Diffusion models deliver state-of-the-art generative performance across diverse modalities but remain computationally expensive due to their inherently iterative sampling process. Existing training-free acceleration methods typically improve numerical solvers for the reverse-time ODE, yet their effectiveness is fundamentally constrained by the inefficiency of the underlying sampling trajectories. We propose A-FloPS (Adaptive Flow Path Sampler), a principled, training-free framework that reparameterizes the sampling trajectory of any pre-trained diffusion model into a flow-matching form and augments it with an adaptive velocity decomposition. The reparameterization analytically maps diffusion scores to flow-compatible velocities, yielding integration-friendly trajectories without retraining. The adaptive mechanism further factorizes the velocity field into a linear drift term and a residual component whose temporal variation is actively suppressed, restoring the accuracy benefits of high-order integration even in extremely low-NFE regimes. Extensive experiments on conditional image generation and text-to-image synthesis show that A-FloPS consistently outperforms state-of-the-art training-free samplers in both sample quality and efficiency. Notably, with as few as 5 function evaluations, A-FloPS achieves substantially lower FID and generates sharper, more coherent images. The adaptive mechanism also improves native flow-based generative models, underscoring its generality. These results position A-FloPS as a versatile and effective solution for high-quality, low-latency generative modeling.

Recent advances in large reasoning models (LRMs) have significantly enhanced long-chain reasoning capabilities over standard large language models (LLMs). However, LRMs often produce unnecessarily lengthy outputs even for simple queries, leading to inefficiencies or even accuracy degradation compared to LLMs. To address this, we propose CP-Router, a training-free, model-agnostic routing framework that dynamically selects between an LLM and an LRM, demonstrated with multiple-choice question answering (MCQA) prompts. The routing decision is guided by the prediction uncertainty estimates derived via Conformal Prediction (CP), which provides rigorous coverage guarantees. To improve uncertainty differentiation across inputs, we introduce Full and Binary Entropy (FBE), a novel entropy-based criterion that adaptively selects the appropriate CP threshold. Experiments across MCQA and QA benchmarks—including mathematics, logical reasoning, and Chinese chemistry—demonstrate that CP-Router efficiently reduces token usage while maintaining or even improving accuracy compared to using LRM alone. We further demonstrate the generality and robustness of CP-Router by extending it to diverse model pairings beyond the LLM–LRM setting.

The low earth orbit (LEO) satellite communication network has recently been proposed by 3GPP as a new paradigm of infrastructure to enhance the capacity and coverage of existing terrestrial wireless networks. However, the mobility of LEO satellite nodes leads to a dynamic environment, which introduces unique challenges for handover and throughput optimization in multi-user access control for LEO networks. We formulate an optimization problem of joint access control and resource allocation to maximize the long-term system throughput and avoid frequent handovers, which is non-deterministic polynomial-time hard. To overcome this challenge problem, we propose a multi-agent deep reinforcement learning algorithm and design the proximal policy optimization (PPO) network structure with the long short-term memory (LSTM) layers. In our proposed algorithm, the centralized trainer node is responsible for training the parameters of all networks, and then each ground user independently makes its own access decisions based on its local observation. We deploy a policy network on each ground user that is able to intelligently access a proper LEO satellite node to maintain high system throughput and avoid frequent handovers over a long period. The simulation results have demonstrated the effectiveness and superiority of our proposed algorithm compared to benchmark schemes in addressing the access control and resource allocation issue for the multi-user LEO satellite network.

Satellite edge federated learning (FL) is an important application paradigm of the integrated artificial intelligence and communication in the future sixth-generation (6G) system. However, achieving efficient FL tasks within highly dynamic, computationally constrained low-earth orbit (LEO) satellite networks while balancing delay and energy consumption remains a significant challenge. Therefore, we explore a satellite-collaborative computation federated learning (SCCFL) system in LEO edge intelligence networks. To this end, we formulate a multi-metric trade-off optimization problem weighted by delay and energy consumption, by jointly optimizing computation satellite (CS) offloading, aggregation satellite (AgS) selection, and computation resource allocation. We propose a multi-metric intelligent alternating (MIA) optimization method, in which each independent agent is responsible for providing decision reference Q-values of specific variables (such as CSs or AgS) for specific metrics (such as delay or energy consumption). The final decision is made based on weighted Q-values. During training, each agent is trained alternately. We also propose a multi-metric computation resource allocation strategy that minimizes delay while reducing the energy consumption as much as possible. Simulation results validate the superiority of our proposed method in terms of FL accuracy and the trade-off between delay and energy consumption.

This paper proposes a novel towed movable antenna (ToMA) array architecture to enhance the physical layer security of airborne communication systems. Unlike conventional onboard arrays with fixed-position antennas (FPAs), the ToMA array employs multiple subarrays mounted on flexible cables and towed by distributed drones, enabling agile deployment in three-dimensional (3D) space surrounding the central aircraft. This design significantly enlarges the effective array aperture and allows dynamic geometry reconfiguration, offering superior spatial resolution and beamforming flexibility. We consider a secure transmission scenario where an airborne transmitter communicates with multiple legitimate users in the presence of potential eavesdroppers. To ensure security, zero-forcing beamforming is employed to nullify signal leakage toward eavesdroppers. Based on the statistical distributions of locations of users and eavesdroppers, the antenna position vector (APV) of the ToMA array is optimized to maximize the users’ ergodic achievable rate. Analytical results for the case of a single user and a single eavesdropper reveal the optimal APV structure that minimizes their channel correlation. For the general multiuser scenario, we develop a low-complexity alternating optimization algorithm by leveraging Riemannian manifold optimization. Simulation results confirm that the proposed ToMA array achieves significant performance gains over conventional onboard FPA arrays, especially in scenarios where eavesdroppers are closely located to users under line-of-sight (LoS)-dominant channels.

Pinching-antenna systems (PASS) have emerged as a promising technology due to their ability to dynamically reconfigure wireless propagation environments. A novel PASS-based multi-user non-orthogonal multiple access (NOMA) framework is proposed by exploiting the waveguide-division (WD) transmission characteristic. Specifically, each NOMA user cluster is served by one dedicated waveguide, and the corresponding pinching beamforming is exploited to enhance the intra-cluster performance while mitigating the inter-cluster interference. Based on this framework, a sum-rate maximization problem is formulated for jointly optimizing power allocation, pinching beamforming, and user scheduling. To solve this problem, a two-step algorithm is developed, which decomposes the original problem into two subproblems. For the joint power allocation and pinching beamforming design, a penalty dual decomposition (PDD) algorithm is proposed to obtain the locally optimal solutions. Specifically, the coupling constraints are alleviated through augmented Lagrangian relaxation, and the resulting augmented Lagrangian (AL) problem is decomposed into four subproblems, which are solved by the block coordinate descent (BCD) method. For the user scheduling, a low-complexity matching algorithm is developed to solve the user-to-waveguide assignment problem. Simulation results demonstrate that 1) the proposed PASS-based NOMA framework under the WD transmission structure achieves significant sum-rate gain over conventional fixed-position antenna systems and orthogonal multiple access (OMA) scheme; and 2) the proposed matching-based user scheduling algorithm achieves near-optimal user-waveguide association with low computational complexity.

Due to the crowded spectrum occupancy and dense user terminals (UTs), the conventional fixed antenna (FA)-based access points (APs) face challenges in realizing massive access and interference cancellation. To address this issue, in this paper we develop a six-dimensional movable antenna (6DMA) enhanced multi-AP coordination system to fully exploit its maximum spatial diversity for coverage enhancement and interference mitigation. First, we model the wireless channels between the APs and UTs to characterize their variation with respect to 6DMA movement, in terms of both the three-dimensional (3D) position and 3D orientation of each distributed AP’s antenna. Then, an optimization problem is formulated to maximize the weighted sum rate of multiple UTs for their uplink transmissions by jointly optimizing the antenna position vector (APV), the antenna orientation matrix (AOM), and the receive combining matrix over all coordinated APs, subject to the constraints on local antenna movement regions. To solve this challenging non-convex optimization problem, we first transform it into a more tractable Lagrangian dual problem. Then, an alternating optimization (AO)-based algorithm is developed by iteratively optimizing the APV and AOM, which are designed by applying the successive convex approximation (SCA) technique and Riemannian manifold optimization-based algorithm, respectively. Moreover, to further reduce the overhead of antenna movement, we propose an offline solution for APV and AOM design based on statistical channel state information (CSI). In addition, we further extend the proposed scheme from uni-polarized to dual-polarized modes for all antennas. Simulation results show that the proposed 6DMA-enhanced multi-AP coordination system can significantly enhance network capacity, and both of the online and offline 6DMA schemes can attain considerable performance improvement compared to the conventional FA-based schemes.

Movable antenna (MA) is an emerging technology that can significantly improve communication performance via the continuous adjustment of the antenna positions. To unleash the potential of MAs in wideband communication systems, acquiring accurate channel state information (CSI), i.e., the channel frequency responses (CFRs) between any position pair within the transmit (Tx) region and the receive (Rx) region across all subcarriers, is a crucial issue. In this paper, we study the channel estimation problem for wideband MA systems. To start with, we express the CFRs as a combination of the field-response vectors (FRVs), delay-response vector (DRV), and path-response tensor (PRT), which exhibit sparse characteristics and can be recovered by using a limited number of channel measurements at selected position pairs of Tx and Rx MAs over a few subcarriers. Specifically, we first formulate the recovery of the FRVs and DRV as a problem with multiple measurement vectors in compressed sensing (MMV-CS), which can be solved via a simultaneous orthogonal matching pursuit (SOMP) algorithm. Next, we estimate the PRT using the least-square (LS) method. Moreover, we also devise an alternating refinement approach to further improve the accuracy of the estimated FRVs, DRV, and PRT. This is achieved by minimizing the discrepancy between the received pilots and those constructed by the estimated CSI, which can be efficiently carried out by using the gradient descent algorithm. Finally, simulation results demonstrate that both the SOMP-based channel estimation method and alternating refinement method can reconstruct the complete wideband CSI with high accuracy, where the alternating refinement method performs better despite a higher complexity.

Movable antenna (MA) has been regarded as a promising technology to enhance wireless communication performance by enabling flexible antenna movement. However, the hardware cost of conventional MA systems scales with the number of movable elements due to the need for independently controllable driving components. To reduce hardware cost, we propose in this paper a novel architecture named cross-linked MA (CL-MA) array, which enables the collective movement of multiple antennas in both horizontal and vertical directions. To evaluate the performance benefits of the CL-MA array, we consider an uplink multiuser communication scenario. Specifically, we aim to minimize the total transmit power while satisfying a given minimum rate requirement for each user by jointly optimizing the horizontal and vertical antenna position vectors (APVs), the receive combining at the base station (BS), and the transmit power of users. A globally lower bound on the total transmit power is derived, with closed-form solutions for the APVs obtained under the condition of a single channel path for each user. For the more general case of multiple channel paths, we develop a low-complexity algorithm based on discrete antenna position optimization. Additionally, to further reduce antenna movement overhead, a statistical channel-based antenna position optimization approach is proposed, allowing for unchanged APVs over a long time period. Simulation results demonstrate that the proposed CL-MA schemes significantly outperform conventional fixed-position antenna (FPA) systems and closely approach the theoretical lower bound on the total transmit power. Compared to the instantaneous channel-based CL-MA optimization, the statistical channel-based approach incurs a slight performance loss but achieves significantly lower movement overhead, making it an appealing solution for practical wireless systems.

