This paper presents a deep neural network (DNN)-based resource allocation framework aimed at maximizing energy efficiency (EE) in beam-hopping (BH) low-Earth orbit (LEO) satellite communication systems. Specifically, in BH-LEO satellite systems, it is challenging to solve the joint optimization of time slot scheduling and transmit power control, due to the intrinsic complexity and non-convex nature of the original mixed-integer nonlinear programming formulation. To mitigate this issue, we propose a strategy to decompose the problem into two tractable subproblems: an integer programming model for time slot allocation and a nonlinear programming model for transmit power allocation. For the time slot allocation subproblem, we employ a dueling double deep Q-network (D3QN), combining the strengths of both dueling and double Q-learning techniques to enable stable and efficient decision-making. Then, for the power allocation subproblem, we design a novel unsupervised DNN (UDNN)-based model that estimates spectral efficiency to indirectly determine transmit power, thereby avoiding the difficulties of solving a non-convex optimization problem for EE maximization. Extensive simulation results show that the proposed D3QN and UDNN-based schemes outperform existing iterative and DNN-based approaches in terms of both EE and outage performance, while achieving a significant reduction in computational overhead.

Multi-beam low Earth orbit (LEO) satellite communication is recognized as a promising technology that delivers high data rates and wide area coverage. However, managing multiple beams and controlling transmit power pose significant challenges due to the impact of inter-beam interference on performance and the impact of power consumption on satellite battery life. Generally, LEO satellite systems focus on reducing the gap between capacity and demand for uneven traffic distributions of ground users. In contrast to conventional approaches, this paper presents a theoretical analysis of the optimal conditions for minimizing satellite transmit power while meeting user traffic demands in multi-beam LEO satellite systems. Based on this analysis, we propose algorithms for user-beam association, beam pattern selection, timeslot scheduling, and power allocation to minimize the total power while satisfying the traffic demand. Simulation results show that the proposed methods have superior performance over conventional schemes in terms of power consumption and capacity-demand gap.

Multibeam low-Earth orbit (LEO) satellite communication is a promising solution for providing high-data rate and wide area coverage. Therefore, satellite communication is introduced into Internet of Things (IoT) networks to support large-scale connectivity. In the satellite communication system, multibeam management and power control are challenging issues because interbeam interference severely affects system performance and power consumption influences the battery life of the satellite. Thus, traditional LEO satellite systems mainly focus on minimizing a capacity-demand gap to develop an effective power reduction algorithm. In contrast to this approach, in this article, we present a theoretical analysis of the optimal conditions for minimizing the transmit power of the satellite while satisfying the traffic demands of users in multibeam LEO satellite-assisted IoT networks. Based on this analysis, we propose algorithms for user-beam association, beam pattern selection, timeslot scheduling, and power allocation to minimize the transmit power while satisfying the traffic demands. In addition, we provide low-complexity algorithms for power minimization to reduce the computational complexity. Simulation results demonstrate that the proposed methods outperform the conventional schemes in terms of power consumption, capacity-demand gap, and computational complexity.

Physical-layer security (PLS) is a new paradigm for secure communication without requiring secret key exchange and management. Moreover, PLS with frequency diverse subarray (FDSA) can better control information leakage in the angle-range domain, which mitigates the security weakness of the phased array caused by its lack of range resolution. In this paper, we propose a three-dimensional (3D) directional modulation (DM) using randomized radiation with FDSA for enhanced PLS, employing a planar array. In addition, nonuniform frequency offsets (FOs) are considered as FO configurations (FOCs) for FDSA to concentrate on the mainlobe and suppress the undesired sidelobes in 3D space, where logarithmically increasing FOC (L-FOC), Hamming window-based FOC (H-FOC), and piecewise trigonometric FOC (P-FOC) are introduced. Characterizing the process of selecting the random subsets for randomized radiation, we provide the exact analysis of the secrecy rate of the proposed scheme. Moreover, FOs applied to FDSA and the number of random subsets are optimized with a genetic algorithm (GA)-based optimization strategy. We evaluate the proposed schemes in terms of secrecy rate and vulnerable volume, where the simulation results verify our analysis and show that nonuniform FOCs are a more favorable choice for FDSA compared to uniform FOC (U-FOC).

