Understanding dynamic 4D environments—3D space evolving over time—is critical for robotic and interactive systems. These applications demand systems that can process streaming point cloud video in real-time, often under resource constraints, while also benefiting from past and present observations when available. However, current 4D backbone networks rely heavily on spatiotemporal convolutions and Transformers, which are often computationally intensive and poorly suited to real-time applications. We propose PointNet4D, a lightweight 4D backbone optimized for both online and offline settings. At its core is a Hybrid Mamba-Transformer temporal fusion block, which integrates the efficient state-space modeling of Mamba and the bidirectional modeling power of Transformers. This enables PointNet4D to handle variable-length online sequences efficiently across different deployment scenarios. To enhance temporal understanding, we introduce 4DMAP, a frame-wise masked auto-regressive pretraining strategy that captures motion cues across frames. Our extensive evaluations across 9 tasks on 7 datasets, demonstrating consistent improvements across diverse domains. We further demonstrate PointNet4D’s utility by building two robotic application systems: 4D Diffusion Policy (DP4) and 4D Imitation Learning (4DIL), achieving substantial gains on the RoboTwin and HandoverSim benchmarks. Code and checkpoints available: https://github.com/yunzeliu/MAP

Humans excel at spatial-temporal reasoning, effortlessly interpreting dynamic visual events from an egocentric viewpoint. However, whether multimodal large language models (MLLMs) can similarly understand the 4D world remains uncertain. This paper explores multimodal spatial-temporal reasoning from an egocentric perspective, aiming to equip MLLMs with human-like reasoning capabilities. To support this objective, we introduce Ego-ST Bench, a novel benchmark containing over 5,000 question-answer pairs across four categories, systematically evaluating spatial, temporal, and integrated spatial-temporal reasoning. Additionally, we propose ST-R1 training paradigm, a video-based reasoning model that incorporates reverse thinking into its reinforcement learning process, significantly enhancing performance. We combine long-chain-of-thought (long-CoT) supervised fine-tuning with Group Relative Policy Optimization (GRPO) reinforcement learning, achieving notable improvements with limited high-quality data. Ego-ST Bench and ST-R1 provide valuable insights and resources for advancing video-based spatial-temporal reasoning research.

Uncrewed aerial vehicles (UAVs) play a pivotal role in ensuring seamless connectivity for Internet of Things (IoT) devices, particularly in scenarios where conventional terrestrial networks are constrained or temporarily unavailable. However, traditional coverage-hole detection approaches, such as minimizing drive tests, are costly, time-consuming, and reliant on outdated radio-environment data, making them unsuitable for real-time applications. To address these limitations, this paper proposes a UAV-assisted framework for real-time detection and recovery of coverage holes in IoT networks. In the proposed scheme, a patrol UAV is first dispatched to identify coverage holes in regions where the operational status of terrestrial base stations (BSs) is uncertain. Once a coverage hole is detected, one or more UAVs acting as aerial BSs are deployed by a satellite or nearby operational BSs to restore connectivity. The UAV swarm is organized based on Delaunay triangulation, enabling scalable deployment and tractable analytical characterization using stochastic geometry. Moreover, a collision-avoidance mechanism grounded in multi-agent system theory ensures safe and coordinated motion among multiple UAVs. Simulation results demonstrate that the proposed framework achieves high efficiency in both coverage-hole detection and on-demand connectivity restoration while significantly reducing operational cost and time.

Movable antenna (MA) technology has recently emerged as a promising paradigm to flexibly reshape wireless channels, thereby enhancing system performance beyond the limits of traditional fixed-position antennas (FPAs). In this paper, we investigate an MA-assisted multiple-input multiple-output (MIMO) simultaneous wireless information and power transfer (SWIPT) network, where an access point (AP) equipped with multiple MAs serves both multi-FPA information decoding users (IDUs) and multi-FPA energy harvesting users (EHUs). Our objective is to maximize the weighted sum harvested power of all EHUs by jointly optimizing the transmit precoding matrices and the positions of MAs, subject to the constraints on the maximum transmit power of the AP, quality of service of each IDU, confined MA moving region, and minimum inter-MA distance. To tackle the non-convexity of this problem, we first transform the original optimization problem into a more tractable equivalent form by leveraging the weighted minimum mean square error (WMMSE) approach. Next, we start with the single-IDU single-EHU scenario and propose an efficient block coordinate descent (BCD) algorithm. Specifically, the transmit precoding matrix and MA positions are iteratively optimized based on the successive convex approximation (SCA) technique. The proposed algorithm is then extended to the general multi-IDU multi-EHU scenario. Finally, simulation results show that the proposed MA-assisted MIMO SWIPT system significantly outperforms conventional FPA systems and other benchmark schemes in terms of the weighted sum harvested power, validating the superiority of MAs in proactively reconfiguring channel conditions to boost energy transfer efficiency.

In this work, we investigate a movable antenna (MA) enhanced secure uplink non-orthogonal multiple access (NOMA) network, where multiple legitimate users, each equipped with an MA, transmit confidential information to the base station in the presence of an eavesdropper. Our goal is to maximize the system's secrecy sum rate by jointly optimizing the positions of MAs, the transmit power of each user, and the receive combining vectors at the base station. To tackle the formulated non-convex problem, we first transform it into a more tractable form and then develop a customized heuristics algorithm by invoking particle swarm optimization (PSO) to obtain a high-quality suboptimal solution. Simulation results validate the efficacy of our proposed algorithm, demonstrating its superior performance over conventional fixed-position antenna (FPA) systems and other baselines.