This paper studies UAV detection by multiple collaborative base stations in an integrated sensing and communication (ISAC) manner. In particular, we propose a computationally efficient UAV 3D localization and velocity estimation approach based on matched filtering (MF) to process the orthogonal frequency division multiplexing (OFDM) sensing signals. The proposed method consists of two main steps: a MF-based preprocessing step at each single base station to efficiently estimate distance, velocity, and angle parameters, and a symbol-level fusion step using a grid searching approach to integrate results from multiple base stations. Compared with traditional multiple signal classification (MUSIC)-based fusion techniques, our approach reduces the overall computational complexity by more than 98.5%. Meanwhile, it demonstrates significantly higher robustness in low SNR conditions (SNR ≤ 0 dB), as evidenced by a reduction in localization error from meter-level to centimeter-level accuracy. In positive SNR conditions (SNR > 0 dB), it also improves the localization and velocity estimation accuracy by approximately 33.5% and 26.3%, respectively. These results demonstrate the practical advantage of the proposed method in real-time UAV sensing application.

Movable antenna (MA) has been regarded as a promising technology to enhance wireless communication performance by enabling flexible antenna movement. To reduce hardware cost, we propose in this paper a novel architecture named cross-linked MA (CL-MA) array, which enables the collective movement of multiple antennas in both horizontal and vertical directions. To evaluate the performance benefits of the CL-MA array, we consider an uplink multiuser communication scenario. Specifically, we aim to minimize the total transmit power while satisfying a given minimum rate requirement for each user by jointly optimizing the horizontal and vertical antenna position vectors (APVs), the receive combining at the base station (BS), and the transmit power of users. To solve this challenging non-convex optimization problem, we develop a low-complexity algorithm based on discrete antenna position optimization. Additionally, to further reduce antenna movement overhead, a statistical channel-based antenna position optimization approach is proposed, allowing for quasi-static APVs over a long time period. Simulation results demonstrate that the proposed CL-MA schemes significantly outperform conventional fixed-position antenna (FPA) systems and closely approach the theoretical lower bound on the total transmit power. Compared to the instantaneous channel-based CL-MA optimization, the statistical channel-based approach incurs a slight performance loss but achieves significantly lower movement overhead, making it an appealing solution for practical wireless systems.

Classifier-free guidance (CFG) has emerged as a pivotal advancement in text-to-image latent diffusion models, establishing itself as a cornerstone technique for achieving high-quality image synthesis. However, under high guidance weights, where text-image alignment is significantly enhanced, CFG also leads to pronounced color distortions in the generated images. We identify that these distortions stem from the amplification of sample norms in the latent space. We present a theoretical framework that elucidates the mechanisms of norm amplification and anomalous diffusion phenomena induced by classifier-free guidance. Leveraging our theoretical insights and the latent space structure, we propose an Angle Domain Guidance (ADG) algorithm. ADG constrains magnitude variations while optimizing angular alignment, thereby mitigating color distortions while preserving the enhanced text-image alignment achieved at higher guidance weights. Experimental results demonstrate that ADG significantly outperforms existing methods, generating images that not only maintain superior text alignment but also exhibit improved color fidelity and better alignment with human perceptual preferences.

In this paper, we propose to employ a movable-antenna (MA) array to enhance unmanned aerial vehicle (UAV) covert communications by fully exploiting the spatial degrees of freedom (DoFs) in large-scale adjustment of UAVs’ positions within broad areas and small-scale movement of MAs within local regions. Specifically, to guarantee fairness, we formulate an optimization problem to maximize the minimum achievable rate over all users via UAV trajectory, transmit beamforming, and antenna position design, subject to a covertness constraint. To solve this non-convex optimization problem, we develop a two-step method to obtain a sub-optimal solution. Specifically, we first design the UAV trajectory under the assumption of ideal beam patterns, which significantly decouples the UAV trajectory optimization and directional transmit beamforming. Then, an alternating optimization algorithm with the successive convex approximation (SCA) technique is developed to optimize the UAV transmit beamforming and MAs’ positions. Simulation results demonstrate that our proposed system design can effectively enhance spectrum-efficiency and stealth of UAV downlink transmissions, significantly outperform conventional systems with fixed-position antenna (FPA) arrays, and closely approach the performance upper bound with ideal beam patterns.

This paper proposes a channel estimation method for movable antenna (MA)-aided wideband communication systems to acquire complete channel state information (CSI), i.e., the channel frequency responses (CFRs) between any position pair within the transmit (Tx) region and the receive (Rx) region across all subcarriers. To start with, we express the CFRs as a combination of the field-response vectors (FRVs), delay-response vector (DRV), and path-response tensor (PRT), which exhibit sparse characteristics and can be recovered by using a limited number of channel measurements at several position pairs of Tx and Rx MAs over a few subcarriers. Specifically, we first formulate the recovery of the FRVs and DRV as a problem of multiple measurement vectors in compressed sensing (MMV-CS), which can be solved via a simultaneous orthogonal matching pursuit (SOMP) algorithm. Next, we estimate the PRT using the least-square (LS) method. Finally, simulation results demonstrate that the proposed SOMP-based channel estimation method can reconstruct the complete wideband CSI with a high accuracy.

Movable antenna (MA) is an innovative technology that can enhance channel condition by altering antenna position within a local area. Integrating MA can further improve the performance of multiuser communications in non-orthogonal multiple access (NOMA) systems. In this paper, we investigate MA enabled NOMA for multiuser downlink communication, where the base station (BS) is equipped with a fixed-position antenna (FPA) array to serve multiple MA enabled user terminals (UTs). An optimization problem is formulated to maximize the minimum achievable rate among all the UTs by jointly optimizing the positions of MAs of each UT, the precoding matrix at BS, and the successive interference cancellation (SIC) decoding indicator matrix at UTs, subject to the limited movement area of the MAs, the maximum transmit power of the BS, and the SIC decoding condition. To solve this non-convex problem, we combine the hippo optimization (HO) method with the alternating optimization (AO) method to obtain a suboptimal solution efficiently. Simulation results show that the proposed algorithm can significantly improve the rate performance of the NOMA system compared to the conventional FPA system as well as other benchmark schemes.

In this paper, we develop a six-dimensional movable antenna (6DMA) enhanced multi-access point (AP) coordination system for coverage enhancement and interference mitigation. First, we model the wireless channels between the APs and UTs to characterize their variation with respect to 6DMA movement, in terms of both the three-dimensional (3D) position and 3D orientation of each distributed AP's antenna. Then, an optimization problem is formulated to maximize the weighted sum rate of multiple UTs for their uplink transmissions by jointly optimizing the antenna position vector (APV), the antenna orientation matrix (AOM), and the receive combining matrix over all coordinated APs, subject to the constraints on local antenna movement regions. To solve this challenging non-convex optimization problem, we first transform it into a more tractable Lagrangian dual problem. Then, an alternating optimization (AO)-based algorithm is developed by iteratively optimizing the APV and AOM, which are designed by applying the successive convex approximation (SCA) technique and Riemannian manifold optimization-based algorithm, respectively. Simulation results show that the proposed 6DMA-enhanced multi-AP coordination system can significantly enhance network capacity, and can attain considerable performance improvement compared to the conventional fixed antenna (FA)-based schemes.

Movable antenna (MA) technology offers promising potential to enhance wireless communication by allowing flexible antenna movement. To maximize spatial degrees of freedom (DoFs), larger movable regions are required, which may render the conventional far-field assumption for channels between transceivers invalid. In light of it, we investigate in this paper MA-enabled near-field communications, where a base station (BS) with multiple movable subarrays serves multiple users, each equipped with a fixed-position antenna (FPA). First, an upper bound on the minimum signal-to-interference-plus-noise ratio (SINR) across users is derived in closed form. A low-complexity algorithm based on zero-forcing (ZF) is then proposed to jointly optimize the antenna position vector (APV) and digital beamforming matrix (DBFM) to approach this bound. Moreover, we further explore the MA design strategy based on statistical channel state information (CSI), with the APV updated less frequently to reduce the antenna movement overhead. Simulation results demonstrate that our proposed algorithms achieve performance close to the derived bound and also outperform the benchmark schemes using dense or sparse arrays with FPAs.

Satellite edge computing promises to provide ubiquitous computation services to meet users’ increasing demands for wide range and high quality of experience (QoE) services by leveraging its global coverage capabilities. However, the highly dynamic variations of low earth orbit (LEO) satellite channels and the uneven distribution of satellite computing resources lead to the difficulty of traditional algorithms and basic reinforcement learning methods to meet the requirements of low delay, low energy consumption and few handovers. Therefore, in this paper, we formulate an optimization problem to jointly design the access selection, computation offloading, and resource allocation in LEO ubiquitous edge computing (UEC) networks to minimize the objective function weighted by delay, energy consumption and handover overhead. To solve this formulated challenging problem, we develop an alternating asynchronous dueling deep Q-network with centralized training distributed execution (Alt-ADDQN-CTDE) algorithm. The proposed method considers the multi-user competitive game and optimal allocation of computation resources, and then finds the optimal decision scheme under convergence by alternately updating the network parameters.Extensive simulations demonstrate that our proposed method is superior in performance and reduces the average optimization objective up to approximately 22.18%, compared with other benchmark methods. Therefore, our proposed method can effectively minimize the delay while minimizing the energy consumption and handover rate as much as possible.

Movable antenna (MA) technology offers promising potential to enhance wireless communication by allowing flexible antenna movement. To maximize spatial degrees of freedom (DoFs), larger movable regions are required, which may render the conventional far-field assumption for channels between transceivers invalid. In light of it, we investigate in this paper MA-enabled near-field communications, where a base station (BS) with multiple movable subarrays serves multiple users, each equipped with a fixed-position antenna (FPA). First, we extend the field response channel model for MA systems to the near-field propagation scenario. Next, we examine MA-aided multiuser communication systems under both digital and analog beamforming architectures. For digital beamforming, spatial division multiple access (SDMA) is utilized, where an upper bound on the minimum achievable rate across users is derived in closed form. A low-complexity algorithm based on zero-forcing (ZF) is then proposed to jointly optimize the antenna position vector (APV) and digital beamforming matrix (DBFM) to approach this bound. For analog beamforming, orthogonal frequency division multiple access (OFDMA) is employed, and an upper bound on the minimum achievable rate among users is also derived. An alternating optimization (AO) algorithm is proposed to iteratively optimize the APV, analog beamforming vector (ABFV), and power allocation until convergence. For both architectures, we further explore MA design strategies based on statistical channels, with the APV updated less frequently to reduce the antenna movement overhead. Simulation results demonstrate that our proposed algorithms achieve performance close to the derived bounds and also outperform the benchmark schemes using dense or sparse arrays with FPAs.

