High-Throughput Satellites (HTS) use beam hopping to handle non-uniform and time-varying ground traffic demand. A significant technical challenge in beam hopping is the computation of effective illumination patterns. Traditional algorithms, like the genetic algorithm, require over 300 seconds to compute a single illumination pattern for just 37 cells, whereas modern HTS typically covers over 300 cells, rendering current methods impractical for real-world applications. Advanced approaches, such as multi-agent deep reinforcement learning, face convergence issues when the number of cells exceeds 40. In this paper, we introduce Tyche, a hybrid computation framework designed to address this challenge. Tyche incorporates a Monte Carlo Tree Search Beam Hopping (MCTS-BH) algorithm for computing illumination patterns and employs sliding window and pruning techniques to significantly reduce computation time. Specifically, MCTS-BH can compute one illumination pattern for 37 cells in just 12 seconds. To ensure real-time computation, we use a Greedy Beam Hopping (G-BH) algorithm, which provides a provisional solution while MCTS-BH completes its computation in the background. Our evaluation results show that MCTS-BH can increase throughput by up to 98.76%, demonstrating substantial improvements over existing solutions.

With the rise of mega-satellite constellations, the integration of hierarchical non-terrestrial and terrestrial networks has become a cornerstone of 6G coverage enhancements. In these hierarchical satellite networks, controllers manage satellite switches within their assigned domains. However, the high mobility of LEO satellites and field-of-view (FOV) constraints pose fundamental challenges to efficient domain partitioning. Centralized control approaches face scalability bottlenecks, while distributed architectures with onboard controllers often disregard FOV limitations, leading to excessive signaling overhead. LEO satellites outside a controller’s FOV require an average of five additional hops, resulting in a 10.6-fold increase in response time. To address these challenges, we propose Eunomia, a three-step domain-partitioning framework that leverages movement-aware FOV segmentation within a hybrid control plane combining ground stations and MEO satellites. Eunomia reduces control plane latency by constraining domains to FOV-aware regions and ensures single-hop signaling. It further balances traffic load through spectral clustering on a Control Overhead Relationship Graph and optimizes controller assignment via the Kuhn-Munkres algorithm. We implement Eunomia on the Plotinus emulation platform with realistic constellation parameters. Experimental results demonstrate that Eunomia reduces request loss by up to 58.3%, control overhead by up to 50.3%, and algorithm execution time by 77.7%, significantly outperforming current state-of-the-art solutions.

Performance Enhancement Proxies (PEPs) are widely used to improve TCP performance in geostationary orbit (GEO) satellite networks, which experience long RTT (approximately 500 ms). As a split TCP-based solution, PEP divides the end-to-end connection into multiple sub-connections, each independently managing its own rate control, including both congestion and flow control. This independence naturally leads to rate imbalances, manifested as an abnormal on-off traffic pattern-a phenomenon confirmed by our experimental observations in real-world GEO satellite networks. Furthermore, we demonstrate that this on-off traffic pattern not only undermines fairness but also reduces throughput, particularly for small-sized flows. To address this problem, we propose PEP-Policer, an automatic rate limiter for PEP to limit the link with higher rate to the lower one. Unlike traditional rate limiting methods that require manual configuration of the target rate, PEP-Policer employs a finite state machine to automatically determine and enforce the appropriate target rate. Moreover, as an add-on, PEP-Policer is compatible with all PEP-based solutions and can be integrated without modifying existing systems. Extensive evaluations in an emulated GEO satellite network show that PEP-Policer improves TCP's fairness and increases goodput by up to 63.0% for Cubic, 49.6% for BBR, and 88.6% for Hybla.

Recently, there has been a surge in the development of advanced intelligent generative content (AIGC), especially large language models (LLMs). For many downstream tasks, it is necessary to fine-tune LLMs using private data. While federated learning offers a promising privacy-preserving solution to LLM fine-tuning, the substantial size of an LLM, combined with high computational and communication demands, makes it hard to apply to handle downstream tasks. More importantly, private edge servers often possess varying computing and network resources in real-world scenarios, introducing additional complexities to LLM fine-tuning. To tackle these problems, we design and implement an automated federated pipeline, named, to fine-tune LLMs on heterogeneous edge servers with minimal training cost and no additional inference latency. firstly identifies the weights to be fine-tuned based on their contributions to the LLM training. It then configures a low-rank adapter for each selected weight within the resource constraints of the edge server and aggregates these local adapters from all edge servers to fine-tune the whole LLM. Finally, it appropriately quantizes the parameters of LLM to reduce memory consumption according to the requirements of edge servers. Extensive experiments demonstrate that expedites model training and achieves higher accuracy than the state-of-the-art benchmarks.

Split federated learning (SFL) has emerged as a promising paradigm to democratize machine learning (ML) on edge devices by enabling layer- wise model partitioning. However, existing SFL approaches suffer significantly from the straggler effect due to the heterogeneous capabilities of edge devices. To address the fundamental challenge, we propose adaptively controlling batch sizes (BSs) and model splitting (MS) for edge devices to overcome resource heterogeneity. We first derive a tight convergence bound of SFL that quantifies the impact of varied BSs and MS on learning performance. Based on the convergence bound, we propose HASFL, a heterogeneity-aware SFL framework capable of adaptively controlling BS and MS to balance communication-computing latency and training convergence in heterogeneous edge networks. Extensive experiments with various datasets validate the effectiveness of HASFL and demonstrate its superiority over state-of-the-art benchmarks.

Recently, the increasing deployment of LEO satellite systems has enabled various space analytics (e.g., crop and climate monitoring), which heavily relies on the advancements in deep learning (DL). However, the intermittent connectivity between LEO satellites and ground station (GS) significantly hinders the timely transmission of raw data to GS for centralized learning, while the scaled-up DL models hamper distributed learning on resource-constrained LEO satellites. Thoughsplit learning(SL) can be a potential solution to these problems by partitioning a model and offloading primary training workload to GS, the labor-intensive labeling process remains an obstacle, with intermittent connectivity and data heterogeneity being other challenges. In this paper, we propose LEO-Split, asemi-supervised(SS) SL design tailored for satellite networks to combat these challenges. Leveraging SS learning to handle (labeled) data scarcity, we construct an auxiliary model to tackle the training failure of the satellite-GS non-contact time. Moreover, we propose a pseudo-labeling algorithm to rectify data imbalances across satellites. Lastly, an adaptive activation interpolation scheme is devised to prevent the overfitting of server-side sub-model training at GS. Extensive experiments with real-world LEO satellite traces (e.g., Starlink) demonstrate that our LEO-Split framework achieves superior performance compared to state-of-the-art benchmarks.

Leveraging the spatiotemporal continuous aware ness of low Earth orbit satellites, space-based spectrum monitoring systems can construct a spectrum situation map (SSM) to facilitate spectrum surveillance and management in mobile cognitive communication networks. However, due to limited on orbit processing capabilities and unfavorable ground-to-satellite transmission environments, spectrum measurements will inevitably be incomplete and corrupted by anomalies. Existing SSM construction methods assume stationary spectral environments and do not consider the dynamic changes in spectrum situation. Considering spectrum state is evolving constantly over time, this paper proposes a robust time-aware online streaming tensor (R TAST) completion algorithm by exploiting the time-frequency space correlation and temporal properties in real-world spectral measurements. Based on compressed wideband sampling, the proposed R-TAST algorithm integrates rank estimation, anomaly removal, and dynamic spectral tensor completion to achieve spectrum situation completion and evolutionary prediction. Numerical analyses conducted on simulated and realistic ray tracing based datasets demonstrate the effectiveness and efficiency of the proposed R-TAST in comparison with state-of-the-art streaming tensor completion and prediction algorithms.

The Satellite-Terrestrial Integrated Network (STIN) enhances end-to-end transmission by simultaneously utilizing terrestrial and satellite networks, offering significant benefits in scenarios like emergency response and cross-continental communication. Low Earth Orbit (LEO) satellite networks offer reduced Round Trip Time (RTT) for long-distance data transmission and serve as a crucial backup during terrestrial network failures. Meanwhile, terrestrial networks are characterized by ample bandwidth resources and generally more stable link conditions. Therefore, integrating Multipath TCP (MPTCP) into STIN is vital for optimizing resource utilization and ensuring efficient data transfer by exploiting the complementary strengths of both networks. However, the inherent challenges of STIN, such as heterogeneity, instability, and handovers, pose difficulties for traditional multipath schedulers, which are typically designed for terrestrial networks. We propose a novel multipath data scheduling approach for STIN, the Adaptive Latency Compensation Scheduler (ALCS), to address these issues. ALCS refines transmission latency estimates by incorporating RTT, congestion window size, inflight and queuing packets, and satellite trajectory information. It further employs adaptive mechanisms for latency compensation and proactive handover management. Implemented in the MPTCP Linux Kernel and evaluated in a simulated STIN testbed, ALCS outperforms existing multipath schedulers, delivering faster data transmission and achieving throughput gains of 9.8% to 44.0% compared to benchmark algorithms.

The rapid deployment of low Earth orbit (LEO) satellite systems has propelled various space-based applications (e.g., agricultural monitoring and disaster response), which increasingly rely on advancements in deep learning (DL). However, ground stations (GS) cannot download such massive raw data for centralized training due to intermittent connectivity between satellites and GS, while the scaled-up DL models pose substantial barriers to distributed training on resource-constrained satellites. Although split learning (SL) has emerged as a promising solution to offload major training workloads to GS via model partitioning while retaining raw data on satellites, limited satellite-GS connectivity and heterogeneity of satellite resources remain substantial barriers. In this paper, we propose S-Leon, an SL framework tailored to tackle these challenges within heterogeneous LEO satellite networks. We develop a satellite early-exit model to eliminate training disruptions during non-contact periods and employ online knowledge distillation to incorporate ground knowledge, further enhancing satellite local training. Moreover, we devise a satellite model customization method that simultaneously accommodates the heterogeneous computation and communication capabilities of individual satellites. Lastly, we develop a partial model-agnostic training strategy to optimize the collaborative training effectiveness across customized satellite models. Extensive experiments with real-world LEO satellite networks demonstrate that S-Leon outperforms state-of-the-art benchmarks.

