We present Ishtar, a power-efficient RISC-V baseband system-on-chip (SoC) tailored for multi-standard integrated sensing and communications (ISAC) in low-altitude wireless networks (LAWNs). Ishtar integrates a hierarchical scheduling scheme and a system-level power-gating architecture that dynamically controls power domains to balance performance and energy efficiency. It supports dynamic task scheduling across heterogeneous protocols using a domain-specific, graph-based representation. Implemented in 40 nm technology and running at 300 MHz, Ishtar achieves better normalized efficiency than state-of-the-art SDR SoCs, delivering real-time multi-standard sniffing under stringent power and area constraints.

Wireless baseband processing (WBP) is a key element of wireless communications, with a series of signal processing modules to improve data throughput and counter channel fading. Conventional hardware solutions, such as digital signal processors (DSPs) and more recently, graphic processing units (GPUs), provide various degrees of parallelism, yet they both fail to take into account the cyclical and consecutive character of WBP. Furthermore, the large amount of data in WBPs cannot be processed quickly in symmetric multiprocessors (SMPs) due to the unpredictability of memory latency. To address this issue, we propose a hierarchical dataflow-driven architecture to accelerate WBP. A pack-and-ship approach is presented under a non-uniform memory access (NUMA) architecture to allow the subordinate tiles to operate in a bundled access and execute manner. We also propose a multi-level dataflow model and the related scheduling scheme to manage and allocate the heterogeneous hardware resources. Experiment results demonstrate that our prototype achieves 2× and 2.3× speedup in terms of normalized throughput and single-tile clock cycles compared with GPU and DSP counterparts in several critical WBP benchmarks. Additionally, a link-level throughput of 288 Mbps can be achieved with a 45-core configuration.

Discovering vulnerabilities in network service is of great significance. Currently, coverage-guided fuzzing (CGF) is widely regarded as the most effective method. However, the efficiency of CGF depends on the quality of initial corpus. The initial corpus is a set of valid input examples used to initiate the fuzzing process. Constructing high-quality initial corpus typically requires manual efforts to understand the implementation details and corresponding protocol specifications, making it difficult to generalize across different protocol implementations.To generate high-quality corpus tailored to service under test (SUT), this paper proposes a protocol-independent smart generation method. The paper introduces a novel service communication model and utilizes active learning algorithms to automatically construct the model. By analyzing the minimum spanning tree of the model, we achieve automatic generation of high-quality corpus that adapts to the SUT.We conduct experiment by generating adaptive corpus for 6 targets of 6 different protocols in ProFuzzBench. Compared to the corpus provided by ProFuzzBench, the corpus generated by our system improve the state coverage of modern protocol fuzzers by 37.1% and discover known real protocol vulnerabilities at a speed 2.47x faster.

Static malware detectors based on machine learning are integral to contemporary antivirus systems, but they are vulnerable to adversarial attacks. While existing research has demonstrated success with adversarial attacks in black-box hard-label scenarios, challenges such as high perturbation rates and incomplete retention of functional integrity remain. To address these issues, we propose a novel black-box hard-label attack method, MiniMal. MiniMal begins with initialized adversarial examples and utilizes binary search and particle swarm optimization algorithms to streamline the perturbation content, significantly reducing the perturbation rate of the adversarial examples. Furthermore, we propose a functionality verification method grounded in file format parsing and control flow graph comparisons to ensure the functional integrity of the adversarial examples. Experimental results indicate that MiniMal achieves an attack success rate of over 98% against three leading machine learning detectors, improving performance by approximately 4.8% to 7.1% compared to state-of-the-art methods. MiniMal reduces perturbation rates to below 40%, making them 9 to 11 times lower than those of previous methods. Additionally, functional verification via Cuckoo Sandbox revealed that the adversarial examples generated by MiniMal retained 100% functional integrity, even with various modifications applied.

Implementing LiDAR-based 3D object detection algorithms in practical autonomous driving situations presents a significant challenge. In current research algorithms, the inherent sparsity and randomness of point cloud data necessitate significant memory usage and frequent data read/write operations during preprocessing. Such demands are not well-suited for terminal devices with stringent real-time requirements and constrained resources. In this paper, we present a packetized processing Pillar Feature Net accelerator for LiDAR 3D object detection. By integrating voxelization and feature extraction into a pipelined architecture, the proposed accelerator significantly reduces the storage requirements for point cloud data and enhances the speed of feature extraction and pseudo-image generation. Experimental results indicate that the proposed method improves the computational throughput from point cloud data to pseudo-image generation by 1.2 times and eliminates the need for off-chip memory access during preprocessing.

Mixed-precision quantization CNNs have become crucial for edge vision algorithms. While model quantization techniques have improved inference efficiency, existing approaches have not fully addressed data coherence in coarse-grained parallelism or adaptation to various 2D data sizes. This study presents a novel CNN accelerator equipped with mixed-precision multiplier and feature linking mechanisms to enhance mixed-precision inference on edge NPUs. The accelerator incorporates reconfigurable multi-precision multiplication support in MAC units, enabling 8-bit/4-bit mixed-precision quantization. A feature linking mechanism is developed to overcome feature map size limitations, achieving scalable processing dimensions. The accelerator design, implemented on a Xilinx Zynq UltraScale+ MPSoC FPGA platform, demonstrates improved hardware performance and enhanced inference speeds for edge device CNN models such as Tiny-YOLOv3 and ResNet18. This work provides a significant advancement in deploying mixed-quantization CNNs on edge NPUs, enhancing the adaptability and performance of edge computing devices for complex neural network models.

Vector processing is crucial for boosting processor performance and efficiency, particularly with data-parallel tasks. The RISC-V ”V” Vector Extension (RVV) enhances algorithm efficiency by supporting vector registers of dynamic sizes and their grouping. Nevertheless, for very long vectors, the static number of RVV vector registers and its power-of-two grouping can lead to performance restrictions. To counteract this limitation, this work introduces Zoozve, a RISC-V vector instruction extension that eliminates the need for strip-mining. Zoozve allows for flexible vector register length and count configurations to boost data computation parallelism. With a data-adaptive register allocation approach, Zoozve permits any register groupings and accurately aligns vector lengths, cutting down register overhead and alleviating performance declines from strip-mining. Additionally, the paper details Zoozve’s compiler and hardware implementations using LLVM and SystemVerilog. Initial results indicate Zoozve yields a minimum 10.10× reduction in dynamic instruction count for fast Fourier transform (FFT), with a mere 5.2% increase in overall silicon area.

Coverage-guided fuzzing (CGF) has proven its outstanding performance on vulnerability detection. However, existing approaches exhibit limitations when handling network service. Restricted by network I/O duration and chronology, long packet sequences crafted by fuzzers incur a substantial execution cost. Test cases with such non-coverage-improving mutations (i.e. redundant mutation) can significantly reduce fuzzing throughput and compromise vulnerability discovery. To address this issue, we propose RPFUZZ, a novel network fuzzing framework designed to systematically reduce redundant mutations: (1) We propose redundant mutation pruning for network service fuzzing. By early terminating redundant mutations’ execution, RPFUZZ can achieve higher throughput. (2) To detect redundant mutation, we propose redundant mutation oracle. This oracle dynamically judges whether a test case is redundant according to current code coverage and value of service-related variables (SRVs). (3)To identify SRVs, we propose an integrated approach combining dynamic call stack analysis with static value-flow graph (VFG) analysis. To evaluate the performance of RPFUZZ, we implement a prototype on top of NYX-NET. We conduct thorough experiments on ProFuzzBench, a benchmark that consists of 12 real-world network services. The results indicate that RPFUZZ achieves over 185% improvement in throughput and 1.02% rise in code coverage compared with NYX-NET. Besides, RPFUZZ has successfully uncovered 1753 unique crashes across 6 network services, including an unreported vulnerability (assigned to CVE-2024-57392) in ProFTPD, which has been well tested. • We propose redundant mutation pruning technique for network service fuzzing. By pruning mutated suffix packet sequence which is non-coverage-improving, network service fuzzer can achieve higher throughout. This is achieved by redundant mutation oracle, which leverage code coverage and identified service-related variables’ (SRVs) value to decide whether continuing current execution is advisable. • To precisely identify SRVs in network services, we propose an identification method combining with call stack analysis and value-flow graph analysis. This method is based on SRV’s programming features, which can be applied in various network service. • Based on technique above, We implement RPFUZZ. RPFUZZ achieved more than 185.92% (average 56.39%) throughput enhancement over NYX-NET, while improving maximum 4.27% code coverage (average +1.02%). It successfully identified 1753 unique crashes across 6 targets without ASAN and a buffer overflow vulnerability in ProFTPD (assigned CVE-2024-57392).