As a superior multicarrier technique utilizing chirp signals for high-mobility communications, affine frequency division multiplexing (AFDM) is envisioned to be a promising candidate for sixth-generation (6G) wireless networks. AFDM is based on the discrete affine Fourier transform (DAFT) with two adjustable parameters of the chirp signals, termed the pre-chirp and post-chirp parameters, respectively. Whilst the post-chirp parameter complies with stringent constraints to combat the time-frequency doubly selective channel fading, we show that the pre-chirp counterpart can be flexibly manipulated for an additional degree of freedom. Therefore, this paper proposes a novel AFDM scheme with the pre-chirp index modulation (PIM) philosophy (AFDM-PIM), which can implicitly convey extra information bits through dynamic pre-chirp parameter assignment, thus enhancing both spectral and energy efficiency. Specifically, we first demonstrate that the subcarrier orthogonality is still maintained by applying distinct pre-chirp parameters to various subcarriers in the AFDM modulation process. Inspired by this property, we allow each AFDM subcarrier to carry a unique pre-chirp signal according to the incoming bits. By such an arrangement, extra bits can be embedded into the index patterns of pre-chirp parameter assignment without additional energy consumption. We derive asymptotically tight upper bounds on the average bit error probability (BEP) of the proposed schemes with the maximum-likelihood detection, and validate that the proposed AFDM-PIM can achieve full diversity under doubly dispersive channels. Based on the derived result, we further propose an optimal pre-chirp alphabet design to enhance the bit error rate (BER) performance via intelligent optimization algorithms. Simulation results demonstrate that the proposed AFDM-PIM outperforms the classical benchmarks.

Integrated sensing and communication (ISAC) systems have the issue of secrecy leakage when using the ISAC waveforms for sensing, thus posing a potential risk for eavesdropping. To address this problem, we propose to employ movable antennas (MAs) and reconfigurable intelligent surface (RIS) to enhance the physical layer security (PLS) performance of ISAC systems, where an eavesdropping target potentially wiretaps the signals transmitted by the base station (BS). To evaluate the synergistic performance gain provided by MAs and RIS, we formulate an optimization problem for maximizing the sum-rate of the users by jointly optimizing the transmit/receive beamformers of the BS, the reflection coefficients of the RIS, and the positions of MAs at communication users, subject to a minimum communication rate requirement for each user, a minimum radar sensing requirement, and a maximum secrecy leakage to the eavesdropping target. To solve this non-convex problem with highly coupled variables, a two-layer penalty-based algorithm is developed by updating the penalty parameter in the outer-layer iterations to achieve a trade-off between the optimality and feasibility of the solution. In the inner-layer iterations, the auxiliary variables are first obtained with semi-closed-form solutions using Lagrange duality. Then, the receive beamformer filter at the BS is optimized by solving a Rayleigh-quotient subproblem. Subsequently, the transmit beamformer matrix is obtained by solving a convex subproblem. Finally, the majorization-minimization (MM) algorithm is employed to optimize the RIS reflection coefficients and the positions of MAs. Extensive simulation results validate the considerable benefits of the proposed MAs-aided RIS-ISAC systems in enhancing security performance compared to traditional fixed position antenna (FPA)-based systems.

The low earth orbit (LEO) satellite communication network has attracted extensive attention owing to its advantages of seamless coverage and low propagation delays, which provides a promising solution to realize massive access for Internet of Things (IoT) devices. In this paper, we study cooperative grant free random access (GF-RA) in LEO satellite communication systems. Specifically, we investigate the joint activity detection and channel estimation (JADCE) problem for multi-input multi-output (MIMO) based massive connectivity. First, we analyze the channel characteristics, and reveal the low-rank and row-sparsity properties of the channel impulse response (CIR) matrix. Accordingly, we transfer the JADCE problem into a low-rank matrix completion problem and a compressive sensing problem, which are solved by a two-stage algorithm efficiently. In the first stage, we design the principal component analysis with adaptive signal space detection (PCA-AASD) algorithm to perform low-rank matrix completion. In the second stage, we employ a sequential sparse Bayesian learning with multiple measurement vector (MMV) algorithm to perform active terminal detection and channel estimation. Finally, a majority voting scheme is utilized to estimate the active terminals by aggregating the estimation of multiple satellites. Simulation results validate that the proposed method achieves lower activity detection error probability and better channel estimation performance than other baseline methods in the literature.

The low-earth orbit (LEO) satellite network has been recognized as a promising technology to enable the ubiquitous coverage and massive connectivity for future sixth-generation (6G) mobile communications. Due to the ultra-dense constellation, efficient beam coverage and interference mitigation are crucial to LEO satellite communication systems, while the conventional directional antennas and fixed-position antenna (FPA) arrays both have limited degrees of freedom (DoFs) in beamforming to adapt to the time-varying coverage requirement of terrestrial users. To address this challenge, we propose in this paper utilizing movable antenna (MA) arrays to enhance the satellite beam coverage and interference mitigation. Specifically, given the satellite orbit and the coverage requirement within a specific time interval, the antenna position vector (APV) and antenna weight vector (AWV) of the satellite-mounted MA array are jointly optimized over time to minimize the average signal leakage power to the interference area of the satellite, subject to the constraints of the minimum beamforming gain over the coverage area, the continuous movement of MAs, and the constant modulus of AWV. The corresponding continuous-time decision process for the APV and AWV is first transformed into a more tractable discrete-time optimization problem. Then, an alternating optimization (AO)-based algorithm is developed by iteratively optimizing the APV and AWV, where the successive convex approximation (SCA) technique is utilized to obtain locally optimal solutions during the iterations. Moreover, to further reduce the antenna movement overhead, a low-complexity MA scheme is proposed by using an optimized common APV over all time slots. Simulation results validate that the proposed MA array-aided beam coverage schemes can significantly decrease the interference leakage of the satellite compared to conventional FPA-based schemes, while the low-complexity MA scheme can achieve a performance comparable to the continuous-movement scheme.

In this paper, we propose utilizing movable antenna (MA) arrays to enhance the low-earth orbit (LEO) satellite beam coverage and interference mitigation. Specifically, given the satellite orbit and the coverage requirement within a specific time interval, the antenna position vector (APV) and antenna weight vector (AWV) of the satellite-mounted MA array are jointly optimized over time to minimize the average signal leakage power to the interference area of the satellite, subject to the constraints of the minimum beamforming gain over the coverage area, the continuous movement of MAs, and the constant modulus of AWV. The corresponding continuous-time decision process for the APV and AWV is first transformed into a more tractable discrete-time optimization problem. Then, an alternating optimization (AO)-based algorithm is developed by iteratively optimizing the APV and AWV, where the successive convex approximation (SCA) technique is utilized to obtain locally optimal solutions during the iterations. Simulation results validate that the proposed MA array-aided beam coverage scheme can significantly decrease the interference leakage of the satellite compared to conventional fixed-position antenna (FPA)-based schemes.

Affine frequency division multiplexing (AFDM), tailored as a novel multicarrier technique utilizing chirp signals for high-mobility communications, exhibits marked advantages compared to traditional orthogonal frequency division multiplexing (OFDM). AFDM is based on the discrete affine Fourier transform (DAFT) with two modifiable parameters of the chirp signals, termed as the pre-chirp parameter and post-chirp parameter, respectively. These parameters can be fine-tuned to avoid overlapping channel paths with different delays or Doppler shifts, leading to performance enhancement especially for doubly dispersive channel. In this paper, we propose a novel AFDM structure with the pre-chirp index modulation (PIM) philosophy (AFDM-PIM), which can embed additional information bits into the prechirp parameter design for both spectral and energy efficiency enhancement. Specifically, we first demonstrate that the application of distinct pre-chirp parameters to various subcarriers in the AFDM modulation process maintains the orthogonality among these subcarriers. Then, different prechirp parameters are flexibly assigned to each AFDM subcarrier according to the incoming bits. By such arrangement, aside from classical phase/amplitude modulation, extra binary bits can be implicitly conveyed by the indices of selected prechirping parameters realizations without additional energy consumption. At the receiver, both a maximum likelihood (ML) detector and a reduced-complexity ML-minimum mean square error (ML-MMSE) detector are employed to recover the information bits. It has been shown via simulations that the proposed AFDM-PIM exhibits superior bit error rate (BER) performance compared to classical AFDM, OFDM and IM aided OFDM algorithms.

A reactive jamming attack, which performs spectrum jamming only during legal signal transmission based on the knowledge of user behaviors, poses a significant threat to wireless communications. In this paper, a novel deceiving approach is proposed for defending reactive jamming in unmanned aerial vehicle (UAV) assisted wireless communications. Specifically, the interaction of multiple legitimate users (LUs) and a malicious user (MU) is modelled as a Stackelberg game, where LUs are the leaders and MU is the follower. We first show that the equilibrium exists when the channel information is known. Then, we propose a multi-agent reinforcement learning (MARL) based method to address the reactive jamming. Finally, we perform simulations to demonstrate the superiority of our proposed method.

In this paper, we investigate the movable antenna (MA)-aided wideband communications employing orthogonal frequency division multiplexing (OFDM) transmissions. Under the general multi-tap field-response channel model, the wireless chan-nel variations in both space and frequency are characterized with different positions of the MAs at the transmitter (Tx) and receiver (Rx) sides. We reveal that the MA positioning can balance between the amplitude and phase over different channel taps. Then, an upper bound on the OFDM achievable rate is derived in closed form when the size of the TxlRx region for antenna movement can be arbitrarily large. Furthermore, we develop a parallel greedy ascent (PGA) algorithm to obtain locally optimal solutions to the MAs' positions for OFDM rate maximization subject to finite-size TxlRx regions. Simulation results demonstrate that the proposed algorithm closely approaches the OFDM rate upper bound with the increase of TxlRx region sizes and outperforms the conventional system with fixed-position antennas (FPAs).

This paper proposes a general channel estimation framework for movable antenna (MA) communication systems. In this framework, the channel state information between the entire transmitter (Tx) and receive (Rx) regions can be re-constructed, so as to find the optimal positions of the MAs for reaping performance gains. Specifically, the field-response channel structure is utilized to represent the channel response in terms of the angles of departure (AoDs), angles of arrival (AoAs), and complex coefficients of the multi-path components (MPCs). Then, the compressed sensing method is employed to jointly estimate the MPC information, i.e., the AoDs, AoAs, and complex coefficients of the paths, with a limited number of channel measurements. Notably, the measurement matrix under the proposed framework is fundamentally determined by the Tx-MA and Rx-MA measurement positions, which further affects the channel estimation performance. In this regard, four MA measurement position setups are proposed, and the channel estimation performance of each setup is further compared. Finally, simulation results show that the complete CSI between the entire Tx and Rx regions can be reconstructed by our proposed channel estimation framework with a high accuracy.

Unmanned aerial vehicles (UAVs)-assisted wireless communication will play an important role in the next-generation mobile communication network. However, the inherent open nature of the signal propagation environment may cause illegal eavesdropping and surveillance from adversaries. In addition, the intergroup co-channel interference among different cells further degrades the system performance. Hence, we consider a generic scenario of multiple UAV base stations (UAV-BSs) and ground users, where multiple terrestrial wardens attempt to detect the transmissions from UAV-BSs to users and a UAV-mounted jammer is employed to generate artificial noise to assist the covert communications. To ensure fairness, we formulate an optimization problem to maximize the minimum of the average rate lower bounds of all users by jointly optimizing user association, bandwidth allocation, UAV transmit power control, and UAV 3-D deployment, subject to the constraints of the detection error probability of each warden. To solve this mixed-integer nonconvex problem, we propose a suboptimal algorithm by applying block coordinate descent (BCD) method to solve three subproblems iteratively. Specifically, in each iteration, the subproblem of user association and bandwidth allocation is solved by a customized genetic algorithm (GA) first, where a closed-form expression for bandwidth allocation is obtained. Second, the subproblem of UAV transmit power control is solved by using successive convex approximation (SCA) techniques. Finally, suboptimal 3-D positions of the UAVs are obtained through particle swarm optimization (PSO)-based algorithm. Extensive simulation results demonstrate the effectiveness and superiority of our proposed algorithm compared to benchmark schemes in terms of improving the minimum of the average rate lower bounds of all users.