Deep Reinforcement Learning (DRL) suffers from uncertainties and inaccuracies in the observation signal in real-world applications. Adversarial attack is an effective method for evaluating the robustness of DRL agents. However, existing attack methods targeting individual sampled actions have limited impacts on the overall policy distribution, particularly in continuous action spaces. To address these limitations, we propose the Distribution-Aware Projected Gradient Descent attack (DAPGD). DAPGD uses distribution similarity as the gradient perturbation input to attack the policy network, which leverages the entire policy distribution rather than relying on individual samples. We utilize the Bhattacharyya distance in DAPGD to measure policy similarity, enabling sensitive detection of subtle but critical differences between probability distributions. Our experiment results demonstrate that DAPGD achieves SOTA results compared to the baselines in three robot navigation tasks, achieving an average 22.03% higher reward drop compared to the best baseline.

With the increasing deployment of Low Earth Orbit (LEO) satellites (e.g., Starlink), rate adaptation has been widely applied in satellite communication systems to rapidly adapt changing satellite-ground channel. However, the state-of-the-art rate adaptation solutions still do not approach the Shannon capacity, since they always operates in discrete steps (e.g., switching between fixed modulation orders and coding rates), leading to abrupt performance deterioration when the channel fluctuates between two thresholds. We propose a new plug-and-play rate adaptation, called ERA-LEO integrated to satellite communication systems without any extra hardware modification. For adapting smoothly to changes in channels, we first design a probabilistic constellation shaping enabled system to provide a continuous and more granular adjustment of the constellation by tuning the probability distribution of symbols. Second, to avoid the frequent feedback, used to select a target probability distribution based on the channel condition, we establish a theoretical model to demonstrate that the SNR of the return link can reliably predict that of the forward link, and present a lightweight prediction mechanism based on neural network. We also implement ERA-LEO prototype using FPGA-based software defined radio platforms. The extensive evaluations demonstrate ERA-LEO achieves an average gain of 2.19 dB compared to state-of-the-art baselines and a 43.08% improvement in network throughput.

Deep reinforcement learning (DRL) has emerged as a promising approach for robotic control, but its real-world deployment remains challenging due to its vulnerability to environmental perturbations. Existing white-box adversarial attack methods, adapted from supervised learning, fail to effectively target DRL agents as they overlook temporal dynamics and indiscriminately perturb all state dimensions, limiting their impact on long-term rewards. To address these challenges, we propose the Adaptive Gradient-Masked Reinforcement (AGMR) Attack, a white-box attack method that combines DRL with a gradient-based soft masking mechanism to dynamically identify critical state dimensions and optimize adversarial policies. AGMR selectively allocates perturbations to the most impactful state features and incorporates a dynamic adjustment mechanism to balance exploration and exploitation during training. Extensive experiments demonstrate that AGMR outperforms state-of-the-art adversarial attack methods in degrading the performance of the victim agent and enhances the victim agent’s robustness through adversarial defense mechanisms.

Recently, large vision-language models (LVLMs) unleash powerful analysis capabilities for low Earth orbit (LEO) satellite Earth observation images in the data center. However, fast satellite motion, brief satellite-ground station (GS) contact windows, and large size of the images pose a data download challenge. To enable near real-time Earth observation applications (e.g., disaster and extreme weather monitoring), we should explore how to deploy LVLM in LEO satellite networks, and design SpaceVerse, an efficient satellite-ground synergistic LVLM inference system. To this end, firstly, we deploy compact LVLMs on satellites for lightweight tasks, whereas regular LVLMs operate on GSs to handle computationally intensive tasks. Then, we propose a computing and communication co-design framework comprised of a progressive confidence network, and an attention-based multi-scale preprocessing, used to identify on-satellite inferring data, and reduce data redundancy before satellite-GS transmission, separately. We implement, and evaluate SpaceVerse on real-world LEO satellite constellations and datasets, achieving a 31.2% average gain in accuracy and a 51.2% reduction in latency compared to state-of-the-art baselines.

While unencrypted information inspection in physical layer (e.g., open headers) can provide deep insights for optimizing wireless networks, the state-of-the-art (SOTA) methods heavily depend on full sampling rate (a.k.a Nyquist rate), and high-cost radios, due to terrestrial and non-terrestrial networks densely occupying multiple bands across large bandwidth (e.g., from 4G/5G at 0.4–7 GHz to LEO satellite at 4–40 GHz). To this end, we present SigChord, an efficient physical layer inspection system built on low-cost and sub-Nyquist sampling radios. We first design a deep and rule-based interleaving algorithm based on Transformer network to perform spectrum sensing and signal recovery under sub-Nyquist sampling rate, and second, cascade protocol identifier and decoder based on Transformer neural networks to help physical layer packets analysis. We implement SigChord using software-defined radio platforms, and extensively evaluate it on over-the-air terrestrial and non-terrestrial wireless signals. The experiments demonstrate that SigChord delivers over 99% accuracy in detecting and decoding, while still decreasing 34% sampling rate, compared with the SOTA approaches.

The LEO (Low Earth Orbit) mega constellation network with beam-hopping scheme has become a promising approach to provide global communications. Most of the research on the beam-hopping scheme design for LEO constellation network has focused on beam-hopping scheduling and power allocation for a single LEO satellite. Few of them considered the cooperation between adjacent satellites covering the overlapping areas. As a result, many satellites illuminate beams to the same areas covered by multiple adjacent satellites wasting the limited onboard resource, and the users located in single coverage areas cannot be served. In this paper, we design a multi-agent reinforcement learning (MARL) based cooperative beam-hopping scheme for the LEO mega constellation network, where the LEO satellites cooperatively schedule the beam-hopping and allocate resource. Specifically, we formulate the beam-hopping problem as a multi-agent model, and propose a Centralized Training and Decentralized Execution (CTDE) algorithm. Various simulations are conducted to evaluate our proposed scheme and the results show that the cooperative beam-hopping outperforms the existing schemes in terms of resource utilization and system throughput.

This article established a novel multi-input multioutput (MIMO) communication network, in the presence of full-duplex (FD) transmitters and receivers with the assistance of dual-side intelligent omni surface (IOS). Compared with the traditional IOS, the dual-side IOS allows signals from both sides to reflect and refract simultaneously, which further exploits the potential of metasurfaces to avoid frequency dependence, and size, weight, and power (SWaP) limitations. By considering both the downlink and uplink transmissions, we aim to maximize the weighted sum rate, subject to the transmit power constraints of the transmitter, the users and the dual-side reflecting and refracting phase shifts constraints. However, the formulated sum rate maximization problem is not convex, hence we exploit the weighted minimum mean square error (WMMSE) approach, and tackle the original problem iteratively by solving two subproblems. For the beamforming matrices optimization of the downlink and uplink, we resort to the Lagrangian dual method combined with a bisection search to obtain the results. Furthermore, we resort to the quadratically constrained quadratic programming (QCQP) method to optimize the reflecting and refracting phase shifts of both sides of the IOS. Simulation results validate the efficacy of the proposed algorithm and demonstrate the superiority of the dual-side IOS.

This paper presents GBSense, an innovative compressed spectrum sensing system designed for GHz-bandwidth signals in dynamic spectrum access (DSA) applications. GBSense introduces an efficient approach to periodic non-uniform sampling, capturing wideband signals using significantly lower sampling rates compared to traditional Nyquist sampling. By integrating time-interleaved analog-to-digital conversion, GBSense overcomes the hardware complexity typically associated with traditional multicoset sampling, providing precise, adjustable sampling patterns without the need for analog delay circuits. The system’s ability to process signals with a 2 GHz radio frequency bandwidth using only a 400 MHz average sampling rate enables more efficient spectrum monitoring and access in wideband cognitive radios. Lab tests demonstrate 100% accurate spectrum detection when the spectrum occupancy is below 100 MHz and over 80% accuracy for occupancy up to 200 MHz. Additionally, an integrated system utilizing a low-power Raspberry Pi processor achieves a low processing latency of around 30 ms per frame, demonstrating the system’s potential for DSA applications in next-generation wireless networks.

Given the wide adoption of multimodal sensors (e.g., camera, lidar, radar) byautonomous vehicles (AVs), deep analytics to fuse their outputs for a robust perception become imperative. However, existing fusion methods often make two assumptions rarely holding in practice: i) similar data distributions for all inputs and ii) constant availability for all sensors. Because, for example, lidars have various resolutions and failures of radars may occur, such variability often results in significant performance degradation in fusion. To this end, we present t-READi, an adaptive inference system that accommodates the variability of multimodal sensory data and thus enables robust and efficient perception. t-READi identifies variation-sensitive yetstructure-specificmodel parameters; it then adapts only these parameters while keeping the rest intact. t-READi also leverages a cross-modality contrastive learning method to compensate for the loss from missing modalities. Both functions are implemented to maintain compatibility with existing multimodal deep fusion methods. The extensive experiments evidently demonstrate that compared with the status quo approaches, t-READi not only improves the average inference accuracy by more than 6% but also reduces the inference latency by almost 15× with the cost of only 5% extra memory overhead in the worst case under realistic data and modal variations.