Low-altitude wireless networks are increasingly vital for the low-altitude economy, enabling wireless coverage in high-mobility and hard-to-reach environments. However, providing reliable connectivity to sparsely distributed aerial users in dynamic three-dimensional (3D) spaces remains a significant challenge. This paper investigates downlink coverage enhancement in vertical heterogeneous networks (VHetNets) beyond 5G, where unmanned aerial vehicles (UAVs) operate as emerging aerial base stations (ABSs) alongside legacy terrestrial base stations (TBSs). To improve coverage performance, we propose a coordinated multi-point (CoMP) transmission framework that enables joint transmission from ABSs and TBSs. This approach mitigates the limitations of non-uniform user distributions and enhances reliability for sparse aerial users. Two UAV deployment strategies are considered:i)random UAV placement, analyzed using stochastic geometry to derive closed-form coverage expressions, andii)optimized UAV placement using a coverage-aware weightedK-means clustering algorithm to maximize cooperative coverage in underserved areas. Theoretical analyses and Monte Carlo simulations demonstrate that the proposed CoMP-enabled VHetNet significantly improves downlink coverage probability, particularly in scenarios with sparse aerial users. These findings highlight the potential of intelligent UAV coordination and geometry-aware deployment to enable robust, adaptive connectivity in low-altitude wireless networks.

With the rapid proliferation of large-scale sensor nodes and smart devices, the energy consumption of wireless networks has increased dramatically, posing significant challenges to the design of energy-efficient communication systems. Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) has recently emerged as a promising technology for enhancing energy efficiency, owing to its capability of reconfiguring the wireless propagation environment and providing full-space user coverage. In this paper, we investigate the application of STAR-RIS in a simultaneous wireless information and power transfer (SWIPT) system, leveraging the spatial beamforming capabilities of multiple-input multiple-output (MIMO) and the frequency diversity gain of orthogonal frequency-division multiplexing (OFDM). In specific, we aim to maximize the system’s weighted energy efficiency, subject to individual users’ energy harvesting and achievable data rate requirements. To address the inherent non-convexity of the formulated problem, we adopt a weighted minimum mean square error (WMMSE)-based reformulation, and develop an efficient algorithm based on successive convex approximation (SCA) and semidefinite programming (SDP). Simulation results validate the performance advantages of the proposed STAR-RIS-aided design over conventional RIS. Furthermore, user-specific weight adjustment enables flexible and fair resource allocation across multiple users.

This paper proposes a unified optimization framework for a power splitting (PS)-based multicarrier multiple-input and multiple-output (MIMO) simultaneous wireless information and power transfer (SWIPT) system with Tomlinson-Harashima pre coding (THP)-based non-linear transceivers or linear transceivers. Our aim is to minimize the transmit power under the sum mean-square-error (MSE) and energy harvesting (EH) constraints. To solve this formulated non-convex problem, we propose a structural solution (SS) which applies the closed-form expressions of equalization matrices, feedback matrices and precoding matrices to establish an equivalent optimization problem in terms of the power allocation and PS ratio. Then the equivalent problem is solved by a two-layer optimization scheme. Simulation results show that the THP-based non-linear transceivers need less transmit power than linear transceivers to achieve the same performance of EH and sum MSE.

Traditional low-power wide-area network (LPWAN) transceivers typically compromise data rates to achieve deep coverage. This paper presents a novel transceiver that achieves high receiver sensitivity and low computational complexity. At the transmitter, we replace the conventional direct sequence spread spectrum (DSSS) preamble with a chirp spread spectrum (CSS) preamble, consisting of a pair of down-chirp and up-chirp signals that are conjugate to each other, simplifying packet synchronization. For enhanced coverage, the payload incorporates continuous phase frequency shift keying (CPFSK) to maintain a constant envelope and phase continuity, in conjunction with DSSS to achieve a high spreading gain. At the receiver, we develop a double-peak detection method to improve synchronization and a non-coherent joint despreading and demodulation scheme that increases receiver sensitivity while maintaining simplicity in implementation. Furthermore, we optimize the preamble detection threshold and spreading sequences for maximum non-coherent receiver performance. The software-defined radio (SDR) prototype, developed using GNU Radio and USRP, along with operational snapshots, showcases its practical engineering applications. Extensive Monte Carlo simulations and field-test trials demonstrate that our transceiver outperforms traditional ones in terms of receiver sensitivity, while also being low in complexity and cost-effective for LPWAN requirements.

The evolution of Internet of Things technologies is driven by four key demands: ultra-low power consumption, high spectral efficiency, reduced implementation cost, and support for massive connectivity. To address these challenges, this paper proposes two novel modulation schemes that integrate continuous phase modulation (CPM) with spread spectrum (SS) techniques. We begin by establishing the quasi-orthogonality properties of CPM-SS sequences. The first scheme, termed IM-CPM-SS, employs index modulation (IM) to select spreading sequences from the CPM-SS set, thereby improving spectral efficiency while maintaining the constant-envelope property. The second scheme, referred to as CIM-CPM-SS, introduces code index modulation (CIM), which partitions the input bits such that one subset is mapped to phase-shift keying symbols and the other to CPM-SS sequence indices. Both schemes are applied to downlink non-orthogonal multiple access (NOMA) systems. We analyze their performance in terms of bit error rate (BER), spectral and energy efficiency, computational complexity, and peak-to-average power ratio characteristics under nonlinear amplifier conditions. Simulation results demonstrate that both schemes outperform conventional approaches in BER while preserving the benefits of constant-envelope, continuous-phase signaling. Furthermore, they achieve higher spectral and energy efficiency and exhibit strong resilience to nonlinear distortions in downlink NOMA scenarios.

