Integrated sensing and communications (ISAC) is emerging as a transformative 6G technology. By employing bistatic passive sensing between base stations (BSs), ISAC shows significant potential for intelligent transport systems (ITS), enabling real-time measurements of multiple vehicles across large areas without extensive hardware modifications. However, bistatic sensing faces significant challenges due to clock asynchronism. Existing phase offset elimination schemes are often limited by insufficient resolution or the introduction of undesired interference. To address these challenges, we propose a novel joint Doppler-delay (DD) estimation scheme that accounts for clock asynchronism, comprising four key components: (1) snapshot augmentation for covariance matrix estimation; (2) a spectrumweighted integrated periodogram-multiple signal classification (SWIP-MUSIC) algorithm for composite timing offset (CTO) estimation; (3) residual phase offset estimation and alignment; and (4) a periodogram-based joint DD estimation algorithm. Simulation results validate the robustness of the proposed method in multi-target scenarios, highlighting its potential to advance traffic monitoring and other sensing applications in the 6G era.

Terahertz (THz) and Non-Orthogonal Multiple Access (NOMA) technologies have illustrated a great potential in the use of large-scale Internet of Things (IoT) applications. However, in most applications, ground sensors (GSs) have poor energy capacity, while THz communication suffers from significant path loss, which leads to a low data transfer rate in air-ground communication system. Fortunately, with the development of unmanned aerial vehicle (UAV) assisted THz-NOMA communication technology, it becomes one of the promising solutions to deal with above challenges. To conserve GSs’ limited energy while enhancing the throughput of THz-NOMA communication system, we utilize a UAV to collect and transmit data from GSs to an aerostat. Then, energy efficiency (EE) of GSs network is maximized by leveraging GSs’ communication resource allocation strategy and UAV trajectory design, while taking into account each GS’s minimum throughput and each GS’s transmission power constraints. The proposed design is a mixed integer non-convex problem, which is generally intractable. To promptly solve the original problem, we divide it into three subproblems: GSs’ communication resource allocation, UAV’s altitude optimization, and horizontal trajectory design. An iterative algorithm is proposed to deal with these subproblems, based on Dinkelbach method, dual decomposition, and convex relaxation techniques. Simulation results show that the proposed iterative algorithm can achieve a higher EE with a shorter convergence time compared with baseline algorithms.

Perceptive mobile networks (PMNs), integrating ubiquitous sensing capabilities into mobile networks, represent an important application of integrated sensing and communication (ISAC) in 6G. In this paper, we propose a practical framework for uplink sensing of angle-of-arrival (AoA), Doppler, and delay in millimeter-wave (mmWave) communication systems, which addresses challenges posed by clock asynchrony and hybrid arrays, while being compatible with existing communication protocols. We first introduce a beam scanning method and a corresponding AoA estimation algorithm, which utilizes frequency smoothing to effectively estimate AoAs for both static and dynamic paths. We then propose several methods for constructing a “clean” reference signal, which is subsequently used to cancel the effect caused by the clock asynchrony. We further develop a signal ratio-based joint AoA-Doppler-delay estimator and propose an AoA-based 2D-FFT-MUSIC (AB2FM) algorithm that applies 2D-FFT operations on the signal subspace, which accelerates the computation process with low complexity. Our proposed framework can estimate parameters in pairs, removing the complicated parameter association process. Simulation results validate the effectiveness of our proposed framework and demonstrate its robustness in both low and high signal-to-noise ratio (SNR) conditions.

The emerging Orthogonal Time Frequency Space (OTFS) technique can achieve stable communication in high-speed mobile scenarios, which combining with multiple-input multiple-output (MIMO) can improve the spectrum and energy efficiency of wireless communication systems. To achieve potential performance gains of OTFS-MIMO systems, this paper considers the precoding design for wideband OTFS-MIMO systems with beam squint effect. We propose a novel precoding structure with compensation modules for eliminating the influence of beam squint and further improving the achievable rate. The simulation results prove that the achievable rate of the wideband OTFS-MIMO system with beam squint has been significantly improved under our proposed scheme.