Recently, a large number of Low Earth Orbit (LEO) satellites have been launched and deployed successfully in space. Due to multimodal sensors equipped by the LEO satellites, they serve not only for communications but also for various machine learning applications. However, a ground station (GS) may be incapable of downloading such a large volume of raw sensing data for centralized model training due to the limited contact time with LEO satellites (e.g. 5 minutes). Therefore,federated learning(FL) has emerged as the promising solution to address this problem via on-device training. Unfortunately, enabling FL on LEO satellites still face three critical challenges: i) heterogeneous computing and memory capabilities, ii) limited downlink/uplink rate, and iii) model staleness. To this end, we proposeFedSNas a general FL framework to tackle the above challenges. Specifically, we first present a novel sub-structure scheme to enable heterogeneous local model training considering different computing, memory, and communication constraints on LEO satellites. Additionally, we propose a pseudo-synchronous model aggregation strategy to dynamically schedule model aggregation for compensating model staleness. Extensive experiments with real-world satellite data demonstrate that FedSN framework achieves higher accuracy, lower computing, and communication overhead than the state-of-the-art benchmarks.

As AI models expand in size, it has become increasingly challenging to deploy federated learning (FL) on resource-constrained edge devices. To tackle this issue,split federated learning(SFL) has emerged as an FL framework with reduced workload on edge devices via model splitting; it has received extensive attention from the research community in recent years. Nevertheless, most prior works on SFL focus only on a two-tier architecture without harnessing multi-tier cloud-edge computing resources. In this paper, we intend to analyze and optimize the learning performance of SFL under multi-tier systems. Specifically, we propose the hierarchical SFL (HSFL) framework and derive its convergence bound. Based on the theoretical results, we formulate a joint optimization problem for model splitting (MS) and model aggregation (MA). To solve this rather hard problem, we then decompose it into MS and MA sub-problems that can be solved via an iterative descending algorithm. Simulation results demonstrate that the tailored algorithm can effectively optimize MS and MA in multi-tier systems and significantly outperform existing schemes.

The low-orbit mega-constellation network (LMCN) is an important part of the space-air-ground integrated network system. An effective satellite-ground interconnection design can result in a stable constellation topology for LMCNs. A naïve solution is accessing the satellite with the longest remaining service time (LRST), which is widely used in previous designs. The Coordinated Satellite-Ground Interconnecting (CSGI), the state-of-the-art algorithm, coordinates the establishment of ground-satellite links (GSLs). Compared with existing solutions, it reduces latency by 19% and jitter by 70% on average. However, CSGI only supports the scenario where terminals access only one satellite, and cannot fully utilize the multi-access capabilities of terminals. Additionally, CSGI's high computational complexity poses deployment challenges. To overcome these problems, we propose the Classification-based Longest Remaining Service Time (C-LRST) algorithm. C-LRST supports the actual scenario with multi-access capabilities. It adds optional paths during routing with low computational complexity, improving end-to-end communications quality. We conduct our 1000 s simulation from Brazil to Lithuania on the open-source platform Hypatia. Experiment results show that compared with CSGI, C-LRST reduces the latency and increases the throughput by approximately 60% and 40%, respectively. In addition, C-LRST's GSL switchings number is 14, whereas CSGI is 23. C-LRST has better link stability than CSGI.

Due to sophisticated deployments of all kinds of wireless networks (e.g., 5G, Wi-Fi, Bluetooth, LEO satellite, etc.), multiband signals distribute in a large bandwidth (e.g., from 70 MHz to 8 GHz). Consequently, for network monitoring and spectrum sharing applications, a sniffer for extracting physical layer information, such as structure of packet, with low sampling rate (especially, sub-Nyquist sampling) can significantly improve their cost- and energy-efficiency. However, to achieve a multiband signals sniffer is really a challenge. To this end, we propose Sums, a system that can sniff and analyze multiband signals in a blind manner. Our Sums takes advantage of hardware and algorithm co-design, multi-coset sub-Nyquist sampling hardware, and a multi-task deep learning framework. The hardware component breaks the Nyquist rule to sample GHz bandwidth, but only pays for a 50 MSPS sampling rate. Our multi-task learning framework directly tackles the sampling data to perform spectrum sensing, physical layer protocol recognition, and demodulation for deep inspection from multiband signals. Extensive experiments demonstrate that Sums achieves higher accuracy than the state-of-the-art baselines in spectrum sensing, modulation classification, and demodulation. As a result, our Sums can help researchers and end-users to diagnose or troubleshoot their problems of wireless infrastructures deployments in practice.

The construction of Low Earth Orbit satellite constellations has recently spurred tremendous attention from both academia and industry. 5G and 6G standards have specified the LEO satellite network as a key component of the mobile network. However, due to the satellites' fast traveling speed, ground terminals usually experience frequent and high-latency handover, which significantly deteriorates the performance of latencysensitive applications. To address this challenge, we propose a parallel handover mechanism for the mobile satellite network which can considerably reduce the handover latency. The main idea is to use plan-based handovers instead of measurementbased handovers to avoid interactions between the access and core networks, hence eliminating the significant time overhead in the traditional handover procedure. Specifically, we introduce a novel network function named Satellite Synchronized Function (SSF), which is designed for being compliant with the standard 5G core network. Moreover, we propose a machine learning model for signal strength prediction, coupled with an efficient handover scheduling algorithm. We have conducted extensive experiments and results demonstrate that our proposed handover scheme can considerably reduce the handover latency by 21× compared to the standard NTN handover scheme and two other existing handover schemes, along with significant improvements in network stability and user-level performance.

Object identification is a pivotal enabling technique for smart home and manufacturing applications. Traditional methodologies for object identification predominantly rely on a singular sensor modality, which inherently limits their ability to furnish a detailed characterization of the target object. Addressing this deficiency, in this paper, we fill this gap by introducing CRFUSION, the first-of-its-kind system that integrates the object RGB image and the radio frequency (RF) signal reflected by the object for fine-grained object identification. CRFUSION leverages the complementary characteristics between visible light and radio frequency modalities to simultaneously determine the category and material of target objects. We design a multifaceted object feature from the RF signal, called the Energy Reflection Factor (ERF), which not only reveals the object texture but complements the image modality for identifying the object category. By integrating the characteristics of radar, we obtain radar feature maps based on the ERF of target objects. Additionally, we have developed a modality fusion network to comprehensively integrate the image and ERF features. We conducted a comprehensive evaluation of CRFUSION using a commercial mmWave radar development board and camera. The results show that CRFUSION achieves a classification accuracy of over 96%, demonstrating its robustness, and potential for application.

Satellites equipped with computing capabilities serve as invaluable access platforms for 5G and beyond (NextG) non-terrestrial networks (NTNs). They facilitate the continuous execution of resource-intensive edge-assisted deep learning (DL) tasks that are offloaded from Internet-of-Things (IoT) user equipment (UEs) in remote areas. To this end, satellite access network (SAN) resources need to be carefully “sliced”, consid-ering both the constrained energy availability and the scarcity of SAN resources. Existing SAN slicing approaches tend to treat offloaded tasks conventionally, overlooking the intricate semantics associated with DL tasks. In this paper, we propose semantic SAN (SemSAN), the first semantic SAN slicing algorithm for NextG AI-native NTNs. Our keen observations reveal that various DL tasks (i) can tolerate different degrees of image compression, and (ii) may yield equivalent model accuracy when employing DNN models with different sizes. These observations inspire us to further exploit the computation capability of a SAN to support more tasks while still minimizing overall energy consumption. After analyzing the characteristics of this optimization problem, we propose an online greedy SemSAN slicing algorithm to approximate its optimal solution. Extensive experiments verify the effectiveness of SemSAN in energy saving and its ability to support a substantial number of tasks, compared with other baselines.

The construction of Low Earth Orbit (LEO) satellite constellations has recently spurred tremendous attention from academia and industry. 5G and 6G standards have specified LEO satellite network as a key component of 5G and 6G networks. However, ground terminals experience frequent, high-latency handover incurred by satellites’ fast travelling speed, which deteriorates the performance of latency-sensitive applications. To address this challenge, we propose a novel handover flowchart for mobile satellite networks, which can considerably reduce the handover latency. The innovation behind this scheme is to mitigate the interaction between the access and core networks that occupy the majority of time overhead by leveraging the predictable travelling trajectory and spatial distribution inherent in mobile satellite networks. Specifically, we design a fine-grained synchronized algorithm to address the synchronization problem due to the lack of control signalling delivery between the access and core networks. Moreover, we minimize the computational complexity of the core network using information such as the satellite access strategy and unique spatial distribution, which is caused by frequent prediction operations. We have built a prototype for a mobile satellite network using modified Open5GS and UERANSIM, which is driven by actual LEO satellite constellations such as Starlink and Kuiper. We have conducted extensive experiments, and the results demonstrate that our proposed handover scheme can considerably reduce the handover latency compared to the 3GPP Non-terrestrial Networks (NTN) and two other existing handover schemes.

This paper focuses on trigger-based (TB) random access (RA) strategies for a multiple-packet reception channel with channel capability$M$($M$-MPR channel), where up to$M$packets can be received simultaneously, while more than$M$concurrent packet transmissions result in collisions and are considered lost. We model the contention for the TB MPR framework and derive the optimal RA strategies that maximize two metrics:i)the normalized saturation throughput, andii)the number of stations successfully occupying the MPR channel within each access round. We generalize the$p$-persistent carrier sense multiple access (CSMA) by enabling it to explore both the MPR dimension and the time dimension to adapt the access probabilities. We also propose suboptimal strategies to reduce the complexity, customized for the considered TB framework. Comprehensive performance evaluations and comparisons with respect to a wide range of system parameters and metrics are provided.