Movable antenna (MA) has been deemed as a promising technology to flexibly reconfigure wireless channels by adjusting the antenna positions in a given local region. In this paper, we investigate the application of the MA technology in both decode-and-forward (DF) and amplify-and-forward (AF) relaying systems, where a relay is equipped with multiple MAs to assist in the data transmission between two single-antenna nodes. For the DF relaying system, our objective is to maximize the achievable rate at the destination by jointly optimizing the positions of the MAs in two stages for receiving signals from the source and transmitting signals to the destination, respectively. To drive essential insights, we first derive a closed-form upper bound on the maximum achievable rate of the DF relaying system. Then, a low-complexity algorithm based on projected gradient ascent (PGA) and alternating optimization (AO) is proposed to solve the antenna position optimization problem. For the AF relaying system, our objective is to maximize the achievable rate by jointly optimizing the two-stage MA positions as well as the AF beamforming matrix at the relay, which results in a more challenging optimization problem due to the intricate coupling variables. To tackle this challenge, we first reveal the hidden separability among the antenna position optimization in the two stages and the beamforming optimization. Based on such separability, we derive a closed-form upper bound on the maximum achievable rate of the AF relaying system and propose a low-complexity algorithm to obtain a high-quality suboptimal solution to the considered problem. Simulation results validate the efficacy of our theoretical analysis and demonstrate the superiority of the MA-enhanced relaying systems to the conventional relaying systems with fixed-position antennas (FPAs) and other benchmark schemes.

Handover (HO) is a critical component of mobility management in the 5th generation (5G) of communication networks, which ensures seamless connectivity and optimal communication performance for user equipment (UE) in motion across different cells. In previous studies, the deep reinforcement learning (DRL) techniques were employed to solve the HO problem. However, for most of these methods, the growing complexity in action space was not considered as the number of UEs increases, leading to inefficient model convergence and HO failures. To address this issue, this paper proposes a novel PPO-MH (Proximal Policy Optimization with Masking for Handover) model for multi-cell selection handover problem. This model calculates the action mask for each UE before each handover using the UE's measurement report, providing prior information for the decision-making process. By dynamically masking base stations (BSs) that do not meet the handover conditions, the model avoids invalid hand overs and improves sampling efficiency. Experimental results demonstrate that the PPO- MH outperforms the traditional PPO across various scenarios, ensuring Quality of Service (QoS) for UEs and reducing the handover frequency. Additionally, PPO- MH converges significantly faster than PPO, which validates the effectiveness of the action mask strategy. Benefiting from these advantages, our methods have broad potential applications, especially in scenarios requiring efficient resource management and low-latency handovers.

Federated learning (FL) enables wireless terminals to collaboratively learn a shared parameter model while keeping all the training data on devices per se. Parameter sharing consists of synchronous and asynchronous ways: the former transmits parameters as blocks or frames and waits until all transmissions finish, whereas the latter provides messages about the status of pending and failed parameter transmission requests. Whatever synchronous or asynchronous parameter sharing is applied, the learning model shall adapt to distinct network architectures as an improper learning model will deteriorate learning performance and, even worse, lead to model divergence for the asynchronous transmission in resource-limited large-scale Internet-of-Things (IoT) networks. This paper proposes a decentralized learning model and develops an asynchronous parameter-sharing algorithm for resource-limited distributed IoT networks. This decentralized learning model approaches a convex function as the number of nodes increases, and its learning process converges to a global stationary point with a higher probability than the centralized FL model. Moreover, by jointly accounting for the convergence bound of federated learning and the transmission delay of wireless communications, we develop a node scheduling and bandwidth allocation algorithm to minimize the transmission delay. Extensive simulation results corroborate the effectiveness of the distributed algorithm in terms of fast learning model convergence and low transmission delay.

Unmanned aerial vehicle (UAV) communications have been widely accepted as promising technologies to support air-to-ground communications in the forthcoming sixth-generation (6G) wireless networks. This paper proposes a novel air-to-ground communication model consisting of aerial base stations served by UAVs and terrestrial user equipments (UEs) by integrating the technique of coordinated multi-point (CoMP) transmission with the theory of stochastic geometry. In particular, a CoMP set consisting of multiple UAVs is developed based on the theory of Poisson-Delaunay tetrahedralization. Effective UAV formation control and UAV swarm tracking schemes for two typical scenarios, including static and mobile UEs, are also developed using the multi-agent system theory to ensure that collaborative UAVs can efficiently reach target spatial positions for mission execution. Thanks to the ease of mathematical tractability, this model provides explicit performance expressions for a typical UE’s coverage probability and achievable ergodic rate. Extensive simulation and numerical results corroborate that the proposed scheme outperforms UAV communications without CoMP transmission and obtains similar performance to the conventional CoMP scheme while avoiding search overhead.

The propagation loss of RF signals is a significant issue in simultaneous wireless information and power transfer (SWIPT) systems. Additionally, ensuring information security is crucial due to the broadcasting nature of wireless channels. To address these challenges, we exploit the potential of active intelligent reflecting surface (IRS) in a multiple-input and multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) SWIPT system. The active IRS provides better beamforming gain than the passive IRS, reducing the “double-fading” effect. Moreover, the noise introduced at the active IRS can be used as artificial noise (AN) to jam eavesdroppers. This paper formulates a secrecy sum-rate maximization problem related to precoding matrices, power splitting (PS) ratios, and the IRS matrix. Since the problem is highly non-convex, we propose a block coordinate descent (BCD)-based algorithm to find a sub-optimal solution. Moreover, we develop a heuristic algorithm based on the zero-forcing precoding scheme to reduce computational complexity. Simulation results show that the active IRS achieves a higher secrecy sum rate than the passive and non-IRS systems, especially when the transmit power is low or the direct link is blocked. Moreover, increasing the power budget at the active IRS can significantly improve the secrecy sum rate.