Fall detection systems are vital for identifying falls and ensuring prompt assistance, reducing the risk of severe injuries. As society progresses and health concerns gain more attention, extensive research has been conducted to mitigate the effects of falls. Integrating these systems with the Internet of Medical Things (IoMT) has significantly advanced healthcare and personal safety. This study proposes MKLS-Net, a deep learning model that combines multikernel (MK) convolution, long-short term memory (LSTM), and self-attention mechanism. MKLS-Net performs feature extraction through MK convolution, passing coarse-grained features to fine-grained ones, minimizing information loss, and improving differentiation between confusing activities. Both LSTM and self-attention help in extracting relatively important information from time series data. The MKLS-Net model demonstrates good fall detection performance on the three publicly available datasets, MobiAct, SisFall, and UniMib-SHAR, with best recognition accuracy of 99.51%, 99.94%, and 99.40%, respectively. In addition, for more analysis, we test the proposed model in the multiclassification stage, and it also shows a high accuracy rate in the SisFall dataset with an average accuracy of 83.91%.

Random access protocols play a pivotal role in the development of Satellite-based Internet of Things (S-IoT). However, existing research often inadequately addresses the rapid increase in device numbers, leading to theoretical models that unconvincingly simulate group behavior based on single-device behavior. Instead, we employ the static floor field method, traditionally used to analyze the evacuation of large numbers of pedestrians from confined spaces, to investigate the competition dynamics for limited preambles among numerous devices within the S-IoT system. This approach offers significant advantages in capturing dynamics and ensuring scalability during massive interactive sessions. Specifically, we extend the analysis model, based on the static floor field method, to address the issue of preamble collisions among a large number of IoT devices. The data obtained from this method closely align with the results from a system-level simulation platform, substantiating the accuracy and effectiveness of our analytical approach. Consequently, the analytical and predictive capabilities of the proposed method lead to the development of intelligent preamble resource allocation strategies based on reinforcement learning, thereby enhancing the overall performance of the S-IoT system. The efficacy of the proposed strategy is further validated by results derived from a simulation platform using authentic orbital data.

Autonomous driving technologies are swiftly evolving, characterized by two main strategies: Single-Vehicle Autonomous Driving (SVAD) and Vehicle-Infrastructure Cooperative Autonomous Driving (VICAD). SVAD depends entirely on the vehicle’s internal sensors and processing capabilities, whereas VICAD benefits from a synergistic network combining roadside infrastructure, connected vehicles, and cloud services to boost safety and efficiency. Nevertheless, VICAD encounters challenges with high-bandwidth data transmission and perception latency. To mitigate these concerns, we introduce an innovative intelligent roadside unit (I-RSU) platform integrating perception, computing, and communication into one cohesive system. The platform features dual neural processing units (NPUs) for the effective extraction of images and LiDAR features, alongside a C-V2X communication module, all realized on a Field-Programmable Gate Array (FPGA). This setup minimizes latency and expenses by enabling computation near the sensors and facilitating selective data transmission. Our system also supports multi-modal fusion, enhancing overall perception and safety. Through extensive real-world trials and simulations, our system demonstrates a substantial reduction in end-to-end latency, providing a scalable solution for VICAD scenarios.

With the continuous advancement of convolutional neural networks (CNNs), many neural network processing units (NPU) have emerged in recent years. NPUs offer advantages such as improved energy efficiency and low latency compared to traditional processors. However, NPUs often struggle to keep pace with the growing complexity of algorithmic models, which limits the implementation of certain AI applications. Central processing units (CPU) are known for their versatility, but are computationally inefficient. Combining the strengths of both architectures could present a viable solution to these challenges. Despite this potential, there is currently insufficient academic focus on CPU/NPU co-computing, and few architectures or toolchains have been developed to support such integration. In this paper, we propose a RISC-V CPU/NPU co-computing-based compilation framework to solve the problem of mapping from algorithms to heterogeneous architectures. To bridge the computational gap between heterogeneous platforms, within ONNX-MLIR, we developed three phases to support operator packing, algorithm adaption, and data coordination. To integrate heterogeneous compilation platforms, we introduce four additional functions to handle task scheduling, weight quantization, weight rearrangement, and memory management. In particular: (1) to optimize the transition overhead, an operator packing technique is introduced by extending the ONNX-MLIR framework with custom operators, which enhances NPU efficiency by reducing the number of transitions between operations. (2) At the aspect of task scheduling, a three-mode task scheduler is developed to allow users to customize task distribution for correctness verification, performance optimization, or detailed analysis on heterogeneous platforms, which demonstrates the versatility of our framework. (3) In terms of memory management, an optimized scheme for CPU/NPU architectures is proposed to ensure accurate data access. This memory management employs a dual-end approach with self-checks and a release-after-use mechanism to further improve efficiency. Experimental results demonstrate that the framework effectively generates instructions for various CNNs, significantly enhancing the efficiency and performance of heterogeneous architectures. It achieves up to a 7.06× reduction in code density and up to a 5.58× improvement in memory usage, narrowing the computational gap between CPUs and NPUs.

With the evolution of wireless communication systems, there is a growing demand for high reliability and low latency in channel coding, particularly in 5G and beyond wireless systems used in applications such as autonomous driving and remote medical services. For the decoding of polar codes, the multi-bit successive cancellation list (MSCL) decoding technique was recently introduced to decrease the decoding latency by decoding several short inner codes in parallel, which preserves high reliability compared to the conventional successive cancellation list (SCL) decoding. However, as parallelism increases, the complexity of the decoding path sorting also increases significantly, which makes it resource-intensive for hardware implementation. To address this issue, this paper proposes a configurable critical-set-based multi-bit successive cancellation list (CS-MSCL) decoding algorithm, which first introduces critical sets to the MSCL decoding for the optimization of path pruning. Subsequently, an enhanced CS-MSCL algorithm is introduced for large list-size MSCL decoding, which can boost the error correction performance. Then, an area-efficient decoding architecture is introduced, which supports the cyclic redundancy check (CRC) and the CS-MSCL decoding compatible with the 5G standard. The proposed decoder is implemented in SMIC 40 nm CMOS technology with a parallelism degree of 8, which has a peak area efficiency of$4.64~\mathrm {Gbps/mm^{2}}$for list size 4 and$2.01~\mathrm {Gbps/mm^{2}}$for list size 8. Compared to state-of-the-art SCL-based decoders, the normalized area efficiency is improved by 7.16% and 17.54% for list sizes 4 and 8, respectively.