The increasingly deeper neural networks hinder the democratization of privacy-enhancing distributed learning, such as federated learning (FL), to resource-constrained devices. To overcome this challenge, in this paper, we advocate the integration of edge computing paradigm and parallel split learning (PSL), allowing multiple edge devices to offload substantial training workloads to an edge server via layer-wise model split. By observing that existing PSL schemes incur excessive training latency and a large volume of data transmissions, we propose an innovative PSL framework, namely, efficient parallel split learning (EPSL), to accelerate model training. To be specific, EPSL parallelizes client-side model training andreduces the dimension of activations' gradientsfor backpropagation (BP) vialast-layer gradient aggregation, leading to a significant reduction in server-side training and communication latency. Moreover, by considering the heterogeneous channel conditions and computing capabilities at edge devices, we jointly optimize subchannel allocation, power control, and cut layer selection to minimize the per-round latency. Simulation results show that the proposed EPSL framework significantly decreases the training latency needed to achieve a target accuracy compared with the state-of-the-art benchmarks, and the tailored resource management and layer split strategy can considerably reduce latency than the counterpart without optimization.

Compressed spectrum sensing (CSS) plays a pivotal role in dynamic spectrum access within mobile cognitive radio networks by offering reduced power consumption and lower hardware costs. The multicoset sampler, a well-known implementation for periodic nonuniform sampling, has been widely studied and is considered a promising architecture for realizing CSS. This paper focuses on the design of the multicoset sampling pattern, aiming at enhancing the isometry property of the sensing matrix. Unlike previous studies which assume a noise-free setup, our work considers the problem in a real-world environment with noise. First, we propose a deterministic algorithm for sampling pattern generation, particularly for specific hardware setup parameters. This algorithm offers strict mutual-coherence control in the multicoset sensing matrix. To address more general hardware configurations, we propose two optimization algorithms. One of them searches for nearly optimal sampling patterns through a random search strategy, while the other employs a greedy pursuit strategy to find a local optimizer. Furthermore, we propose an algorithm to iteratively optimize the sampling pattern between consecutive spectrum sensing windows by minimizing a restricted version of mutual coherence. The excellent performance of our proposed algorithms has been demonstrated through numerical experiments and has been verified on a self-developed hardware platform.

The integration of a near-space information network (NSIN) with the reconfigurable intelligent surface (RIS) is envisioned to significantly enhance the communication performance of future wireless communication systems by proactively altering wireless channels. This paper investigates the problem of deploying a RIS-integrated NSIN to provide energy-efficient, ultra-reliable and low-latency communications (URLLC) services. We mathematically formulate this problem as a resource optimization problem, aiming to maximize the effective throughput and minimize the system power consumption, subject to URLLC and physical resource constraints. The formulated problem is challenging in terms of accurate channel estimation, RIS phase alignment, and effective solution design. We propose a joint resource allocation algorithm to handle these challenges. In this algorithm, we develop an accurate channel estimation approach by exploring message passing and optimize phase shifts of RIS reflecting elements to further increase the channel gain. Besides, we derive an analysis-friendly expression of decoding error probability and decompose the problem into two-layered optimization problems by analyzing the monotonicity, which makes the formulated problem analytically tractable. Extensive simulations have been conducted to verify the performance of the proposed algorithm. Simulation results show that the proposed algorithm can achieve outstanding channel estimation performance and is more energy-efficient than diverse benchmark algorithms.

Conclusion Security and ease of provisioning of IoT devices onto cloud infrastructure is instrumental to the deployment of IoT devices. With not much information available on the ease of secure provisioning of IoT devices couple with inherent challenge of these devices in encrypting data before transmission to the cloud due to their constrained nature, this work implements a low-cost client-side encryption algorithm based on the AES to carry out data encryption at the IoT device’s communication end using a SAMG55 microprocessor, following which the device is securely provisioned to a cloud platform as shown in Fig. 3. Related work and direction for future work is further detailed in Online Resources 1 and Online Resources 2.

In this article, we investigate an unmanned aerial vehicle (UAV)-assisted Internet of Things (IoT) system in a sophisticated 3-D environment, where the UAV’s trajectory is optimized to efficiently collect data from multiple IoT ground nodes. Unlike existing approaches focusing only on a simplified 2-D scenario and the availability of perfect channel state information (CSI), this article considers a practical 3-D urban environment with imperfect CSI, where the UAV’s trajectory is designed to minimize data collection completion time subject to practical throughput and flight movement constraints. Specifically, inspired by the state-of-the-art deep reinforcement learning approaches, we leverage the twin-delayed deep deterministic policy gradient (TD3) to design the UAV’s trajectory and we present a TD3-based trajectory design for completion time minimization (TD3-TDCTM) algorithm. In particular, we set an additional information, i.e., the merged pheromone, to represent the state information of the UAV and environment as a reference of reward which facilitates the algorithm design. By taking the service statuses of the IoT nodes, the UAV’s position, and the merged pheromone as input, the proposed algorithm can continuously and adaptively learn how to adjust the UAV’s movement strategy. By interacting with the external environment in the corresponding Markov decision process, the proposed algorithm can achieve a near-optimal navigation strategy. Our simulation results show the superiority of the proposed TD3-TDCTM algorithm over three conventional nonlearning-based baseline methods.

International audience

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.

The space-air-ground integrated network (SAGIN) has drawn increasing attention for its benefits, such as wide coverage, high throughput for 5G and 6G communications. As one of the links, space-air communications between multiple unmanned aerial vehicles (UAVs) and Ka-band orbiting low earth orbit (LEO) satellites face a crucial challenge in tracking the 3D dynamic channel information. This paper exploits a statistical dynamic channel model called the multi-dimensional Markov model (MD-MM), which investigates the more realistic spatial and temporal correlation in the sparse UAVs-satellite channel. Specifically, the spatial and temporal probabilistic relationships of multi-user (MU) hidden support vector, single-user (SU) joint hidden support vector, and SU hidden value vector are investigated. The specific transition probabilities that connect the SU and MU hidden support vector for both azimuth and elevation directions are defined. Moreover, based on the proposed MD-MM, we derive a novel multi-dimensional dynamic turbo approximate message passing (MD-DTAMP) algorithm for tracking the 3D dynamic channel in multiple UAVs systems. Furthermore, we also develop a gradient update scheme to recursively find the azimuth and elevation offset for 3D off-grid estimation. Numerical results verify that the proposed algorithm shows superior 3D channel tracking performance with smaller pilot overhead and comparable complexity.

To boost the performance of wireless communication networks, unmanned aerial vehicles (UAVs) aided communications have drawn dramatically attention due to their flexibility in establishing the line of sight (LoS) communications. However, with the blockage in the complex urban environment, and due to the movement of UAVs and mobile users, the directional paths can be occasionally blocked by trees and high-rise buildings. Intelligent reflection surfaces (IRSs) that can reflect signals to generate virtual LoS paths are capable of providing stable communications and serving wider coverage. This is the first paper that exploits a three-dimensional geometry dynamic channel model in IRS- assisted UAV-enabled communication system. Moreover, we develop a novel deep learning based channel tracking algorithm consisting of two modules: channel pre-estimation and channel tracking. A deep neural network with off-line training is designed for denoising in the pre-estimation module. Moreover, for channel tracking, a stacked bi-directional long short term memory (Stacked Bi-LSTM) is developed based on a framework that can trace back historical time sequence together with bidirectional structure over multiple stacked layers. Simulations have shown that the proposed channel tracking algorithm requires fewer epochs to convergence compared to benchmark algorithms. It also demonstrates that the proposed algorithm is superior to different benchmarks with small pilot overheads and comparable computation complexity.

Compressive sensing (CS) is a technique frequently adopted in wireless communications. By utilizing CS, a receiver could sense the state of channels with sub-Nyquist analog to digital converters when signals are sparse. Traditional CS methods struggle with non-sparse signals due to their intrinsic sparsity assumption. Therefore, we propose using deep learning (DL) to solve the vector support recovery problem with channels’ high occupancy. The simulation results show that the proposed CS framework powered by DL can perform better than a traditional CS analytical benchmark, both in high and low channel occupation regions. We also observe that the ML can work under a lower sampling rate than traditional CS methods. To process data sampled with high channel numbers, a divide and conquer tactic is implemented.

Abstract The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

The Internet of Things (IoT) pervades every aspect of our daily lives and industrial productions since billions of interconnected devices are deployed everywhere of the globe. However, the seamless IoT unveils a number of physical-layer threats, such as jamming and spoofing that decrease the communication performance and the reliability of the IoT systems. As the process of identifying the modulation format of signals corrupted by noise and fading, automatic modulation classification (AMC) plays a vital role in physical-layer security as it can detect and identify the pilot jamming, deceptive jamming, and sybil attacks. In this article, we propose a novel cyclic correntropy vector (CCV)-based AMC method using long short-term memory densely connected network (LSMD). Specifically, cyclic correntropy model-driven feature CCV is first extracted using the received signals as it contains both the second-order and the higher order characteristics of cyclostationary. Then, the extracted CCV feature is put into the data-driven LSMD which mainly consists of long short-term memory (LSTM) network and dense network (DenseNet). Moreover, an additive cosine loss is utilized to train the LSMD for maximizing the interclass feature differences and minimizing the intraclass feature variations. Simulations demonstrate that the proposed CCV-LSMD method yields superior performance than other recent schemes.

