To tackle the distinctive challenges of satellite mobility—challenges that exert significant impacts on both the reliability of communication links and the efficiency of wireless power transfer (WPT)—we propose a distributed satellite-fusion cell-free (SFcf) integrated sensing and communication (ISAC) architecture. Traditional adaptive beamforming, which performs reactive compensation based on estimated channel state information (CSI), faces inherent limitations in achieving robust data transmission and efficient energy delivery within such high-dynamic environments, primarily due to its intrinsic latency. To surmount this bottleneck, we put forward a novel closed-loop ISAC framework that facilitates a proactive paradigm for the joint transfer of wireless information and power. The core concept lies in estimating the physical source of channel variation—namely the satellite’s motion parameters—and utilizing these parameters to enable predictive adaptation of both communication and energy beams. In this framework, sensing-derived Doppler and time of arrival (ToA) parameters are fed back to actively pre-compensate for channel mismatch, ensuring communication signals are robust and that energy beams remain precisely focused on the moving target. Therefore, our proposed SFcf-ISAC framework is specifically designed to validate this effective synergy, enabling sustainable 6G satellite-ground connectivity through high-efficiency WPT, remote sensing and resilient high-mobility communication.

As the number of Internet of Things (IoT) devices continues to surge, particularly in remote areas, ensuring efficient and reliable connectivity has become a critical challenge. The integration of Narrowband IoT (NB-IoT) with Non-Terrestrial Networks (NTN) offers a promising solution. However, in Low Earth Orbit (LEO) satellite environments, existing random access (RA) procedures face significant issues, especially with high user density, leading to increased collisions and reduced access efficiency. While progress has been made in areas like delay compensation and Doppler effect management, challenges in collision mitigation remain. This paper proposes two innovative strategies to enhance RA performance in NB-IoT NTN systems: a non-orthogonal frequency hopping pattern for the narrowband physical random access channel (NPRACH) preamble, which increases the number of available preambles by allowing controlled overlap in frequency hopping sequences, thus reducing collision rates; and an adaptive access mechanism, which dynamically adjusts access parameters like backoff intervals and access barring factors based on network conditions to improve access success rates. Simulation results demonstrate the effectiveness of these improvements, especially in high-traffic environments, providing a more efficient and reliable solution for future NB-IoT deployments in NTN.

As a supplement to terrestrial communication networks, satellite edge computing can break through geographical limitations and provide on-orbit computing services for people in some remote areas to achieve truly seamless global coverage. Considering time-varying channels, queue delays, and dynamic loads of edge computing satellites, we propose a multiagent task offloading and resource allocation (MATORA) algorithm with weighted latency as the optimization goal. It is a mixed integer nonlinear problem decoupled into task offloading and resource allocation subproblems. For the offloading subproblem, we propose a distributed multiagent deep reinforcement learning algorithm, and each agent generates its own offloading decision without knowing the prior knowledge of others. We show that the resource allocation problem is convex and can be solved using convex optimization methods. The experiment shows that the proposed algorithm can better adapt to the change of channel and the dynamic load of edge computing satellite, and it can effectively reduce task latency and task drop rate.

Recent studies have shown that system capacity is very important for cellular networks. In this paper, we consider maximizing the weighted sum-rate of the cellular network downlink and uplink, where each cell consists of a full-duplex (FD) base station (BS) and half-duplex (HD) users. Federated learning (FL) can train models in the absence of centralized data, which can achieve privacy protection of user data. A low Earth orbit (LEO) satellite edge computing system (LSECS) can be formed by placing the mobile edge computing (MEC) servers on LEO satellites, which greatly increases the processing capacities of the satellites. Therefore, we consider a combination of FL and MEC and propose an FL-based computation offloading algorithm to maximize the weighted sum-rate while ensuring the security of user data. We consider solving the sub-channel assignment and power allocation problems using deep reinforcement learning (DRL) algorithms with excellent global search capabilities. The simulation results show that our proposed algorithm achieves the maximum weighted sum-rate compared with the baseline algorithms and excellent convergence.

Low Earth Orbit (LEO) satellite is one of the most promising infrastructures for realizing global high-speed interconnection services. Accurate satellite-to-ground channel state information (CSI) is crucial for meeting these demands, as it plays a vital role in ensuring the reliability and efficiency of communication links. However, the high dynamics and long delays in LEO satellite communications pose challenges to the effectiveness of the obtained CSI, resulting in channel aging issues. To this end, we propose a temporal-frequency transformer (TFformer) based channel prediction scheme to predict CSI from both temporal and frequency perspectives. Specifically, to tackle the challenge of accurately extracting time-domain features of fast time-varying satellite-to-ground channels, we propose the frequency transform block (FTB) and temporal frequency attention (TFA) modules to achieve frequency domain feature extraction and temporal-frequency feature fusion. The FTB module is proposed to extract features of CSI in the frequency domain by establishing the mapping between time and frequency domain. Meanwhile, we propose the TFA module to achieve the effective combination of frequency and temporal features by assigning distinct attention weights to different temporal-frequency components. To further address the dynamic characteristics of satellite-to-ground channels, we propose the mixture of experts (MoE) module to extract the variation trends of key parameters as representative features. Moreover, the proposed TFformer achieves linear computational complexity with negligible information loss by randomly selecting Fourier components. Numerical results show that our proposed TFformer outperforms the conventional and deep learning-based channel prediction schemes in both prediction accuracy and system-level performance.

The low earth orbit (LEO) satellite system is a key point in the research of the next generation network, 6G. Phased array antenna is an important component of the LEO satellite antenna system, and its research and development have attracted more and more attention. LEO satellites require a reduction in the number of antenna arrays to achieve the effect of simplifying the array structure. Based on the above, this paper proposes an array sparse algorithm based on the combination of amplitude density and genetic algorithm (ADGA) to simplify antenna array arrangement. The task is transformed into an array sparse problem with the goal of minimizing the peak side lobe level (PSLL) of the beam while meeting a certain sparsity rate and ensuring that the width of the main lobe is basically unchanged. The non-iterative algorithm based on the amplitude density is used to quickly give an initial solution of array sparseness. Then, the improved genetic algorithm is used for further optimization, where the array element symmetry constraints is proposed to enhance the optimization effect. The simulation results show that, compared with the genetic algorithm and the traditional analytical algorithm, the proposed algorithm reduces the number of array elements and effectively reduces the PSLL of the beam generated by the LEO satellite under the premise of keeping the main lobe width basically unchanged.