In this work, we propose a novel algorithm for subchannel and power allocation for Internet of Things (IoT) networks using downlink multicarrier nonorthogonal multiple access (MC-NOMA). Unlike single-carrier NOMA, MC-NOMA can utilize multiple subchannels, which is more suitable for supporting the massive connectivity of IoT users. However, in MC-NOMA, the joint subchannel and power allocation problem leads to a mixed-integer nonlinear programming problem, which is challenging to find an optimal solution. Therefore, in this article, we reformulate the joint subchannel and power allocation problem into a binary decision problem for subchannel allocation with a mathematical analysis of power allocation. Then, using the transformed problem, we propose a subchannel allocation scheme for MC-NOMA to improve the sum rate and outage performances compared with the conventional approaches. Even though many prior studies on the power allocation for MC-NOMA focused on deep learning-based methods to achieve an optimal solution with low complexity, it is difficult to jointly optimize the maximum power of each subchannel and the power for each NOMA user. Thus, we propose a deep learning-based training algorithm to optimize the maximum per-subchannel power with a mathematical analysis of power allocation for NOMA users. In addition, we introduce the user selection algorithm to avoid performance loss due to an outage user, where the presented algorithm can select users satisfying the data rate requirement. Through simulations, we show that the proposed subchannel and power allocation schemes have outstanding sum rate and outage performances compared with the existing schemes.

Frequency diverse subarrays (FDSA) with linear frequency offsets (Lin-FO) can generate mainlobe to be confined around the desired receiver on an angle-range plane, which mitigates vulnerabilities in conventional randomized radiation techniques implemented with a phased array. However, the angle-range-coupled beampattern of subarrays imposes limitations on improving the secrecy rate. In this paper, we propose randomized radiation using FDSA with nonlinear FO designs for enhanced physical-layer security (PLS) in mmWave communications. Non-linear FO designs, including logarithmically increasing FO (Log-FO), Hamming window-based FO (HW-FO), and piecewise trigonometric FO (PT-FO), enable the formation of angle-range-decoupled beampatterns focused on the intended receiver. The exact secrecy rate of the proposed scheme is derived and validated through simulations. The evaluation of various FO designs reveals reduced vulnerable areas and highlights the impact of transmit power on their performance, offering valuable insights for FO design selection.

To achieve a global coverage and enhance throughput, low Earth orbit (LEO) satellites have been adopted in B5G and 6G communications, because they can provide lower latency and higher service density compared to those with higher orbital altitude. However, in the LEO satellite networks, ground users may observe significant Doppler, which should be estimated and compensated for reliable communications. Thus, in this paper, we provide a comprehensive review of the existing studies on Doppler characterization and compensation.

In the above article, Hong et al. proposed a frequency region beamforming scheme exploiting frequency diverse subarray. We found that there is a mathematical flaw in precoding vector normalization in their sidelobe randomization scheme called the inverted subarray subset technique (ISST). We show that it is not only a matter of how to define and interpret the array factor, but it leads to the wrong performance optimization and misoperation of their own proposed scheme, which may cause detrimental security risks. Furthermore, to avoid any false conclusions in the future study caused by the irrational normalization, we also present the related techniques and their correct normalization.

A wireless sensor network (WSN) refers to a network of sensor nodes that collaboratively work to sense, monitor, and control their surrounding environments. As WSNs are integrated into the Internet of Things, it is crucial to protect the network against malicious security attacks considering its wide applicability, such as military monitoring, healthcare, and civilian applications. Thus, in this paper, we consider a physical-layer security technique exploiting analog cooperative beamforming (ACB), where multiple sensor nodes create a virtual antenna array (VAA) and locally adapt their phases. As WSNs inherently have clustered topology, we model the VAA elements' locations by Gaussian distributions. The secrecy capacity of the ACB with the Gaussian-distributed elements is derived in a closed-form expression. The theoretical and numerical results indicate that the ACB-based schemes provide a better secrecy rate compared to the conventional co-located antenna array-based schemes. In addition, the ACB with Gaussian-distributed elements can better suppress side-lobe, which causes undesired information leakage to certain directions, compared to the ACB with uniformly distributed elements. Furthermore, we investigate the impacts of fading channel and phase estimation error in the ACB.

Internet of things (IoT) is a smart technology that connects anything anywhere at any time. Intelligent device-to-device (D2D) communication, in which devices will communicate with each other autonomously without any centralized control, is an integral part of the Internet of Things (IoT) ecosystem. Thus, for D2D applications such as local file sharing or swarm sensing, we study communications between devices in proximity in ultra-dense urban environments, where devices are stacked vertically and dispersed in the horizontal plane. To reflect the spatiotemporal correlation inherently embedded in the D2D communications, we model and analyze clustered D2D networks in three-dimensional (3D) space based on Thomas cluster process (TCP), where the locations of clusters follow Poisson point process, and cluster members (devices) are normally distributed around their cluster centers. We assume that multiple device pairs in the network can share the same frequency band simultaneously. Thus, in the presence of cochannel interference from both the same cluster and the other clusters, we investigate the coverage probability and the area spectral efficiency of the clustered D2D networks in 3D space.