Gaining accurate channel state information (CSI) through a low-cost scheme has always been difficult in wireless communication systems. One of the current research directions is to obtain the CSI from the channel knowledge map (CKM) based on the users’ location. However, the direct utilization of CSI in CKM is hindered due to the sensitivity of instantaneous CSI to time-varying scattering environments and positioning errors. To address this issue, this paper proposes a channel prediction scheme that combines the CKM with historical user CSI to enhance the beamforming performance in multiple-input multiple-output (MIMO) systems. Specifically, the joint-orthogonal matching pursuit algorithm is used to accurately reconstruct the user channel with high precision using a limited number of pilots, and the multi-path components tracking algorithm is employed to extract the common and independent support sets of paths from the estimated channel and the CKM. Lastly, an adaptive and low-complexity predictor is utilized to obtain the future user CSI. The proposed scheme has been evaluated using multiple measured channel datasets, the results indicate a significant improvement in predicting channel cosine similarity compared to directly using the CSI from CKM and existing schemes.

Wireless baseband processing (WBP) serves as an ideal scenario for utilizing vector processing, which excels in managing data-parallel operations due to its parallel structure. However, conventional vector architectures face certain constraints such as limited vector register sizes, reliance on power-of-two vector length (VL) multipliers, and vector permutation capabilities tied to specific architectures. To address these challenges, we have introduced an instruction set extension (ISE) based on RISC-V known as unlimited vector processing (UVP). This extension enhances both the flexibility and efficiency of vector computations. UVP employs a novel programming model that supports non-power-of-two register groupings (RGs) and hardware strip mining, thus enabling smooth handling of vectors of varying lengths while reducing the software strip-mining burden. Vector instructions are categorized into symmetric and asymmetric classes, complemented by specialized load/store strategies to optimize execution. Moreover, we present a hardware implementation of UVP featuring sophisticated hazard detection mechanisms, optimized pipelines for symmetric tasks such as fixed-point multiplication and division, and a robust permutation engine for effective asymmetric operations. Comprehensive evaluations demonstrate that UVP significantly enhances performance, achieving up to$3.0\times $and$2.1\times $speedups in matrix multiplication and fast Fourier transform (FFT) tasks, respectively, when measured against lane-based vector architectures. Our synthesized register transfer level (RTL) for a 16-lane configuration using SMIC 40-nm technology spans 0.94 mm2and achieves an area efficiency of 21.2 GOPS/mm2.

Mutation-based fuzzing effectively discovers defects in JS engines. High-quality mutations are key for the performance of mutation-based fuzzers. The choice of the underlying representation (e.g., a sequence of tokens, an abstract syntax tree, or an intermediate representation) defines the possible mutation space and subsequently influences the design of mutation operators. Current program representations in JS engine fuzzers center around abstract syntax trees and customized bytecode-level intermediate languages. However, existing efforts struggle to generate semantically valid and meaningful mutations, limiting the discovery of defects in JS engines.

DataBase Management Systems (DBMS) are essential software for efficient data storage, management, and analysis, playing a crucial role in modern data-intensive applications. Vulnerabilities in DBMS can significantly threaten data security and application functionality, impacting millions of software systems. While fuzz testing(fuzzing) is a prevalent technique for uncovering DBMS vulnerabilities, there is limited research on employing Neural Network Language Models (NNLMs) for this purpose. This paper introduces NNTailor, a novel approach using NNLMs based on AST code fragments for DBMS fuzzing. A key advantage of this method is its effectiveness in black-box testing scenarios. We evaluated NNTailor on SQLite and PostgreSQL, demonstrating its capability to generate effective SQL test cases.

Throughout various convolutional neural network (CNN) models, the sparsity increases as the network deepens, which poses significant potential to model compression and hardware acceleration. In this paper, a dual-factor hybrid-grained pruning method is introduced to make a good balance between model compression and accuracy preservation. The pro-posed pruning method combines hardware-friendly unstructured vector-level pruning with structured filter-level pruning to explore multiple grains of sparsity in CNNs. The architecture of the corresponding hardware accelerator is then proposed based on the row-based convolution dataflow, which could fully utilize the hybrid sparsity to accelerate CNN processing. Experimental results demonstrate that the proposed method increases the compression rate by 1.08× while causing 0.21% accuracy loss compared to the state-of-the-art filter pruning method in VGG16, and 2.39% hardware resource increase compared to the accelerator without sparsity optimization.

FIR (Finite Impulse Response) filters are widely used in digital communication systems, digital image processing, and many other fields. A great deal of research has been done on the flexible configuration of FIR filters, particularly on the dynamic adjustment of coefficients and orders. Existing FIR filter structures can be configured to a higher-order filter for a lower-order use, leading to low hardware utilization. This paper presents a dynamically configurable architecture for FIR filters based on a novel architecture with systolic arrays and serial multiply accumulators (MACs). This design can be configured to a higher-order filter or to several independent lower-order filters, thus increasing utilization. We demonstrate a 2048-order FIR filter that can be configured to a minimum of 16 orders and a maximum of 128 channels using only 256 multipliers and adders.

With the development of the 5G communication system, research on propagation characteristics of millimeter-wave (mm-wave) band has aroused significant attention. In particular, it is imperative to understand the similarities between the instantaneous channel characteristics of mm-wave band and sub-6 GHz band, which can be leveraged to facilitate beamforming and user scheduling. Towards this end, this paper presents a channel measurement campaign at both 4.9 GHz and 28 GHz in an urban micro-cell scenario using a virtual antenna array and conducts a detailed comparative study on the channel statistics. The measurement includes line-of-sight and non-line-of-sight propagation environments. Parameter extraction and analysis including power delay profiles (PDPs), path loss (PL), root mean square (RMS) delay spread (DS), and azimuth of arrival (AOA) shows that the 28 GHz channel has similar statistics compared to 4.9 GHz, while the 28 GHz band exhibits much fewer multipath components (MPCs). These results show that it is possible to leverage low-band channel estimations to facilitate the high-band, or vice versa.

Accurate channel state information (CSI) is crucial for optimizing system performance in wireless communications. One of the current research directions is to extract the CSI from the channel knowledge map (CKM) based on users' location. While such an approach is promising for large-scale CSI acquisition, e.g., pathloss, it is still questionable whether CKM can help to predict instantaneous CSI which is very sensitive to positioning error and scattering environment changes. To answer this question, this paper proposes a channel prediction scheme that combines the CKM with historical user CSI to improve the beamforming performance in multiple-input multiple-output (MIMO) systems. Specifically, it utilizes the complex amplitude information of multi-path components (MPCs) and employs a low-complexity method to predict the future MIMO CSI. Through experiments based on real-world channel data, the results demonstrate that the proposed scheme outperforms state-of-the-art ones that use the CKM alone or autoregressive schemes without CKM. In the non-line-of-sight scenarios, when the positioning error exceeds 0.525 m, small-scale CSI in CKM provides little gain. In line-of-sight environments, the threshold for the usability of small-scale CSI in terms of positioning error is approximately 0.725 m or slightly higher. When the positioning error is less than 0.225 m, the CKM is beneficial to predict instantaneous CSI in both scenarios.

Abstract The format string vulnerability is a common software vulnerability. A well-constructed format string can read and modify arbitrary memory addresses, causing serious system problems. Existing automated exploit generation solutions for format string vulnerability are unable to cope with the limitations imposed by the vulnerability defense mechanism Address Space Layout Randomization (ASLR) and the program itself on vulnerability exploitation. In this paper, to address the above challenges, we propose FormatAEG, the first automatic exploitation framework for format string vulnerabilities that can bypass ASLR defense and the program's own constraints. Specifically, we first proposed an arbitrary address reading and writing method based on a format string vulnerability, which can modify the target address data by directly arranging the target address or automatically searching and utilizing the pointer chain in the stack. Then, we propose a vulnerability reentry method based on global offset table (GOT) hijacking, which hijacks the program control flow by modifying function addresses in the GOT, making the vulnerability reentrant. In the experimental section, we evaluated FormatAEG using 20 Capture The Flag programs from top international tournaments and two real-world programs with format string vulnerabilities. The evaluation results show that with ASLR defense turned on, FormatAEG successfully detects format string vulnerability in 19 of these programs and generates exploit code for 15 of them. Compared with existing tools, FormatAEG detected 11 more format string vulnerabilities and generated 13 more exploit codes.