Space-ground integrated networks (SGINs) have attracted substantial research interests due to their wide area coverage capability, where spectrum sharing is employed between the satellite and terrestrial networks for improving the spectral efficiency (SE). We further improve the SE by conceiving a cell-free system in SGINs, where the full-duplex (FD) multi-antenna APs simultaneously provide downlink and uplink services at the same time and within the same frequency band. Furthermore, power domain (PD) non-orthogonal multiple access (NOMA) is employed as the multiple access (MA) technique in the cell-free system. To achieve a performance enhancement, the sum-rate maximization problem is formulated for jointly optimizing the power allocation factors (PAFs) of the NOMA downlink (DL), the uplink transmit power, and both the beamformer of the satellite and of the APs. Successive convex approximation (SCA) and semi-definite programming (SDP) are adopted to transform the resultant non-convex problem into an equivalent convex one. Our simulation results reveal that 1) our proposed system outperforms the well-known approaches (i.e., frequency division duplex (FDD) and small cell systems) in terms of its SE; 2) our proposed optimization algorithm significantly improves the networking performance; 3) the conceived SIC order design outperforms the fixed-order design at the same complexity.

In this paper, we propose a multi-roadside unit (RSU) assisted mobile crowd computing framework for intelligently connected vehicle (ICV) networks, where vehicles within RSUs’ coverage act as workers to provide their computation and communication resources for computing resource limited vehicle user equipments (VUEs). Physical (PHY) layer security is used to secure computation task offloading and results feedback in time-varying vehicular channels. Artificial noise (AN) assisted adaptive wiretap coding is adopted to enhance the security of offloading links. With PHY security, the intended receiver can decode secret message while eavesdropper cannot. A modified exhaustive two-dimensional (2D) search algorithm is proposed to optimize transmission rate and secrecy rate in an effective secrecy throughput maximization problem, and a multi-agent twin delayed deep deterministic policy gradient algorithm (MATD3) is utilized to assign VUEs’ tasks without a central controller, where a reward function is defined according to the computing costs, including execution time, energy consumption, and price paid for computing. Finally, simulations verify the effectiveness of the proposed framework.

Sparse code multiple access (SCMA) technology has been widely studied thanks to its outstanding overload performance, which provides greater device access on limited time-frequency resources. We propose a variational learning based end-to-end SCMA network model termed as V-SCMA. In our model, SCMA codebooks are generated by a deep neural network (DNN) encoder. Motivated by the idea of variational learning, we derive the posterior probability of SCMA codeword send by each user from the point view of binary coding using the variational method. Then, we design an SCMA decoding network to learn this process of approaching the real posterior probability. The SCMA decoding network designed by us is a truncated recurrent neural network, which is simpler than other networks. In addition, a novel loss function is proposed to optimize V-SCMA. Compared with previous works, the new loss function is a tighter variational lower bound which improves the BER performance. Simulation results show that the proposed V-SCMA offers a better bit error rate (BER) performance and a lower computational complexity than conventional schemes.

In this paper, we investigate unmanned aerial vehicle (UAV) assisted physical layer security in multi-beam satellite enabled vehicle communications. Particularly, the UAV is exploited as a relay to improve the secure satellite-to-vehicle link, and simultaneously serves as a jammer by deliberately generating artificial noise (AN) to confuse Eve. The satellite beamforming (BF) and UAV power allocation (PA) are jointly optimized to maximize the secrecy rate of the legitimate user within a target beam while guaranteeing the quality of service (QoS) of users within other beams. Since the problem is nonconvex, we first convert it into an equivalent two-stage problem. Then, the outer-stage problem is solved by using one-dimensional search, and the inner-stage problem is transformed to a bi-convex problem by using the semi-definite relaxation (SDR) and Charnes Cooper transformation. To solve the inner-stage bi-convex problem, we propose an iterative alternating optimization algorithm, where the optimal BF is obtained by semi-definite programming (SDP), and the optimal UAV PA is subsequently obtained by solving the reformulated fractional programming problem with an iterative Dinkelbach method. The tightness of SDR and the complexity of our proposed approach are analyzed, and extensive simulations are carried out to evaluate the effectiveness of our proposed approach.

Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) system with lens antenna array can achieve extremely high data rates with limited radio frequency (RF) chains. It brings challenges to channel estimation due to the gap between the number of RF chains and antennas. To solve this problem, by analyzing and exploiting the unique power distribution (PD) of the beamspace channel (BC) that differs from the general sparse signals, we propose a PD-based estimation scheme for the sparse BC. Specifically, we transform the issue into the direction of arrival (DOA) and complex gain estimation for each multipath component after PD-based support estimation. Meanwhile, we propose a method to reduce error propagation. Besides, we provide a lower bound for the error of the proposed PD-based scheme and explain the parameters that influence the performance. Finally, the numerical simulation results confirm the advantage of the proposed method over the conventional channel estimation ones in terms of accuracy and overhead. Meanwhile, the simulation results also prove the proposed proposition about the lower bound of normalized mean squared error.

Sparse code multiple access (SCMA) has excellent application prospects due to its high spectral efficiency and accsess capacity. However, due to the nonorthogonal characteristic of SCMA in code domain, the codebook needs to be manually designed for all communication scenarios, and the receiver has high computational complexity. To address this issue, deep neural network-aided SCMA (DNN-SCMA) is proposed, but it is difficult to capture channel state information (CSI) in the dynamic and time-varying communication scenarios, which hinders the overall learning and optimization fot end-to-end communications. This paper proposes a stochastic channel model with conditional generative adversarial network (CGAN) for DNN-aided SCMA in a data-driven way. Particularly, a model-free learning method is adopted to accurately learn different types of random channel models, which realizes effective acquisition of dynamic channel information. Finally, the end-to-end training is achieved through the use of back propagation (BP), and then by an iterative training of the composed networks, the end-to-end loss can be optimized in a supervised manner. Results show the feasibility of CGAN-based channel modeling in end-to-end DNN-SCMA.