The emergence of generative pre-trained transform-ers (GPTs) will thoroughly change the application of sixth generation mobile communications (6G) networks. Therefore, it is necessary to design new network architectures to support ubiq-uitous deployment and real-time applications of GPTs. Aiming to integrate GPTs and the 6G network, this paper investigates the typical application scenarios of 6G+GPTs and summarizes the requirements of network key performance indicators (KPIs). Then, to address the complex and dynamically changing commu-nication environment, a self-adaptive 6G network architecture is proposed based on autonomous learning and self-optimization. Additionally, a novel mechanism based on attack samples is studied to improve the security of applying GPTs in 6G networks. Finally, we demonstrate that the proposed network architecture and security mechanism can satisfy the KPIs and improve robustness effectively. Overall, this paper provides a theoretical basis for the support of native GPTs with a novel 6G network architecture.

Prompted by the significant advancements in image processing technologies and their diverse range of applications, remote sensing satellites are poised for rapid expansion. Nonetheless, offloading the vast amount of remote sensing satellite images to the ground gateway station is inefficient due to the exorbitant costs induced by satellite links, while the limited resources of individual satellites hinder local task processing. With the advancement of the network function virtualization (NFV) technology, a new paradigm for service function chain (SFC) has emerged, which can significantly improve the flexibility and resource utilization of network services and alleviate resource conflicts by dividing large services into smaller ones organized in the form of SFCs. As mega-constellations (e.g., Starlink) developed, the number of low earth orbit (LEO) satellites is increasing. By dividing services into small sub-services and organizing them into SFCs throughout the LEO network, services that cannot be completed by a single satellite can be accomplished through multi-satellite cooperation. However, the quality of the remote sensing service is positively correlated with its latency, and the rapidly changing topology of LEO networks also adds complexity to the SFC placement. Hence, how to select appropriate satellites to place the SFC and modulate service levels, in order to obtain better remote sensing results within an acceptable latency, remains a question. To address these issues, this paper proposes the FloodSFCP, an SFC placement method that aims to increase service quality and decrease latency through offline training and online optimization via deep reinforcement learning, taking into account the variation in LEO network topology. By introducing NoisyNet, Dueling, and N-step learning, we improve the model’s generalization ability and reduce the state space, thus enhancing convergence speed while reducing decision and training time. Experimental results demonstrate that FloodSFCP significantly improves service quality while reducing total decision costs.

In mobile edge computing (MEC), one of the key issue is to optimize the offloading policy and the allocation of communication and computation resources among multiple mobile users (MUs). For different MUs, deadline of their computation tasks may be heterogeneous. It becomes more challenging as the computation burden of each computation task turns to be a random variable, which may even conform to uncertain probabilistic distribution. To address these issues, this work studies the offloading of asynchronous computation task and resource allocation in a MEC system supporting multiple MUs. Only with the mean and variance about uncertain computation burden, an optimization problem to minimize the weighted sum of energy consumption of multiple MUs is formulated, which is non-deterministic and non-convex, and is hard to solve. To overcome this challenge, we transform it into a deterministic problem, but is still non-convex. In order to solve the non-convex deterministic optimization problem, we decompose the problem into two levels. A heuristic algorithm is proposed for the upper-level to solve an ordering problem and a combination of alternative descend method, successive convex approximation (SCA), and Karush-Kuhn-Tucker (KKT) condition investigating are utilized for the lower-level problem.

With the emergence of compute-intensive and latency-sensitive vehicular applications, vehicular fog computing (VFC) has been proposed for catering to the thriving demands for computing and communication resources close to vehicles. In VFC scenarios where multiple tasks need to be offloaded simultaneously, the data, often coming from multiple sources, must be transmitted at a high data-rate in parallel. An antenna array system, a set of multiple connected antennas which work together as a single antenna, could achieve a significantly higher data-rate than a traditional single antenna. However, data-rate of the antenna array system may decrease due to the presence of interference. On the other hand, an antenna array system consumes more energy than a single antenna, which is antagonistic to vehicles powered by limited electricity. To address these challenges, we propose EAAV, a multi-task allocation strategy that enables multiple tasks to be offloaded concurrently in antenna array empowered VFC. EAAV aims at reducing the transmission power consumption while maintaining a high transmission data-rate, taking into account the mobility of vehicles and communication interference. We transform the multi-task allocation problem into a convex solvable one and evaluate the effectiveness of EAAV based on real-world vehicle trajectories. Compared with the existing task allocation strategy, EAAV improves the average transmission data-rate by up to 8.2% and reduces the average power consumption by up to 38.3%.