With an aggravated aging population, Parkinson disease has become a neurodegenerative disease affecting millions of elderly people, therefore, it is crucial to establish a companion diagnostic system that can provide timely and accurate diagnostic results for patients with Parkinson disease. To address the problems of misdiagnosis and under-diagnosis of Parkinson disease, an auxiliary diagnosis system for Parkinson disease (MI-GA) based on mutual information and genetic algorithm is proposed. In the feature extraction process, genetic algorithm is used to approximate the unimportant features in the clinical features of patients. And the mutual information is introduced to assign weights for each clinical feature, and then the weights are applied to the KNN algorithm to achieve the accuracy of the diagnosis results. The experimental performance indicated that the method can significantly alleviate the noise data disturbance of the clinical features of Parkinson disease and improve the accuracy of Parkinson's disease diagnosis.

Physical-layer authentication (PLA) can provide lightweight security solutions for the next-generation Internet of Things (IoT) networks. This paper adopts and modifies the spreading code authentication techniques initially designed for the Global Navigation Satellite System to apply PLA to narrowband IoT networks. In particular, the chip values of the PHY-layer preamble are replaced by a message-generated tag at the transmitter. At the receiver, two kinds of test statistics, cross-correlation and double-correlation values, are computed to decide the authenticity of a received signal. Also, the closed-form expressions of the corresponding optimal thresholds for the hypothesis tests are explicitly derived. Afterward, the single-frame authentication schemes are extended to multi-frame authentication, where several frames are jointly authenticated. Simulation results of the proposed schemes, along with the prototype validation of the double-correlation scheme based on the GNU Radio/USRP SDR platform, corroborate the effectiveness of the PLA strategies.

Energy-efficient scheduling and resource allocation strategies help reduce interference and extend the lifetime of power-limited Internet of Things (IoT) devices. This paper focuses on improving the transmission efficiency and working time of power-limited data acquisition equipment, e.g., low-power consumption IoT sensors. In particular, the cognitive device tunes its transmission time and power rationally to avoid interference and recharges itself by conducting energy harvesting. Inspired by the concept of the age of information, we coin the concept of the value of update (VoU) and use it to guide devices to upload data in a timely manner and optimize the key parameters through a deep deterministic policy gradient (DDPG) neural network to maximize the long-term VoU. Finally, extensive simulations are conducted to demonstrate the effectiveness and robustness of the proposed scheme.

In Internet of Things (IoT) networks, edge learning for data-driven tasks provides intelligent applications and services. As the network size becomes large, different users may generate distinct datasets. Thus, to suit multiple edge learning tasks for large-scale IoT networks, this paper considers efficient communication under a task-oriented principle by using the collaborative design of wireless resource allocation and edge learning error prediction. In particular, we start with multi-user scheduling to alleviate co-channel interference in dense networks. Then, we perform optimal power allocation in parallel for different learning tasks. Thanks to the high parallelization of the designed algorithm, extensive experimental results corroborate that the multi-user scheduling and task-oriented power allocation improve the performance of distinct edge learning tasks efficiently compared with the state-of-the-art benchmark algorithms.

Simultaneous wireless information and power transfer (SWIPT) has emerged as an appealing solution to prolonging the lifetime of low-power Internet of Things (IoT) networks. Meanwhile, an intelligent reflecting surface (IRS) can reconstruct a favorable wireless propagation environment for IoT terminals to achieve high spectrum and energy efficiencies. To take full advantage of these two technologies, this article studies the optimization of an IRS-assisted multiple-input and multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) SWIPT system with nonlinear energy harvesting (EH) model. In particular, we aim to maximize the achievable data rate by jointly designing the transmit precoding matrices, the IRS matrix, as well as the power splitting (PS) ratio subject to the transmit power and harvested power constraints. Since the formulated problem is highly nonconvex, we develop an alternating optimization (AO)-based algorithm to find a high-quality suboptimal solution. Moreover, we further design a heuristic algorithm based on a two-stage optimization strategy to reduce the computational complexity. Simulation results verify that the proposed AO-based algorithm can significantly improve the achievable data rate compared to conventional benchmarks, and the proposed heuristic low-complexity algorithm can achieve comparable performance to the AO-based algorithm.

Wireless powered backscatter communications (WPBC) is capable of implementing ultra-low-power communication, thus promising in the Internet of Things (IoT) networks. In practice, however, it is challenging to apply WPBC in large-scale IoT networks because of its short communication range. To address this challenge, this paper exploits an unmanned ground vehicle (UGV) to assist WPBC in large-scale IoT networks. In particular, we investigate the joint design of network planning and dynamic resource allocation of the access point (AP), tag reader, and UGV to minimize the total energy consumption. Also, the AP can operate in either half-duplex (HD) or full-duplex (FD) multiplexing mode. Under HD mode, the optimal cell radius is derived and the optimal power allocation and transmit/receive beamforming are obtained in closed form. Under FD mode, the optimal resource allocation, as well as two suboptimal ones with low computational complexity, is developed. Simulation results disclose that dynamic power allocation at the tag reader rather than at the AP dominates the network energy efficiency while the AP operating in FD mode outperforms that in HD mode concerning energy efficiency.