Abstract This paper investigates the relay selection problem and proposes a three‐stage relay selection strategy with power allocation (TRSPA) for a spectrum‐sensing‐based full‐duplex (FD) user relaying cooperative non‐orthogonal multiple access (CNOMA) scheme. Uniformly‐distributed strong user relays in the investigated scheme help a weak user communicates with the base station in an efficient and reliable way. The proposed TRSPA strategy maximizes the transmission data rate of the selected relay while ensuring successful transmissions for the weak user by precisely narrowing down relay candidates step‐by‐step and dynamically allocating optimal power coefficients. Exact and asymptotic outage probabilities and ergodic rates are worked out. Accordingly, diversity orders and spatial multiplexing gains are derived. We further exploit the impact of self‐interference (SI) on TRSPA for FD‐CNOMA and then compare its performance with TRSPA applied in other relaying modes, that is half‐duplex and orthogonal multiple access. Finally, simulation results reveal that: (i) theoretical derivation results are correct; (ii) TRSPA always outperforms other relay selection strategies in terms of outage probability and ergodic rate; and (iii) TRSPA for FD‐CNOMA in a real‐world scenario achieves better performance than other relaying modes in spite of the adverse effect of SI in FD mode.

For the future sixth-generation (6G) wireless communication networks, improved metrics are expected to provide connectivity of massive devices, which brings new challenges for 6G networks extending to modern radio access for Internet of Things (IoT) applications. We consider a 6G enabled nonterrestrial network working in remote areas in this article, where an on-demand unmanned aerial vehicles (UAVs) provides the connectivity services. To improve the energy efficiency (EE), a method combining nonorthogonal multiple access (NOMA) and spatial modulation (SM) techniques is proposed and termed spatial NOMA (S-NOMA). Particularly, by employing multiple input multiple output (MIMO), SM only activates partial transmit antennas in per symbol interval, which can provide large data rate with less interantenna interference (IAI). Moreover, a power allocation optimization method subject to EE for S-NOMA scheme is proposed. Specifically, the antenna selection bits are determined by all users, which improves EE for all users instead of the selected one. Besides, the capacity expressions of S-NOMA are derived, then the EE performance of S-NOMA is analyzed. In addition, simulation results show that the proposed S-NOMA with energy-efficient power allocation performs better EE performance compared with the conventional NOMA.

Fifth generation with high dimensions and capabilities is expected to fulfil the requirements of the Tactile Internet. Tracking provides strong support for intuitive interaction with interfaces by hands, eyes, bodies, etc. Such interfaces can be used in the Tactile Internet for interaction with real and virtual objects. The trackers based on tracking-by-detection framework rely on manual feature detectors for robust tracking, which is particularly useful for specific objects like humans but cannot handle generic tracking problems. Therefore, a sparse feature learning method beyond manual design is proposed to learn features from the samples sampled during tracking. The basic idea is to learn a dictionary from the samples in the previous frames and construct feature representations to represent the object for detection of the location in the current frame. The samples are patches centered at the keypoints based on an adaptive features from accelerated segment test (FAST) detector with local threshold. The dictionary is learned with sparse coding for sparse representations and atoms of the dictionary are grouped to describe the local orientation of these samples. The integrated features are built after rectification of the sparse representations. The correlation filter is used to infer the object location from the sparse features. The qualitative and quantitative experimental results on OTB100 show the advantage of the proposed tracker against current state-of-the-art trackers in terms of accuracy.

Mobile sensor networks (MSNs) are good candidates for large-scale unattended surveillance applications. However, it is challenging to track moving targets due to their complex dynamic behaviors. Semiflocking algorithms have been proven to be efficient in controlling MSNs in both area coverage and target tracking applications. While many existing literatures on the study of semiflocking algorithms often assume an area of interest (AoI) to be regular and with unified traversal cost, the uneven and rough landscapes in real-life applications have imposed extra challenges and raised demands for new management strategies. In this article, a mobility map is used to incorporate different costs associated with irregular terrains which results in different maximum allowed speeds on nodes in different regions. In order to reduce target detection time and node energy consumption, a heuristic search algorithm is developed to find time-efficient and feasible paths between nodes and sensing targets. Under the proposed algorithm, nodes can effectively select a target to track or search for new targets in the AoI. Results of extensive experiments show that semiflocking-controlled nodes together with path planning can reach their targets faster with lower energy consumption compared to three exiting flocking-based algorithms.

In indoor localization, reliability and optimization analysis are the most vital factors to be considered. Numerous modern-day services require precise location information for their application. Location fingerprinting using WLAN is the most acknowledged technique for indoor localization purposes. Both accuracy and coverage can be enhanced by deploying the WLAN access points (APs) appropriately. In this article, an optimization problem is formulated for reliable localization systems. An AP deployment strategy ensuring coverage along with a selection strategy to choose an optimal configuration for reducing the localization error has been implemented. A hybrid technique is proposed to select the optimal APs configuration that merges the traditional fingerprint difference and geometric dilution of precision-based methods. A distinguishing feature of this work is the inclusion of two significant constraints, which are the consideration of walls and people attenuation factor in the optimization process. Simulations are conducted to test and verify the practicality of the proposed technique through a number of comparison test cases. The results demonstrate that our proposed technique outperforms the previously existing techniques.

Semi-flocking algorithms have been demonstrated to be efficient in maneuvering MSNs in multi-target tracking tasks. In many real-world applications, targets can be assigned with different priorities according to their importance of being tracked. However, existing semi-flocking algorithms normally assume the importance of all targets to be identical, which may not allocate resources in an efficient manner. In this paper, we propose a target evaluation method that incorporates priorities of the targets in the assessment process. Based on the evaluation results, mobile agents decide to track a target or continue to scan the terrain via a probabilistic task switching mechanism. Simulation results indicate a higher effectiveness of the proposed method in target tracking and area coverage when compared with two existing semi-flocking algorithms.

