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 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.

We propose a robust transceiver design for a covert integrated sensing and communications (ISAC) system with imperfect channel state information (CSI). Considering both bounded and probabilistic CSI error models, we formulate worst-case and outage-constrained robust optimization problems of joint transceiver beamforming and radar waveform design to balance the radar performance of multiple targets while ensuring the communications performance and covertness of the system. The optimization problems are challenging due to the non-convexity arising from the semi-infinite constraints (SICs) and the coupled transceiver variables. In an effort to tackle the former difficulty, S-procedure and Bernstein-type inequality are introduced for converting the SICs into finite convex linear matrix inequalities (LMIs) and second-order cone constraints. A robust alternating optimization framework referred to alternating double-checking is developed for decoupling the transceiver design problem into feasibility-checking transmitter- and receiver-side subproblems, transforming the rank-one constraints into a set of LMIs, and verifying the feasibility of beamforming by invoking the matrix-lifting scheme. Numerical results are provided to demonstrate the effectiveness and robustness of the proposed algorithm in improving the performance of covert ISAC systems.

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.

We explore a satellite internet of things (SIoT) system, wherein multiple IoT users are allowed to access the SIoT in a grant-free manner. Such a manner may incur the severe co-frequency interference (CFI), and decease the user capacity in terms of the number IoT users successfully accessing the SIoT. To this end, we first design two beam preprocessing oriented user grouping methods, called user distribution based user grouping (UDUG) and user position based user grouping (UPUG), respectively. With respect to the UDUG, we propose the regional statistical channel state information based multi-beam processing (RsCSI-MBP). For the UPUG, we conceive the user statistical channel state information with position error based MBP (UsCSIPe-MBP). Both the RsCSI-MBP and the UsCSIPe-MBP schemes can mitigate inter-beam CFI relying on MBP, wherein the optimized beamforming vectors are obtained by using generalized Rayleigh quotient. Furthermore, the user capacity, preamble collision probability and packet loss probability are analyzed for the RsCSI-MBP and UsCSIPe-MBP schemes. Numerical results demonstrate that the UsCSIPe-MBP scheme outperforms the RsCSI-MBP and the conventional seven-color frequency-reuse multibeam schemes in terms of a higher user capacity and a lower packet loss probability, even considering the satellite mobility, phase and amplitude inconsistence of the antenna array used.

Industrial Internet of Things (IIoT) usually utilizes 2.4-GHz unlicensed frequency band, which is also heavily used by many other communication systems, such as ZigBee, WiFi, Bluetooth, etc. Therefore, the lack of spectrum resources has become a key technical bottleneck to restrict the development of IIoT. Integrating cognitive radio (CR) into IIoT, Cognitive IIoT (CIIoT) can cope with the spectrum resource shortage by accessing the frequency bands licensed to primary user (PU). However, spectrum sensing and access control must be performed to avoid bringing severe interference to the PU. In this article, an integrated cooperative spectrum sensing (CSS) and access control model is proposed to improve the transmission performance of the CIIoT while guaranteeing the CSS’s detection probability and controlling the interference to the PU. This model is optimized to maximize the total throughput of IIoT in each frame by jointly optimizing sensing time, the number of sensing nodes and the transmit power for each node under the constraints of the minimum detection probability, the total power control, the interference control, and the minimum rate for each node. The optimization problem is solved by the joint optimization of spectrum sensing and access control. A simultaneous CSS and access control model is also proposed to increase the communication time by using one time slot to perform CSS and access control simultaneously. The simulation results show that there exist optimal sensing and control parameters to maximize the total throughput of CIIoT.

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.

Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted non-orthogonal multiple access (NOMA) communication systems are investigated in its vicinity, where a STAR-RIS is deployed within a predefined region for establishing communication links for users. Both beamformer-based NOMA and cluster-based NOMA schemes are employed at the multi-antenna base station (BS). For each scheme, the STAR-RIS deployment location, the passive transmitting and reflecting beamforming (BF) of the STAR-RIS, and the active BF at the BS are jointly optimized for maximizing the weighted sum-rate (WSR) of users. To solve the resultant non-convex problems, an alternating optimization (AO) algorithm is proposed, where successive convex approximation (SCA) and semi-definite programming (SDP) methods are invoked for iteratively addressing the non-convexity of each sub-problem. Numerical results reveal that 1) the WSR performance can be significantly enhanced by optimizing the specific deployment location of the STAR-RIS; 2) both beamformer-based and cluster-based NOMA prefer asymmetric STAR-RIS deployment.

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.

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.

In this paper, we study the secrecy provisioning downlink transmission in an aerial-assisted network, where the unmanned aerial vehicle (UAV) serves as an aerial platform to provide secure transmission for the mobile users (MUs) with coexist of Internet of Things (IoT) nodes (INs). Specifically, secure transmission is required for MUs to combat eavesdropping attacks and a desired successful transmission probability should be ensured for INs to receive the public instruction massages. To improve the secrecy rates (SRs) for MUs, we consider an eavesdropper-free area, i.e., protected zone, surrounding the UAV. With non-orthogonal multiple access (NOMA) for MUs, the power allocation to each MU is optimized to maximize the minimum secrecy rate of MUs within the protected zone, under the constraints of successful receiving probability requirements for INs. To solve this problem, we first prove that the max-min SR can be obtained when SRs of all users are equal, and then a dichotomy-based successive power allocation policy is proposed. Numerical results show that higher max-min secrecy rate can be achieved by our proposed power allocation policy than the traditional policy.