This paper considers an unmanned aerial vehicle (UAV)-assisted communication system for data collection in urban areas, where multiple UAVs are dispatched to harvest data from multiple ground user equipments (UEs). We adopt a blockage-aware channel model to characterize the practical blockage effects for air-to-ground (A2G) links caused by buildings. Aiming to minimize the mission completion time while satisfying the data collection requirements of UEs, we formulate a problem by jointly optimizing the UAV three-dimensional (3-D) trajectory and resource allocation, including the UE scheduling and subcarrier assignment. To solve the formulated non-convex combinatorial programming problem, we propose a suboptimal algorithm that solves two subproblems iteratively. Specifically, in each iteration, the trajectory design subproblem jointly optimizes the UAVs’ waypoints and time slot length to decrease the mission completion time, which is solved by employing block successive convex approximation (BSCA). For the resource allocation subproblem, we develop a heuristic algorithm for UE scheduling and subcarrier assignment to increase the collected data volume for a given time duration. Simulation results demonstrate the superior performance of the proposed algorithm in terms of mission completion time compared to benchmark schemes.

Reconfigurable intelligent surface (RIS) has drawn extensive attentions as a promising alternative for classical phased-array antennas at the massive multiple-input multiple-output (MIMO) transmitter, leading to cost-effective data transmission that is especially desirable at terahertz (THz) frequencies. In this article, we consider a multi-user MIMO (MU-MIMO) system equipped with a RIS-assisted transmitter: A RIS array is illuminated by unmodulated THz carriers through a feeding antenna, which is equally divided into a number of subarrays (SAs). Each activated SA serves one unique user equipment (UE) via directional beams. Then we develop a spectrum- and energy- efficient MU-MIMO scheme for THz communications by performing index modulation (IM) on the array-of-SA structure of the RIS, abbreviated as RIS-SA-IM. Specifically, the indices of the RIS-SAs allocated to different UEs, defined as SA allocation pattern (SAPs), are flexibly controlled by the information bits at each symbol period. Hence, aside from classical amplitude/phase modulation, additional energy-free bits (referred to asindex bits) can be conveyed implicitly by the chosen SAP at the transmitter, thus attaining superior enhancement on spectrum- and energy-efficiencies. Furthermore, we design a distributed mapping rule between the SAPs and index bits, which guarantees that the index information for each UE is exclusively determined by the index of its allocated RIS-SA. Hence, the proposed mapping rule can enable localized demodulation of the index bits without inter-UE data exchange. In particular, a general form of the distributed mapping rule is provided based on the binary-tree structure, which can be extended to arbitrary number of UEs and index bits. Additionally, the error performance of the proposed RIS-SA-IM is evaluated through pairwise error probability (PEP) calculations. Theoretical and simulation results demonstrate the superiority of our proposed RIS-SA-IM over its classical non-IM-aided counterpart.

Movable antenna (MA) is a new technology with great potential to improve communication performance by enabling local movement of antennas for pursuing better channel conditions. In particular, the acquisition of complete channel state information (CSI) between the transmitter (Tx) and receiver (Rx) regions is an essential problem for MA systems to reap performance gains. In this paper, we propose a general channel estimation framework for MA systems by exploiting the multi-path field response channel structure. Specifically, the angles of departure (AoDs), angles of arrival (AoAs), and complex coefficients of the multi-path components (MPCs) are jointly estimated by employing the compressed sensing method, based on multiple channel measurements at designated positions of the Tx-MA and Rx-MA. Under this framework, the Tx-MA and Rx-MA measurement positions fundamentally determine the measurement matrix for compressed sensing, of which the mutual coherence is analyzed from the perspective of Fourier transform. Moreover, two criteria for MA measurement positions are provided to guarantee the successful recovery of MPCs. Then, we propose several MA measurement position setups and compare their performance. Finally, comprehensive simulation results show that the proposed framework is able to estimate the complete CSI between the Tx and Rx regions with a high accuracy.

Movable antenna (MA) is an innovative technology that facilitates the repositioning of antennas within the transmitter/receiver area to enhance channel conditions and communication performance. This paper proposes a new base station (BS) architecture employing multiple MAs for improving the multiuser network performance. First, the uplink multiple access channel (MAC) is modeled to capture the characteristics of the variation of wireless channels caused by the movement of MAs at the BS. Subsequently, we propose to maximize the minimum achievable rate among multiple users for MA-aided multiuser uplink transmissions by joint optimization of the MAs’ positions, their receive combining at the BS, and the transmit power of users, subject to the MAs’ positions-related constraints and the maximum transmit power of each user. To tackle this highly non-convex max-min fairness problem, we propose a two-loop iterative algorithm based on the particle swarm optimization (PSO). Specifically, the outer-loop updates the positions of a set of particles, where each particle’s position corresponds to one realization of the antenna position vector (APV) of all MAs. The inner-loop conducts the fitness evaluation for each particle, determining the max-min achievable rate for multiple users based on the current APV. Therein, for given APV, the receive combining matrix at the BS and the transmit power for each user are optimized using the block coordinate descent (BCD) technique. To further reduce the computational complexity, we develop an alternating optimization (AO)-based algorithm via iteratively updating the APV, combining matrix, and transmit power. Finally, extensive simulations demonstrate that the antenna position optimization for MAs-aided BSs can significantly improve the rate performance as compared to conventional BSs with fixed-position antennas (FPAs).

In this paper, we propose to deploy multiple unmanned aerial vehicle (UAV) mounted base stations to serve ground users in outdoor environments with obstacles. In particular, the geographic information is employed to capture the blockage effects for air-to-ground (A2G) links caused by buildings, and a realistic blockage-aware A2G channel model is proposed to characterize the continuous variation of the channels at different locations. Based on the proposed channel model, we formulate the joint optimization problem of UAV three-dimensional (3-D) positioning and resource allocation, by power allocation, user association, and subcarrier allocation, to maximize the minimum achievable rate among users. To solve this non-convex combinatorial programming problem, we introduce a penalty term to relax it and develop a suboptimal solution via a penalty-based double-loop iterative optimization framework. The inner loop solves the penalized problem by employing the block successive convex approximation (BSCA) technique, where the UAV positioning and resource allocation are alternately optimized in each iteration. The outer loop aims to obtain proper penalty multipliers to ensure the solution of the penalized problem converges to that of the original problem. Simulation results demonstrate the superiority of the proposed algorithm over other benchmark schemes in terms of the minimum achievable rate.

Movable antenna (MA) has emerged as a promising technology to enhance wireless communication performance by enabling the local movement of antennas at the transmitter (Tx) and/or receiver (Rx) for achieving more favorable channel conditions. As the existing studies on MA-aided wireless communications have mainly considered narrow-band transmission in flat fading channels, we investigate in this paper the MA-aided wideband communications employing orthogonal frequency division multiplexing (OFDM) in frequency-selective fading channels. Under the general multi-tap field-response channel model, the wireless channel variations in both space and frequency are characterized with different positions of the MAs. Unlike the narrow-band transmission where the optimal MA position at the Tx/Rx simply maximizes the single-tap channel amplitude, the MA position in the wideband case needs to balance the amplitudes and phases over multiple channel taps in order to maximize the OFDM transmission rate over multiple frequency subcarriers. First, we derive an upper bound on the OFDM achievable rate in closed form when the size of the Tx/Rx region for antenna movement is arbitrarily large. Next, we develop a parallel greedy ascent (PGA) algorithm to obtain locally optimal solutions to the MAs’ positions for OFDM rate maximization subject to finite-size Tx/Rx regions. To reduce computational complexity, a simplified PGA algorithm is also provided to optimize the MAs’ positions more efficiently. Simulation results demonstrate that the proposed PGA algorithms can approach the OFDM rate upper bound closely with the increase of Tx/Rx region sizes and outperform conventional systems with fixed-position antennas (FPAs) under the wideband channel setup.

A reactive jamming attack, which performs spec-trum jamming only during legal signal transmission based on the knowledge of sensor behaviors, poses a significant threat to wireless sensing networks (WSNs). In this paper, a novel deceiving approach is proposed for defending reactive jamming in dynamic WSNs. Specifically, when the maximum transmission power is given, we first formulate the anti-jamming process as an optimization problem to maximize the average received power while eliminating the effects of the jamming attack. Then the interaction between reactive jamming and legitimate sensors is modeled with the Markov decision process (MDP). Finally, a deep Q network (DQN) based jamming deceiving method is proposed to solve the formulated optimization problem. Simulation results show that the proposed anti-jamming scheme can converge quickly and is superior to the classical counterparts in terms of the mean of received signal power.

The programmable metasurface (MTS), which can enhance the signal quality by flexibly manipulating the electromagnetic (EM) responses of reflected waves, has emerged as a promising technology for multiple-input multiple-output (MIMO) transmission due to its superior energy efficiency and cost-effective hardware implementations. In this paper, we consider a multi-user MIMO (MU-MIMO) system equipped with a MTS-based multi-feed transmitter, where an array of metallic elements is split into several subarrays (SAs), each irradiated by a unique radio-frequency (RF) feed. Additionally, each user is allocated with one unique RF-feed-SA pair for data services. Then we develop a novel index modulation (IM) scheme based on this array-of-SA (AoSA) structure of the MTS, named as MTS-SA -IM. More specifically, the allocation strategy of the SAs to different users, termed as SA allocation pattern (SAP), is flexibly controlled by the information bits. In other words, aside from classical amplitude/phase modulation bits, additional binary bits, referred to as index bits, can be embedded into the indices of the allocated SAs without extra power consumption, leading to enhancement of both spectrum and energy efficiencies. Furthermore, we propose a distributed mapping rule design between the index bits and SAPs, which guarantees that the index bits at each user are exclusively dependent on the index of its own allocated SA. By constructing a binary-tree structure, a general form of the proposed distributed mapping rule is generated recursively, which can be extended to the cases of arbitrary number of users. Simulation results demonstrate that the proposed MTS is capable of achieving desirable performance gain over its classical counterpart.

This paper proposes to deploy multiple unmanned aerial vehicle (UAV) mounted base stations to serve ground users collaboratively in outdoor environments with obstacles. In particular, the geographic information is employed to capture the blockage effects for air-to-ground (A2G) links caused by buildings, and a realistic blockage-aware A2G channel model is proposed to characterize the continuous variation of the channel at different locations. Based on the proposed channel model, we formulate a joint design problem of UAV three-dimensional (3-D) positioning and resource allocation, including the user association and subcarrier allocation, to maximize the minimum achievable rate among users. We propose a suboptimal iterative algorithm to solve the mixed-integer non-convex optimization problem. Specifically, the UAV positioning and resource allocation are alternately optimized in each iteration by employing the successive convex approximation (SCA) and matching theory, respectively. Simulation results reveal that the proposed algorithm outperforms several benchmark schemes in terms of the minimum achievable rate.