Spectrally efficient frequency division multiplexing (SEFDM) is a promising non-orthogonal multi-carrier technique to improve spectral efficiency, by compressing the inter-carrier interval relative to orthogonal frequency division multiplexing (OFDM) systems. However, by breaking the orthogonality among subcarriers, the self- introduced inter-carrier interference (ICI) severely restrains the achievable transmission rate and poses great challenges in designing the receiver with ICI cancellation. In this paper, we first characterize the statistical distribution of ICI and then derive a closed-form expression of the signal-to- interference-plus-noise ratio (SINR). After that, an efficient time-domain ICI mitigation approach is proposed to improve the achievable SINR and the spectral efficiency of the SEFDM system. Numerical results verify the analytical expressions for the cumulative distribution function (CDF) of ICI and the achievable SINR. In addition, it is shown that the spectral efficiency can be significantly improved by adopting our proposed ICI mitigation approach.

Internet of Things as an essential integrated part of the future wireless communication system provides ubiquitous connectivity and information exchange to enable a range of applications and services, which has triggered spectrum resource pressure, multiple access, bandwidth efficiency, and security issues. Focusing on these issues, a high spectral efficiency secure access (HSESA) scheme based on dual nonorthogonal is proposed first in this paper. The scheme which can be recognized as a dual nonorthogonal scheme is designed by the nonorthogonal multiplexing and nonorthogonal multiple access. Particularly, HSESA scheme is equipped with secure multiplexing by using security matrix to improve physical layer security. Moreover, spectral efficiency analysis is given and the throughput of HSESA has been derived. Moreover, iterative detection (ID) and maximum likelihood (ML) are, respectively, combined with message passing algorithm (MPA) as detection schemes, and their respective performance advantages are analyzed. Simulation results show that the detection scheme using ID combined with MPA has lower complexity, while ML combined with MPA has better bit error rate performance, and the spectral efficiency is also enhanced by the proposed HSESA.

Cloud computing provides powerful computing ability of mobile devices in the Internet of Things (IoT) networks. However, the large amounts of data interaction with cloud suffers bandwidth limit and energy efficiency for data processing and transmission, and the energy consumption of data processing is far less than data transmission. In this paper, offloading is considered in transmission to improve the battery lifetime by employing a spectral-energy efficient transmission scheme with efficient computing in IoT-Cloud. The offloaded resource can be saved to serve more services if the physical air interface is designed efficiently. In addition, many personal things are unloaded to the IoT-Cloud which creates a risk of privacy and security. The improved offloading efficiency of data transmission scheme secure truncating orthogonal frequency division multiplexing (STOFDM) is generated by deliberately truncating the orthogonal frequency division multiplexing signal in time domain. Particularly, the truncations are selected by a dynamic random private matrix based on the proposed offloading power amplifier theorem. The corresponding legitimate receiver is designed with private mapping using the efficient fast Fourier transformation (FFT) for offloading computation. Moreover, the closed-form expression for the FFT-based STOFDM system is analyzed and be verified by simulation results. In light of the analysis, the STOFDM performs intercarrier interference as an orthogonal sequence is partially transmitted, which degrades the reliability of transmission link. Further, two enhanced detectors with low computing-complexity is also given to improve the performance of Bob while restrict eavesdropper's reception and further provides offloading computation.

Future Internet of Things should contain space segment and terrestrial segment. In addition, the multibeam satellite communication systems, especially working in S shared band, have gained more attention, which plays a significant role in providing direct-to-user satellite mobile services. Besides, due to the limited on-board resources, it is increasingly urgent to improve resource utilization. Taking the interbeam interference, channel conditions, delay factor, capacity, bandwidth utilization variance into consideration, a novel joint resource allocation algorithm is proposed in this paper. Interbeam interference coefficient matrix derived from frequency reuse is established to measure the level of co-channel interference. Moreover, the proposed algorithm can allocate resources flexibly according to specific traffic requirements and channel conditions. A novel joint power and bandwidth allocation algorithm is proposed by optimizing throughput and approximation problem of actual requirement. The optimal solution to this optimization problem can be obtained by golden section theory and subgradient iteration. The evaluation results demonstrate that the proposed algorithm can maximize the capacity, minimize the bandwidth utilization variance, and it can also allocate resource intelligently adapting to the user requirements and channel conditions.

Compared with the cloud radio access network (C-RAN), heterogeneous C-RAN with the high-power node entity which separates the control and broadcast functionalities from the baseband processing unit (BBU) pool. It makes the user access, resource allocation, load balancing more flexible, which also makes the intralayer interference and interlayer interference more complex. Therefore, the heavy inverse operations for dense matrices and the complicated power allocation algorithms in beamforming perform large floating-point calculations per second in BBU pool. As frequency resources grow scarcer, green communication with high energy efficiency (EE) and low carbon emissions has raised significant concerns. In this paper, we focus on the EE advantages achieved by selectively cooperative transmission and associated power consumption model. A joint channel matrix sparseness and normalized water-filling resource allocation algorithm is proposed and formulated to improve EE at different user density through mathematical derivation. By reducing the computation complexity of cooperative transmission, the proposed scheme decreases the digital baseband power consumption, which is more adaptable for tidal phenomenon. Simulation results show that the proposed algorithm can effectively reduce the energy consumption of baseband and improve the EE of the system.

In this paper, a novel full-duplex cooperative non-orthogonal multiple access (FD CNOMA) system is proposed, where users intend to exchange messages with the assistance of a decode-and-forward relay. To characterize the potential performance gain brought by the proposed FD CNOMA scheme, the outage probability and ergodic rate are analyzed. Specifically, the closed-form expressions for the outage probabilities, diversity orders, ergodic rates, and system throughputs in delay-limited and delay-tolerant transmission modes are derived under the realistic assumption of imperfect self-interference cancellation. Furthermore, to present the comprehensive performance evaluation, both perfect and imperfect successive interference cancellations (SICs) are taken into consideration. Simulations are performed to validate the accuracy of the derivation results and to illustrate the outstanding performance of the proposed scheme in low signal-to-noise ratio region compared with half-duplex CNOMA system and cooperative orthogonal multiple access system. Our results show that under the conditions of both perfect and imperfect SICs, outage probability floors and ergodic rate ceilings exist for the proposed FD CNOMA scheme due to the inter-user interference among superimposed NOMA signals and the residual self-interference caused by the imperfect self-interference cancellation.