Internet-of-Things (IoT) devices characterized by low power and low processing capabilities do not exactly fit into the provision of existing security techniques due to their constrained nature. Classical security algorithms that are built on complex cryptographic functions often require a level of processing that low-power IoT devices are incapable to effectively achieve due to limited power and processing resources. Consequently, the option for constrained IoT devices lies in either developing new security schemes or modifying existing ones to be more suitable for constrained IoT devices. In this work, an efficient security algorithm for constrained IoT devices, based on the advanced encryption standard, is proposed. We present a cryptanalytic overview of the consequence of complexity reduction together with a supporting mathematical justification, and provisioned a secure element (ATECC608A) as a tradeoff. The ATECC608A doubles for authentication and guarding against implementation attacks on the associated IoT device (ARM Cortex M4 micro-processor) in line with our analysis. The software implementation of the efficient algorithm for constrained IoT devices shows up to 35% reduction in the time it takes to complete the encryption of a single block (16 B) of plain text, in comparison to the currently used standard AES-128 algorithm, and in comparison to current results in literature at 26.6%.

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.

Cellular connectivity for a massive number of Unmanned Aerial Vehicles (UAVs) will overcrowd the radio spectrum and cause spectrum scarcity. Incorporating Cognitive Radio (CR) with UAVs (Cognitive-UAV-Radios) has been proposed to overcome such an issue. However, the broadcasting nature of CR and the dominant line-of-sight links of UAV makes the Cognitive-UAV-Radios susceptible to jamming attacks. In this paper, we propose a framework to detect smart jammer, which locates and attacks the UAV commands with low Jamming-to-Signal-Power-Ratio (JSR). Smart jammer is more challenging than the types of jammers that always require high power values. Our work focuses on learning a Dynamic Bayesian Network (DBN) to model and analyze the signals' behaviour statistically. A Markov Jump Particle Filter (MJPF) is employed to perform predictions and consequently detect jamming signals. The results are satisfactory in terms of detection probability and false alarm rate that outperform the conventional Energy Detector approach.

Millimeter Wave (mmWave) band can be a solution to serve the vast number of Internet of Things (IoT) and Vehicle to Everything (V2X) devices. In this context, Cognitive Radio (CR) is capable of managing the mmWave spectrum sharing efficiently. However, Cognitive mmWave Radios are vulnerable to malicious users due to the complex dynamic radio environment and the shared access medium. This indicates the necessity to implement techniques able to detect precisely any anomalous behaviour in the spectrum to build secure and efficient radios. In this work, we propose a comparison framework between deep generative models: Conditional Generative Adversarial Network (C-GAN), Auxiliary Classifier Generative Adversarial Network (AC-GAN), and Variational Auto Encoder (VAE) used to detect anomalies inside the dynamic radio spectrum. For the sake of the evaluation, a real mmWave dataset is used, and results show that all of the models achieve high probability in detecting spectrum anomalies. Especially, AC-GAN that outperforms C-GAN and VAE in terms of accuracy and probability of detection.

The development of the Internet-of-Things (IoT) security is comparatively slower than the pace of the IoT innovations. The seamless IoT network operates in an untrusted environment and is exposed to many malicious active attacks. As the process of identifying the modulation format of signals is corrupted by noise and fading, automatic modulation classification (AMC) can be viewed as an effective approach to counter physical-layer threats for IoT as it can detect and identify the pilot jamming, deceptive jamming, and Sybil attacks. Nowadays, data-driven deep learning (DL) techniques, which are capable of extracting discriminative features and perform better robustness to channel and noise conditions, have drawn widespread attention. The deep residual network (ResNet) has a strong representative ability, which can learn latent information repeatedly from the received signals and improve the classification accuracy. Meanwhile, the gated recurrent unit (GRU), which is capable of exploiting temporal information of the received signal can expand the dimension of the signal features for satisfactory classification performance. Considering the advantages of the above networks, this article proposes a novel gated recurrent residual neural network (GrrNet) for feature-based AMC, where the amplitude and phase of the received signal are utilized as the inputs of GrrNet. In GrrNet, a ResNet extractor module is first designed to extract the highly representative features and then temporal information is obtained by the subsequent GRU module which is capable of processing the representative features with the arbitrary length for modulation classification. Moreover, extensive simulations are conducted to verify the classification performance and robustness of the proposed GrrNet and it is shown that GrrNet outperforms other recent DL-based AMC methods. Moreover, the influence of the network parameters, symbol length, and frequency offset on performance is also explored.

In this article, we investigate resource allocation with edge computing in Internet-of-Things (IoT) networks via machine learning approaches. Edge computing is playing a promising role in IoT networks by providing computing capabilities close to users. However, the massive number of users in IoT networks requires sufficient spectrum resource to transmit their computation tasks to an edge server, while the IoT users were developed to have more powerful computation ability recently, which makes it possible for them to execute some tasks locally. Then, the design of computation task offloading policies for such IoT edge computing systems remains challenging. In this article, centralized user clustering is explored to group the IoT users into different clusters according to users' priorities. The cluster with the highest priority is assigned to offload computation tasks and executed at the edge server, while the lowest priority cluster executes computation tasks locally. For the other clusters, the design of distributed task offloading policies for the IoT users is modeled by a Markov decision process, where each IoT user is considered as an agent which makes a series of decisions on task offloading by minimizing the system cost based on the environment dynamics. To deal with the curse of high dimensionality, we use a deep Q-network to learn the optimal policy in which deep neural network is used to approximate the Q-function in Q-learning. Simulations show that users are grouped into clusters with optimal number of clusters. Moreover, our proposed computation offloading algorithm outperforms the other baseline schemes under the same system costs.

The space-air-ground integrated network (SAGIN) aims to provide seamless wide-area connections, high throughput and strong resilience for 5G and beyond communications. Acting as a crucial link segment of the SAGIN, unmanned aerial vehicle (UAV)-satellite communication has drawn much attention. However, it is a key challenge to track dynamic channel information due to the low earth orbit (LEO) satellite orbiting and three-dimensional (3D) UAV trajectory. In this paper, we explore the 3D channel tracking for a Ka-band UAV-satellite communication system. We firstly propose a statistical dynamic channel model called 3D two-dimensional Markov model (3D-2D-MM) for the UAV-satellite communication system by exploiting the probabilistic insight relationship of both hidden value vector and joint hidden support vector. Specifically, for the joint hidden support vector, we consider a more realistic 3D support vector in both azimuth and elevation direction. Moreover, the spatial sparsity structure and the time-varying probabilistic relationship between degree patterns named the spatial and temporal correlation, respectively, are studied for each direction. Furthermore, we derive a novel 3D dynamic turbo approximate message passing (3D-DTAMP) algorithm to recursively track the dynamic channel with the 3D-2D-MM priors. Numerical results show that our proposed algorithm achieves superior channel tracking performance to the state-of-the-art algorithms with lower pilot overhead and comparable complexity.

Compressive sensing techniques have been proposed in real-time wideband spectrum sensing (WSS) to achieve sub-Nyquist sampling rate. Multicoset sampler has been widely discussed in the literature to acquire compressed samples of wideband signals. In millimeter-wave bands with even larger bandwidth of multi-gigahertz, the timing offsets of the multicoset sampler should be fine-controlled at finer resolutions and becomes more prone to undesired skews. We hereby analyze the impact of timing skews of the multicoset sampler, which is unavoidable and has to be taken into account in the real-world implementations. The error in compressed measurements caused by timing skews is quantified for the cases of unknown and known skews respectively, which can be indicative of the sparse recovery and system-level performances in the design of practical multicoset-sampler-based WSS systems.

Channel estimation is crucial to beamforming techniques in directional millimetre wave (mmWave) communications, which is generally designed based on channel state information with the assumption that the channel is static. However, due to the Doppler effect caused by the mobility of the users in highly mobile applications, the mmWave channel is changing rapidly. Spatial channel covariance, defined by long-term statistic information of channels, is a promising solution to reduce channel estimation frequency, and which can be used to design hybrid precoders. In this paper, we investigate compressive sensing based spatial channel covariance estimation for hybrid mmWave multiuser (MU) multiple input multiple output (MIMO) system. The updated sparse Bayesian learning (Updated-SBL) algorithm is proposed which is achieved by reducing the total squared mutual coherence of the sensing matrix in it. Simulations demonstrate that the total squared mutual coherence of the proposed Updated-SBL algorithm is dramatically reduced and the superiority of the proposed algorithm is validated by comparing to the other benchmark methods.

In this paper, we consider resource allocation for edge computing in internet of things (IoT) networks. Specifically, each end device is considered as an agent, which makes its decisions on whether offloading the computation tasks to the edge devices or not. To minimize the long-term weighted sum cost which includes the power consumption and the task execution latency, we consider the channel conditions between the end devices and the gateway, the computation task queue as well as the remaining computation resource of the end devices as the network states. The problem of making a series of decisions at the end devices is modelled as a Markov decision process and solved by the reinforcement learning approach. Therefore, we propose a near optimal task offloading algorithm based on ϵ-greedy Q-learning. Simulations validate the feasibility of our proposed algorithm, which achieves a better trade-off between the power consumption and the task execution latency compared to these of edge computing and local computing modes.

Compressive sensing has been applied in wideband spectrum sensing to achieve sub-Nyquist sampling. Prior information of the multiband spectrum occupancy, e.g. from geo-location database, can be utilized by compressive spectrum sensing (CSS) to enhance the sensing performance. However, these priors are prone to be partially missing and may also contain incorrect information. We hereby propose a CSS scheme aided by priors and robust to priors imperfections, and moreover, a novel and practical algorithm to provide robust channel sparsity estimation needed by the CSS scheme. Simulations show prominent enhancement of detection performance and lower iteration counts by employing priors in the proposed CSS scheme.