Fall detection (FD) systems are crucial for identifying falls and ensuring timely assistance, thus reducing the risk of serious injuries. With the development of society and increasing attention to health issues, researchers have conducted extensive studies on falls to reduce the severe sequelae of falls. Integrating FD systems with the Internet of Things (IoT), particularly the Internet of Medical Things (IoMT), has significantly advanced healthcare and personal safety. This dynamic relationship between FD technology and IoT has opened up new vistas for monitoring and assisting individuals, particularly the elderly and those with health conditions that make them prone to falls. This article presents a review of wearable sensor-based FD techniques. We classify the detection methods into their categories from an algorithmic perspective: threshold-based, conventional machine learning-based, and deep learning-based methods. In addition, we identify and summarize the available data sets that can be used to evaluate the performance of the introduced methods. This review aims to provide researchers with a better comprehension of the FD problem, intending to foster further advancements in the field.

This paper examines the use of the fluid limit, a mathematical tool used to analyze Age of Information (AoI) in networks, to study the long-term average AoI for scheduling optimization in multiaccess networks with transmission frequency constraints. Two types of problems that have been studied in the literature but remain unsolved are revisited under this approach, wherein multiple agents transmit over an error-prone multiaccess channel, and long-term transmission frequency constraints are considered with either a resource constraint or a minimum throughput requirement. Previous works have derived the Whittle’s Index (WI) policy, max-weight policy and average AoI lower bound, however without optimality guarantee or closed-form performance analysis. This work advances the field by deriving closed-form optimal AoI and achieving scheduling policies for both problems, by utilizing the fluid limit tool and transforming the original high-dimensional Markov decision process into solving a set of partial derivative equations. As a result, threshold-based scheduling policies with closed-form threshold expressions are obtained which can be proven to be asymptotically optimal when the number of agents is large. Numerical simulations are conducted to demonstrate the performance optimality of the proposed schemes and analytical results.

Autonomous vehicle platooning has tremendous potential to relieve the burden of Vehicular Edge Computing (VEC) by sharing resources with nearby vehicles. Therefore, fleet formation and resource orchestration within vehicle platoons have recently ignited significant research interest. However, most fleet formation works focus on the intra-platoon configuration and information exchange, but few consider trajectory matching and joining willingness. Likewise, in multi-platoon scenarios, static resource orchestration for a single platoon no longer meets the demand from dynamic resource scheduling. To tackle these problems, we proposed the SFO scheme, an adaptive taskScheduling based on incentive fleetFormation and metrizable resourceOrchestration. First, we design a fleetFormation algorithm based onTrajectory matching andJoining willingness (FTJ) to ensure the stable underlying architecture. Second, we use theWeightedSum ofEnergyConsumption (WSEC) as the performance metric for resource orchestration and formulate the time-average WSEC minimization problem. Third, anAdaptive taskScheduling underPartitionableApplications and variableResources (ASPAR) is proposed for an asymptotic optimal solution in reaction to the changeable backlog of the timeout queue. Finally, our numerical results demonstrate that our approach is superior to other latest and classic works in energy consumption and execution latency.

Open radio access network (O-RAN) is considered as a viable method for reducing the cost and enhancing the energy efficiency of cellular networks, due to its native incorporation of intelligence and open interfaces. However, the processing delay of software-based wireless protocol stacks has hindered its development. This paper presents the implementation of parallel computing acceleration for an LTE baseband system based on single program multiple data (SPMD) methodology, and proposes detailed optimization strategies for the time-consuming synchronization and OFDM modulation modules in the system. Experiment results based on the implicit SPMD program compiler (ISPC) show that continuous memory access has a significant impact on the final acceleration effect. Moreover, the processing speed of software-based physical layer can be increased up to 10–30 times through SPMD.

In this paper, the fluid limit tool is applied to analyze long-time age of information (AoI) for multiaccess networks with standard automatic repeat request (ARQ) protocol under resource constraints. Heterogeneous agents transmit over a error-prone channel, and a long-time average resource constraint is imposed to limit the transmission frequency. In this system setting, existing works have derived the Whittle index policy and performance lower bound, however with no optimality guarantee or closed-form performance analysis. This work is based on the fluid limit approach, a powerful mathematical tool to analyze AoI, and a threshold-based scheduling policy is proposed, which can be proved with asymptotic optimality. Meanwhile, the theoretical performance of the scheme is explicitly obtained, and the numerical simulations are carried out to validate the performance.

The sensing and communication layers are both integral parts of the Internet-of-Things. Most of recent studies on sensory status update treat the information sensing and sensory data communication problems separately (i.e., a decoupled approach) and optimize specific latency metrics such as age of information relying on simplified models of communication networks or sensory traffic. In this paper, we propose a deeply integrated sensing and communication scheduling (S2) framework based on status-error-triggered update, focusing specifically on multiaccess wireless networks. We first analyze a motivating example consisting of two-state Markov sensors, showing that when both optimized, S2 outperforms the decoupled approach significantly. For sensors with random-walk state transitions, the closed-form Whittle's index with arbitrary status tracking error functions is presented and the indexability is established. Furthermore, a mean-field approach is applied such that the decentralized status update medium access control design is solved explicitly, for both homogeneous nodes and heterogeneous nodes in terms of status transition behaviors. According to the numerical results, the performance of the proposed S2 scheme is close to the optimum and better than the decoupled approach. In addition, a potential application of dynamic Channel State Information (CSI) update is presented, with CSI generated by a commercial ray-tracing simulator.

With the increasing on-board demand for intelligent connected vehicles (ICVs), the fifth-generation (5G) wireless systems are being massively utilized in vehicular networks. As an essential component, content retrieval in the ICV provides a basis for vehicle-to-vehicle or vehicle-to-infrastructure data interaction for many applications. However, content access is still subject to performance degradation due to congested communication channels, diverse requests patterns, and intermittent network connectivity. To mitigate these issues, in-network caching in 5G-enabled ICV has been leveraged to benefit content access by allowing edge nodes to store content for data generators. In this paper, we propose an in-network caching scheme to support various provisions of data sharing in the ICVs by exploring the advantages of information-centric networks (ICN). We first divide each on-board service into several content units. Then, we place these units at the ICV and small cell base stations (SBSs) to reduce the content retrieval delay, further model the proposed system as an integer nonlinear program (INLP) and attain the optimal QoE (Quality of Experience) by placing content units at appropriate cache entities. Finally, we verify the effectiveness and correctness of our proposed model through extensive simulations.

The acquisition of channel state information (CSI) is a vital and challenging task in wireless communications, especially for high mobility scenarios with rapidly varying channels. Channel prediction, as an effective approach to acquire CSI, is considered a promising approach to improve the communication performance. However, most of the current prediction methods with the assumption of slowly varying channel components cannot cope with rapid variation. In this paper, we propose a novel channel prediction scheme to incorporate the mobility of both transceivers and scatterers. Based on the estimated mobility parameters, the component-wise channel extraction results are derived and then the channel frequency response is predicted. In addition, the prediction performance and the computational complexity of the proposed scheme are analyzed and evaluated, and the relationship between the mobility parameter estimation error and the channel component prediction accuracy is revealed as well. Through predicted results based on the simulated and measured data, the performance of the proposed scheme surpasses the conventional schemes with genie-aided information.