We investigate a novel downlink multicarrier direct-sequence code division multiple access (MC-DS-CDMA) resource allocation scheme in the context of low earth orbit (LEO) satellite-ground integrated networks (SGINs). In contrast to the existing MC-DS-CDMA works which mainly focus on delay-tolerant services in terrestrial networks, we consider the heterogeneous delay traffic and take the unique characteristics of LEO satellite systems into account. Specifically, we exploit the channel information, the delay requirement, the buffer state, and the visible time of users to construct a utility function and formulate the resource allocation optimization problem in presence of co-channel interferences (CCI) in LEO satellite-ground heterogeneous systems. We transform the original nonconvex optimization problem into two convex ones and use the Lagrange dual decomposition method to derive the solution. We also design an efficient algorithm dynamically scheduling subcarriers, codes, and transmission power of MC-DS-CDMA. The simulation results confirm the superiority of our work in terms of lower average delay and higher overall throughput.

To achieve maximum throughput in named data networking (NDN) with consideration of fairness among each source-sink pair, we model the routing problem in NDN as a mixed-integer maximum concurrent flow problem. Taking advantage of the smart forwarding framework in NDN, we obtain an approximate optimal routing and forwarding solution with our improved Fleischer and random rounding (FRR) algorithm. We analyze the complexity of the Fleischer algorithm more comprehensively and give the best scaling method to achieve the least iterations. In addition, we further reduce the complexity of the Fleischer algorithm so that it iterates only once, thus getting a heuristic algorithm which is called multicommodity multipath (MCMP). Using ndnSIM-based simulation, we compare our proposed routing algorithms with the existing routing algorithms, including flooding, k-shortest path, and best single path routing, and the simulation results prove that FRR and MCMP can outperform other algorithms both in throughput and latency, and it can also be concluded that the best number of paths for k-shortest path routing should be close to and less than the average node degree of the network. For studying routing optimization with caching, we conduct simulations with different in-network cache capacity and different caching policies. The simulation results show that although the LRU can achieve the best performance under the same cache capacity, the cache capacity plays a decisive role. All routing algorithms produce better performance with caching, but the FRR still achieves the best performance, and follows by MCMP.

Recently, unmanned aerial vehicle (UAV) has been considered as a promising technique in mobile edge computing networks, and enhances the performances of ultra-reliable and low-latency services. In this paper, we propose a UAV-aided low latency mobile edge computing network with millimeter wave (mmWave) backhaul. There are two types of communication links in our network, the UAV link and the ad hoc link. We jointly consider the network resource allocation and the UAV trajectory design in our proposed problem, which is a non-convex mixed integer nonlinear programming (MINLP). For solving the proposed problem, we give a novel algorithm framework. We adopt generalized Benders decomposition (GBD) as the outer loop algorithm to separate the integer variables and continuous variables. In the inner loop, the continuous primal problem is solved by the joint alternating direction method of multipliers (ADMM), Dinkelbach algorithm and successive convex approximation (SCA) algorithm. The simulation results show that our proposed system architecture and algorithm framework can achieve low latency for the time-sensitive task in mobile edge computing.

With the increasing demands for large-scale computing in Internet of Things network, fog computing emerges as a potential solution. However, the time and energy costs are the bottlenecks for developing fog computing. In this paper, we investigate the green fog computing by maximizing the network utility function considering energy efficiency with the constraints of power and interference. The proposed problem is a large-scale mixed integer nonlinear programming. To deal with such kind of problems, we design an algorithm framework to solve the problem in a distributed and parallel manner. The outer loop of the problem is based on the Benders decomposition to divide the integer variables and continuous variables into the master problems and subproblems, respectively. In the subproblem, we use the Dinkelbach algorithm to transform the fractional programming into an equivalent solvable form. In the inner loop, the large-scale problem with only continuous variables is handled by the alternating direction method of multipliers algorithm. For the master problem, we propose a centralized branch-and-bound algorithm to deal with the complexity. We also discuss the properties and performances of our algorithm. Finally, the simulation results indicate that our proposed algorithm is energy-efficient and time-saving.

Fog computing is a promising approach to alleviate the computation burden in traditional mobile networks to meet the increasing application demands. Such a complicated system is typically challenging and requires distributed solutions. In this paper, we investigate the resource allocation problem in fog computing to maximize the utility function from the energy efficiency perspective. The formulated problem is a mix integer nonlinear programming problem, which is NP-hard. We adopt a modified distributed inner convex approximation (NOVA) to approximate the problem first. Then, the Benders decomposition algorithm is applied to deal with integer variables. In the subproblem, we use the Dinkelbach algorithm to transform the fractional programming into an equivalent parametric subtractive form. Furthermore, the subproblem is decomposed distributedly, which enables users to update without information exchange. The simulation results indicate the effectiveness of the proposed algorithm.