Mobile sensor networks (MSNs) are useful in monitoring outdoor environments. Semi-flocking algorithms have been proven to be efficient in controlling MSNs in area sensing and target tracking applications. Even though outdoor environments may consist of irregular regions with different traverse costs, existing semi-flocking algorithms assume an area of interest (AoI) to be regular and with uniform costs. Such an assumption limits target tracking capabilities of a MSN. In this work, we model operating terrains using a mobility map that incorporates the speed limits of different sub-regions. To gather the required number of mobile sensor nodes for target tracking, an A* heuristic search algorithm is used to find time-efficient paths connecting nodes to targets. Nodes can acquire navigation information using the A*-based path planning module and then traverse along the time-efficient paths until they reach the targets. Simulation results verify the effectiveness of the proposed method over an existing semi-flocking algorithm.

Since presented by K. Fall, the new-type network Delay and Disruption Tolerant Network (DTN) came to become a hot area of research. In recent years, many kinds of DTN routing algorithms have been proposed. With the characteristics of long time delay, high packet loss rate and intermittent connectivity. Satellite networks also belong to a kind of DTN. But meanwhile, they have their unique characteristics of satellite nodes regular running in orbits and predictable inter-satellite links on and off, for which deterministic routing algorithm is more suited in Satellite DTN networks. This paper focuses on studying the theoretical knowledge of a kind of deterministic DTN routing algorithm, Earliest-Delivery(ED) algorithm. And combining with the characteristics of the satellite network above, we propose an improved ED algorithm from three aspects of distributed routing, routing update strategy and tag - based dynamic storage scheme. Ultimately, we simulate the LEO/MEO two-layer satellite delay tolerant network in OPNET, and analyze the performance of two algorithms upon packet delivery ratio, average end-to-end delay and the number of packets destroyed in the storage queue. The result shows the improved algorithm performs better in satellite DTN network.

Integrated satellite-terrestrial network combines both advantages of satellite and the terrestrial network and it can achieve all-day seamless coverage and broad coverage areas. Cognitive satellite spectrum sharing scheme increases spectrum utilization significantly, but it can cause satellite network and terrestrial network intensive co-frequency interference. However, the exclusion zone makes the signal to interference and noise ratio (SINR) of the satellite-terrestrial link increase significantly and the performance of throughput, energy efficiency (EE) and inter-cell fairness have not been improved. Therefore, continuous optimization of inter-cell fairness and energy efficiency will improve the Quality of Experience (QoE). Thus, we investigate an integrated satellite terrestrial spectrum sharing scheme based on cognitive frequency band isolation, where both inter-cell fairness and user density are considered. Moreover, integrated satellite terrestrial network overlapping coverage framework is firstly proposed, where the optimization of EE or SINR can be flexibly adapt to integrate satellite-terrestrial network. Furthermore, a joint soft frequency reuse strategy can achieve maximum throughput. Finally, the numerical results show that the performance of EE, SINR, throughput and the inter- cell fairness of proposed scheme are superior to the traditional integrated satellite-terrestrial spectrum sharing scheme.

Modulated wideband converter (MWC) is a blind undersampling system for sparse multiband signal. By the available hardware and compressed sensing method, it can realize energy-efficient wideband spectrum sensing under sub-Nyquist sampling rate. In this paper, we propose an optimized circulant measurement matrix constructing method which is suitable for the MWC system, aiming at the problem that the random measurement matrix is hard to implement in hardware. Simulation results show that the MWC system can reconstruct the original signal with high probability using the proposed method. And its signal to noise ratio (SNR) and sampling channel number performance are improved 8dB and 4 channels respectively compared with traditional Toeplitz measurement matrix and circulant measurement matrix.

Considering the fairness between the users in physical-layer multigroup multicasting, the Max-Min Fairness (MMF) precoding problem is investigated under the per-antenna power constraints. A suboptimal precoding algorithm is proposed by decoupling the problem into a precoding direction vector design problem and a power allocation problem. Particularly, the SLNR (Signal to Leakage plus Noise Ratio) metric is considered in the precoding direction vector design to properly mitigate the inter-group interference. Moreover, aiming at the MMF criterion, the power allocation procedure can guarantee fairness between users. The simulation results show that the proposed algorithm has less complexity than the primary MMF algorithm at the cost of some performance loss, while has better performance than a low complex algorithm, and consequently provides a solution for the tradeoff between complexity and performance.

Sparse code multiple access (SCMA), as one of the most promising candidate techniques for the fifth generation communications system, is a nonorthogonal multiple access scheme which can provide large scale connections. Its philosophy is to map coded bits directly to multidimensional sparse codewords, and the message passing algorithm (MPA) is utilized to detect the multiuser signals. However, the relatively high computation of MPA detection may lower the performance when SCMA is implemented in practical applications. The partial marginalization MPA (PM-MPA) helps to reduce the computation of original MPA detection. In this paper, an improved detection scheme based on PM-MPA is proposed. Our analysis and simulation shows that compared with PM-MPA, the improved PM-MPA (IPM-MPA) can obtain a lower bit error ratio. Besides, the simulation also shows that, to achieve the same performance, the IPM-MPA is less complex than PM-MPA.

The Internet of Things (IoT) is a new network that connects massive devices which have the communication ability. With the requirement of various services and the wireless spectrums becoming increasingly scarce, we consider a nonorthogonal multicarrier transmission scheme that is spectrum efficient frequency division multiplexing (SEFDM) as the downlink transmission scheme for IoT network. SEFDM has the merit of improved bandwidth usage efficiency, but the serious inter carrier interference (ICI) will be introduced by multiplexing overlapped carriers. Thus, the signal detection is challenged for recovering the signal which is suffered from ICI due to the loss of the orthogonality. In this paper, a low complexity detector based on quasi-orthogonality compensation (QOC) is proposed in the downlink receiver. And the complexity of the QOC detector is also analyzed. Moreover, a novel detector which joint QOC and fixed sphere decoding (FSD) algorithm is proposed. The bit error rate performances of QOC and QOC-FSD detectors are evaluated by numerical simulations. Numerical results show that the QOC and QOC-FSD detectors can achieve better performance than conventional iterative detection (ID) and ID-FSD, respectively. Furthermore, QOC-FSD detector performs a lower complexity than ID-FSD detector.