Beamforming for multi-antenna wireless communication systems has been widely studied and applied in practice. However, its performance in high mobility scenarios deteriorates dramatically due to severe channel aging. This issue stimulates the research of channel prediction in high mobility scenarios. This paper studies the general high-mobility Doppler domain wireless channel characterization and reveals that: 1) the Doppler frequency of the cluster corresponding to the near scatterers varies approximately linearly over short periods; 2) the linear-fitting characteristics of autoregressive model leads to poor channel prediction performance; 3) the parameter estimation algorithms of the chirp model which encompasses the characteristics are particularly complex. Given the shortcomings of existing works, this paper adopts the short-time Fourier transform to pre-estimate the parameter range and leverages the polynomial Fourier transform to obtain the initial values of the parameters. The downhill simplex algorithm is used to search for the fine-grained values of the parameters. In addition, the orthogonal matching pursuit algorithm is utilized to reconstruct the sparse signal. Evaluation results under the COST2100 channel model and a channel measurement campaign indicate that the proposed scheme can enhance the channel prediction accuracy or reduce the computing overhead compared to the existing matrix completion and polynomial iteration method.

The popularity of coverage-guided greybox fuzzers has led to a tsunami of security-critical bugs that developers must prioritize and fix. Knowing the capabilities a bug exposes (e.g., type of vulnerability, number of bytes read/written) enables prioritization of bug fixes. Unfortunately, understanding a bug's capabilities is a time consuming process, requiring (a) an understanding of the bug's root cause, (b) an understanding how an attacker may exploit the bug, and (c) the development of a patch mitigating these threats. This is a mostly-manual process that is qualitative and arbitrary, potentially leading to a misunderstanding of the bug's capabilities.

Network slicing is a critical driver for guaranteeing the diverse service level agreements (SLA) in 5G and future networks. Recently, deep reinforcement learning (DRL) has been widely utmzed for resource allocation in network slicing. However, existing related works do not consider the performance loss associated with the initial exploration phase of DRL. This paper proposes a new performance-guaranteed slicing strategy with a soft and hard hybrid slicing setting. Mainly, a common slice setting is applied to guarantee slices’ SLA when training the neural network. Moreover, the resource of the common slice tends to precisely redistribute to slices with the training of DRL until it converges. Furthermore, experiment results confirm the effectiveness of our proposed slicing framework: the slices’ SLA of the training phase can be guaranteed, and the proposed algorithm can achieve the near-optimal performance in terms of the SLA satisfaction ratio, isolation degree and spectrum efficiency after convergence.

Nowadays, with the development of wireless communication systems, high-frequency bands are utilized together with low-frequency bands to provide better system throughput. However, systems based on high-frequency bands suffer from coverage problems significantly. In this paper, we consider utilizing the sounding signals from two frequency bands to improve the channel estimation accuracy of both bands, and therefore enhance the coverage. An algorithm based on Distributed Compressed Sensing (DCS) is proposed, combining with Simultaneous Orthogonal Matching Pursuit (SOMP) technology. We verify the algorithm performance by simulations and real-world channel measurements, which show that the proposed method can improve the channel estimation accuracy of two frequency bands with the same pilot cost, compared with the system wherein the channels are estimated independently for each band.

In a system with energy-constrained sensors, each transmitted observation comes at a price. The price is the energy the sensor expends to obtain and send a new measurement. The system has to ensure that sensors’ updates are timely, i.e., their updates represent the observed phenomenon accurately, enabling services to make informed decisions based on the information provided. If there are multiple sensors observing the same physical phenomenon, it is likely that their measurements are correlated in time and space. To take advantage of this correlation to reduce the energy use of sensors, in this paper we consider a system in which a gateway sets the intervals at which each sensor broadcasts its readings. We consider the presence of battery-powered sensors as well as sensors that rely on Energy Harvesting (EH) to replenish their energy. We propose a Deep Reinforcement Learning (DRL)-based scheduling mechanism that learns the appropriate update interval for each sensor, by considering the timeliness of the information collected measured through the Age of Information (AoI) metric, the spatial and temporal correlation between readings, and the energy capabilities of each sensor. We show that our proposed scheduler can achieve near-optimal performance in terms of the expected network lifetime.

The increasing demand for road freight has raised tremendous attention to vehicle platooning, which reduces air resistance and improves fuel economy. To achieve a small and safe spacing between vehicles while ensuring platoon stability, wireless communication assistance is indispensable. However, the imperfection of communication brings degradation of platooning performance (i.e., spacing error) and more possibilities for adversarial attacks. This article proposes a prediction-assisted platooning mechanism from the perspective of performance optimization, wherein each vehicle establishes its local platoon model based on the information received from the communication network, thereby reducing information latency. Then, to secure the system against malicious vehicles, we carry out analysis and design of a detection algorithm for a typical attack type, i.e., the data-injection attack. The detection is based on three indicators: 1) absolute spacing error; 2) spacing prediction error; and 3) acceleration prediction error. The advantages of the novel platooning mechanism and detection algorithm are ultimately demonstrated on a road-traffic simulation platform that considers the imperfection of realistic vehicle perceptions and Cellular-Vehicle-to-Everything (C-V2X) communication.

Fuzzing has emerged as the most effective bug-finding technique. The output of a fuzzer is a set of proof-of-concept (PoC) test cases for all observed "unique'' crashes. It costs developers substantial efforts to analyze each crashing test case. This, mostly manual, process has lead to the number of reported crashes out-pacing the number of bug fixes. Automatic crash deduplication techniques, which mostly rely on coverage profiles and stack hashes, are supposed to alleviate these pressures. However, these techniques both inflate actual bug counts and falsely conflate unrelated bugs. This hinders, rather than helps, developers, and calls for more accurate techniques.

In this paper, we adopt the fluid limits to analyze Age of Information (AoI) in a wireless multiaccess network with many users. We consider the case wherein users have heterogeneous i.i.d. channel conditions and the statuses are generate-at-will. Convergence of the AoI occupancy measure to the fluid limit, represented by a Partial Derivative Equation (PDE), is proved within an approximation error inversely proportional to the number of users. Global convergence to the equilibrium of the PDE, i.e., stationary AoI distribution, is also proved. Based on this framework, it is shown that an existing AoI lower bound in the literature is in fact asymptotically tight, and a simple threshold policy, with the thresholds explicitly derived, achieves the optimum asymptotically. The proposed threshold-based policy is also much easier to decentralize than the widely-known index-based policies which require comparing user indices. To showcase the usability of the framework, we also use it to analyze the average non-linear AoI functions (with power and logarithm forms) in wireless networks. Again, explicit optimal threshold-based policies are derived, and average age functions proven. Simulation results show that even when the number of users is limited, e.g., 10, the proposed policy and analysis are still effective.

A typical Internet-of-Things (IoT) system consists of three major layers: 1) sensing; 2) communication; and 3) application (i.e., actuation and control) layers. The co-design of these layers has been studied for over two decades, dating back to the concept of communication, computing, and control, i.e., 3C, convergence in the 1990s. Nowadays, with the emergence of wireless-networked machine-type applications, such as connected autonomous driving and factory automation, this co-design is more urgently desired than ever to meet the stringent quality-of-service requirements thereof. To realize this goal, the 5G wireless network of today has mainly focused on the communication part and strived to reliably achieve low air-interface communication delay, i.e., ultra-reliable and low-latency communications (uRLLC). However, more and more wireless communications in IoT are based on status updates instead of general content delivery. The current uRLLC design is insufficient to characterize the status update quality, and thus is unable to optimize for timely status update with constrained wireless resources. Therefore, the performance of computing and control in IoT networks that rely highly on wireless communications is suboptimal.

