치 팀을 운영하는 개인용 이동기기 공유 서비스에서 서비스 가용성을 높일 수 있는 기기들의 재배치 기법을 설계하고 그 성능을 평가한다. 재배치 시간 혹은 거리를 줄이기 위해 Simulated annealing 기법을 적용하는데 과보유지역, 부족지역, 팀별 할당을 포함한 각 스케줄을 정수 벡터로 표현한 후 원소간 교환과 스케줄의 평가 과정으로 구성된 진화과정을 반복한다. 동일한 속성 간으로 제한된 원소 교환에 의해 스케줄의 유효성을 유지하며 평가 과정에서 중복이동을 고려하여 팀별 작업 시간을 구하고 그 최대값을 전체 비용을 산정한다. 파이썬 언어로 구현된 프로토타입에 기반한 실험 결과, 제안된 기법은 실험에서 주어진 이동기기들의 지역적 분포와 수요에 있어서 참조 기법에 비해 재배치 비용을 60 %까지 감소시킨다.

In low Earth orbit (LEO) satellite communication systems, obtaining accurate channel state information (CSI) is crucial for achieving high performance. Least squares (LS) channel estimation is a simple conventional channel estimation scheme, but it does not account for compensating for channel estimation errors. In this paper, we propose a channel estimation scheme with a machine learning-based denoising network for massive multiple-input single-output LEO satellite communication systems. Our proposed scheme uses a denoising convolutional neural network to reduce channel estimation errors from the LS estimator. The numerical results demonstrate that our proposed machine learning-based denoising network effectively improves the accuracy of the estimated channel from the LS channel estimator.

In parallel channels, it is well known that the waterfilling is optimal power allocation that maximizes the sum achievable rate. However, the waterfilling requires iterative calculations, so may not be suitable especially when the number of total channels is very large or the delay constraint is very tight. In this paper, we propose machine learning-based power loading to reduce the computational complexity of power allocation in massive Gaussian parallel channels, which emulates on-off power allocation, where some of the channels equally share total transmit power. Our proposed scheme adopts a deep neural network structure that takes channel gains with total transmit power and returns an on-off power allocation strategy. The numerical results show that our proposed scheme achieves almost the same performance with the on-off power allocation with reduced complexity.

Index coding, a.k.a. coded multicasting, is one of the promising transmission schemes that can reduce the number of transmissions by leveraging the stored data in the users' memory. With multiple antennas, index coding gain can be further multiplied by spatially multiplexing but joint design of index code and beamforming has not been addressed well in literature. This paper figures out the gain of spatially multiplexed index code by jointly designing the multicast beamformer and index code. Since the optimal joint design problem itself is NP-hard, we take a two-step approach. At the first step, for an arbitrary index code, conjugate gradient-based multi-group multicast beamforming is designed. At the second step, the index code minimizing the total transmission time is selected. Our simulation results exhibit a substantial gain of spatially multiplexed index code by the proposed scheme.

Laparoscopic surgery is a technique in which surgeons insert laparoscopic instruments into the body through a small incision and conduct surgical operations. It offers a shorter recovery period, less blood loss, and less postoperative pain than open surgeries. In order to maximize these benefits of this surgery technique, it is important to localize the lesion accurately without palpation, minimizing the surgical area. It is because surgeons cannot palpate the organ during laparoscopic surgeries. For accurate localization, we propose a lesion localization system (LLS). This adopts an endoscopic clip with a small-size magnet as a marker and an improved magnetometer as a probe for measuring the magnetic field from the magnet. The magnetometer, which consists of two Hall sensors, can cancel out the fluctuating magnetic baseline mainly caused by external environments so that it can detect a smaller change in the target magnetic field. As a result, the LLS improves the detection range up to 40 mm while using a weak magnet with a small volume of 17.1 mm3, which is compatible with the use of commercial clip appliers. An error of less than 0.1% is achieved at a distance of 40 mm, and the maximum error is kept below 6% even when the rotation angle is varied to ±90°. This LLS does not cause discomfort to the surgery operation because it uses a marker small enough and does not require external coils.

In this paper, we propose random beam-based non-orthogonal multiple access (NOMA) for low latency multiple-input single-output (MISO) broadcast channels, where there is a target signal-to-interference-plus-noise power ratio (SINR) for each user. In our system model, there is a multi-antenna transmitter with its own single antenna users, and the transmitter selects and serves some of them. For low latency, the transmitter exploits random beams, which can reduce the feedback overhead for the channel acquisition, and each beam can support more than a single user with NOMA. In our proposed random beam-based NOMA, each user feeds a selected beam index, the corresponding SINR, and the channel gain, so it feeds one more scalar value compared to the conventional random beamforming. By allocating the same powers across the beams, the transmitter independently selects NOMA users for each beam, so it can also reduce the computational complexity. We optimize our proposed scheme finding the optimal user grouping and the optimal power allocation. The numerical results show that our proposed scheme outperforms the conventional random beamforming by supporting more users for each beam.