Technologies such as cognitive radio and dynamic spectrum access rely on spectrum sensing which provides wireless devices with information about the radio spectrum in the surrounding environment. One of the main challenges in wireless communications is the interference caused by malicious users on the shared spectrum. In this manuscript, an artificial intelligence enabled cognitive radio framework is proposed at system‐level as part of a cyclic spectrum intelligence algorithm for interference mitigation in wideband radios. It exploits the cyclostationary feature of signals to differentiate users with different modulation schemes and an artificial neural network as classifier to detect potential malicious users. A dataset consisting of experimental modulated and dynamic signals is recorded by spectrum measurements with an in‐house software defined radio testbed and then processed. Cyclostationary features are extracted for each detected signal and fed to a neural network classifier as training and testing data in a complex and dynamic scenario. Results highlight a classification rate of in most of cases, even at low transmission power. A comparison with two previous works with hand‐crafted features, which employ an energy detector‐based classifier and a naive Bayes‐based classifier, respectively, is discussed.

In this paper, we investigate optimal schemes to manage time scheduling of multiple modules, including spectrum sensing, radio frequency (RF) energy harvesting (RFH) and ambient backscatter communication (ABCom) by maximizing data transmission rate in Internet of Things networks. We first detect ambient RF signals with high signal power as the RF resource of RFH and ABCom by using spectrum sensing with energy detection techniques. Specifically, compressive sensing (CS) is adopted to detect the wideband RF signals with improving spectrum sensing efficiency at the same time. We formulate a joint optimization problem to manage time scheduling parameter and power allocation ratio. In addition, we propose to find the threshold of spectrum sensing for ABCom communications by analyzing the outage probability of backscatter communications. Numerical results demonstrate that the optimal schemes using spectrum sensing are achieved with better transmission rates. The designed time scheduling scheme with CS is confirmed to be more efficient, and the superiorities become more obvious with the increase of network operation time. Moreover, the optimal scheduling parameters and power allocation ratios are obtained. Simulations illustrate that the threshold of spectrum sensing for backscatter communications is obtained by analyzing the outage probability of backscatter communications.

In this paper, the efficient resource allocation for the uplink transmission of wireless powered Internet of Things (IoT) networks is investigated. We adopt LoRa technology as an example in the IoT network, but this paper is still suitable for other communication technologies. Allocating limited resources, like spectrum and energy resources, among a massive number of users faces critical challenges. We consider grouping wireless powered IoT users into available channels first and then investigate power allocation for users grouped in the same channel to improve the network throughput. Specifically, the user grouping problem is formulated as a many to one matching game. It is achieved by considering IoT users and channels as selfish players which belong to two disjoint sets. Both selfish players focus on maximizing their own utilities. Then we propose an efficient channel allocation algorithm (ECAA) with low complexity for user grouping. Additionally, a Markov decision process is used to model unpredictable energy arrival and channel conditions uncertainty at each user, and a power allocation algorithm is proposed to maximize the accumulative network throughput over a finite-horizon of time slots. By doing so, we can distribute the channel access and dynamic power allocation local to IoT users. Numerical results demonstrate that our proposed ECAA algorithm achieves near-optimal performance and is superior to random channel assignment, but has much lower computational complexity. Moreover, simulations show that the distributed power allocation policy for each user is obtained with better performance than a centralized offline scheme.

Compressive sensing (CS) techniques have been proposed for wideband spectrum sensing applications to achieve sub-Nyquist-rate sampling. The complexity of CS recovery algorithm and the detection performance against noise are two of the main challenges of the implementation of compressive spectrum sensing (CSS). We hereby propose CSS scheme based on orthogonal matching pursuit (OMP) with the aid of spectrum sparsity order estimation enabled by detecting the signal subspace dimensionality directly from sub- Nyquist measurements. The computational effort of spectrum recovery can be saved superlinearly with the reduction of iterations. With the estimated spectrum sparsity order, the OMP algorithm is proposed to run only an explicit and a fraction of iterations compared to the cases where such estimation is absent. Besides, the estimation of active channel number also enables blind and hard decision of channel occupancy where threshold adaption for energy detection is avoided. Moreover, the detection performance of the proposed CSS scheme by simulation shows superior robustness against noise compared to the energy detection scheme.

Millimeter wave band (mmWave) integrates with a wide variety of signals under manifold communication standards due to its high-capacity feature, which enables mmWave beam sensing to serve a valuable function in discriminating different signals. In this paper, we propose a novel frame structure consisting of variant beam sensing process and data transmission process. In the beam sensing process, multi-power-level beam sensing method is conducted in every direction to discriminate multi-users under multiple standards. The sensing duration varies with the number of directions. Several performance metrics are correspondingly proposed to quantify the beam sensing for multiple mmWave users, such as the probability of correct detection and the false alarm probability. In the second process, the signal with the biggest received signal-to-noise ratio (SNR) is given priority to communicate. On this base, sensing-throughput tradeoff is analyzed to balance the time division between two processes for throughput maximization. Finally, numerical evaluations and simulations are conducted to verify the correctness of the proposed methods.

The increasing number of Internet of Things (IoT) objects has been a growing challenge of the current spectrum supply. To handle this issue, the IoT devices should have cognitive capabilities to access the unoccupied portion of the wideband spectrum. However, most IoT devices are difficult to perform wideband spectrum sensing using either conventional Nyquist sampling system or sub-Nyquist sampling system since both power-hungry sampling components and intricate sub-Nyquist sampling hardware are unrealistic in the power-constrained IoT paradigm. In this paper, we propose a blind joint sub-Nyquist sensing scheme by utilizing the surround IoT devices to jointly sample the spectrum based on the multicoset sampling theory. Thus, only the off-the-shelf low-rate analog-to-digital converters on the IoT devices are required to form coset samplers and only the minimum number of coset samplers are adopted without the prior knowledge of the number of occupied channels and signal-to-noise ratios. Moreover, to further reduce the number of coset samplers and transfer part of the computational burden from the IoT devices to the core network, we adopt the data from geo-location database when applicable. The experimental results on both simulated and real-world signals verify the theoretical results and effectiveness of the proposed scheme. At the meanwhile, it is shown that the adaptive number of coset samplers could be adopted without causing the degradation of the detection performance and the number of coset samplers could be further reduced with the assists from geolocation database even when the obtained information is partially correct.

Spectrum sensing is a proactive way in cognitive radio systems to achieve dynamic spectrum access; and compressive spectrum sensing (CSS) techniques alleviate the demand for high-speed sampling in wideband spectrum sensing. Most existing literature discusses Neyman-Pearson channel energy detection and threshold adaption schemes to achieve an optimal performance of detection in a conventional non-compressive spectrum sensing scenario. However, in the CSS, it is found that the channel energy statistics and optimal threshold depend not only on noise energy but also on compression ratio, sparsity of spectrum, and nature of recovery algorithms. To investigate the channel energy statistics of recovered spectrum, we postulate a statistical model of channel energy for CSS and propose a learning algorithm based on a mixture model and expectation-maximization techniques. In addition, having verified the validity of the postulated model, we propose a practical threshold adaption scheme for CSS aiming to maintain constant false alarm rates in channel energy detection. In simulations, it is shown that the postulated channel energy statistic models with parameters learned by the proposed learning algorithm fit well with empirical distributions under circumstances of various channel models and recovery algorithms. Moreover, it is presented that the proposed threshold adaption scheme maintains the false alarm rate near the predefined constant, which in turn validates the postulated model.

Collaborative compressive spectrum sensing techniques are adopted to achieve spectrum reconstruction at sub-Nyquist sampling rate as well as to improve detection reliability in cognitive radio networks. However, most of the existing research of compressive spectrum sensing only focuses on the sparsity in the frequency domain. In pursuit of achieving compressive sampling further beyond Nyquist rate and better recovery performance, this paper presents a two- dimensional (2-D) compressive spectrum sensing scheme in a centralized collaborative setting by applying compression in both frequency and spatial dimensions. To exploit sparsity in spatial dimension, an algorithm based on ant colony optimization aiming at permuting power spectrum observations from users is proposed. Numerical simulations show the superior compressibility and recovery performance of the proposed 2-D compression scheme, as well as effectiveness of the proposed permutation algorithm.

The increasing number of Internet of things (IoT) objects has been a growing challenge of the current spectrum supply. To handle this issue, the IoT devices should have cognitive capabilities to detect and access the unoccupied portion of the wideband spectrum. However, most IoT devices are difficult to perform wideband spectrum sensing using either conventional Nyquist sampling system or sub-Nyquist sampling system since both the power-hungry sampling components and specialized sub-Nyquist sampling hardware are unrealistic in the power-constrained IoT paradigm. In this paper, we propose a blind sub-Nyquist sensing scheme by utilizing the surround IoT devices to jointly sample the spectrum based on the multi-coset sampling theory. Thus, only the off-the-shelf low- rate analog-to-digital converters (ADCs) on the IoT devices are required to form coset samplers and only the minimum number of coset samplers are adopted without the prior knowledge of the number of occupied channels. The experimental results on both the simulated and real-time signals verify the theoretical results and the effectiveness of the proposed scheme. At the meanwhile, it is shown that the adaptive number of coset samplers could be adopted without causing the degradation of the detection performance.

To maximize spectrum access opportunities for white space devices, incorporating real-time spectrum sensing with geolocation database is a promising approach to enhance detection resolution. Advanced spectrum sensing techniques are needed to quickly and accurately identify spectrum occupancy over a wide frequency range. However, the stringent requirements from the wideband signal acquisition and processing pose a major implementation challenge in compact devices with limited energy storage and computational capabilities. In this paper, an efficient joint scheme of sub-Nyquist wideband spectrum sensing with geolocation database is proposed to ensure accurate detection of the surrounding spectrum with reduced number of measurements. Subspace augmented greedy algorithm is modified to incorporate a priori information from geolocation database, therefore enabling local spectrum sensing to be performed only on a limited number of potentially vacant channels over TV White Space. Theoretical analysis and experimental results on the simulated and real-time signals show that the proposed joint scheme improves the sensing sensitivity with lower computation complexity, while the updated channel information from local sensing reduces the risk of interferences to the primary users.