We consider a base station supporting two types of traffics, i.e., status update traffic and timely throughput traffic. The goal is to improve the information freshness of status update traffic while satisfying timely throughput constraints. Age of Information (AoI) is adopted as a metric for information freshness. We first propose an age-aware policy that makes scheduling decisions based on the current value of AoI directly. Given timely throughput constraint, an upper bound of the weighted average AoI under this policy is provided. To evaluate policy performance, it is important to obtain the minimum weighted average AoI achievable given timely throughput constraint. A low complexity method is proposed to estimate a lower bound of this value. Furthermore, inspired by the estimation procedure, we design an age-oblivious policy that does not rely on the current AoI to make scheduling decisions. Surprisingly, simulation results show that the weighted average AoI of the age-oblivious policy is comparable to that of the age-aware policy, and both are close to the lower bound.

It is becoming increasingly clear that an important task for wireless networks is to minimize the age of information (AoI), i.e., the timeliness of information delivery. While mainstream approaches generally rely on the real-time observation of user AoI and channel state, there has been little attention to solve the problem in a complete (or partial) absence of such knowledge. In this article, we present a novel study to address the optimal blind radio resource scheduling problem in orthogonal frequency division multiplexing access (OFDMA) systems towards minimizing long-term average AoI, which is proven to be the composition of time-domain-fair clustered round-robin and frequency-domain-fair intra-cluster sub-carrier assignment. Heuristic solutions that are near-optimal as shown by simulation results are also proposed to effectively improve the performance upon presence of various degrees of extra knowledge, e.g., channel state and AoI.

In a wireless network that conveys status updates from sources (i.e., sensors) to destinations, one of the key issues studied by existing literature is how to design an optimal source sampling strategy on account of the communication constraints which are often modeled as queues. In this paper, an alternative perspective is presented—a novel status-aware communication scheme, namelyparallel communications, is proposed which allows sensors to be communication-agnostic. Specifically, the proposed scheme can determine, based on an online prediction functionality, whether a status packet is worth transmitting considering both the network condition and status prediction, such that sensors can generate status packets without communication constraints. We evaluate the proposed scheme on a Software-Defined-Radio (SDR) test platform, which is integrated with a collaborative autonomous driving simulator, i.e., Simulation-of-Urban-Mobility (SUMO), to produce realistic vehicle control models and road conditions. The results show that with online status predictions, the channel occupancy is significantly reduced, while guaranteeing low status recovery error. Then the framework is applied to two scenarios: a multi-density platooning scenario, and a flight formation control scenario. Simulation results show that the scheme achieves better performance on the network level, in terms of keeping the minimum safe distance in both vehicle platooning and flight control.

Real-time status update in future vehicular networks is vital to enable control-level cooperative autonomous driving. Cellular Vehicle-to-Everything (C-V2X), as one of the most promising vehicular wireless technologies, adopts a Semi-Persistent Scheduling (SPS) based Medium-Access-Control (MAC) layer protocol for its sidelink communications. Despite the recent and ongoing efforts to optimize SPS, very few work has considered the status update performance of SPS. In this paper, Age of Information (AoI) is first leveraged to evaluate the MAC layer performance of C-V2X sidelink. Critical issues of SPS, i.e., persistent packet collisions and Half-Duplex (HD) effects, are identified to hinder its AoI performance. Therefore, a piggyback-based collaboration method is proposed accordingly, whereby vehicles collaborate to inform each other of potential collisions and collectively afford HD errors, while entailing only a small signaling overhead. Closed-form AoI performance is derived for the proposed scheme, optimal configurations for key parameters are hence calculated, and the convergence property is proved for decentralized implementation. Simulation results show that compared with the standardized SPS and its state-of-the-art enhancement schemes, the proposed scheme shows significantly better performance, not only in terms of AoI, but also of conventional metrics such as transmission reliability.

This paper studies the status update performance in wireless sensor networks when status, describing the physical reality that is being sensed, is temporally and spatially correlated. The status is modeled as a time-varying Gauss-Markov Random Field (GMRF), whereby the estimation error of status update at the fusion center is analyzed. The transmission latency introduced by wireless networks is modeled as exponentially distributed random variables. We extend the existing queuing analysis results for Age of Information (AoI) with uncorrelated sources to GMRF in the considered scenario. Closed-form expressions of average remote estimation error are obtained for both one- and two-dimensional GMRFs assuming the exponential time-correlation function, both First-Come First-Served (FCFS) and Last-Come First-Served (LCFS) service disciplines, and a single wireless link. The analytical results are then extended to scenarios wherein multi-packet reception, i.e., multiple concurrent wireless links, is enabled; the difficulty of analyzing obsolete updates in this case is addressed leveraging a reasonable approximation validated by theoretical analysis in the regime where the number of sensors is far more than that of wireless links. Monte-Carlo simulation results are also presented which agree with our theoretical analysis. Based on the results, optimal time and spatial domain sampling rates (e.g., sensor density) can be obtained, providing helpful guidance to wireless sensor deployment.

Ultra-Reliable and Low-Latency Communications (URLLC) services in vehicular networks on millimeter-wave bands present a significant challenge, considering the necessity of constantly adjusting the beam directions. Conventional methods are mostly based on classical control theory, e.g., Kalman filter and its variations, which mainly deal with stationary scenarios. Therefore, severe application limitations exist, especially with complicated, dynamic Vehicle-to-Everything (V2X) channels. This paper gives a thorough study of this subject, by first modifying the classical approaches, e.g., Extended Kalman Filter (EKF) and Particle Filter (PF), for non-stationary scenarios, and then proposing a Reinforcement Learning (RL)-based approach that can achieve the URLLC requirements in a typical intersection scenario. Simulation results based on a commercial ray-tracing simulator show that enhanced EKF and PF methods achieve packet delay more than 10 ms, whereas the proposed deep RL-based method can reduce the latency to about 6 ms, by extracting context information from the training data.

Wireless communications for status update are becoming increasingly important, especially for machine-type control applications. Existing work has been mainly focused on Age of Information (AoI) optimizations. In this paper, a status-aware predictive wireless interface design, networking and implementation are presented which aim to minimize the status recovery error of a wireless networked system by leveraging online status model predictions. Two critical issues of predictive status update are addressed: practicality and usefulness. Link-level experiments on a Software-Defined-Radio (SDR) testbed are conducted and test results show that the proposed design can significantly reduce the number of wireless transmissions while maintaining a low status recovery error. A Status-aware Multi-Agent Reinforcement learning neTworking solution (SMART) is proposed to dynamically and autonomously control the transmit decisions of devices in an ad hoc network based on their individual statuses. System-level simulations of a multi dense platooning scenario are carried out on a road traffic simulator. Results show that the proposed schemes can greatly improve the platooning control performance in terms of the minimum safe distance between successive vehicles, in comparison with the AoI-optimized status-unaware and communication latency-optimized schemes-this demonstrates the usefulness of our proposed status update schemes in a real-world application.

Vehicular communications, considered as one of the key technologies for autonomous driving, are still faced with significant challenges, e.g., distributed resource allocation in the direct communication mode. In Cellular Vehicle-to-Everything (C-V2X), a Semi-Persistent Scheduling (SPS) based Medium-Access-Control (MAC) layer protocol is adopted by 3rd Generation Partnership Project (3GPP) for its sidelink communications. However, the performance of either the SPS scheme in standard or the state-of-the-art enhancement works is not satisfactory. In this paper, we propose a piggyback-based collaboration method and firstly introduce Age of Information (AoI) to evaluate the status update performance of C-V2X. In addition, the closed-form AoI performance is obtained. Through extensive simulations, the proposed scheme exhibits better performance than the current schemes, both in terms of AoI and transmission reliability.