The 5th generation mobile networks (5G) and their key technologies need to provide higher spectrum efficiency utilization and smooth integration of broadband and narrowband systems. Filter bank multi-carrier transmission with Offset Orthogonal Amplitude Modulation (FBMC-OQAM) has great advantages to achieve this goal by combating multipath effects and offering high-speed data rate in wireless channels. In this paper, an improved partial transmit sequence(PTS) scheme employing particle swarm optimization (PSO) algorithm is proposed to reduce the peak-to-average-power ratio (PAPR) of the FBMC-OQAM system. Compared with the conventional PTS scheme, the proposed scheme takes the overlapping structure of FBMC-OQAM signal into account, and develop a segmentation scheme based on windowing for the PTS scheme (W-PTS). To reduce the computational complexity, we further propose a PSO-PTS scheme, which significantly reduces the computational complexity. Simulation results show that the proposed scheme could provide a significant performance in PAPR reduction with a relative low complexity.

Deep cross-modal retrieval methods have shown their competitiveness among different cross-modal retrieval algorithms. Generally, these methods require a large amount of training data. However, aggregating large amounts of data will incur huge privacy risks and high maintenance costs. Inspired by the recent success of federated learning, we propose the federated cross-modal retrieval (FedCMR), which learns the model with decentralized multi-modal data. Specifically, we first train the cross-modal retrieval model and learn the common space across multiple modalities in each client using its local data. Then, we jointly learn the common subspace of multiple clients on the trusted central server. Finally, each client updates the common subspace of the local model based on the aggregated common subspace on the server, so that all clients participated in the training can benefit from federated learning. Experiment results on four benchmark datasets demonstrate the effectiveness proposed method.

As a forerunner in 5G ecosystem construction and industry application, Narrowband Internet of Things (NB-IoT) will be inevitably coexisting with legacy Long-Term Evolution (LTE) system. To meet the key performance indicators defined in 5G standard, it is imperative for NB-IoT to mitigate the LTE interference. By virtue of the strong temporal correlation of NB-IoT signal, this paper develops a sparse recovery algorithm based on K-means clustering, which iteratively clusters the correlation coefficients between the measurement vector and each column of observation matrix. Compared with the ideal case without interference, extensive simulation results demonstrate the effective recovery of the proposed algorithm.

Computation efficiency (CE) is crucial to mobile edge computing (MEC) for intelligent Internet of Things (IoT) applications, in addition to energy efficiency. To improve CE, this paper designs a backscatter-assisted wireless powered MEC network, where IoT terminals can partially offload their computation tasks via hybrid offloading through harvest-then-transmit protocol and/or backscatter communications. In particular, a CE maximization problem is formulated from a system perspective and an iterative algorithm is developed to tackle the problem, by using the Dinkelbach and Lagrangian duality methods. Simulation results demonstrate the superiority of the proposed scheme over competing ones and the flexibility in achieving trade-offs between different computation and communication modes.

This work investigates the energy-efficient resource allocation for layered-division multiplexing (LDM) based non-orthogonal multicast and unicast transmission in cell-free massive multiple-input multiple-output (MIMO) systems, where each user equipment (UE) performs wireless information and power transfer simultaneously. To begin with, the achievable data rates for multicast and unicast services are derived in closed form, as well as the received radio frequency (RF) power at each UE. Based on the analytical results, a nonsmooth and nonconvex optimization problem for energy efficiency (EE) maximization is formulated, which is however a challenging fractional programming problem with complex constraints. To suit the massive access setting, a first-order algorithm is developed to find both initial feasible point and the nearly optimal solution. Moreover, an accelerated algorithm is designed to improve the convergence speed. Numerical results demonstrate that the proposed first-order algorithms can achieve almost the same EE as that of second-order approaches yet with much lower computational complexity, which provides insight into the superiority of the proposed algorithms for massive access in cell-free massive MIMO systems.

This work investigates energy-efficient power allocation for layered-division multiplexing (LDM) based non-orthogonal multicast and unicast transmission of cell-free massive multiple-input multiple-output systems. In particular, the achievable data rates for multicast and unicast services are derived. Based on the analytical results, a nonsmooth and nonconvex optimization problem for energy efficiency (EE) maximization is formulated, which is however a challenging fractional programming problem. For ease of mathematical tractability, the smooth and successive convex approximations are exploited to transform the original optimization problem into a sequence of quasi-concave problems and, then, Dinkelbach's method is applied to solve the resultant problems. Simulation results demonstrate that the LDM-based transmission achieves higher EE than orthogonal multiplexing schemes.

This paper studies the joint transceiver design and receive power splitting (PS) optimization for a multicarrier multiple-input and multiple-output (MIMO) simultaneous wireless information and power transfer (SWIPT) system. We aim to maximize the harvested power at the receiver under a sum mean-square error (MSE) constraint and a total transmit power constraint. By first deriving the linear minimum MSE equalization matrices, we obtain a non-convex optimization problem involving the transmit precoding matrices and the receive PS ratio. For a given PS ratio, we show that the problem can be recast as a semidefinite programming (SDP) problem, which can be solved with the interior point algorithm. Then, by employing the unimodal property of the objective function with respect to the PS ratio, we propose a Golden-section search method to find the optimal PS ratio efficiently. Further, to reduce the complexity of solving the inner SDP problems, we exploit the optimal structure of the precoding matrices to transform the original matrix-based optimization problem into a scalar-based optimization problem, for which the closed-form solution is obtained through convex optimization techniques. Simulations are provided to validate the superior performance of our proposed solutions.

As specified in Release 13 specification of the 3rd Generation Partnership Project (3GPP), narrowband physical uplink shared channel (NPUSCH) is a critical physical layer component of narrowband Internet-of-Things (NB-IoT) system. This paper designs and implements an efficient NPUSCH receiver by using modified discrete Fourier transform channel estimation and exponential moving average interpolation. Moreover, three key blocks including channel equalization, soft demodulation and soft combining are implemented for a full receive processing chain. Extensive simulation results corroborate that the designed receiver obtains lower block error rate than the benchmark specified by 3GPP.