To enable dynamic spectrum access of machine-to- machine (M2M) communications for internet of thigns (IoT) applications, incorporating real-time wideband spectrum sensing with geo- location database is a promising approach to reducing complexity and promoting low latency. However, high sampling rate and high speed signal processing of the wideband signal acquisition pose a major implementation challenge in compact M2M/IoT devices with limited power supply and low computation capabilities. In this paper, to reduce the required sampling rate without degrading the detection performance, a hybrid sub- Nyquist wideband spectrum sensing scheme with geo-location database is proposed. The iteratively reweighted least squares (IRLS) algorithm is modified to incorporate a priori information from geo-location database, therefore enabling local spectrum sensing to be performed only on a limited number of potentially vacant TV channels. Theoretical analyses and experimental results show that the proposed hybrid scheme improves detec- tion performance with reduced sub-Nyquist sampling ratio and demonstrates a better detection capability over lower signal-to- noise ratio (SNR) regions.

Nowadays, wideband spectrum sensing, as one of the vital technologies of cognitive radio (CR), has the potential to find more temporarily available frequency bands to meet the growing demands of wireless services. As the vast number of samples are required to be collected and processed, traditional wideband spectrum sensing methods become inefficient and cause large energy consumption. Therefore, many theoretical work focus on applying compressive sensing (CS) into wideband spectrum sensing to alleviate this issue. In this paper, to verify the CS-based spectrum sensing scheme in real-world scenarios, a real-time compressive spectrum sensing testbed is proposed to process the real-time data collected from the TV white space (TVWS) spectrum. The proposed testbed consists of two parts: a senor node, and a real-time signal processing platform based on National Instruments (NI) LabVIEW software to process the spectral data and control the sensor.

In this paper, we consider cognitive radio networks in which energy constrained secondary users (SUs) can harvest energy from the randomly deployed power beacons. A new frame structure is proposed for the considered networks. In the considered network, a wireless power transfer model is proposed, and the closed-form expressions for the power outage probability are derived. In addition, in order to reduce the energy consumption at SUs, sub-Nyquist sampling are performed at SUs. Subsequently, compressive sensing and matrix completion techniques are invoked to recover the original signals at the fusion center by utilizing the sparsity property of spectral signals. Throughput optimizations of the secondary networks are formulated into two linear constrained problems, which aim to maximize the throughput of a single SU and the whole cooperative network, respectively. Three methods are provided to obtain the maximal throughput of secondary networks by optimizing the time slots allocation and the transmit power. Simulation results show that the maximum throughput can be improved by implementing compressive spectrum sensing in the proposed frame structure design.

Nowadays, people usually connect to the Internet through a multitude of different devices. Video streaming takes the lion's share of the bandwidth, and represents the real challenge for the service providers and for the research community. At the same time, most of the connections come from indoor, where Wi-Fi already experiences congestion and coverage holes, directly translating into a poor experience for the user. A possible relief comes from the TV white space (TVWS) networks, which can enhance the communication range thanks to sub-GHz frequencies and favorable propagation characteristics, but offer slower datarates compared with other 802.11 protocols. In this paper, we show the benefits that TVWS networks can bring to the end user, and we present CABA, a connection aware balancing algorithm able to exploit multiple radio connections in the favor of a better user experience. Our experimental results indicate that the TVWS network can effectively provide a wider communication range, but a load balancing middleware between the available connections on the device must be used to achieve better performance. We conclude this paper by presenting real data coming from field trials in which we streamed an MPEG dynamic adaptive streaming over HTTP video over TVWS and Wi-Fi. Practical quantitative results on the achievable quality of experience for the end user are then reported. Our results show that balancing the load between Wi-Fi and TVWS can provide a higher playback quality (up to 15% of average quality index) in scenarios in which the Wi-Fi is received at a low strength.

This paper investigates the physical layer security of non-orthogonal multiple access (NOMA) in large-scale networks with invoking stochastic geometry. Both single-antenna and multiple-antenna aided transmission scenarios are considered, where the base station (BS) communicates with randomly distributed NOMA users. In the single-antenna scenario, we adopt a protected zone around the BS to establish an eavesdropper-exclusion area with the aid of careful channel ordering of the NOMA users. In the multiple-antenna scenario, artificial noise is generated at the BS for further improving the security of a beamforming-aided system. In order to characterize the secrecy performance, we derive new exact expressions of the security outage probability for both single-antenna and multiple-antenna aided scenarios. For the single-antenna scenario, we perform secrecy diversity order analysis of the selected user pair. The analytical results derived demonstrate that the secrecy diversity order is determined by the specific user having the worse channel condition among the selected user pair. For the multiple-antenna scenario, we derive the asymptotic secrecy outage probability, when the number of transmit antennas tends to infinity. Monte Carlo simulations are provided for verifying the analytical results derived and to show that: 1) the security performance of the NOMA networks can be improved by invoking the protected zone and by generating artificial noise at the BS and 2) the asymptotic secrecy outage probability is close to the exact secrecy outage probability.

In this paper, the application of non-orthogonal multiple access (NOMA) into K-tier heterogeneous networks (HetNets) is investigated. A new promising transmission framework is proposed, in which massive multiple-input multiple-output (MIMO) is employed in macro cells and NOMA is adopted in small cells. For maximizing the biased average received power at mobile users, a massive MIMO and NOMA based user association scheme is developed. In an effort to evaluate the performance of the proposed framework, analytical expressions for the spectrum efficiency of each tier are derived using stochastic geometry. Simulation results are presented to verify the accuracy of the proposed analytical derivations and confirm that NOMA is capable of enhancing the spectrum efficiency of the network compared to the orthogonal multiple access (OMA) based HetNets.

Wideband spectrum sensing is a critical functionality in cognitive radio networks to enable dynamic spectrum sharing, but entails a major implementation challenge in compact commodity radios with restricted energy and computation capabilities. Exploiting jointly sparse nature of multiband signals, this paper proposes an efficient blind sub-Nyquist cooperative wideband spectrum sensing scheme, which reduces energy consumption in wideband signal acquisition, processing and transmission, with performance guarantee. In contrast to traditional sub-Nyquist approaches where a wideband signal or its power spectrum is first reconstructed from compressed samples, the proposed scheme locates occupied channels by recovering the signal support jointly from multiple secondary user (SU) measurements. Based on subspace decomposition, the low-dimensional measurement matrix computed at each SU from local sub-Nyquist samples can reduce transmission overhead while improving noise robustness. Numerical analysis and simulation results show that the proposed scheme can achieve good detection performance as well as reduce computation and implementation complexity in comparison with conventional cooperative wideband spectrum sensing schemes.

Wideband spectrum sensing is regarded as one of the key features in cognitive radio systems. Compressive sensing (CS) has recently become one of the promising techniques to deal with the Nyquist sampling rate bottleneck of wideband spectrum sensing. Theoretical analyses and simulation have shown that CS could achieve high detection probability and low false alarm for wideband spectrum sensing. However, implementation of CS on the real-time signals and real-time processing poses significant challenges due to the iterative nature of the CS algorithms. In this paper, we propose a novel adaptively regularized iterative reweighted least squares (AR-IRLS) algorithm to implement the real-time signal recovery on the CS based wideband spectrum sensing. The proposed algorithm moves estimated solutions along an exponential-linear path by regularizing weights with a series of non- increasing penalty terms, which significantly speeds up the convergence of reconstruction and provides high fidelity guarantee to cope with the varying bandwidths and power levels of occupied channels. The proposed algorithm presents robustness against different sparsity levels at low compressive ratio without degradation on the reconstruction performance, and is tested on the real-time signals over TV white space spectrum after having been validated on the simulated signals. Both the simulation and real-time experiments show that the proposed algorithm outperforms the conventional iterative reweighted least squares (IRLS) algorithms in terms of convergence speed, reconstruction accuracy, and compressive ratio requirement.

In this paper, the physical layer security of applying non-orthogonal multiple access (NOMA) in large-scale networks is investigated. In the considered scenario, both the NOMA users and eavesdroppers are spatially randomly deployed. A protected zone around the source node is adopted to enhance the security of a random network. In order to characterize the secrecy performance of the considered scenario, new exact and asymptotic expressions for the security outage probability are derived. These analytical results demonstrate that the secrecy diversity order is m, which is determined by the user with poor channel condition. Monte Carlo simulations are provided to verify the derived analytical results. Furthermore, it is also confirmed that the secure performance of the NOMA networks can be improved by either enlarging the scope of the protected zone or reducing the scope of the user zone.

Cognitive radio (CR) has emerged as one of the most promising candidate solutions to improve spectrum utilization for future wireless networks. A crucial requirement for future CR networks is the ability to sense wideband spectrum. Recently, compressive sensing (CS) has been proposed as a solution to replace high speed analog-to-digital converters. CS enables sub-Nyquist sampling for wideband spectrum sensing by exploiting the sparse nature of spectrum occupancy. In this paper, the CS based spectrum sensing model is firstly summarized. The measurement matrix designs and the hardware implementations of CS based wideband spectrum sensing algorithms are then explored. The implementations of CS with real-time signals over TV white space spectrum in CR is further established. This paper is concluded by providing outlooks and open research challenges on the practical implementation of CS in wideband spectrum sensing in cognitive radio.

