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.

Unmanned aerial vehicles (UAVs) are regarded as powerful assistance for emergency communications due to their disregard for the limitations of the geographic environment. In this paper, we consider a multi-UAV-assisted wireless emergency communication system, where UAVs are applied as aerial base stations to serve terrestrial device-to-device users (DUs). Our goal is to maximize the UAVs' energy efficiency (EE) through the user grouping strategy with a joint optimization scheme regarding UAVs' trajectories and transmit power. To deal with the resultant mix-integer non-linear programming problem, we divide the optimization process into two stages. In the first stage, we discretize the trajectory into a set of stop points (SPs). Then, the grouping of DUs is achieved by pre-planning the location and optimization range of SPs. In the second stage, with the determined DU grouping strategy, we apply Dinkelbach method and successive convex approximation to convert the original problem into a solvable convex optimization problem. Finally, simulation results verify the effectiveness of our proposed algorithm, which has better performance compared with benchmark schemes in the low user-density region.

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.

The strong interference suffered by users can be a severe problem in cache-enabled networks (CENs) due to the content-centric user association mechanism. To tackle this issue, multi-antenna technology may be employed for interference management. In this paper, we consider a user-centric interference nulling (IN) scheme in two-tier multi-user multi-antenna CEN, with a hybrid most-popular and random caching policy at macro base stations (MBSs) and small base stations (SBSs) to provide file diversity. All the interfering SBSs within the IN range of a user are requested to suppress the interference at this user using zero-forcing beamforming. Using stochastic geometry analysis techniques, we derive a tractable expression for the area spectral efficiency (ASE). A lower bound on the ASE is also obtained, with which we then consider ASE maximization, by optimizing the caching policy and IN coefficient. To solve the resultant mixed integer programming problem, we design an alternating optimization algorithm to minimize the lower bound of the ASE. Our numerical results demonstrate that the proposed caching policy yields performance that is close to the optimum, and it outperforms several existing baselines.

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.

Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is a promising technology that aids in achieving full-space coverage on both sides of the surface, by splitting the incident signal into transmitted and reflected signals. This paper investigates the resource allocation problem in a STAR-RIS-assisted multi-carrier communication networks. To maximize the system sum-rate, a joint optimization problem comprising of the channel assignment, power allocation, and transmission and reflection beamforming at the STAR-RIS for orthogonal multiple access (OMA) is first formulated. To solve this challenging problem, we first propose a channel assignment scheme utilizing matching theory and then invoke the alternating optimization-based method to optimize the resource allocation policy and beamforming vectors iteratively. Furthermore, the sum-rate maximization problem for non-orthogonal multiple access (NOMA) with flexible decoding orders is investigated. To efficiently solve it, we first propose a location-based matching algorithm to determine the sub-channel assignment, where a transmitted user and a reflected user are grouped on a sub-channel. Based on thistransmission-and-reflectionsub-channel assignment strategy, a three-step approach is proposed, which involves the optimization of decoding orders, beamforming-coefficient vectors, and power allocation, by employing semidefinite programming, convex upper bound approximation, and geometry programming, respectively. Numerical results unveil that: 1) For OMA, a general design that includes the same-side user-pairing for channel assignment is preferable, whereas for NOMA, the proposed transmission-and-reflection scheme can achieve comparable performance to the exhaustive search-based algorithm. 2) The STAR-RIS-aided NOMA network significantly outperforms networks employing conventional RISs and OMA.

Interference management is an interesting topic to be studied in cache-enabled heterogeneous networks (HetNets). In this work, by carefully exploring the relationships among the nearest macro base station (MBS), the nearest small base station (SBS) that stores the requested file, and the nearest SBS that does not store the requested file of a typical user, we propose a location-aware cross-tier cooperation (LCC) scheme to mitigate the cross-tier interference in cache-enabled HetNets. Taking the successful transmission probability (STP) as the performance metric, we firstly derive the expression of it using stochastic geometry. Then, a local-optimal random caching strategy (LORC) that maximizes the STP is obtained. Finally, simulation results verify the effectiveness of the proposed LCC scheme, and demonstrate the advantages of the LORC over three existing caching strategies.

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.

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.