Passive intermodulation (PIM) often limits the performance of satellite communication systems with multicarriers. The PIM interference has a peculiarity of timevarying and non-Markov. While the existing digital signal processing methods have limited effects, we propose a novel adaptive real-time recursion learning neural network (RTRLNN) which is suitable for dynamic PIM in satellite communications. The proposed novel RTRLNN method has fast convergence with its adaptive learning rate and accurate approximation ability. In this paper, a cancellation system with RTRLNN algorithm is designed for the PIM interference in satellite communications. The system has two processing sections which are pilot slot and data transmission slot. The system extracts the feature of PIM interference and trains itself in the pilot slot while achieves cancellation of PIM interference in the data transmission slot. Simulation results are presented on time and frequency domain illustrating the effectiveness of PIM interference cancellation. Compared with the modified least mean square(LMS) method, the proposed novel RTRLNN method significantly decreases the bit error rate(BER) of PIM interference. Furthermore, the proposed novel RTRLNN method shows an enhancement of 10dB in SIR gain while the Eb/N0 fixed 10dB.

To foster the rollout of 5G in unserved areas, 3GPP has kicked off a study item on new radio to support nonterrestrial networks (NTNs). Due to ultra‐wideband of laser, laser communication is very promising for the feeder links of NTNs; however, imprecise temporal synchronization hinders its deployment, which results from a combination of propagation delay, velocity, acceleration, and jerk of NTN platform. The prior synchronization algorithms are inapplicable to the temporal synchronization in laser communications due to the extremely high data rate and Doppler shift. This paper is devoted to addressing the temporal synchronization problem in laser communications. In particular, we first observe the sparsity of laser signal in time‐frequency domain. On top of this observation, we propose a new sparsity‐aware algorithm for temporal synchronization without carrier aid through sparse discrete polynomial‐phase transform and sparse discrete fractional Fourier transform. Subsequently, we implement the proposed algorithm via designing a hardware prototype. To further evaluate its performance, we conduct extensive simulations, and the results demonstrate the effectiveness of the proposed algorithm in terms of good accuracy, low power consumption, and low computational complexity, suggesting its attractiveness for the feeder links of 5G NTNs.

The number of array elements significantly influences the computational complexity of space-borne phased array antenna systems. When the satellite is on-orbit, the performances of space-borne phased array antennas will be easily influenced by the environment and devices aging. Therefore, the space-borne antennas need to be regularly calibrated after satellite is launched. However, the complexity of calibration system increases in direct ratio to the radio frequency (RF) chain number of phased array antenna system. To solve the problem, a low complexity calibration method is proposed, whose system complexity is irrelevant to the number of array elements and calibration time is short. This method is especially suitable for large-scale space-borne phased array antennas. Simulation and experimental results of relative amplitude/phase inaccuracy probability under different SNR illustrate that when the value of SNR is higher than 16dB, the measurement accuracy of relative power (amplitude) and phase can reach ±0.1dB and ±1°, respectively. The simulation and experiment results also provide a significant basis to the link budget of the space-borne phased array antenna systems.

In this paper, we investigate the energy-efficient power control for device-to-device (D2D) communications underlaying cellular networks with max-min fairness, where uplink resource blocks allocated to one cellular user equipment are reused by multiple D2D pairs to improve the frequency reuse factor, and the minimum individual energy efficiency (EE) is maximized. This is a generalized fractional programming (GFP) problem, and is hard to tackle due to its non-concave nature, which means the complexity of global optimal solution is unaffordable. In order to give sub-optimal solution with reasonable complexity, we first transform the GFP problem into equivalent optimization problem in a parametric subtractive form, and then add constraints on the co-channel interferences to convert the non-concave GFP problem into concave one. The sub-optimal solution, which can be obtained through solving the deduced concave problem based on sophisticated convex optimization methods, gives a tight lower bound on the optimal EE. Simulation results are presented to demonstrate the effectiveness of the proposed scheme.