The blockchain technology has achieved tremendous success in open (permissionless) decentralized consensus by employing Proof of Work (PoW) or its variants, whereby unauthorized nodes cannot gain a disproportionate impact on consensus beyond their computational power. However, PoW-based systems incur a high delay and low throughput, making them ineffective in dealing with the real-time Internet-of-Things (IoT) applications. On the other hand, the Byzantine fault-tolerant (BFT) consensus algorithms with better delay and throughput performance cannot be employed in permissionless settings due to vulnerability to Sybil attacks. In this article, we present a Sybil-proof wireless network coordinate-based Byzantine consensus (SENATE), which has the merits of both real-time consensus reaching and Sybil-proof, i.e., it is based on the conventional BFT consensus framework yet works in open systems of wireless devices where faulty nodes may launch Sybil attacks. As in a Senate, in the legislature, where the quota of senators per state (district) is a constant irrespective with the population of the state, “senators” in SENATE are selected from participating distributed nodes based on their wireless network coordinates (WNCs) with a fixed number of nodes per district in the WNC space. Elected senators then participate in the subsequent consensus reaching process and broadcast the result. Thereby, the SENATE is a proof against Sybil attacks since pseudonyms of a faulty node are likely to be adjacent in the WNC space and hence fail to be elected. The simulation results reveal that the SENATE can achieve real-time consensus (consensus delay under one second) in a network of hundreds of nodes.

We consider a star-topology wireless network for status update where a central node collects status data from a large number of distributed machine-type terminals that share a wireless medium. The Age of Information (AoI) minimization scheduling problem is formulated by the restless multi-armed bandit. A widely-proven near-optimal solution, i.e., the Whittle's index, is derived in closed-form and the corresponding indexability is established. The index is then generalized to incorporate stochastic, periodic packet arrivals and unreliable channels. Inspired by the index scheduling policies which achieve near-optimal AoI but require heavy signaling overhead, a contention-based random access scheme, namely Index-Prioritized Random Access (IPRA), is further proposed. Based on IPRA, terminals that are not urgent to update, indicated by their indices, are barred access to the wireless medium, thus improving the access timeliness. A computer-based simulation shows that IPRA's performance is close to the optimal AoI in this setting and outperforms standard random access schemes. Also, for applications with hard AoI deadlines, we provide reliable deadline guarantee analysis. Closed-form achievable AoI stationary distributions under Bernoulli packet arrivals are derived such that AoI deadline with high reliability can be ensured by calculating the maximum number of supportable terminals and allocating system resources proportionally.

Future wireless access networks need to support diversified quality of service (QoS) metrics required by various types of Internet-of-Things (IoT) devices, e.g., age of information (AoI) for status generating sources and ultra low latency for safety information in vehicular networks. In this paper, a novel inner-state driven random access (ISDA) framework is proposed based on distributed policy learning, in particular a cross-entropy method. Conventional random access schemes, e.g., p-CSMA, assume state-less terminals, and thus assigning equal priorities to all. In ISDA, the inner-states of terminals are described by a time-varying state vector, and the transmission probabilities of terminals in the contention period are determined by their respective inner-states. Neural networks are leveraged to approximate the function mappings from inner-states to transmission probabilities, and an iterative approach is adopted to improve these mappings in a distributed manner. Experiment results show that ISDA can improve the QoS of heterogeneous terminals simultaneously compared to conventional CSMA schemes.

Information source sampling and update scheduling have been treated separately in the context of real-time status update for age of information optimization. In this paper, a unified sampling and scheduling (S2) approach is proposed, focusing on decentralized updates in multiaccess wireless networks. To gain some insights, we first analyze an example consisting of two-state Markov sources, showing that when both optimized, the unified approach outperforms the separate approach significantly in terms of status tracking error by capturing the key status variation. We then generalize to source nodes with random-walk state transitions whose scaling limit is Wiener processes, the closed-form Whittle's index with arbitrary status tracking error functions is obtained and indexability established. Furthermore, a mean-field approach is applied to solve for the decentralized status update design explicitly. In addition to simulation results which validate the optimality of the proposed S2scheme and its advantage over the separate approach, a use case of dynamic channel state information (CSI) update is investigated, with CSI generated by a ray-tracing electromagnetic software.

Motivated by the increasingly urgent demands for delivering fresh information, the age-of-information (AoI) has recently been introduced as an important metric for evaluating the timeliness performance of information update systems and has shed light on a number of research studies. Nevertheless, the most common goal of the existing works does not characterize the value of information freshness from the users' perspective. In this paper, we introduce the concept of effective AoI (EAoI) to quantify the freshness of the information users utilize for decision-making. We consider a general request-response model, which captures both proactive information update and timely information delivery, for investigating the scheduling problem with respect to EAoI minimization. By decomposing the scheduling problem into multiple computationally tractable subproblems, we propose request-aware scheduling policies for static and dynamic request models, respectively. The numerical results show that serving users requests proactively can reduce time-average EAoI in both scenarios.

In many applications, status information of a general spatial process, in contrast to a point information source, is of interest. In this paper, we consider a system where status information is drawn from a random field and transmitted to a fusion center through a wireless multiaccess channel. The optimal density of spatial sampling points to minimize the remote status estimation error is investigated. Assuming a one-dimensional Gauss Markov random field and an exponential correlation function, closed-form expressions of remote estimation error are obtained for First-Come First-Served (FCFS) and Last-Come First-Served (LCFS) service disciplines. The optimal spatial sampling density for the LCFS case is given explicitly. Simulation results are presented which agree with our analysis.

In a typical Internet of Things (IoT) application where a central controller collects status updates from multiple terminals, e.g., sensors and monitors, through a wireless multiaccess uplink, an important problem is how to attain timely status updates autonomously. In this paper, the timeliness of the status is measured by the recently proposed age-of-information (AoI) metric; both the theoretical and practical aspects of the problem are investigated: we aim to obtain a scheduling policy with minimum AoI and, meanwhile, requires little signaling exchange overhead. Toward this end, we first consider the set of arrival-independent and renewal policies; the optimal policy thereof to minimize the time-average AoI is proved to be a round-robin policy with one-packet (latest packet only and others are dropped) buffers (RR-ONE). The optimality is established based on a generalized Poisson-arrival-see-time-average theorem. It is further proved that RR-ONE is asymptotically optimal among all policies in the massive IoT regime. The AoI steady-state stationary distribution under RR-ONE is also derived. An implementation scheme of RR-ONE is proposed which can accommodate dynamic terminal appearances with little overhead. In addition, considering scenarios where packets cannot be dropped, a Lyapunov optimization-based max-AoI-weight policy is proposed which achieves better performance compared with state-of-the-art.

The high density and heterogeneous mobility of users in many applications pose challenges for the channel acquisition in massive multiple-input-multiple-output (MIMO) systems. For such scenarios, we propose an intermittent channel estimation (ICE) scheme to save pilot resources, which utilizes the aged channel state information (CSI) based on the temporal correlations of user channels. The optimal CSI update pattern to maximize the achievable sum rate is obtained by solving a formulated multichain Markov decision process (MDP), which is denoted by ICE-MDP. Furthermore, to reduce the computational complexity of the MDP, we relax the constraint of the CSI update pattern design problem and convert it into a convex optimization problem, whose solution is denoted by ICE-CVX. The simulations validate the close-to-optimal performance and the computational efficiency of ICE-CVX and show that the ICE scheme can significantly outperform a conventional scheme which persistently updates the CSI of all users.