This paper studies the joint transceiver and power splitting (PS) ratio design for a multicarrier multiple-input and multiple-output (MIMO) simultaneous wireless information and power transfer (SWIPT) system. We present a unified optimization framework based on the minimization of a general mean square error (MSE) objective function, which includes the most commonly used criteria, such as arithmetic MSE, geometric MSE and maximum MSE minimizations. The optimal equalization matrices are first derived. Then we propose a two-layer scheme to jointly optimize the precoding matrices and the PS ratio. In the inner layer, a structural solution for the precoding matrices is derived based on the Schur-convexity/concavity of the objective function, with which the precoding optimization problems are solved by different closed-form power allocations. In the outer layer, we show that the optimized objective functions obtained by the inner-layer optimization are unimodal with respect to the PS ratio. This enables us to find the optimal PS ratio very efficiently by exploiting the Golden-section search. Simulations are provided to compare the achievable rate and error rate performances of the proposed transceiver schemes.

As of the specification Release 13 completed by the 3rd Generation Partnership Project (3GPP) in June 2016, narrowband Internet-of-Things (NB-IoT) has attracted great attention in both academia and industry. Some new features were further specified in subsequent Releases 14 and 15. In light of these specifications, efficient downlink receiver design is critical to the implementation of NB-IoT, due to the strictly limited hardware resources at a receiver. Conforming to Release 15, this paper develops an efficient downlink receiver by jointly accounting for the synchronization, channel estimation and soft combination for repetitive transmissions. Simulation results demonstrate that both the detection probability for the narrowband primary synchronization signal (NPSS) and the block error rate (BLER) for the narrowband physical downlink sharing channel satisfy the benchmarks designated by 3GPP.

Narrowband Internet of Things (NB-IoT) is a powerful technology for massive machine-type communications, which is imperative in the forthcoming 5G wireless communications. Unlike the long-time evolution (LTE) protocol, in the NB-IoT protocol specified by the third generation partnership project (3GPP), the narrowband physical random access channel (NPRACH) is newly introduced and its receiver performance is critical to the success of an NB-IoT system. In this article, an optimal activity detection scheme is first designed by using the Neyman-Pearson criterion. Then, a low-complexity iterative search algorithm is developed for the joint estimation of residual carrier frequency offset (RCFO) and timing advanced (TA), avoiding the effect of phase ambiguity. Finally, simulation results collaborate on the effectiveness and efficiency of the proposed receiver.

This paper studies a wireless-powered amplify-and- forward (AF) relaying system, where the relay harvests energy from the source or itself using an antenna switching protocol. At the first time slot, each antenna of the relay either receives information or harvests energy from the source and, then, a subset of antennas is chosen to forward information while the remaining antennas continue to harvest energy at the second time slot, which enables energy recycling. To maximize the energy efficiency of the relaying system, a mixed- integer non- linear fractional-combinatorial problem is formulated, which is however mathematically intractable. Accordingly, a suboptimal low-complexity iterative algorithm is developed, including joint power allocations at the source and relay and antenna switching at the relay. In particular, the joint power allocations is first performed by the BCD and Dinkelbach algorithms for a given antenna combination in the inner iteration while a low-complexity greedy algorithm is adopted to select the optimal antenna subset in the outer iteration. Simulation results demonstrate that energy recycling benefits higher energy efficiency.

This paper studies the end-to-end performance of dual-hop amplify-and-forward (AF) relaying systems, where the relays have no constant power supplies but can harvest energy from both the desired signal and nearby co-channel interferences (CCIs). A hybrid energy harvesting (EH) protocol, which is a combination of the existing time switching (TS) and power splitting (PS) protocols, is adopted at the relays. To enhance system performance, an opportunistic relay selection is exploited and the extreme value theory is applied to analyze the asymptotic throughput of the system. Our analysis reveals that when the number of relays (N) is sufficiently large, the system through- put scales with ln ln N. Moreover, the hybrid EH protocol is demonstrated to outperform both TS and PS protocols while it is equivalent to PS protocol in the high signal-to-noise-ratio (SNR) region.

In this paper, we consider minimum bit-error rate (BER) transceiver design for multiple-input multiple-output (MIMO) decode-and-forward (DF) relay systems employing single-carrier transmission with frequency-domain equalization (SC-FDE). The problem is formulated as the minimization of the end to-end (e2e) BER subject to a joint source and relay transmit power constraint. Since the e2e-BER is highly non-convex in terms of the complex matrix optimization variables, solving the optimization problem directly is challenging. By resorting to an upper bound on the e2e-BER and by assuming an optimal sum power budget splitting for the source and relay, we show that the problem can be reduced to the optimization of two equivalent point-to-point MIMO systems. This enables us to derive the optimal eigen-structure of the precoders and the matrix optimization problem simplifies into a convex power allocation problem involving real scalar variables. Primal decomposition is further applied to solve the resulting convex problem in a layered manner, where closed-form solutions are obtained for the inner subproblems. Simulation results are provided to confirm the BER performance of the proposed transceiver design for SC-FDE based MIMO DF relay systems.