Operating at ultra-high frequency (UHF), TV white space (TVWS) can achieve long-distance communication and good in-building penetration, and has attracted increasing attention of regulators, researchers and stakeholders. This paper explores the potential of TVWS for network provisioning within a cluster of buildings, through a succession of tests. Different transmission distances, from 10m to over 120m, and through multiple layers of walls as well as complex transmission environment imposed by other factors like office and construction facilities, are considered. Further, a compact ultra-wide band (UWB) printed monopole antenna is designed for the client white space terminal, and compared with a commercial directional UHF antenna on the same client. Measurement results show that the in-house compact antenna achieves fast network speed and a high signal-to-interference-plus-noise ratio (SINR), and it is orientation independent.

To underpin the predicted growth of the Internet of Things (IoT), a highly scalable, reliable and available connectivity technology will be required. Whilst numerous technologies are available today, the industry trend suggests that cellular systems will play a central role in ensuring IoT connectivity globally. With spectrum generally a bottleneck for 3GPP technologies, TV white space (TVWS) approaches are a very promising means to handle the billions of connected devices in a highly flexible, reliable and scalable way. To this end, we propose a cognitive radio enabled TD-LET test-bed to realize the dynamic spectrum management over TVWS. In order to reduce the data acquisition and improve the detection performance, we propose a hybrid framework for the dynamic spectrum management of machine-to-machine networks. In the proposed framework, compressed sensing is implemented with the aim to reduce the sampling rates for wideband spectrum sensing. A noniterative reweighed compressive spectrum sensing algorithm is proposed with the weights being constructed by data from geolocation databases. Finally, the proposed hybrid framework is tested by means of simulated as well as real-world data.

The rising popularity of wireless services resulting in spectrum shortage has motivated dynamic spectrum sharing to facilitate efficient usage of the underutilized spectrum. Wideband spectrum sensing is a critical functionality to enable dynamic spectrum access by enhancing the opportunities of exploring spectral holes, but entails a major implementation challenge in compact commodity radios that only have limited energy and computation capabilities. In contrast to the traditional sub-Nyquist approaches where a wideband signal or its power spectrum is first reconstructed from compressed samples, this paper proposes a sub-Nyquist wideband spectrum sensing scheme that locates occupied channels blindly by recovering the signal support, based on the jointly sparse nature of multiband signals. Exploiting the common signal support shared among multiple secondary users (SUs), an efficient cooperative spectrum sensing scheme is developed, in which the energy consumption on wideband signal acquisition, processing, and transmission is reduced with detection performance guarantee. Based on subspace decomposition, the low-dimensional measurement matrix, computed at each SU from local sub-Nyquist samples, is deployed to reduce the transmission and computation overhead while improving noise robustness. The theoretical analysis of the proposed sub-Nyquist wideband sensing algorithm is derived and verified by numerical analysis and further tested on real-world TV white space signals. It shows that the proposed scheme can achieve good detection performance as well as reduce the computation and implementation complexity, in comparison with the conventional cooperative wideband spectrum sensing schemes.

In this paper, we present a novel hybrid framework combining compressive spectrum sensing with geo-location database to find spectrum holes in a decentralized cognitive radio. In the hybrid framework, a geo-location database algorithm is proposed to be stored locally at secondary users (SUs) to remove the extra transmission link to a centralized remote geo-location database. Specifically, by utilizing the output of the locally stored geo-location database algorithm, a data-assisted noniteratively reweighted least squares (DNRLS)-based compressive spectrum sensing algorithm is proposed to improve detection performance under sub-Nyquist sampling rates for wideband spectrum sensing, and to reduce the computational complexity of signal recovery. In addition, an efficient method for the calculation of maximum allowable equivalent isotropic radiated power in TV white space (TVWS) is also designed to further support SUs. The convergence and complexity of the proposed DNRLS algorithm are analyzed theoretically. Furthermore, the proposed framework is pioneered on real-time “from air” signals and data after having been validated by simulated signals and data in TVWS.

In this paper, we consider a cognitive radio network in which energy constrained secondary users (SUs) can harvest energy from the randomly deployed power beacons. A new frame structure with four time slots, namely, energy harvesting, spectrum sensing, energy harvesting and data transmission is proposed. In the energy harvesting slot, a new wireless power transfer (WPT) scheme in a bounded power transfer model is proposed to enable power SUs wirelessly. Closed-form expression for the power outage probability of the proposed WPT scheme is derived. In the spectrum sensing slot, we propose to utilize the compressive sensing technique which enables sub-Nyquist sampling to further reduce the energy consumption at SUs. Throughput of the secondary network with the proposed frame structure is formulated into a nonlinear constraint problem. Three optimization methods are provided to obtain the maximal throughput of secondary network by optimizing the time slots allocation and the transmit power of SUs.

Spectrum sensing is one of the key technologies to realize dynamic spectrum access in cognitive radio (CR). In this paper, a novel database-augmented spectrum sensing algorithm is proposed for a secondary access to the TV White Space (TVWS) spectrum. The proposed database-augmented sensing algorithm is based on an existing geo-location database approach for detecting incumbents like Digital Terrestrial Television (DTT) and Programme Making and Special Events (PMSE) users, but is combined with spectrum sensing to further improve the protection to these primary users (PUs). A closed-form expression of secondary users' (SUs) spectral efficiency is also derived for its opportunistic access of TVWS. By implementing previously developed power control based geo-location database and adaptive spectrum sensing algorithm, the proposed database-augmented sensing algorithm demonstrates a better spectrum efficiency for SUs, and better protection for incumbent PUs than the exiting stand-alone geo-location database model. Furthermore, we analyze the effect of the unregistered PMSE on the reliable use of the channel for SUs.

This paper studies the physical layer security in cognitive relay network (CRN) with multiple antennas in the presence of multiple eavesdroppers. Under spectrum sharing scenario and orthogonal space-time block code (OSTBC) transmission, we derive both the exact and asymptotic expressions of secrecy outage probability over Rayleigh fading channels and give the reliability-security tradeoff analysis. The results which have been verified by Monte Carlo simulation show that the loss of the secrecy outage performance caused by an increase of eavesdroppers number can be totally overcome by multiple antenna diversity. It's illustrated that increasing the number of antennas can also improve both reliability and security of the system. Besides, the asymptotic analysis indicates the secrecy diversity order is only determined by number of antennas and is independent with number of eavesdroppers. The secrecy array gain is relevant with both number of antennas and eavesdroppers. In other words, the existence of eavesdroppers will degrade the secrecy outage performance but will not affect the diversity order of the system.

TV White Spaces (TVWS) technology allows wireless devices to opportunistically use locally-available TV channels enabled by a geolocation database. The UK regulator Ofcom has initiated a pilot of TVWS technology in the UK. This paper concerns a large- scale series of trials under that pilot. The purposes are to test aspects of white space technology, including the white space device and geolocation database interactions, the validity of the channel availability/powers calculations by the database and associated interference effects on primary services, and the performances of the white space devices, among others. An additional key purpose is to perform research investigations such as on aggregation of TVWS resources with conventional resources and also aggregation solely within TVWS, secondary coexistence issues and means to mitigate such issues, and primary coexistence issues under challenging deployment geometries, among others. This paper provides an update on the trials, giving an overview of their objectives and characteristics, some aspects that have been covered, and some early results and observations.

Secondary operation in TV White Space (TVWS) calls for fast and accurate spectrum sensing over a wide bandwidth, which challenges the traditional spectrum sensing methods operating at or above Nyquist rate. Sub-Nyquist sampling has attracted significant interests for wideband spectrum sensing, while existing algorithms can only work for sparse spectrum with high computation and hardware complexity. In this paper, we propose a novel sub-Nyquist wideband sensing algorithm that can work for the non-sparse spectrum without sampling at full bandwidth through the use of multiple low-speed Analog-to-Digital Converters (ADCs) based on sparse Fast Fourier Transform (sFFT). The proposed permutation and filtering algorithm achieves the wideband sensing regardless of signal sparsity with low hardware complexity. In contrast to existing sub-Nyquist approaches, the proposed wideband sensing algorithm subsamples the wideband signal, and then directly estimates its frequency spectrum. The mathematical model of the proposed sub-Nyquist wideband sensing algorithm is derived and verified by numerical analysis over TVWS signals. The proposed algorithm shows considerable detection performance on wideband signals as well as reduces the runtime and implementation complexity in comparison with conventional wideband sensing algorithm.

Cooperative spectrum sensing is employed in Cognitive Radio (CR) networks to reliably detect Primary User (PU) transmissions by fusing the sensed data of multiple Secondary Users (SUs). The local detection reliability of an individual SU is closely related to its channel condition. In this paper, we propose a scheme that uses SNR to evaluate the reliability of each individual SU's local decision. We optimize the number of SUs for the sensing based on their channel conditions to achieve the optimal global detection probability at the fusion centre. Simulation results show that the proposed algorithm is robust against noise uncertainty with the optimal number of SUs and better receiver operating characteristic (ROC) performance is obtained in comparison to conventional schemes.

Interference mitigation between femtocells and the surrounding macrocells is one of the major challenges in femtocell deployment. This paper proposes a system architecture of using TV White Space (TVWS) in LTE femtocell networks, which includes: (i) a Geo-location database to obtain locally available TVWS information, and (ii) a new resource allocation scheme using the locally available TVWS to mitigate the downlink cross-tier interference between macrocell users and nearby femtocells. A two-tier multi-femtocell simulator is established to demonstrate the system performance. Simulations at different scenarios are conducted to compare the performance of the traditional all-shared resource allocation scheme, dynamic resource partitioning scheme and the proposed scheme. Simulation results show that the proposed scheme has better downlink interference mitigation performance in comparison with the other two schemes.