Reconfigurable intelligent surface (RIS) can significantly enhance the service coverage of Tera-Hertz massive multiple-input multiple-output (MIMO) communication systems. However, obtaining accurate high-dimensional channel state information (CSI) with limited pilot and feedback signaling overhead is challenging, severely degrading the performance of conventional spatial division multiple access. To improve the robustness against CSI imperfection, this paper proposes a deep learning (DL)-based rate-splitting multiple access (RSMA) scheme for RIS-aided Tera-Hertz multi-user MIMO systems. Specifically, we first propose a hybrid data-model driven DL-based RSMA precoding scheme, including the passive precoding at the RIS as well as the analog active precoding and the RSMA digital active precoding at the base station (BS). To realize the passive precoding at the RIS, we propose a Transformer-based data-driven RIS reflecting network (RRN). As for the analog active precoding at the BS, we propose a match-filter based analog precoding scheme considering that the BS and RIS adopt the LoS-MIMO antenna array architecture. As for the RSMA digital active precoding at the BS, we propose a low-complexity approximate weighted minimum mean square error (AWMMSE) digital precoding scheme, and further design a model-driven deep unfolding active precoding network (DFAPN) by combining the proposed AWMMSE scheme with DL. Then, to acquire accurate CSI at the BS for the investigated RSMA precoding scheme to achieve higher spectral efficiency, we propose a CSI acquisition network (CAN) with low pilot and feedback signaling overhead. The proposed DL-based RSMA scheme for RIS-aided Tera-Hertz multi-user MIMO systems can exploit the advantages of RSMA and DL to improve the robustness against CSI imperfection, thus achieving higher spectral efficiency with lower signaling overhead.

We deploy an unmanned aerial vehicle (UAV) equipped with a large-scale uniform planar array (UPA) to serve multiple ground users in millimeter-wave band. Particularly, the practical UAV jitter is carefully considered, which may affect the beam gains and impact the communication quality. To provide a stable service, we first model the attitude change of the UPA caused by UAV jitter. Then, an optimization problem is formulated to maximize the minimum achievable rate of the users by optimizing the position and robust hybrid beamforming of the UAV. To solve the non-convex problem with highly coupled variables, a two-stage optimization strategy is developed. The first stage aims to decouple the beamforming from the original problem and design the UAV deployment under the assumption of an ideal beam pattern. The second stage aims to design robust hybrid beamforming with the obtained UAV position. Specifically, we first design analog beamforming for wide beams to cover the potential jitter angle range for each user via a chirp sequence-inspired method. Then, with equivalent channel estimation, we design digital beamforming by combining zero-forcing and water-filling power allocation algorithms. Extensive simulation results show the performance superiority of the proposed solution compared to the benchmark algorithms.

Due to the advantage of achieving scalable connectivity and low-latency communications, air-ground integrated mesh networks (AGIMNs) will play an important role in the next generation wireless communication systems. However, due to the heterogeneous character of AGIMNs, it is challenging to manage the network and optimize the communication resources for improving the end-to-end (E2E) performance. Therefore, in this paper, we study a joint routing and time-frequency resource scheduling problem aiming at minimizing the total weighted E2E delay for heterogeneous AGIMNs. To capture the features of this complex system, we mathematically model the network constraints and formulate an optimization problem. To solve the original nonconvex problem, a suboptimal solution is proposed. First, we propose an optimal minimum-weight routing method, in which the hop count, conflict delay, and contention delay are taken into consideration. Then, we transform the time-frequency resource scheduling subproblem into a series of tractable problems for maximizing the number of active links through channel assignment in successive time slots. Finally, the successive convex approximation (SCA) technique is utilized to solve the channel assignment problem per time slot. Extensive simulation results show the performance superiority of the proposed solution compared to the benchmarks in terms of the total E2E delay.

This paper investigates the massive connectivity of low Earth orbit (LEO) satellite-based Internet-of-Things (IoT) for seamless global coverage. We propose to integrate the grant-free non-orthogonal multiple access (GF-NOMA) paradigm with the emerging orthogonal time frequency space (OTFS) modulation to accommodate the massive IoT access, and mitigate the long round-trip latency and severe Doppler effect of terrestrial–satellite links (TSLs). On this basis, we put forward a two-stage successive active terminal identification (ATI) and channel estimation (CE) scheme as well as a low-complexity multi-user signal detection (SD) method. Specifically, at the first stage, the proposed training sequence aided OTFS (TS-OTFS) data frame structure facilitates the joint ATI and coarse CE, whereby both the traffic sparsity of terrestrial IoT terminals and the sparse channel impulse response are leveraged for enhanced performance. Moreover, based on the single Doppler shift property for each TSL and sparsity of delay-Doppler domain channel, we develop a parametric approach to further refine the CE performance. Finally, a least square based parallel time domain SD method is developed to detect the OTFS signals with relatively low complexity. Simulation results demonstrate the superiority of the proposed methods over the state-of-the-art solutions in terms of ATI, CE, and SD performance confronted with the long round-trip latency and severe Doppler effect.

With the rapid development of electronic and information technologies, the Internet of Everything (IoE) has become one of the trendiest topics in both academia and industry. Therein, many types of space/air/ground platforms need to be connected to networks for breaking down the isolation of information islands and providing various services. Space/air/ground platforms, such as satellites, unmanned aerial vehicles (UAVs), airships, balloons, terrestrial vehicles, and high-speed trains (HSTs) have emerged for accomplishing various complex tasks. Wireless communication is one of the most important technologies to support the real-time delivery of control commands and mission-related data. On the other hand, the space-air-ground integrated network has become a promising paradigm for the six-generation (6G) mobile communication network, where the aerospace and terrestrial vehicles may need to connect to existing mobile cellular networks or act as base stations (BSs) or relays to assist terrestrial wireless communications. To meet the ever-increasing demands of high capacity, wide coverage, low latency, and strong robustness for communications, it is promising to adopt large-scale antenna arrays at the transceivers to obtain considerable array gains and improve the channel quality. Antenna array-enabled beamforming technologies can facilitate spectrum reuse, interference mitigation, coverage enhancement, and physical-layer security. Antenna arrays can also be used to promote the sensing capability of space/air/ground networks, where the sensing information may be carefully processed to assist communications. However, enabling antenna array for space/air/ground communication networks poses specific, distinctive, and tricky challenges in antenna array design, physical layer, multiple access control layer, and network layer. As a result, numerous new research issues require to be addressed, which cover a wide range of disciplines including communication theory, network theory, antenna theory, signal processing, protocol design, resource allocation, optimization, hardware implementation, and experimentation.

Antenna arrays have a long history of more than 100 years and have evolved closely with the development of electronic and information technologies, playing an indispensable role in wireless communications and radar. With the rapid development of electronic and information technologies, the demand for all-time, all-domain, and full-space network services has exploded, and new communication requirements have been put forward on various space/air/ground platforms. To meet the ever increasing requirements of the future sixth generation (6G) wireless communications, such as high capacity, wide coverage, low latency, and strong robustness, it is promising to employ different types of antenna arrays (e.g., phased arrays, digital arrays, and reconfigurable intelligent surfaces, etc.) with various beamforming technologies (e.g., analog beamforming, digital beamforming, hybrid beamforming, and passive beamforming, etc.) in space/air/ground communication networks, bringing in advantages such as considerable antenna gains, multiplexing gains, and diversity gains. However, enabling antenna array for space/air/ground communication networks poses specific, distinctive and tricky challenges, which has aroused extensive research attention. This paper aims to overview the field of antenna array enabled space/air/ground communications and networking. The technical potentials and challenges of antenna array enabled space/air/ground communications and networking are presented first. Subsequently, the antenna array structures and designs are discussed. We then discuss various emerging technologies facilitated by antenna arrays to meet the new communication requirements of space/air/ground communication systems. Enabled by these emerging technologies, the distinct characteristics, challenges, and solutions for space communications, airborne communications, and ground communications are reviewed. Finally, we present promising directions for future research in antenna array enabled space/air/ground communications and networking.

In this paper, we study to employ geographic information to address the blockage problem of air-to-ground links between UAV and terrestrial nodes. In particular, a UAV relay is deployed to establish communication links from a ground base station to multiple ground users. To improve communication capacity, we first model the blockage effect caused by buildings according to the three-dimensional (3-D) geographic information. Then, an optimization problem is formulated to maximize the minimum capacity among users by jointly optimizing the 3-D position and power allocation of the UAV relay, under the constraints of link capacity, maximum transmit power, and blockage. To solve this complex non-convex problem, a two-loop optimization framework is developed based on Lagrangian relaxation. The outer-loop aims to obtain proper Lagrangian multipliers to ensure the solution of the Lagrangian problem converge to the tightest upper bound on the original problem. The inner-loop solves the Lagrangian problem by applying the block coordinate descent (BCD) and successive convex approximation (SCA) techniques, where UAV 3-D positioning and power allocation are alternately optimized in each iteration. Simulation results confirm that the proposed solution significantly outperforms three benchmark schemes and achieves a performance close to the upper bound on the UAV relay system.

In this paper, we propose to employ multiple unmanned aerial vehicle (UAV) base stations to serve ground users in the millimeter-wave (mmWave) frequency bands. To improve the spectrum efficiency, uniform planar arrays are equipped at the UAVs and users for compensation of the high path loss and for mitigation of interference. We formulate a problem to jointly optimize the UAV positioning, user clustering, and hybrid analog-digital beamforming (BF) for the maximization of user achievable sum rate (ASR), subject to a minimum rate constraint for each user. Since the problem is highly non-convex and involves high-dimensional variable matrices and combinatorial programming variables, we develop a suboptimal solution via alternating optimization, successive convex optimization, and combinatorial optimization. First, we design the UAV positioning and user clustering under the assumption of ideal beam patterns, which significantly decouples the UAV positioning and directional BF. Then, the transmit and receive BF variables are successively optimized to approach the ideal beam patterns. Our simulation results verify the convergence and superiority of the proposed algorithm. Significant performance gains can be obtained compared to some benchmark schemes in terms of the ASR, and the proposed hybrid BF solution closely approaches a performance bound given by fully-digital BF.

As the commercial launch of the fifth-generation (5G) wireless communications gets near, the trend from the Internet of Things (IoT) to the Internet of Everything (IoE) is emerging. Due to the advantages of the high mobility, high Line-of-Sight (LoS) probability and low labor cost, unmanned aerial vehicles (UAVs) may play an important role in the future IoT communication networks, e.g., data collection in remote areas. In this article, we study the 3-D placement and resource allocation of multiple UAV-mounted base stations (BSs) in an uplink IoT network, where the balanced task for the UAV-BSs, the limited channel resource, and the signal interference are taken into consideration. In the considered system, the total transmission power of IoT devices is minimized, subject to a signal-to-interference-and-noise ratio (SINR) threshold for each device. First, aiming to balance the task of each UAV, we propose a clustering algorithm based on an improved$K$-means method to divide IoT devices into several groups so that the number of devices in each group is roughly the same. Then, based on matching theory, a modified-Hungarian-based dynamic many–many matching (HD4M) algorithm is designed for assigning subchannels to IoT devices, which can efficiently mitigate the interference. Finally, we jointly optimize the transmission power of IoT devices and the altitudes of UAVs via an alternating iterative method. The simulation results show that the total transmission power decreases significantly after applying the proposed algorithms.

Recently, unmanned aerial vehicles (UAVs) have attracted lots of attention because of their high mobility and low cost. This article investigates a communication system assisted by multiple UAV-mounted base stations (BSs), aiming to minimize the number of required UAVs and to improve the coverage rate by optimizing the three-dimensional (3D) positions of UAVs, user clustering, and frequency band allocation. Compared with the existing works, the constraints of the required quality of service (QoS) and the service ability of each UAV are considered, which makes the problem more challenging. A three-step method is developed to solve the formulated mixed-integer programming problem. First, to ensure that each UAV can serve more number of users, the maximum service radius of UAVs is derived according to the required minimum power of the received signals for the users. Second, an algorithm based on artificial bee colony (ABC) algorithm is proposed to minimize the number of required UAVs. Third, the 3D position and the frequency band of each UAV are designed to increase the power of the target signals and to reduce the interference. Finally, simulation results are presented to demonstrate the superiority of the proposed solution for UAV-assisted communication systems.