Adaptive compressed spectrum sensing (ACSS) can effectively save sampling resources in wideband spectrum sensing. Almost all of the existing ACSS algorithms are based on the discrete multitone signal model. However, real-world spectra are always multiband signals. In this paper, we derive mathematical models and algorithms enabling the ACSS suitable for multiband signals, which can save sampling resources and has lower computational complexity. Firstly, we introduce the multicoset sampling system into ACSS to sample multiband signals. Besides, we propose a leave-one-out cross-validation (LOOCV) based ACSS scheme with low sampling costs. To save sampling resources, we choose only one sampling channel as a testing subset to validate reconstructed signal and repeat this several times with different sampling channels. Then, we use the mean of the multiple validation results to determine the accuracy of the reconstructed signal. To reduce computational complexity, we propose a LOOCV-ACSS algorithm, in which we only perform the least square method several times in the LOOCV procedure, rather than the complicated compressed sensing reconstruction algorithms. Numerical simulations and real-world signal test results demonstrate that our derivation and algorithms are effective to reduce the sampling cost while keeping the same performance as conventional algorithms.

Compared with traditional compressed spectrum sensing, adaptive compressed spectrum sensing can greatly reduce the sampling rate. However, it requires complex optimization algorithms for signal reconstruction and extra samples to validate the accuracy of the reconstructed signal, which makes real-time signal processing impossible. In order to further reduce the number of samples and enable low complexity greedy algorithms, e.g., orthogonal matching pursuit (OMP), in the adaptive compressed spectrum sensing, we propose a cross validation (CV) OMP algorithm (CV-OMP) for the adaptive compressed spectrum sensing. The proposed CV-OMP algorithm embeds the CV process into an OMP algorithm, so it does not require extra samples as testing set to validate the accuracy of the reconstructed signal. Furthermore, the accuracy of the reconstructed signal is verified by the CV, which is used as the criterion to stop the iteration, so that the proposed CV-OMP algorithm does not require the prior knowledge of sparsity order for signal reconstruction. Simulation results indicate that the proposed CVOMP algorithm achieves better reconstruction performance than that of conventional OMP algorithms, and also effectively reduce the number of samples in the adaptive compressed spectrum sensing.

The large amount of deployed smart devices put tremendous traffic pressure on networks. Caching at the edge has been widely studied as a promising technique to solve this problem. To further improve the successful transmission probability (STP) of cache-enabled cellular networks (CEN), we combine the cooperative transmission technique with CEN and propose a novel transmission scheme. Local channel state information (CSI) is introduced at each cooperative base station (BS) to enhance the strength of the signal received by the user. A tight approximation for the STP of this scheme is derived using tools from stochastic geometry. The locally optimal content placement strategy of this scheme is obtained using a numerical method to maximize the STP. Simulation results demonstrate the optimal strategy achieves significant gains in STP over several comparative baselines with the proposed scheme.

Unmanned aerial vehicles (UAVs) have been considered as effective flying base stations (FBSs) to provide on- demand wireless communications. Equipped with computation resource, UAVs are also capable of offering computation offloading opportunities for the mobile users (MUs) in mobile edge computing (MEC) networks. However, due to the small hardware and load capacity, UAVs can only supply limited computation and energy resource. It is thus challenging for UAVs to guarantee the quality of service (QoS) of MUs, while minimizing their total resource consumptions. Toward this end, instead of using all resource for every single task, we propose an intelligent resource allocation algorithm based on reinforcement learning, which enables UAVs to make energy-efficent and computation-efficent allocation decisions intelligently. Then, we take UAVs as learning agents by forming resource allocation decisions as actions and designing a reward function with the aim of minimizing the weighted resource consumptions. Each UAV performs the algorithm only based on its local observations without information exchange among different UAVs. Simulation results show that the proposed reinforcement learning based approach outperforms the benchmark algorithms in terms of weighted consumptions in a whole time period.

The large amount of devices deployed for the Internet of Things (IoT) cause a tremendous traffic burden on the cloud server. Caching content at the edge of IoT networks is a promising technology to alleviate the traffic load. However, how to further improve the successful transmission probability (STP) of cache‐enabled IoT networks is still an open issue. In this study, the authors propose a novel local channel state information based joint transmission (LC‐JT) scheme for cache‐enabled IoT networks and design the optimal content placement probability at the cooperative edge base stations, correspondingly. First, they derive an upper bound and a tight approximation for the STP of LC‐JT scheme using stochastic geometry. Next, an algorithm is proposed to maximise the approximation of STP in LC‐JT by optimising the placement probability vector, which is a non‐convex optimisation problem. By utilising some properties of the STP, they obtain the globally optimal solutions in specific cases. Moreover, the locally optimal solutions in general cases are obtained by using the gradient projection method. Finally, numerical results show the optimised content placement strategy can achieve significant gains in STP over several comparative baselines. It verifies that the strategy with LC‐JT can considerably enhance the STP in cache‐enabled IoT networks.