As the indispensable supplement of terrestrial communications, Low Earth Orbit (LEO) satellite network is the crucial part in future space‐terrestrial integrated networks because of its unique advantages. However, the effective and reliable routing for LEO satellite network is an intractable task due to time‐varying topology, frequent link handover, and imbalanced communication load. An Extreme Learning Machine (ELM) based distributed routing (ELMDR) strategy was put forward in this paper. Considering the traffic distribution density on the surface of the earth, ELMDR strategy makes routing decision based on traffic prediction. For traffic prediction, ELM, which is a fast and efficient machine learning algorithm, is adopted to forecast the traffic at satellite node. For the routing decision, mobile agents (MAs) are introduced to simultaneously and independently search for LEO satellite network and determine routing information. Simulation results demonstrate that, in comparison to the conventional Ant Colony Optimization (ACO) algorithm, ELMDR not only sufficiently uses underutilized link, but also reduces delay.

Efficient path planning has an utmost importance in the domain of autonomous navigation. Even though shortest path planning has been well discussed in the past, the same techniques might not always be used to find the fastest path in outdoor environments due to the inability of mobile agents to travel at their peak speed everywhere in irregular terrains. Mobility maps are an effective way for dealing with such irregularities. In this paper, we first introduce a grid-based mobility maps for representing speed limitations in outdoor terrains. Then, we propose a heuristic for finding the fastest path on such maps. The proposed heuristic is proven to be both admissible and consistent. Therefore, it can be used with A*-like heuristic search algorithms for obtaining fastest paths efficiently. Simulation results provided in this paper verify the optimality of paths that are found with the help of the proposed heuristic.

Correlation-based algorithms are low-complexity spectrum sensing methods requiring little knowledge on primary signals or noise signals. However, their detection performance severely degrades in the low signal-to-noise ratio (SNR) regime with low signal correlation, which happens to be quite common in practice. In this paper, a weighted correlation- based spectrum sensing scheme and its simplified blind detection scheme are proposed to effectively improve the detection performance. The two proposed algorithms adequately exploit both the auto- correlation and the cross-correlation function statistical characteristics of received signals and assign a proper weight to each term in the test statistics of them, enlarging the differences of test statistics with or without the existence of primary users (PUs), making it easier to detect PUs and thus greatly promoting the detection performance. The weighting operation is verified to be effective from two aspects. The two proposed algorithms are proved robust against noise uncertainty. The false alarm probabilities and detection probabilities are analyzed in the low SNR regime, and their approximate analytical expressions are derived based on the central limit theorem. The analyses are verified through simulations. Experiments with simulated multi-antenna signals show that the proposed detectors can significantly outperform other correlation-based detectors with about 2.2-dB detection performance gain.

Algorithms based on signal correlation have low computational complexity and require little knowledge on primary signals or noise signals. However, their detection performance becomes relatively terrible in low signal-to-noise ratio (SNR) regime with weak signal correlation, which happens to be quite common in practical systems. In this paper, a weighted blind spectrum sensing algorithm based on signal correlation is proposed to effectively improve the detection performance on the basis of above-mentioned advantages of correlation- based detection. This proposed algorithm adequately exploits the auto-correlation (AC) and cross-correlation (CC) characteristics of received signals and assigns a proper weighting coefficient to each term in the test statistic of our algorithm, enlarging the difference of test statistic with or without the existence of primary users (PUs) and thus greatly promoting the detection performance. False alarm and detection probabilities are analyzed thoroughly in the low-SNR regime, and their approximate analytical expressions are derived based on the central limit theorem (CLT). Simulations are presented to verify the analyses. Experiments show that the proposed detection can significantly outperform other correlation-based algorithms.

We study the improved cuckoo search algorithm to promote the capability of spectrum sensing in satellite cognitive systems in this paper. We consider the application scenario of satellite network, which has relatively concise structures and flexible networking modes. The sharable frequency bands are divided into a number of subchannels, and cognitive users should discover unoccupied channels to access. In this case, the actual utilization of the spectrum is sparse as well as the changes in spectrum usage, so we propose an improved cuckoo search algorithm that adjusts dynamically the step size and switching/discovery probability in order to avoid redundant search and increase the convergence rate. Furthermore, we use simulations to study the performance of our proposed algorithm, which show the proposed scheme is noticeably efficient compared with the sequential search and a cuckoo search scheme with fixed step size and switching/discovery probability.

In this paper, a cooperative satellite system consisting of two satellites is considered. After building the channel model, the channel capacity is analysed. Since the propagation time of the signal via different satellites is not equal, a scheme to handle the propagation time difference is proposed. Considering the various propagation time difference of different users, the scheme takes advantage of a proper kind of Space-Time Trellis Code (STTC) to maintain the performance of the transmission. The simulation results show that the STTC has better performance than other coding schemes and can maintain the performance for the users with different propagation time difference.

Modulated wideband converter system offers an under- sampling method aiming at the issue that sparse wideband signals require high sampling rates. In this paper, we propose a multi-bit pseudo-random measurement matrices construction method based on discrete chaotic sequences since Bernoulli random measurement matrix currently used in an MWC system is difficult to be implemented by hardware. Simulation results show that an MWC system can still perform sub-Nyquist sampling and achieve exact recovery with overwhelming probability while adopting the proposed pseudo-random sequences as measurement matrices. Thus our method is able to save system resources greatly on condition that the reconstruction performance hardly changes compared with Bernoulli matrix.