In this paper, we investigate millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) networks with multiple unmanned aerial vehicle (UAV) mounted base stations (BSs). Uniform planar arrays are equipped at the UAV-BSs to perform hybrid analog-digital beamforming (BF) for compensation of the high path loss of mmWave channels and for mitigation of intra-cell and/or inter-cell interference. We jointly optimize the UAV-BS positioning, user assignment, and hybrid BF for maximization of the achievable sum rate (ASR) of the users, subject to a minimum rate constraint for each user. A sub-optimal solution for the resulting high-dimensional and non-convex problem is developed by exploiting alternating optimization, successive convex optimization, and combinatorial optimization. Our simulation results verify the convergence of the proposed algorithm and demonstrate significant performance gains compared to two benchmark schemes in terms of the ASR.

This paper proposed a novel electrocardiogram (ECG) automatic diagnose system for health assistance and rescue related with cardiovascular diseases. This system consists of three parts: 1) Data acquisition subsystem, this subsystem acquires ECG data from wearable devices on users' body and transmit them to the cloud server. 2) Deep learning analysis subsystem, with the help of convolutional neural network, the important feature lied inside ECG signal can be extract for abnormal heart condition detection. Hierarchical residual modules provide the network the ability to see seconds of signal and make a decision through the combination of features. Meanwhile, the global max pooling layer on top of the network enables it to capture the most important feature across the whole ECG signal with periodicity. This subsystem is a crucial part for cardiac status based health caring. 3) Back-stage management subsystem, methodical data storage and management were conducted in this subsystem, which also provides the users an interface to access their healthy data and body status. Assembling these three parts of system, real-time ECG diagnose for people in need and timely medical rescue can be implemented.

Unmanned aerial vehicle (UAV) with flexible mobility and low cost has been a promising technology for wireless communication. Thus, it can be used for wireless data collection in Internet of Things (IoT). In this article, we consider millimeter-wave (mmWave) communication on a UAV platform, where the UAV base station (UAV-BS) serves multiple ground users, which generate big sensor data. Both the deployment of the UAV-BS and the beamforming design have essential impact on the throughput of the system. Thus, we formulate a problem to maximize the achievable sum rate of all the users, subject to a minimum rate constraint for each user, a position constraint of the UAV-BS, and a constant-modulus (CM) constraint for the beamforming vector. We solve the nonconvex problem with two steps. First, by introducing the approximate beam pattern, we solve the deployment and beam gain allocation subproblem. Then, we utilize the artificial bee colony (ABC) algorithm to solve the beamforming subproblem. For the global optimization problem, we find the near-optimal position of the UAV-BS and the beamforming vector to steer toward each user, subject to an analog beamforming structure. The simulation results demonstrate that the proposed solution can achieve a more superior performance than the present random steering beamforming strategy in terms of achievable sum rate.

In this paper, a full-duplex unmanned aerial vehicle (FD-UAV) relay is employed to increase the communication capacity of millimeter-wave (mmWave) networks. Large antenna arrays are equipped at the source node (SN), destination node (DN), and FD-UAV relay to overcome the high path loss of mmWave channels and to help mitigate the self-interference at the FD-UAV relay. Specifically, we formulate a problem for maximization of the achievable rate from the SN to the DN, where the UAV position, analog beamforming, and power control are jointly optimized. Since the problem is highly non-convex and involves high-dimensional, highly coupled variable vectors, we first obtain the conditional optimal position of the FD-UAV relay for maximization of an approximate upper bound on the achievable rate in closed form, under the assumption of a line-of-sight (LoS) environment and ideal beamforming. Then, the UAV is deployed to the position which is closest to the conditional optimal position and yields LoS paths for both air-to-ground links. Subsequently, we propose an alternating interference suppression (AIS) algorithm for the joint design of the beamforming vectors and the power control variables. In each iteration, the beamforming vectors are optimized for maximization of the beamforming gains of the target signals and the successive reduction of the interference, where the optimal power control variables are obtained in closed form. Our simulation results confirm the superiority of the proposed positioning, beamforming, and power control method compared to three benchmark schemes. Furthermore, our results show that the proposed solution closely approaches a performance upper bound for mmWave FD-UAV systems.

In this paper, we investigate the combination of non-orthogonal multiple access and millimeter-wave communications (mmWave-NOMA). A downlink cellular system is considered, where an analog phased array is equipped at both the base station and users. A joint Tx-Rx beamforming and power allocation problem is formulated to maximize the achievable sum rate (ASR) subject to a minimum rate constraint for each user. As the problem is non-convex, we propose a sub-optimal solution with three stages. In the first stage, the optimal power allocation with a closed form is obtained for an arbitrary fixed Tx-Rx beamforming. In the second stage, the optimal Rx beamforming with a closed form is designed for an arbitrary fixed Tx beamforming. In the third stage, the original joint Tx-Rx beamforming and power allocation problem is reduced to a Tx beamforming problem by using the previous results, and a boundary-compressed particle swarm optimization (BC-PSO) algorithm is proposed to obtain a sub-optimal solution. Extensive performance evaluations are conducted to verify the rational of the proposed solution, and the results show that the proposed sub-optimal solution can achieve a significantly better performance in terms of ASR compared with those of the state-of-the-art schemes and the conventional mmWave orthogonal multiple access (mmWave-OMA) system.

The integration of non-orthogonal multiple access in millimeter-Wave communications (mm Wave-NOMA) can significantly improve the spectrum efficiency and increase the number of users in the fifth-generation (5G) mobile communication and beyond. In this paper, we consider a downlink mm Wave-NOMA cellular system, where the base station is mounted with an analog beamforming phased array, and multiple users are served in the same time-frequency resource block. To guarantee user fairness, we formulate joint beamforming and power allocation problem to maximize the minimal achievable rate among the users, i.e., we adopt the max–min fairness. As the problem is difficult to solve due to the non-convex formulation and high dimension of the optimization variables, we propose a sub-optimal solution, which makes use of the spatial sparsity in the angle domain of the mm Wave channel. In the solution, the closed-form optimal power allocation is obtained first, which reduces the joint optimization problem into an equivalent beamforming problem. Then, an appropriate beamforming vector is designed. The simulation results show that the proposed solution can achieve a near-upper-bound performance in terms of achievable rate, which is significantly better than that of the conventional mm Wave orthogonal multiple access (mm Wave-OMA) system.

This paper investigates the application of non-orthogonal multiple access in millimeter-Wave communications (mmWave-NOMA). Particularly, we consider downlink transmission with a hybrid beamforming structure. A user grouping algorithm is first proposed according to the channel correlations of the users. Whereafter, a joint hybrid beamforming and power allocation problem is formulated to maximize the achievable sum rate, subject to a minimum rate constraint for each user. To solve this non-convex problem with high-dimensional variables, we first obtain the solution of power allocation under arbitrary fixed hybrid beamforming, which is divided into intra-group power allocation and inter-group power allocation. Then, given arbitrary fixed analog beamforming, we utilize the approximate zero-forcing method to design the digital beamforming to minimize the inter-group interference. Finally, the analog beamforming problem with the constant-modulus constraint is solved with a proposed boundary-compressed particle swarm optimization algorithm. The simulation results show that the proposed joint approach, including user grouping, hybrid beamforming and power allocation, outperforms the state-of-the-art schemes and the conventional mmWave orthogonal multiple access system in terms of achievable sum rate, and energy efficiency.

This paper is concerned with path planning for unmanned aerial vehicles (UAVs) flying through low altitude urban environment. Although many different path planning algorithms have been proposed to find optimal or near-optimal collision-free paths for UAVs, most of them either do not consider dynamic obstacle avoidance or do not incorporate multiple objectives. In this paper, we propose a multiobjective path planning (MOPP) framework to explore a suitable path for a UAV operating in a dynamic urban environment, where safety level is considered in the proposed framework to guarantee the safety of UAV in addition to travel time. To this aim, two types of safety index maps (SIMs) are developed first to capture static obstacles in the geography map and unexpected obstacles that are unavailable in the geography map. Then an MOPP method is proposed by jointly using offline and online search, where the offline search is based on the static SIM and helps shorten the travel time and avoid static obstacles, while the online search is based on the dynamic SIM of unexpected obstacles and helps bypass unexpected obstacles quickly. Extensive experimental results verify the effectiveness of the proposed framework under the dynamic urban environment.

In this paper, we explore non-orthogonal multiple access (NOMA) in millimeter-wave (mm-wave) communications (mm-wave-NOMA). In particular, we consider a typical problem, i.e., maximization of the sum rate of a 2-user mm-wave-NOMA system. In this problem, we need to find the beamforming vector to steer towards the two users simultaneously subject to an analog beamforming structure, while allocating appropriate power to them. As the problem is non-convex and may not be converted to a convex problem with simple manipulations, we propose a suboptimal solution to this problem. The basic idea is to decompose the original joint beamforming and power allocation problem into two sub-problems which are relatively easy to solve: one is a power and beam gain allocation problem, and the other is a beamforming problem under a constant-modulus constraint. Extension of the proposed solution from 2-user mm-wave-NOMA to more-user mm-wave-NOMA is also discussed. Extensive performance evaluations are conducted to verify the rational of the proposed solution, and the results also show that the proposed sub-optimal solution achieves close-to-bound sum-rate performance, which is significantly better than that of time-division multiple access.

In this paper, we investigate the combination of two key enabling technologies for the fifth generation wireless mobile communication, namely millimeter-wave (mm-wave) communications and non-orthogonal multiple access (NOMA). In particular, we consider a typical two-user uplink mm-wave-NOMA system, where the base station equips an analog beamforming structure with a single radio-frequency chain and serves two NOMA users. An optimization problem is formulated to maximize the achievable sum rate of the two users while ensuring a minimal rate constraint for each user. The problem turns to be a joint power control and beamforming problem, i.e., we need to find the beamforming vectors to steer to the two users simultaneously subject to an analog beamforming structure, and meanwhile control appropriate power on them. As direct search for the optimal solution of the non-convex problem is too complicated, we propose decomposing the original problem into two sub-problems that are relatively easy to solve: one is a power control and beam gain allocation problem, and the other is an analog beamforming problem under a constant-modulus constraint. The rationale of the proposed solution is verified by extensive simulations, and the performance evaluation results show that the proposed sub-optimal solution achieves a close-to-bound uplink sum-rate performance.

This paper is concerned with the design of routing protocol capable of congestion mitigation for drone-cells communication networks where drone-cells remain stationary in the sky as relays. All of the (distance or hop-count based) existing routing protocols can perform well when the network is lightly loaded. Once the network is heavily loaded, a large number of packets might be backlogged in queues of network nodes since these protocols can not be aware of the network congestion condition. In this paper, we propose a queuing delay and transmission delay based routing protocol (QDTD) to relieve the network congestion caused by heavily loaded traffic. First, QDTD designs a novel ForWard-Back (FWB) queue architecture that significantly reduces the number of queues maintained at each network node. Second, both queuing delay and transmission delay are leveraged as a routing metric to enhance the performance of QDTD. Experimental results show that QDTD can effectively relieve the network congestion and reduce the overall network delay and achieve high throughput.