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.

The shortage of spectrum resources has limited the development of Internet of Things (IoT). Fifth generation (5G) network can flexibly support a variety of devices and services, which makes it possible to combine 5G with IoT. In this paper, a novel multichannel IoT is proposed to dynamically share the spectrum with 5G communication, where an IoT node including transmitter and receiver is designed to perform 5G communication and IoT communication simultaneously. The subchannel sets allocated for 5G communication and IoT communication are defined by two complementary spectrum marker vectors, respectively. Two independent spectrum sequences are generated by calculating the inner products of spectrum marker vectors, presudo-random phases and power scaling vectors. Two time-domain fundamental modulation waveforms generated by the inverse fast Fourier transform of the spectrum sequences are used to modulate 5G data and IoT data, respectively. The receiver can detect the data using the same spectrum marker vectors as the transmitter. The BER performances of the system using binary modulation and cyclic code shift keying modulation in the cases of spectrum marker error and multiple access are analyzed, respectively. A subchannel and power optimization unit is formulated as a joint optimization problem, which seeks to maximize the 5G throughput under the constraints of minimal IoT throughput, maximal power, and maximal interference. An alternative optimization problem is proposed to maximize the IoT throughput while guaranteeing the minimal 5G throughput. A joint optimization algorithm based on Lagrange dual decomposition is proposed to achieve the optimal solution. Simulation results indicate that the proposed IoT can improve the 5G throughput significantly while the IoT throughput is guaranteed.

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.

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.

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.

This paper explores the optimal filtering problem for microgrid state estimation considering packet losses in the internet of things (IoT) networks. The considered IoT networks collect microgrid information from smart sensors and send control messages to actuators where sensors and actuators are linked over a lossy network. Explicitly, the distribution power system incorporating renewable distributed energy resources such as wind turbine is represented as a state-space model where IoT element such as smart sensors are deployed to obtain measurements. This sensing information is transmitted to the fusion center through a lossy IoT communication network where measurements are lost. This paper proposes an optimal estimator based on the mean squared error between the actual states and its estimate. Afterwards, a state feedback controller is designed based on the semidefinite programming approach under the condition of packet dropouts in the IoT network. The efficacy of the proposed approaches are demonstrated by presenting an environment-friendly microgrid model incorporating distributed energy resources.

In cognitive radio (CR), the spectrum sensing of the primary user (PU) may consume some electrical power from the battery capacity of the secondary user (SU), yielding to decrease the transmission power of the SU. In this paper, a multislot simultaneous spectrum sensing and energy harvesting model is proposed, which uses the harvested radio frequency (RF) energy of the PU signal to supply the spectrum sensing. In the proposed model, the sensing duration is divided into multiple sensing slots consisted of one local-sensing subslot and one energy-harvesting subslot. If the presence of the PU is detected in the local-sensing subslot, the SU will harvest RF energy of the PU signal in the energy-harvesting slot, otherwise, the SU will continue spectrum sensing. The global decision is obtained through combining local sensing results from all the sensing slots by adopting "OR Rule". A joint optimization problem of sensing time and time splitter factor is proposed to maximize the throughput of the SU under the constraints of probabilities of false alarm and detection and energy harvesting. The simulation results have shown that the proposed model can improve the maximal throughput of the SU obviously compared to the traditional sensing-throughput tradeoff model.

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.

Different from conventional sub-Nyquist sampling methods for sparse multiband signal, the modulated wideband converter system provides a feasible way that can solve the case when carrier frequency is unknown. In this structure, there is a block entitled continuous to finite (CTF) to find the support of origin signal with some prior information about bands of signal. In this paper, we consider the challenging problem of determine the support of signal, whose known the number of the bands and the maximum bandwidth of each of the bands. We propose another approach to achieve the spectrum position sensing for sparse multiband signal with finite rate of innovation (FRI). First, represent the multiband signal as another sparse form which is the type of FRI signal in another transform domain. Then, use FRI theory to process this signal and confirm the support of origin signal without prior information about bands. Finally, obtain the spectrum position of multiband signal for future application like monitoring, interference or interception.

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.

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.

In this paper, we study the power allocation for both regenerative and non-regenerative relay transmission over Rayleigh fading channels in cognitive networks. Based on the analyses of features of cognitive networks, a relevant interference model is first built over Rayleigh fading channels. Then, we propose a combined power allocation strategy in order to minimize the outage probabilities in the cooperative communications. For regenerative system, we give a closed-form expression for the power allocation by taking into account the characteristics of the fading channels. For non-regenerative system, we utilize pattern search algorithm to solve the optimization problem since the objective function is complex and uneasy to be figured out directly. Numerical results show that the system performances with optimum power allocation outperform those with uniform power allocation whereas lower outage probabilities can be obtained.