Unmanned aerial vehicles (UAVs) have been recently considered as a flying platform to provide wide coverage and relaying services for mobile users (MUs). Mobile edge computing (MEC) is developed as a new paradigm to improve quality of experience of MUs in future networks. Motivated by the high flexibility and controllability of UAVs, in this study, the authors study a multi‐UAV‐enabled MEC system, in which UAVs have computation resources to offer computation offloading opportunities for MUs, aiming to reduce MUs' total consumptions in terms of time and energy. Considering the rich computation resource in the remote cloud centre, they propose the MUs‐Edge‐Cloud three‐layer network architecture, where UAVs play the role of flying edge servers. Based on this framework, they formulate the computation offloading issue as a mixed‐integer non‐linear programming problem, which is difficult to obtain an optimal solution in general. To address this, they propose an efficient Q ‐learning based computation offloading algorithm (QCOA) to reduce the complexity of optimisation problem. Numerical results show that the proposed QCOA outperforms benchmark offloading policies (e.g. random offloading, traversal offloading). Furthermore, the proposed three‐layer network architecture achieves a 5% benefits compared with the traditional two‐layer network architecture in terms of MUs' energy and time consumptions.

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.

Cache-enabled unmanned aerial vehicles (UAVs) have been envisioned as a promising technology for many applications in future urban wireless communication. However, to utilize UAVs properly is challenging due to limited endurance and storage capacity as well as the continuous roam of the mobile users. To meet the diversity of urban communication services, it is essential to exploit UAVs’ potential of mobility and storage resource. Toward this end, we consider an urban cache-enabled communication network where the UAVs serve mobile users with energy and cache capacity constraints. We formulate an optimization problem to maximize the sum achievable throughput in this system. To solve this problem, we propose a deep reinforcement learning-based joint content placement and trajectory design algorithm (DRL-JCT), whose progress can be divided into two stages: offline content placement stage and online user tracking stage. First, we present a link-based scheme to maximize the cache hit rate of all users’ file requirements under cache capacity constraint. The NP-hard problem is solved by approximation and convex optimization. Then, we leverage the Double Deep Q-Network (DDQN) to track mobile users online with their instantaneous two-dimensional coordinate under energy constraint. Numerical results show that our algorithm converges well after a small number of iterations. Compared with several benchmark schemes, our algorithm adapts to the dynamic conditions and provides significant performance in terms of sum achievable throughput.

In this paper, we present a new system of Duffing oscillator array to detect weak chirp signal utilizing the characteristics of chaotic system, which are the sensitivity to the initial condition and the immunity to the noise. Based on the Lyapunov Exponents (LE) calculated in the Wolf method, we can discriminate the output state of each oscillator in chaotic array quickly and accurately. Meanwhile, the system we proposed can detect the weak chirp signal using the property of the critical state and overcome the limitation of the previous research on chaotic system which can only be used in periodic signal detection. The contributions of this paper are as follows. On one side, we introduce a chaotic system to detect the weak chirp signal, which is the standard signal in radar communication, under ultra-low signal-to-noise ratio (SNR) effectively; On the other side, the previous research on chaotic system take the large-scale periodic state as the sign of whether the weak signal exist or not, while the proposed system in this paper utilizes the critical state's characteristics to estimate the frequency modulation rate (FMR) of the weak chirp signal novelly. Simulation results demonstrate that the proposed system can achieve a satisfactory performance in strong background noise with SNR low to -20dB, while traditional linear detection method does not work in the same condition.