This paper is concerned with providing radio access network (RAN) elements (supply) for flash crowd traffic demands. The concept of multi-tier cells [heterogeneous networks (HetNets)] has been introduced in 5G network proposals to alleviate the erratic supply–demand mismatch. However, since the locations of the RAN elements are determined mainly based on the long-term traffic behavior in 5G networks, even the HetNet architecture will have difficulty in coping up with the cell overload induced by flash crowd traffic. In this paper, we propose a proactive drone-cell deployment framework to alleviate overload conditions caused by flash crowd traffic in 5G networks. First, a hybrid distribution and three kinds of flash crowd traffic are developed in this framework. Second, we propose a prediction scheme and an operation control scheme to solve the deployment problem of drone cells according to the information collected from the sensor network. Third, the software-defined networking technology is employed to seamlessly integrate and disintegrate drone cells by reconfiguring the network. Our experimental results have shown that the proposed framework can effectively address the overload caused by flash crowd traffic.

In this paper, we study hierarchical codebook design for channel estimation in millimeter-wave (mmWave) communications with a hybrid precoding structure. Due to the limited saturation power of the mmWave power amplifier, we consider the per-antenna power constraint (PAPC). We first propose a metric, termed generalized detection probability (GDP), to evaluate the quality of an arbitrary codeword. This metric not only enables an optimization approach for mmWave codebook design, but also can be used to compare the performance of two different codewords/codebooks. To the best of our knowledge, GDP is the first such metric, particularly for mmWave codebook design. We then propose a heuristic approach to design a hierarchical codebook exploiting beam widening with the multi-RF-chain sub-array (BMW-MS) technique. To obtain crucial parameters of BMW-MS, we provide two solutions, namely, a low-complexity search (LCS) solution to optimize the GDP metric and a closed-form (CF) solution to pursue a flat beam pattern. Performance comparisons show that BMW-MS/LCS and BMW-MS/CF achieve very close performances, and they outperform the existing alternatives under the PAPC.

In millimeter-wave (mmWave) MIMO systems, while a hybrid digital/analog precoding structure offers the potential to increase the achievable rate, it also faces the challenge to lower channel estimation time due to a large number of antennas at both Tx/Rx sides. In this paper, the channel estimation is realized via multi-beam search, and a new hierarchical multi-beam search scheme, which uses a pre- designed analog hierarchical codebook, is proposed. Performance evaluations show that, compared with a state-of- the-art scheme, the proposed scheme not only achieves a higher success rate to acquired multiple beams under typical system settings, but also greatly reduces the channel estimation time.

In this study, the authors propose a lattice reduction (LR)‐based doubly iterative receiver for joint channel estimation and detection in multiple‐input–multiple‐output (MIMO) bit‐interleaved coded modulation systems. For the inner iteration loop of the receiver, LR‐based randomised sampling detection is employed to enjoy the tradeoff between performance and complexity while for the outer iteration loop, the expectation–maximisation (EM)‐based iterative channel estimation using sampling results is proposed to achieve the maximum likelihood channel estimation performance. Besides, a modified computational efficient EM‐based channel estimation approach is also derived to reduce the complexity further. Simulation results demonstrate that the proposed doubly MIMO iterative receiver can have comparable bit‐error rate performance with a reasonable computational complexity.

In millimeter-wave communication, large antenna arrays are required to achieve high power gain by steering toward each other with narrow beams, which poses the problem to efficiently search the best beam direction in the angle domain at both Tx and Rx sides. As the exhaustive search is time consuming, hierarchical search has been widely accepted to reduce the complexity, and its performance is highly dependent on the codebook design. In this paper, we propose two basic criteria for the hierarchical codebook design, and devise an efficient hierarchical codebook by jointly exploiting sub-array and deactivation (turning-off) antenna processing techniques, where closed-form expressions are provided to generate the codebook. Performance evaluations are conducted under different system and channel models. Results show superiority of the proposed codebook over the existing alternatives.

Due to the requirement of establishing several independent links, multi-device multi-path beamforming (BF) training becomes rather time-consuming in 60-GHz millimeterwave communications. To reduce training overhead, a novel BF training scheme is proposed in this paper. The scheme includes a simultaneous training strategy for multiple devices and multiple paths, and a corresponding multi-path beam-pair detection algorithm. The training pilot of the source device is broadcasted to all destination devices. Subsequently, in each destination device, the proposed detection is separately conducted to detect significant-path-related beam pairs from the channel responses, via exploiting the time resolvability and space sparsity of physical paths. Finally, the training results are successively fed back to the source device. The proposed scheme achieves simultaneous training of multiple devices and multiple paths, and obtains significant reduction of overhead. Comprehensive simulations are presented to verify the effectiveness of this scheme.

Owing to the requirement of power‐hungry ultra‐high‐speed analogue‐to‐digital converters (ADCs), 60 GHz wireless local area network systems face the challenge of high energy consumption in idle listening (IL). To cope with this problem, a 1 bit‐sampling IL (1BS‐IL) scheme is proposed in this paper. This scheme includes a mixed 1/ M ‐bit‐sampling receiver, and a corresponding packet detection algorithm termed 1BS generalised likelihood ratio test (1BS‐GLRT). The receiver enables packet detection in IL with a 1 bit ADC, which considerably lowers down the energy consumption; besides, the 1BS‐GLRT algorithm guarantees promising performance on packet detection. Once detecting the arrival of a packet, the receiver switches to an original multi‐bit sampling precision, that is, M ‐bit, ADC to demodulate the packet, which maintains the demodulation performance. Comparisons show that 1BS‐IL not only significantly reduces energy consumption, but also achieves competitive detection performance.

We investigate Tx/Rx joint beamforming in millimeter-wave communications (MMWC). As the multipath components (MPCs) have different steering angles and independent fadings, beamforming aims at achieving array gain and diversity gain in this scenario. A sub-optimal beamforming scheme is proposed to find the antenna weight vectors (AWVs) at Tx/Rx via iterative eigenvalue decomposition (EVD), provided that full channel state information (CSI) is available at both the transmitter and receiver. To make this scheme practically feasible in MMWC, a corresponding training approach is suggested to avoid the channel estimation and iterative EVD computation. As in fast fading scenario, the training approach may be time-consuming due to frequent training; another beamforming scheme, which exploits the quasi-static steering angles in MMWC, is proposed to reduce the overhead and increase the system reliability by multipath grouping (MPG). The scheme first groups the MPCs and then concurrently beamforms toward multiple steering angles of the grouped MPCs, so that both array gain and diversity gain are achieved. Performance comparisons show that, compared with the corresponding state-of-the-art schemes, the iterative EVD scheme with the training approach achieves the same performance with a reduced overhead and complexity, whereas the MPG scheme achieves better performance with approximately equivalent complexity.

In order to improve the capacity and spectral efficiency of geostationary orbiting satellite collocation system, a cooperative transmission scheme is proposed in this paper. With the cooperative beamforming at the ground control station, a satellite spot beam can be split into virtual beams, which can support radio resources multiplexing for selected users. A cooperative gain is defined and further studied to evaluate the enhancement of cooperative and non-cooperative system. Then, according to this cooperative gain, a ground user selection criterion is derived, in which equivalent distance mismatch can be tolerated under the constraint of capacity degradation. Furthermore, the expectation of selectable ground users is analyzed to show the feasibility of our proposed scheme. Simulation results demonstrate that with the proposed cooperative transmission scheme, a high cooperative gain can be achieved.

Due to the requirement of power-hungry multi-Gsps analog-to-digital converters (ADCs), 60-GHz wireless local-area network (WLAN) systems face the challenge of high power consumption in idle listening (IL). To cope with this problem, a 1-bit-sampling IL (1BS-IL) scheme is proposed in this paper. This scheme includes a mixed 1/M-bit-sampling receiver, and a corresponding packet detection algorithm termed 1-bit-sampling generalized likelihood ratio test (1BS-GLRT). The receiver enables packet detection in IL with a 1-bit ADC, which considerably lowers down the power consumption; besides, the 1BS-GLRT algorithm guarantees promising performance on packet detection. Once detecting the arrival of a packet, the receiver switches to an original sampling precision (i.e., M-bit) ADC to demodulate the packet, which maintains the demodulation performance. Comparisons show that 1BS-IL not only significantly reduces power consumption, but also achieves competitive detection performance.

Due to human blockage and device mobility, 60GHz wireless local-area network (WLAN) faces the challenge of communication-link outage. To cope with this problem, a double-link beam tracking scheme is proposed in this paper. This scheme includes a search strategy of transmission and alternative links, and an in-packet double-link tracking and switching method. The transmission and alternative links are simultaneously tracked against device mobility, through probing neighboring beams; once the human blockage is detected in transmission link, the system can thus switch to the alternative link directly. Besides, the proposed scheme finds the finish of human blockage quickly through in-packet double-link tracking, and enables the system to communication in the maximal received-power link. The comparison between the proposed scheme and the IEEE 802.11ad scheme is conducted. The comparison shows that the proposed scheme significantly reduces the probability of outage, and achieves an improved throughput.

Estimation of the multipath channel in 60‐GHz communications is challenging, because the channel may be sparse or dense during beam training. Specifically, because of the variation of the number of non‐zero taps, it is hard for common estimators to obtain robust and prominent performance. In order to address this problem, the authors propose a sparse/dense channel estimation with non‐zero tap detection (SDCE‐NTD). The estimation is conducted in a three‐stage fashion, including initial estimation with the unstructured least‐square (LS) algorithm, non‐zero‐tap detection with the generalised likelihood ratio test approach, and posterior estimation with the structured LS algorithm. The false‐alarm and detection probability of the tap detector, as well as the mean square error (MSE) of SDCE‐NTD, are derived and confirmed via simulations. Comparisons are conducted between SDCE‐NTD and the common estimators in the beam training scenarios, where both dense and sparse channels exist. Results show that SDCE‐NTD reveals a significant gain in terms of MSE over both the conventional LS algorithm, which does not exploit the sparse nature of the channel, and the matching pursuit algorithm, which endeavours to exploit the sparsity. In addition, it is also demonstrated that the proposed estimator can approach the lower bound with high signal‐to‐noise ratio.

Power amplifier (PA) non‐linearity is extremely significant for 60 GHz millimetre‐wave communications, which distorts signal constellation seriously. In dealing with this problem, the commonly used digital predistortion (DPD) becomes unaffordable for 60 GHz systems in both power consumption and price. This study proposes to handle PA non‐linearity with distorted constellation (DCS) estimation and demodulation for 60 GHz systems with single‐carrier frequency‐domain equalisation transmission, which has a low implementation cost. Employing the least‐square (LS) algorithm, the DCS is estimated both before and after equalisation, where LS after equalisation has a lower computational complexity than LS before equalisation. Subsequently, the estimated DCS, instead of the standard constellation, is used as a criterion for demodulation, which is shown to achieve a significant improvement on the symbol‐error‐rate (SER) performance, especially when the non‐linear distortion is severe. Moreover, compared with DPD, the proposed method achieves a better SER performance and behaves more robustly in the low range of input power back‐off (IBO), whereas achieving a close SER performance in the high‐IBO range.

In this paper, a novel lower-power multigigabit Viterbi decoder architecture is proposed for 60 GHz wireless personal-area network (WPAN) systems. Since add-compare-select (ACS) computation is the main bottleneck in decoding speed of Viterbi decoders, a balanced add-select-register-compare (BASIC) architecture is put forward to reduce critical path of ACS with low complexity. This work develops an 8-parallel BASIC Viterbi decoder for IEEE 802.15.3c standard to accomplish high-throughput and low-power goals. Based on synthesized results in 0.13 μm CMOS technology, the proposed decoder achieves up to 4 Gb/s throughput with energy efficiency 0.104 nJ/bit at 1.2 V.