In this paper, we consider the robust transceiver design for a single-carrier frequency-domain equalization (SC-FDE) based two-way amplify-and-forward (AF) relay system with imperfect channel state information (CSI). We formulate the optimization problem for the relay filter and destination equalizers as the maximization of the achievable bit rate (ABR) of the system subject to a relay transmit power constraint. Due to the lack of analytical tractable expression for the system ABR under imperfect CSI, solving the optimization problem directly is challenging. Thereby, a lower bound on the link ABR is first derived and adopted in the objective function. Based on the lower bound, the optimal equalizers at the two terminal nodes can be determined in closed form. Subsequently, the optimization of the relay filter is transformed into a convex power allocation problem and an efficient algorithm is proposed to find its global optimal solution. Numerical results are provided to confirm the ABR performance of the proposed robust two-way AF relaying strategy.

In this paper, we consider the robust transceiver design for a multi-hop full-duplex decode-and-forward (DF) relay system employing single-carrier transmission with frequency-domain equalization (SC-FDE). We take into account the effect of imperfect channel state information (CSI) where the CSI errors are modeled as Gaussian random variables with known statistics. The design of the precoding at the transmitter and the equalization at the receiver is formulated as an optimization problem with the objective to minimize two relevant performance metrics, namely, the sum mean-square error (MSE) and the maximum MSE across the different hops, subject to separate transmit power constraints for the nodes. We show that the equalization filters can be optimized individually at the receiving nodes and take the form of robust Wiener filters. However, due to the loopback/backward interference, the transmit signals in the different hops are coupled and the transmit precoding problem leads to a nonconvex power allocation problem in the frequency domain. To find the optimal power allocation, we first employ a sequential geometric programming (sGP) approach, which uses the condensation technique to transform the objective function into a posynomial and then solves a sequence of standard GP problems. The sGP approach requires global channel knowledge at a central node and the involved subproblems admit only numerical solutions. To gain further insight into the structure of the problem, we also consider an alternating optimization (AO) approach for power allocation where convex programming problems and difference of convex programming problems are solved in an alternating manner. The resulting AO algorithm admits closed-form solutions in each iteration step and requires less signaling overhead compared to the centralized sGP scheme. Numerical results for the MSEs and achievable rates of the proposed robust schemes are provided, showing that the proposed sGP and AO algorithms yield significant performance gains compared to conventional half-duplex relay systems and nonrobust full-duplex designs.

This paper investigates physical layer security with opportunistic scheduling in a downlink wireless network with multiple asymmetrically located legitimate users (LUs) and eavesdroppers. We employ the cumulative distribution function (CDF)-based scheduling policy to guarantee fairness among LUs in arbitrary fading channels while exploiting multiuser diversity. Under this scheduling framework, the closed-form expressions for the secrecy throughput and secrecy outage probability are derived, illustrating the interplay among the system parameters such as the channel statistics and the number of LUs and eavesdroppers. In order to investigate the exploited multiuser diversity gain, the normalized secrecy throughput, i.e., the secrecy throughput for a given LU normalized by the probability of it being selected, is analyzed and is proved to achieve a double-logarithmic growth when the number of LUs in the network increases to infinity. In addition, we derive the secrecy diversity order through an asymptotic analysis of intercept probability and prove that the secrecy diversity order is equal to the number of LUs in the system, implying that full diversity is achieved by the CDF-based scheduling.

In this paper, we consider the robust transceiver design for a two-hop full-duplex decode-and-forward relay system employing single-carrier transmission with frequency-domain equalization (SC-FDE). The design problem for the transmit precoding and receive equalization is formulated as an optimization problem with the objective to minimize the sum mean-squared error (MSE) of the two hops subject to separate node transmit power constraints. We show that the equalization filters can be optimized individually at the receiving nodes and take the form of robust Wiener filters. However, due to the loopback interference, the transmissions in the two hops are coupled and the transmit precoding problem boils down to a non-convex power allocation problem in the frequency domain. An alternating optimization approach is proposed to obtain the power allocation where convex programming problems and difference of convex programming problems are solved in an alternating manner. Numerical results are provided to validate the MSE and the achievable rate of the proposed robust schemes, showing that significant performance gains can be achieved compared to conventional half-duplex systems and non-robust full-duplex designs.

In this paper, we study the robust design of the relay beamforming (rBF) and destination equalization (dEQ) filters for broadband multiuser multi-relay networks employing single-carrier frequency-division multiple access (SC-FDMA) and orthogonal frequency-division multiple access (OFDMA). Thereby, we consider the realistic case where only imperfect channel state information is available for rBF and dEQ filter optimization. Our goal is to maximize a lower bound for the weighted achievable bit rate (ABR) of the network, subject to either individual relay power constraints (Ind-PCs) or an aggregate relay power constraint (Agg-PC). We first derive the optimal dEQ filters and the phases of the optimal rBF filter coefficients, which are independent of the power constraints. For the Agg-PC, the amplitude optimization of the rBF filter coefficients is decomposed into two subproblems, which correspond to the optimization of the power allocation across the relays and the power allocation across the users and subcarriers, respectively. We obtain a closed-form structural solution for the first subproblem by fixing the powers across users and subcarriers, and the global optimal solution for the second subproblem. For the Ind-PCs, the corresponding optimization problem is formulated as a reverse-convex problem with convex constraints. Subsequently, the constrained convex concave procedure is applied to approximate the original non-convex problem with a sequence of convex problems, which can be efficiently solved using convex optimization techniques. Simulation results validate the excellent performance of the proposed robust rBF and dEQ filter designs and show their superiority compared to conventional non-robust and naive relaying schemes.