In this paper, we considered a practical system where the number of base station antennas serving tens users is large but finite. The signal must be collected before detection, and the optimal maximum a posteriori (MAP) detector has high computational complexity that grows exponentially with the number of users. Even the suboptimal MMSE-SIC (soft interference cancellation) requires complexity proportional to the cube of the number of the antenna units. In this paper, we proposed a distributed detection scheme done at each antenna unit separately, termed complex Gaussian belief propagation algorithm (CGaBP), for multicell multi-user detection. The multiuser detection problem is reduced to a sequence of scalar estimations, and detecting each individual user using CGaBP is asymptotically equivalent to detecting the same user through a scalar additive Gaussian channel with some degradation in the signal-to-noise ratio (SNR) of the desired user due to the collective impact of interfering users. The degradation is determined by the unique fixed-point of state evolution equations. Numerical results show that CGaBP has low complexity and overhead, and achieves optimal data estimates for Gaussian symbols, and is better than MMSE-SIC for finite-alphabet symbols.

In this paper, we proposed a distributed detection scheme done at each antenna unit separately, termed complex Gaussian belief propagation algorithm (CGaBP), for multicell multi-user detection in imperfect channel state information (CSI) scenarios. The multi-user detection problem is reduced to a sequence of scalar estimations, and detecting each individual user using CGaBP is asymptotically equivalent to detecting the same user through a scalar additive Gaussian channel with some degradation in the signal-to-noise ratio (SNR) of the desired user due to the collective impact of interfering users. The degradation is determined by the unique fixed-point of state evolution equations. We demonstrate the performance of the CGaBP through simulations. The performance of the CGaBP algorithm is better than that of the MMSE-SIC (soft interference cancellation) detector. However, the CGaBP algorithm degrades in the presence of imperfect CSI due mainly to pilot contamination in channel estimation.

Joint decoding of all users' data symbols can be distributed across a network of interacting base stations by message passing techniques. Unfortunately, for discrete belief propagation, the computational complexity grows exponentially with the number of interfering users at each base station, and the messaging overhead grows linearly with the order of modulation constellation. In this paper, the complex Gaussian belief propagation algorithm (CGaBP) is proposed for finite-alphabet symbols. The multi-user detection problem is reduced to a sequence of scalar estimation, and detecting each individual user using CGaBP is asymptotically equivalent to detecting the same user through a scalar additive Gaussian channel with some degradation in the signal-to-noise ratio (SNR) of the desired user due to the collective impact of interfering users. Numerical results show that the proposed method is of low complexity and overhead, and achieves near-optimal data estimates for Gaussian symbols. Moreover, for finite-alphabet symbols, the performance is better than limited cooperation via clustering in the sense of the bit error rate.

This paper introduces the Cloud-based Radio Access Network (C-RAN) architecture into heterogeneous network (HetNet), in which distributed antennas are connected to a cloud-based baseband processing unit through optical fibers. Among various opportunities realized by this architecture, our focus in this paper is on the spectral efficiency (SE) advantages achieved by cooperative transmission and its associated power consumption that may affect the energy efficiency (EE) of the system. A simple but efficient pre-coding scheme is proposed to reduce the computation complexity of cooperative transmission, thus lowering the associated power consumption, and a detailed power model is then developed to benchmark the various sources of energy consumption in C-RAN. Through detailed simulation, an early performance evaluation of the potentially energy efficient C-RAN implementation was demonstrated.

Cognitive radio has outstanding advantages in solving scarcity of spectrum resource and low utilization rate of spectrum in current wireless communication. Spectrum sensing method in multi-bit decision model can effectively improve the detection performance when comparing with the conventional 1- bit decision model. Aiming at the actual scenarios that there may exist malicious users in the network, this paper proposes a multi- bit decision adaptive cooperative spectrum sensing algorithm based on trust valuations. In this algorithm, each cognitive user firstly makes local decision, and then the fusion center proceeds weighted fusion and makes the final decision. Moreover, the increment of each cognitive user's trust valuation is obtained according to the difference between its local decision and all cognitive nodes' weighted average decision results, and then to update each cognitive user's trust valuation. Furthermore, the weighted coefficient for the next detection using each cognitive user's trust valuation is obtained. The simulation results show that the system performance of detection probability, the system false dismissal probability and the system error probability can be improved. And the proposed algorithm can effectively improve the detection performance when there existing individual malicious users, especially to the system false alarm probability is constant.

Due to the respective developments of various radio access technologies, some geographical areas may be covered simultaneously by different wireless networks. The cooperation of these networks will not only meet the diverse quality of service requirements of mobile users but also efficiently utilize the resources of systems. As an important component of radio resource management, call admission control (CAC) is to decide whether a new call or a handover request can be accepted into a resource-constrained network. In such heterogeneous wireless environment, it needs more accurate metrics to measure the performances of mobility especially inside a cellular. In this paper, we propose a new equivalent cost ring based model to represent the complex heterogeneous wireless environment in a simplified and unified way. The coverage of base station is divided into regions based on the basic data rate. Using this model, we derive the mobility parameters such as new call blocking and handover failure probabilities. The system performances of heterogeneous networks are validated by simulations.

In overlay heterogeneous wireless networks, many mobile terminals may operate vertical handover simultaneously which is defined as Group Vertical Handover (GVHO). Conventionally, handover decision schemes designed for a single user may cause network congestion and increasing handover blocking probability in GVHO scenario. To provide seamless handover with QoS guarantee, a fuzzy clustering method (FCM) is proposed by combining the concurrent traffic attributes of GVHO users and the status of available candidate networks. After the combination, users are able to select the proper network based on the dynamic diagram, which is generated by FCM. The collision probability can also be decreased through allocating handover request properly in a specific time window. Compared to the other two schemes, the simulation results show that the proposed scheme reduces the handover blocking probability about 17% and 25% (the maximum rate) respectively.

With the increase of science project in size and complexity, it is necessary to integrate and share global research resources for distributed experimentation and simulation. This paper presents an architecture of Cooperative Remote Laboratories (CRLab) with scalability, flexibility and heterogeneity. It consists of a central Web server, simulation servers, instrument servers and experiment devices, which provide four logical functions including Parallel Computing, Virtual Instrument, Collaborative Simulation and Web 2.0. Based on these functions with interactive online features, the research can be more efficient and convenient just via a simple Web browser.