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 Internet of Things (IoT) provides enormous connections of devices and sensors with different applications. It is an enabling technology for smart city, intelligent transportation systems, environmental monitoring, security surveillance, smart homes, satellite and space information network, ocean monitoring, and unmanned border awareness systems, just to name a few. IoT as a high-density network will take the burden of massive data generated by different kinds of terminals and sensors. Dramatic growth in IoT has created a shortage in the available radio spectrum. Wireless communications services in IoT such as cellular phones, tablets, and wireless Internet access have to compete with existing users in radar, government and military communications, environmental monitoring, and other IoT applications. The strategy to increase the efficiency of spectrum sharing among the enormous users in IoT. Besides, the IoT applications demand more and better functionality and performance from new electronic devices; these demands translate into greater energy consumption demands. The gap between energy storage and demand continues to grow and the battery technologies for energy storage are not expected to increase tremendously in the coming years. Furthermore, reducing signal transmission power can lessen interference among devices in IoT. Energy-efficient protocols and network architectures will further reduce the number of transmissions and extend the battery life of IoT devices (IoTDs). Therefore, it is essential to pursue fundamental research on new components, techniques, and architectures to achieve energy-efficient sensing, communications, and networking in a shared spectrum environment for IoT.

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.

Complicated channel environment of wireless mesh networks makes the spectrum resources around each node different, which leads to the difference of link availability around each cluster head nodes. In this paper, an adaptive ant colony routing based on link availability is proposed for low outage probability in this environment. The algorithm combines spectrum selection and ant colony routing, in order to avoid high switching frequency when sending and receiving data in a process of communication path selection. We use the MAC layer spectrum intermittent method to estimate the spectral state on the link, and improve ant colony algorithm with adaptive routing to get global dynamic optimization. The node only needs to save the local link state information, instead of maintaining the whole network state information. The simulation results show that, this method has good ability of global optimization, and compared to classical AODV routing algorithm, adaptive routing has lower outage probability.

In this paper, we investigates the problem of computing the optimal trajectories of multiple mobile elements (e.g. robots, vehicles, aircrafts, etc.) to minimize energy consumption in the Wireless Sensor Networks (WSNs). We present a clustering algorithm, EmCA (An Effective mTSP based Clustering Algorithm), based on the multiple Traveling Salesman Problem (mTSP). It is designed to solve mTSP as the first step, which can transform mTSP to multiple standard TSP. Numerical simulation indicates that the algorithm can obtain a serial of uniform cluster. For each cluster, the density of sensor nodes has been minimized, and then the total travel distance has been further minimized.

With the increasing demand and the consensus algorithm widely used for clock synchronization in wireless sensor network, we propose a consensus-based distributed clock synchronization protocol (CDCS). The protocol based on the random broadcast scheme has advantage of being asynchronous, robust to node failure or appearance and totally distributed. We consider a network represented by a directed graph, which is simplified according to rules of the protocol, and simulations are presented to evaluate different synchronization schemes, where the proposed protocol shows promising performance.

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.

This paper investigates the problem of computing the optimal trajectories of multiple mobile elements (e.g. robots, vehicles, aircrafts, etc.) to minimize energy consumption in the Wireless Sensor Networks (WSNs). We consider the real situation, take full advantage of sensor's communication range and construct the multiple Traveling Salesman Problem with Communication Range (mTSP-CR). An optimization-based two-stage algorithm is proposed to solve mTSP-CR where a relaxed optimization problem is solved in the first stage and a modified TSP-algorithm is implemented to obtain a feasible solution in the second stage. Numerical simulation indicates that the algorithm can obtain a feasible solution for mTSP-CR. Communication range is beneficial to minimize the total travel distance and to save energy in WSNs.

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.

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.

Zigbee is widely used in WSN because of its low power consumption and high reliability. A control and information collection system based on ZigBee is designed in this paper. It can be visit anywhere via gateway. A testbed is set up and its transmission performance is tested. Then an improvement is made on environment information collection cycle to meet the requirement of energy saving.

Transmit beamforming is a traditional technique for multi-antenna network. Recently, idea of beamforming is applied in wireless sensor network, which can extent its communication distance significantly. An extendable scheme is proposed for carrier synchronization of distributed beamforming in this paper. All slave nodes adjust local oscillation with the selected master node's oscillation to synchronize carrier frequency. Phase is synchronized at receiving terminal by means that master node rollover signal from each slave node and send it back to counteract phase differences. Simulation is given to analyze its performance. Results show that signal is strengthened at receiving terminal. Besides, circuit prototype is recommended. Effects of node mobility are discussed.

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.