In millimeter-wave communications (MMWC), multipath components (MPCs) have different steering angles and independent fadings. Park and Pan recently proposed a simple scheme for both transmitter and receiver to concurrently beamform towards multiple steering angles of MPCs to achieve both array gain and diversity gain. However, when the number of MPCs is greater than that of the transmit or receive antennas, a solution of antenna weight vector (AWV) does not exist. To cope with this problem, two multipath grouping (MPG) schemes, namely MPG-steering vector grouping and MPG-channel vector grouping, are proposed. These schemes group the MPCs that have close steering angles, and define an equivalent MPC for each non-empty group in both the transmitter and receiver. As the number of non-empty groups is always no larger than that of antennas in both the transmitter and receiver, a solution of AWV is guaranteed. Moreover, performance comparisons show that the two proposed MPG schemes achieve not only full diversity, but also an even better array gain than that of the scheme proposed by Park and Pan.

Due to the extremely high oscillation frequency of 60 GHz systems, phase noise (PN) imported at both transmitter (Tx) and receiver (Rx) is significant, which degrades the transmission performance. This paper proposes an architecture employing iterative Tx and Rx PN compensation (ITR-PNC) for 60 GHz systems with single-carrier frequency-domain equalization (SC-FDE) transmission. The ITR-PNC iteratively performs PNC before equalization (PNC-BE) and PNC after equalization (PNC-AE), which are mainly set to manage Rx PN (RPN) and Tx PN (TPN), respectively. The PNC-BE and PNC-AE both exploit the one-tap least mean square (LMS) algorithm for PN extraction (PNE). In PNC-AE, the decision feedback result is used as the reference signal for PNE. And in PNC-BE, the reference signal is the signal replica, which is generated via the decision result and the estimated TPN of last iteration, and the estimated channel response. Comprehensive simulations indicate that the proposed architecture employing the proposed ITR-PNC achieves competitive bit-error-rate (BER) performance with only two iterations, for the cases whether only RPN or both TPN and RPN are taken into consideration.

Beamforming is necessary in 60GHz millimeter-wave communications in order to compensate for high path loss. A complete beamforming algorithm includes codebook design and beam training. The existing beamforming protocol of IEEE 802.15.3c standard has following flaws: (1) Not all three beamforming pattern types (quasi-omni pattern, sector and beam) have their own codebooks; (2) Almost every beam points to two directions, one of which is expected but the other one is unwanted; (3) Setup time of beam training is long. This paper proposes a codebook design and corresponding training procedure based on circular antenna array with a two-layer-structure. It allows every pattern type to own its corresponding codebook and highly directional antenna radiation pattern. The training setup time is reduced to around half of 3c training time. Performance evaluations also manifest that the proposed algorithm leads to less antenna gain loss and more robust performance than the 3c standard.

60 GHz communication supporting multigigabit data rate is a popular choice of industry for next generation short distance wireless communications. However, multi-Gsps ADC becomes a challenge in 60 GHz systems which have ultra wide Nyquist bandwidth. To reduce sampling rate of ADC in the estimation stage, we propose a joint signal-to-noise ratio (SNR) and channel estimation algorithm using compressed sensing (CS) theory. In 60 GHz systems, CS encoding and decoding strategies are optimized to maximize benefits from the design of pilots and estimators. For pilot design, m-sequence rather than conventional Bernoulli random sequence is selected owning to a better average restricted isometry property; for estimator design, a quasi-optimal channel and noise power estimation is put forward underlying signal subspace provided by CS algorithm. Simulation results show that the proposed algorithm reduces the sampling rate of ADC to 9.1% Nyquist bandwidth of 60 GHz communication. Moreover, the algorithm with this compressed sampling efficiently outperforms classical least square algorithm with Nyquist sampling as SNR exceeds 7 dB.

The direct-conversion transceiver is an attractive architecture for 60 GHz wireless systems to meet the low-cost and low-power-consumption requirements, but it suffers from the IQ imbalance problem seriously. This paper adopts secondorder moment estimation to extract frequency-independent IQ imbalance parameters, and performs IQ compensation based on the estimation results. In order to achieve robust estimation, we employ a specific preamble sequence, where adjacent symbols have a fixed phase difference of π/2. The proposed estimation excels in the following aspects: (1) it completely eliminates the impact of inter-symbol interference (ISI) and carrier phase offset (CPO); (2) it is not degraded by carrier frequency offset (CFO), since the impact of which only lies in the negligible phase offset during only a symbol duration; (3) it has low computational complexity and can be implemented on hardware platform expediently. Simulation results indicate that the estimation algorithm has competitive performance over a large range of IQ imbalance, and with IQ compensation the bit-error-rate (BER) performance can be improved to that of the ideal case.

Rapid detection of the arrival of a packet is challenging in burst wireless communications, where many parameters are unknown, such as signal power, noise power and carrier phase offset. Due to the unknown noise power, it is hard to set appropriate thresholds for the existing common detectors under the Neyman-Pearson criterion, which results in vulnerable acquisition and poor performance. In order to solve this problem, we propose the generalized likelihood-ratio test (GLRT) approach for sequence-aided packet acquisition in this paper. GLRT detection is formulated under multipath channel. Moreover, false alarm probability and detection probability of GLRT are derived and confirmed via simulations. Comparisons are conducted between GLRT and the common detectors, as well as GLRT under different channels. Results show that GLRT basically reveals more competitive and robust performance than the common detectors in practice, with only a little extra hardware cost by using the provided recursive computation structure. Additionally, it is shown that fading results in significant deterioration for GLRT; whereas GLRT has ability to exploit multipath diversity to reduce fading effect and improve acquisition performance.

The signal constellation of 60 GHz millimeter wave (mmWave) communications is distorted by power amplifier (PA) nonlinearity seriously and the bit error rate (BER) performance declines, due to the huge bandwidth and high equivalent isotropic radiated power (EIRP), especially for quadrature amplitude modulation (QAM) signals. This paper aims at non-data-aided (NDA) distorted constellation (DC) estimation and demodulation to deal with PA nonlinearity. An estimation algorithm of expectation-maximization (EM) is proposed and the corresponding Cramer-Rao lower bounds (CRLBs) are calculated, then the estimated DC is used as reference for demodulation. Simulation results indicate that the means of the estimation results are unbiased and the mean square errors (MSEs) reach the CRLBs under easy requirements on signal-to-noise ratio (SNR) and output power back-off (OBO). Additionally, with PA nonlinearity, the BER performance of the presented DC demodulation is close to that of the ideal PA case, and much superior to that of the traditional standard constellation (SC) demodulation.

The nonlinear distortion caused by power amplifier (PA) nonlinearity distorts the constellation and degrades the bit error rate (BER) performance of 60 GHz millimeter wave (mmWave) wireless local area network (WLAN) easily, due to the huge bandwidth and high equivalent isotropic radiated power (EIRP), especially for quadrature amplitude modulation (QAM) signals. This paper aims at data-aided (DA) distorted constellation (DC) estimation and demodulation to resist PA nonlinearity. A maximum likelihood (ML) estimation algorithm is proposed, and the corresponding Cramer-Rao lower bounds (CRLBs) are computed. Furthermore, we implement the demodulation based on the DC rather than the standard constellation (SC). Simulation results indicate that the means of the estimated parameters are unbiased and the mean square errors (MSEs) approach the CRLBs. Meanwhile, the proposed DC demodulation with PA nonlinearity achieves BER performance close to the ideal PA case, and evidently outperforms the traditional SC demodulation especially in the high-code-rate or uncoded case.

Traditional Delay-Locked-Loop (DLL) method can be hardly used in high-rate Direct-Sequence Ultra-Wideband (DS-UWB) systems due to dense multipath environment, low spreading factor and high-frequency clock. Most of the existing researches on tracking in UWB scenario focus on low-rate Impulse-Radio UWB (IR-UWB). In this paper, a non-Numerical Controlled Oscillator (NCO) periodical-pilot-assisted tracking method is proposed for practical high-rate DS-UWB systems. Analytical expressions of Equivalent Probability Density Function (EPDF) of timing error in locked state, Mean Square Error (MSE, or termed timing jitter) and Mean Time to Lose Lock (MTLL) are derived. Numerical results are presented, and then confirmed by means of extensive computer simulation results. These results corroborate our theoretical analysis, and show performance superiority of the proposed method over traditional DLL. Additionally, this proposed method has been used and justified in our practical DS-UWB system.

Tracking constitutes a major challenge in Ultra-Wideband (UWB)communications due to dense multipath environment and low power consumption. Most of the existing studies on tracking fall within low rate Impulse Radio UWB (IR-UWB) scope, with the assumption that Inter-Frame Interference (IFI) or Inter-Symbol Interference (ISI) is absent. In this paper, we settle on high rate Direct-Sequence UWB (DS-UWB) systems, in which ISI is inevitable. A Periodical-Pilot-Assisted Tracking Loop with RAKE combining, which we termed PPATL, is proposed for high rate DS-UWB scenario. The proposed tracking loop can effectively combat ISI and capture multipath energy. Analytical expressions of Probability Density Function (PDF) of timing error in locked state, Mean Square Error (MSE, or termed timing jitter) and Mean Time to Lose Lock (MTLL) are derived. Numerical results are presented and then confirmed by means of extensive computer simulation results. These results corroborate our theoretical analysis. Additionally, this proposed PPATL has been used and justified in our realistic DS-UWB system.

Many acquisition schemes have been proposed and investigated for Ultra-Wideband (UWB) systems, however, most of them setup for low duty-cycle Impulse Radio UWB (IR-UWB) signals, with the assumption that the pulse repetition time is greater than the maximum excess delay of channels, so that intra- and inter-frame interference can be bypassed. This assumption limits the transmission rate and cannot hold in high-rate Direct-Sequence UWB (DS-UWB) systems. In this paper, we propose a two-step data-aided acquisition scheme for high-rate DS-UWB systems. The first step is to quickly acquire an arbitrary train sequence; a two-stage correlation architecture is employed to combat Inter-Symbol Interference (ISI) and collect multipath energy, so as to speed up acquisition. Correspondingly, the second step is to search for the first arriving path with a maximum gap based search method (also new proposed), so as to improve Bit Error Rate (BER) performance. Closed-form expressions of Mean Acquisition Time (MAT), Overall Acquisition Probability (OAP) and False Alarm Rate (FAR) are derived. Based on them, parameter selections that minimize MAT and maximize OAP are discussed under certain FAR constrain. Simulations corroborate our theoretical results.

This paper analyzes the interference in Direct Sequence Spreading Spectrum (DSSS) systems with Selective RAKE (SRAKE) receivers where channel delay is comparable with spreading sequence length. We build an accurate output model of SRAKE receiver and derive the output Signal to Interference-Noise Ratio (SINR) and Bit Error Rate (BER) of SRAKE receiver, taking multipath effect into consideration. Based on this analysis, we showed that in frequency selective channels with SRAKE receiver, system become interference limited as transmit power increases. Besides, there exists a critical spreading factor. When spreading factor is lower than this critical value, Eb/N0have to increase dramatically to compensate the multipath effect. This provide a criterion to adaptively choose the optimal spreading factor under different channel conditions.