Abstract This paper investigates the channel selection and slot time configuration in a cognitive radio network with a number of potential channels. Each channel alternates between ON state (i.e., the primary user is using the channel) and OFF state (i.e., the primary user does not use the channel), and the state evolution process is modeled as a continuous‐time Markov process. The traffic parameters (the transition rates) of the Markov process also evolve with time, modeled as a discrete‐time Markov process. A secondary user adopts a slotted structure with dynamic slot length. At each slot, the secondary user needs to determine which channel to sense and, if the channel is sensed idle, how long the slot length should be. Considering both the amount of data that the secondary user can transmit and the duration when the secondary user interferes with primary activities, a reward definition is given. Based on the reward definition, an adaptive channel selection and slot length configuration method is proposed, which includes a reward maximization procedure to maximize the achieved reward and an update procedure for the channel state belief vector and traffic parameter state belief vector. Numerical results are given to demonstrate the effectiveness and features of the proposed method. Copyright © 2016 John Wiley & Sons, Ltd.

In this paper, we investigate the energy-efficient resource allocation problem for the device-to- device (D2D) communications overlaying LTE networks, where the D2D user equipment (UE) shares the spectrum with the cellular UE in an orthogonal way such that the interference between them is completely eliminated. We consider both the non- orthogonal and orthogonal resource allocation strategies for D2D communications, where the resources allocated to different D2D pairs are non-orthogonal and orthogonal respectively. In the non-orthogonal strategy, the interference exists among different D2D pairs, and the resource allocation only concerns the transmit power control for each D2D pair; whereas in the orthogonal strategy, there is no interference, and the resource allocation concerns both the resource block (RB) allocation and the transmit power control. In the two strategies, the related resource allocation problems are firstly formulated as a fractional programming (FP) problem and a mixed-integer nonlinear fractional programming (MINLFP) problem respectively, both of which are then transformed into equivalent optimization problems in parametric subtractive form by exploiting the property of FP. As the transformed equivalent problems are non-concave, we develop the sub-optimal energy-efficient resource allocation schemes by solving them based on Dinkelbach and Powell-Hestenes-Rockafellar augmented Lagrangian methods. Simulation results demonstrate the effectiveness of the proposed schemes and show that the non-orthogonal strategy outperforms the orthogonal one in terms of the energy efficiency.

Device-free localization (DFL) based on received signal strength (RSS) measurements functions by measuring RSS variation due to the presence of the target. The accuracy of a certain localization method closely depends on the accuracy of the measurement model itself. Existing models have been found not accurate enough under certain circumstances as they cannot explain some phenomena observed in DFL practices. In light of this, we propose a new model to characterize the RSS variation, which invokes diffraction theory and regards the target as a cylinder instead of a point mass. It is observed that the proposed model agrees well with experimental measurements, particularly when the target crosses the link or is in the vicinity of the link. Since the proposed measurement model is highly nonlinear, a particle filter-based tracking method is used to generate the approximate Bayesian estimate of the target position. As a performance benchmark, we have also derived the posterior Cramér-Rao lower bound of DFL for a diffraction model. A field test has shown that the proposed diffraction model may improve the tracking accuracy at least by 45% in a single-target case and by 27% in a double-target case.

Location of a source is of considerable interest in wireless sensor networks. A novel algorithm for source location by utilizing the time-of-arrival (TOA) measurements of a signal received at spatially separated sensors under non-line-of-sight (NLOS) environments is proposed. The algorithm is based on quadratic programming, which is a special type of mathematical optimization problem. Comparisons of performance with other algorithms are made, and Monte Carlo simulations are performed. Simulation results show that the proposed algorithm gives better results.

We present a robust threshold setting strategy with modest computational complexity for frequency domain interference excision in direct sequence spread-spectrum (DSSS) commu-nication systems. The proposed strategy calculates the threshold by multiplying the minimum value of the averaged squared magnitude with a predefined scaling factor. An analytical framework for choosing the scaling factor is developed based on the principle of constant false-alarm rate (CFAR). Numerical results indicate that the new strategy outperforms existing ones in a wide range of partial-band jamming scenarios.

Channel estimation is critical in designing broadband multiple input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) systems, a parametric channel estimation method based on least square (LS) criteria is proposed in this paper, and the MSE performance using optimal training pilots is also given here, which proves this estimation method can improve the estimation precision greatly in sparse channel. Since such method needs the multi-path time delays information of the channel, the probabilistic data association (PDA) method was employed to estimate the multi-path time delays. Simulation results show that both the bit error rate (BER) and the mean square error (MSE) performance of the proposed method are better than the traditional LS channel estimation method.