In this paper, an optimal link selection and data rate assignment algorithm for spatial time division multiple access (STDMA) medium access control (MAC) protocols is exploited. The motivation for this study is jointly searching for optimal links and corresponding rates in simultaneous transmission environments in order to maximize the throughput capacity in each time slot. A mathematical formulation for exploring such links and rates combination under power and delay constrains is developed. Then, we transform this formulation into a standard knapsack problem (KP). Finally, we solve this KP using discrete dynamic programming (DDP) and propose a joint optimal link and rate search (JOLRS) algorithm which can be generically embedded with any existing STDMA MAC protocol. Through theoretical and experimental studies, we show that our JOLRS algorithm significantly outperforms baseline alternatives and similar existing algorithms in throughput, power consumption, and PER performance.

Multihop spatial time division multiple access (STDMA) medium access control (MAC) protocols constitute an important building block of wireless networks. There are not many practical power control algorithms that can optimally tradeoff power consumption against transmission rates with a reasonable computational complexity. In this paper, we introduce an energy-efficient distributed power control algorithm for STDMA MAC protocols. The motivation for this study is two fold, namely, maximizing the spatial reuse of the system resources and maximizing power efficiency. We develop a mathematical formulation for maximizing spatial reuse and power efficiency under discrete SINR and rate constrains. After proving that power is a convex function of data rates in our problem, we demonstrate that our problem in simultaneous transmission environments can be reduced to a linear programming (LP) problem. Then, we solve this LP problem using dynamic programming (DP). Finally, based on our proposed solution, we propose a low complexity optimal power control (OPC) algorithm which can be generically embedded within any existing STDMA MAC protocol. Through analytical and experimental studies, we show that our power control algorithm cannot only significantly improve the throughput, power consumption, and delay performance of STDMA MAC protocols compared to their baseline alternatives, but also outperform existing STDMA algorithms.

With the development of economics, an increasing number of people are involved in the road traffic. Therefore, the traffic security has been paid much more attention recently. The Traffic Monitoring Control System (MCS) can effectively supervise traffic participants so as to reduce the potential security risks. An Intelligent Traffic MCS scheme based on embedded Web server by applying several kinds of technologies like camera and Radio-frequency Identification (RFID) in the Vehicular Ad-Hoc Network (VANET) is designed and implemented in this paper. This system combines image surveillance system with vehicle identification information monitoring, which provides new ideas and methods for the real road monitoring.

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.

In this paper, we exploit a distributed power control algorithm for spatial time division multiple access (STDMA) medium access control (MAC) protocols. Our algorithm aims at maximizing the number of simultaneous transmissions while concurrently minimizing the corresponding transmission powers in a given time slot. We formulate the problem of interest as a linear programming (LP) problem subject to discrete constraints. Then, we solve the problem using dynamic programming (DP). Based on our solution, we propose a low complexity optimal power control algorithm which can be generically embedded into any existing STDMA MAC protocol. Through analytical and experimental studies, we show that not only our power control algorithm significantly improves the throughput performance and the power consumption of STDMA MAC protocols compared to their baseline alternatives but it also outperforms the existing power control algorithms devised for STDMA MAC protocols.

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 this work, robust transceiver optimization algorithms are proposed for multi-user multiple-input multiple-output (MIMO) interference network in which only imperfect channel state information (CSI) is available at both transmitters and receivers. The errors of the CSI are assumed to be norm bounded, and the mean square errors (MSE) are served as quality of service targets to be minimized. Considering the impact of channel uncertainty, robust algorithms that minimize the maximum sum MSE and minimize the maximum per-user MSE with per transmitter power constraint are proposed. Each transceiver design algorithm can be decomposed into two subproblems, and the optimization alternates between the transmitters and receivers. Iterative algorithms that design one precoder or decoder each time while leave others as fixed are proposed. Such problem can be recast into convex semidefinite programming (SDP) problems. Numerical results are presented which show the effectiveness and robustness of proposed algorithms when CSI errors exist.

In this paper, an onboard Asynchronous Transfer Mode (ATM) switching fabric based on Ant Algorithm with blocking avoidance is proposed in satellite ATM network. The onboard ATM switching fabric is an improved Benes network based on Ants Flooding Mechanism and Blocking Avoidance Mechanism to solve bandwidth-limited and delay-bounded problems in satellite communication network. Compared with traditional self-routing Benes network, better performances of proposed onboard ATM switching fabric are achieved.