In this work, we propose a robust cooperative relay beamforming (rBF) design for single-carrier frequency-division multiple access (SC-FDMA) based multiuser multi-relay systems with imperfect channel state information. We maximize a lower bound on the achievable bit rate (ABR) of the network, subject to an aggregate relay transmit power constraint. Employing the primal decomposition technique, we decompose the problem into two subproblems: the rBF coefficient optimization and the relay power allocation. For a given power allocation across the frequency tones, a closed-form solution for the rBF matrices is obtained first. Subsequently, the convexity of the remaining power allocation problem is then proved, and efficient convex optimization methods are employed to find the global optimum. Simulation results validate the excellent performance of the proposed rBF schemes and show their superiority compared to conventional non-robust designs.

In this paper, we propose a joint transceiver design for single-carrier frequency-domain equalization (SC-FDE) based multiple-input multiple-output (MIMO) relay systems. To this end, we first derive the optimal minimum mean-squared error linear and decision-feedback frequency-domain equalization filters at the destination along with the corresponding error covariance matrices at the output of the equalizer. Subsequently, we formulate the source and relay precoding matrix design problem as the minimization of a family of Schur-convex and Schur-concave functions of the mean-squared errors at the output of the equalizer under separate power constraints for the source and the relay. By exploiting properties of the error covariance matrix and results from majorization theory, we derive the optimal structures of the source and relay precoding matrices, which allows us to transform the matrix optimization problem into a scalar power optimization problem. Adopting a high signal-to-noise ratio approximation for the objective function, we obtain the global optimal solution for the power allocation variables. We illustrate the excellent performance of the proposed system and compare it to that of conventional orthogonal frequency-division multiplexing MIMO relay systems based on computer simulations.

In this paper, we propose a joint transceiver design for multiple-input multiple-output (MIMO) relay systems employing single-carrier frequency-domain equalization (SC-FDE). We first derive the optimal minimum mean-squared error (MMSE) frequency-domain linear equalization filter at the destination and the associated stream-wise MSEs at the output of the equalizer. Subsequently, we optimize the source and relay precoding matrices for various optimality criteria by minimizing a general function of the MSEs subject to separate source and relay power constraints. The structures of the optimal source and relay precoding matrices are obtained in closed form and the remaining power allocation problems are solved using an alternating optimization algorithm. Simulation results show that the proposed SC-FDE designs outperform orthogonal frequency-division multiplexing based MIMO relay systems in terms of both uncoded and coded bit error rate.

We consider cooperative beamforming (BF) for block-based single-carrier frequency-domain equalization (SC- FDE) in a wireless network consisting of one single-antenna source, one single-antenna destination, and multiple multi- antenna relays. Adopting the minimum mean squared error as optimality criterion, the optimal frequency-domain linear equalization (LE) and decision-feedback equalization (DFE) receivers are derived and corresponding objective functions for relay BF matrix optimization are specified. For a sum relay power constraint, we obtain the structure of the optimal relay BF matrices in closed form. While the structure of the optimal relay BF matrices is identical for LE and DFE as well as for an idealized matched filter receiver, the solution of the remaining power allocation problem depends on the adopted receiver. The power allocation problem is shown to be convex for all considered receivers and an efficient numerical algorithm for finding the optimal power allocation is provided. Furthermore, to reduce complexity, two suboptimal power allocation schemes assigning identical powers to all relays and/or frequencies are proposed and shown to lead to only a small loss in performance and a remarkable robustness against imperfect channel state information.

In this work, we investigate cooperative frequency-domain beamforming (FD-BF) for single-carrier frequency-domain equalization (SC-FDE) systems. In particular, we propose optimal and suboptimal FD-BF schemes for frequency-domain linear equalization (FD-LE) and an idealized matched filter receiver. For both receiver structures, we derive the optimal processing at the destination and develop corresponding expressions for the signal-to-interference-plus-noise ratio (SINR). For a given power allocation, the optimal FD-BF filters maximizing the SINR are obtained in closed form and the power allocation problem is shown to be convex. For computation of the optimal power allocation a numerical method is developed and two low-complexity suboptimal power allocations are provided. Simulation results confirm the excellent performance of all proposed FD-BF schemes.

In this paper, we propose a novel equalization scheme combining the Tomlinson-Harashima precoding (THP) and linear precoding for frequency selective fading channels. The newly introduced linear precoder at the transmitter is jointly designed with the feedforward filter (FFF) and the feedback filter (FBF) of the THP unit, aiming to minimize the mean-square-error (MSE) of the receive signal. The final solutions obtained are closely related to a novel matrix decomposition named Geometric-Mean Decomposition. It is shown that the new scheme eventually attains the receive SNR/SINR upper bound of the conventional non-linear equalization systems, thus enjoys much better error performance. Furthermore, the implementation issue is considered by proposing a suboptimal scheme which enables the designer to take a tradeoff between the system performance and complexity. Numerical results are presented to demonstrate the superior performance of the proposed schemes.

Full-rate full-diversity (FRFD) space-time block codes (STBCs), which are originally designed for independent and identically distributed (i.i.d.) multiple-input multiple-output (MIMO) channels, may suffer from severe performance degradation in the presence of channel correlation. A feasible solution is to adopt a linear precoder to match the eigen beams of these codes with that of the channels. The precoder design addressed in the literature is applicable only to the cases of orthogonal/quasi-orthogonal STBCs in semi-correlated MIMO channels, where the minimum codeword difference matrices (CDM) are either equal, or reducible, to the identity matrix. A general FRFD STBC, however, can have multiple CDMs which, when interacting with a general doubly correlated MIMO channel of covariance matrix feedback, call for a new design criterion for precoding which is the focus of this paper. Furthermore, a modified sphere decoder is also proposed to resolve the problem of degenerated equivalent channel matrix caused by the precoding at low SNRs. Numerical results are presented to demonstrate the superior performance of the resulting optimal precoder when used alongside various real- and complex-rotation based FRFD STBCs.