In this letter, we prove that decouple-and-forward (DCF) relaying for dual-hop orthogonal space-time block code (OSTBC) transmissions achieves the maximum diversity order attainable by dual-hop MIMO relaying systems. Decoupling at the relay transforms received OSTBC signals into independent and maximal-ratio combined single-input single-output (SISO) signals, which provides the receive diversity gain, and also enables the relay to use OSTBCs again so as to achieve the transmit diversity gain. In obtaining the diversity order, DCF relaying needs a coherence interval of the length of OSTBC transmissions, which is shorter than the requirement assumed in other works that provide the maximum diversity. Numerical results are also presented to verify the proof and give an insight to the relationship between the diversity order and antenna configurations.

We present a study on the outage probability of multihop wireless communication systems with multiple-input multiple-output (MIMO) link based on the transmit antenna selection and the maximal-ratio combining (MRC) at the receiver. A nonregenerative system (NS) is investigated with an ideal amplifying gain. MIMO channels are assumed in uncorrelated Rayleigh fading.We derive a moment generating function (MGF) of the reciprocal of the end-to-end signal-to-noise ratio (SNR) and obtain a closed-form approximation on the outage probability through the numerical inversion of a Laplace transform. Numerical results show that the presented outage is exactly matched with the outage probability when assuming the ideal relay gain. For more practical gains, the result is shown to be a lowerbound that gets tight at high average SNR as well as for a small number of hops and/or of antennas. We also compare the outage probabilities of nonregenerative MIMO relaying with a regenerative counterpart for multiple hops.

In this letter, a BER study is presented for the end- to-end performance of dual-hop wireless communication systems employing transmit diversity with orthogonal space-time block codes (OSTBCs), where a nonregenerative or regenerative relay is equipped with a single antenna operating over flat Rayleigh fading channels. More specifically, we provide probability density functions (PDFs) and moment generating functions (MGFs) for the end-to-end SNR of the dual-hop OSTBC transmissions and then present its BER performance overM-ary QAM and PSK modulations, respectively. Numerical investigation shows that the analytic BER provided in the letter makes an exact match with the simulation result in various multiple-antenna transmission scenarios. The result also shows how the number of antennas equipped at the source and destination affects the end-to-end performance.

Greedy transmission scheduling achieves great capacity by maximally exploiting independent time-varying channels across different mobile users. The improvement in capacity, however, depends on the degree of completeness of the channel quality information (CQI) fed back from the receiver to the transmitter. To be motivated by an insight that too many CQI feedbacks may rather impair the capacity gain, due to causing congestion in feedback link, this letter proposes a quality-based CQI reporting (QBR) scheme where the CQIs are fed back to the transmitter only for receivers whose signal quality is above a predefined threshold. The capacity is provided in terms of the threshold and feedback-error rate. The results show that QBR achieves outstanding performance when the feedback error is present. In addition, it quickly approaches an unimpaired ideal capacity, as the number of users increases if the error is not assumed

This paper proposes a power cost of mobility that is needed to compensate for fast fading. By the power cost of mobility we mean the additional SINR (signal-to-interference-plus-noise ratio) that is needed to meet the signal quality because the user is mobile. That is, the power cost of mobility in this paper is defined as the increment in power-control target SINR due to fast fading. We consider closed-loop power control (CLPC) based on SNR (signal-to-noise ratio) instead of SINR because it is difficult to analyze statistically power-controlled interference. The channel is modeled as flat Rayleigh fading. The probability density function (pdf) of the received SNR is derived in terms of the power control updating rate, maximum SNR, and Doppler frequency of the fading channel. In cellular systems with quadrature phase shift keying (QPSK) modulation, we study the power cost with various power control updating rates and Doppler frequencies of the fading channel.

In this paper, it is shown that, in DS/CDMA mobile systems, halving or doubling the spreading factor (HSF or DSF) when retransmission is requested possibly improves the throughput. Given transmit power, DSF essentially decreases probability of packet error (PPE) by increasing the signal energy per information bit. It, however, doubles the time duration needed for transmitting the original packet. On the other hand, HSF increases PPE. It, however halves the time duration required to carry the original packet. Thus, the efficiency of HSF in DSF as a retransmission strategy depends on the amount of increased or reduced PPF after HSF or DSF is selected. With achieving given residual error probability (REP) in CDMA systems, the effective throughput is evaluated in this paper to find conditions with which HSF or DSF achieves better performance than using the original one. Analytic results show that HSF or DSF performs better when relatively small or big changes in their PPE's are present, respectively.