Some work has been done with the DTN convergence layer adapters (CLAs) such as TCP-based CLA (i.e., TCPCL), Licklider transmission protocol CLA (i.e., LTPCL) and user datagram protocol (UDP)-based CLA (i.e., UDPCL) in space communication, running as an individual protocol. Because each DTN CLA is designed for a different type of communication link, using any single CLA under BP is generally inefficient over a heterogeneous end-to-end interplanetary infrastructure. In this paper, we propose to use a hybrid of DTN CLAs for efficient file transmission in interplanetary environment, i.e. using one CLA for one hop of the end-to-end path and a different CLA for another hop of the path. Based on experimental investigation of DTN protocols over a typical relay-type of interplanetary infrastructure, we found that a hybrid of TCPCL and LTPCL has significant goodput advantage over single-CL-protocol configurations at long link delays, especially with a high BER, and a transmission with LTPCL utilized over a long-delay hop is more tolerant of long channel delay and high channel error.

Delay/disruption tolerant networking (DTN) offers a new solution to highly stressed communications in space environments. It is considered one of the most suitable technologies to be employed in space internetworking. To date, little work has been done in investigating how to achieve the best performance of DTN transmission over lossy, long-delay space channels. In this paper, we present an experimental investigation of how the throughput performance of Licklider transmission protocol (LTP)-based DTN is affected by the LTP flow control “window” size, established by the Number of Sessions (NOS) and Number of Bytes per Session (NBS) characterizing the LTP channel. The intent of the work is, in particular, to find a relationship between the window size and throughput performance of DTN in a typical lossy and long delay cislunar communications infrastructure. One major conclusion is that a bigger NOS generally results in higher throughput than a low NOS, and NBS does not affect the throughput significantly.

Because of its parity check matrix has good systematicness, the minimum distance between codes has linear relationship with code length, AR4JA (accumulate-repeat-4-jagged-accumulate) codes is thought to be one of the most suitable error control channel codes for deep space reliable communication in the future. In order to reduce the modified min-sum algorithm decoder hardware implementation complexity of AR4JA codes, a method of constraint about clipping threshold is proposed in the paper, based on the analysis of the effects of modify factor, clipping threshold and input quantization on the performance. The simulation results show that when the modify factor A is 0.75, received signal clipping threshold cth is 4, and use 8Q5 quantization, the decoding performance of the modified min-sum algorithm for AR4JA codes is close to the theoretical value. Meanwhile, in this condition it can reduce the hardware implementation complexity for just need addition, comparison and shift operation, which is beneficial to realize miniaturization of deep space communication receiver.

Given that the conventional Selected Mapping (SLM) scheme needs to multiply pseudo-random phase sequences to the transmitted data meanwhile transmitting the whole side information, an exhaustive entropy and chaotic phase sequence based SLM method was proposed for reduction the peak-to-average power ratio (PAPR) of Orthogonal frequency division multiplexing (OFDM) in the paper. In the novel algorithm, exhaustive entropy was used to evaluate the randomness of phase sequence, chaotic sequences to reduce the large side information and improved Lorenz sequence digitalization method to enlarge sequence entropy, expanding the number of the candidate phase sequences vector space with the lower transmitted parameters of phase sequence. Simulation results show that different quantization methods of chaotic sequence have different exhaustive entropy and PAPR values. Furthermore, compared with the traditional SLM method, the new scheme proposed in the paper has better performance in reducing PAPR of OFDM system by about 0.1-0.5dB.

In this paper, an optimized transmitting power allocation scheme between the source and its cooperator is presented, minimizing the derived closed-form approximated outage probability expressions for the separately adopted maximum ratio combining (MRC) algorithm and selective diversity combining (SDC) algorithm at the satellite. The circumstance considered is the uplink of a dual-hop land mobile satellite cooperative diversity communication system with decode-and-forward (DF) protocol. Simulation results illustrate that the optimized power allocation scheme excellently outperforms the conventional uniform power allocation scheme on outage probability. The preferable choice of the combing algorithm between MRC and SDC in terms of the minimal outage probabilities changes with the variation of the maximum power allowed to consuming for transmitting a packet.

One of the issues of mobility management in a low Earth orbit (LEO) satellite network is the high-frequency location binding update initiated by mobile nodes (MNs). To solve this problem, we propose a location management scheme based on dual location area (LA) in an IP/LEO satellite network. The proposed scheme uses two kinds of LA, the fixed Earth station LA and satellite LA, to manage the location of the MNs together. MNs operate the binding update procedures only when they are moving out of both of the two LAs last registered. Geographical location information of MN is used in the binding update procedures, so that the network can page the idle MNs near their last registered location first, to enhance the probability of paging success. A detailed description of the implementation of the scheme is provided. Mathematical analysis shows that the proposed scheme reduces the location management cost and minimizes the influences of the distance between MN and its home agent. Paging cost is also reduced by introducing geographical location information in the binding update procedures.

Given that single-state model can only describe single channel environment, while multiple-state model brings more parameters and more complicated simulation work, a wide-area environment two-state channel model including ideal state and non-ideal state was proposed, according to the principle that model should accurately depict the real propagation characteristic of the channel with relative simplicity and feasibility. Meanwhile, simulation of the model was implemented dynamically with filters method and markov chain. Based on the experimental data of Iridium system measured by Communication Research Center, Harbin Institute of Technology, China, model parameters under three kinds of typical channel environments were fitted by using least squares error criterion and the method of linear least squares. The validity of the model is verified too.

A fair and adaptive call admission control algorithm for multimedia low Earth orbit (LEO) satellite networks was proposed. Based on current call dropping probability of destination cell, this algorithm reserves bandwidth for handoff calls using double threshold method. To avoid the discrimination of quality of service (QoS) caused by the allocation based on fair bandwidth, this algorithm adopts the bandwidth allocation rule based on fair QoS. Simulation results show that the proposed algorithm can accurately and adaptively reserve bandwidth, present satisfactory call blocking probability and greatly reduce handoff call dropping probability, while guarantees the high bandwidth utilization.