Channel state information (CSI) plays a vital role in wireless communication systems. However, the CSI acquisition overhead is an enormous obstacle to realize the system performance improvements promised by massive connectivity and massive multiple-input-multiple-output (MIMO). To alleviate this overhead, this paper proposes a remote channel inference framework by probing the channels occupied by a source base station (BS) and inferring the channels of target BSs at geographically separated sites. The work generalizes existing literature which mainly focuses on utilizing the CSI linear correlations of adjacent antennas, by adopting a model-free deep learning framework to investigate non-linear dependence among remote CSI. The existence of such cross-BS CSI dependence is first shown by calculating the mutual information between remote channels, and the Cramér-Rao lower bound of remote CSI inference performance based on a one-ring channel model. Inspired by this finding, modern deep learning approaches are leveraged to perform remote channel inference in heterogeneous networks for both single user and multi-user scenarios. The simulation results based on ray tracing data show evident performance advantages over conventional methods, under both homogeneous and heterogeneous frequency coverage. The proposed framework achieves beamformer inference accuracy within 4.6% of the genie-aided optimum at the cost of sweeping only two beams.

Timely status updates are crucial to enabling applications in the massive Internet of Things (IoT). This paper measures the data-freshness performance of a status update system with an energy-harvesting transmitter, considering the randomness in information generation, transmission, and energy harvesting. The performance is evaluated by a non-linear function of age of information (AoI) that is defined as the time elapsed since the generation of the most up-to-date status information at the receiver. The system is formulated as two queues with status packet generation and energy arrivals both assumed to be Poisson processes. With negligible service time, both first-come-first-served (FCFS) and last-come-first-served (LCFS) disciplines for arbitrary buffer and battery capacities are considered, and a method for calculating the average penalty with non-linear penalty functions is proposed. The average AoI, the average penalty under exponential penalty function, and the AoI's threshold violation probability are obtained in a closed form. When the service time is assumed to follow exponential distribution, a matrix geometric method is used to obtain the average peak AoI. The results illustrate that under the FCFS discipline, the status update frequency needs to be carefully chosen according to the service rate and energy arrival rate in order to minimize the average penalty.

Timely and accurate knowledge of channel state information (CSI) is necessary to support scheduling operations at both physical and network layers. In order to support pilot-free channel estimation in cell sleeping scenarios, we propose to adopt a channel database that stores the CSI as a function of geographic locations. Such a channel database is generated from historical user records, which usually can not cover all the locations in the cell. Therefore, we develop a two-step interpolation method to infer the channels at the uncovered locations. The method firstly applies the K-nearest-neighbor method to form a coarse database and then refines it with a deep convolutional neural network. When applied to the channel data generated by ray tracing software, our method shows a great advantage in performance over the conventional interpolation methods.

Channel state information (CSI) is of vital importance in wireless communication systems. Existing CSI acquisition methods usually rely on pilot transmissions, and geographically separated base stations (BSs) with non-correlated CSI need to be assigned with orthogonal pilots which occupy excessive system resources. Our previous work adopts a data-driven deep learning based approach which leverages the CSI at a local BS to infer the CSI remotely, however the relevance of CSI between separated BSs is not specified explicitly. In this paper, we exploit a model-based methodology to derive the Cramer-Ran lower bound (CRLB) of remote CSI inference given the local CSI. Although the model is simplified, the derived CRLB explicitly illustrates the relationship between the inference performance and several key system parameters, e.g., terminal distance and antenna array size. In particular, it shows that by leveraging multiple local BSs, the inference error exhibits a larger power-law decay rate (w.r.t. number of antennas), compared with a single local BS; this explains and validates our findings in evaluating the deep-neural-network-based (DNN-based) CSI inference. We further improve on the DNN-based method by employing dropout and deeper networks, and show an inference performance of approximately 90% accuracy in a realistic scenario with CSI generated by a ray-tracing simulator.

In this paper, the wireless uplink is considered for status update with a large number of terminals. Thy key problem we address is that whether spatial multiplexing of multiple terminals, enabled by the massive multiple-input multiple-output technology, can help to improve the scaling law of age-of-information versus the number of terminals, on account of the mandatory pilot overhead. Based on a queuing theory analysis, we show that the proposed elastic spatial multiplexing scheme, which assigns an optimized pilot length that is smaller than the number of transmitting terminals on account of random packet arrivals, can indeed improve the scaling law compared with the optimal scaling law without spatial multiplexing, by a factor that is related to the packet lengths and arrival rates. Simulation results are provided to validate our findings.

One of the most challenging issues in deploying massive multiple-input multiple-output (MIMO) systems is the significant radio-frequency (RF) front-end complexity, hardware cost and power consumption. Towards this end, the beamspace-MIMO based approach is a promising solution. In this paper, we first show that traditional beamspace-MIMO approaches suffer from spatial power leakage and imperfect channel statistics estimation. A beam combination module is hence proposed, which consists of a small number (compared with the number of antenna elements) of low-resolution (possibly one- bit) digital (discrete) phase shifters after beamspace transformation to further compress the beamspace signal dimensionality, such that the number of RF chains can be reduced beyond beamspace transformation and beam selection. The optimum discrete beam combination weights are obtained based on the branch-and-bound (BB) approach. The key to the BB-based solution is to solve the embodied sub- problem, whose solution is derived in a closed-form. Link-level simulation results based on realistic channel models and LTE parameters are presented which show that the proposed schemes can reduce the number of RF chains by up to 25% with a one-bit phase-shifter-network.

Vehicular cloud computing (VCC) is proposed to effectively utilize and share the computing and storage resources on vehicles. However, due to the mobility of vehicles, the network topology, the wireless channel states and the available computing resources vary rapidly and are difficult to predict. In this work, we develop a learning-based task offloading framework using the multi-armed bandit (MAB) theory, which enables vehicles to learn the potential task offloading performance of its neighboring vehicles with excessive computing resources, namely service vehicles (SeVs), and minimizes the average offloading delay. We propose an adaptive volatile upper confidence bound (AVUCB) algorithm and augment it with load-awareness and occurrence-awareness, by redesigning the utility function of the classic MAB algorithms. The proposed AVUCB algorithm can effectively adapt to the dynamic vehicular environment, balance the tradeoff between exploration and exploitation in the learning process, and converge fast to the optimal SeV with theoretical performance guarantee. Simulations under both synthetic scenario and a realistic highway scenario are carried out, showing that the proposed algorithm achieves close-to- optimal delay performance.

We consider a system where multiple terminals transmit their randomly generated status updates to a base station (BS) sharing a wireless multiaccess uplink channel. The problem of interest, especially in massive Internet-of-Things systems, is that how to schedule the terminals to minimize the time-average age-of-information in a decentralized manner, namely terminals transmit autonomously without signalling exchange (overhead) with the BS or other terminals. Towards this end, the round-robin with one-packet buffers (the newest packet at each terminal only) policy (RR-ONE) is proposed and proved optimal among arrival-independent renewal (AIR) policies. In addition to its simple structure which is instrumental for decentralized implementation, RR-ONE is further proved asymptotically (massive terminals) optimal among all policies, including centralized and non-causal policies.

In vehicular cloud computing (VCC) systems, the computational resources of moving vehicles are exploited and managed by infrastructures, e.g., roadside units, to provide computational services. The offloading of computational tasks and collection of results rely on successful transmissions between vehicles and infrastructures during encounters. In this paper, we investigate how to provide timely computational services in VCC systems. In particular, we seek to minimize the deadline violation probability given a set of tasks to be executed in vehicular clouds. Due to the uncertainty of vehicle movements, the task replication methodology is leveraged which allows one task to be executed by several vehicles, and thus trading computational resources for delay reduction. The optimal task replication policy is of key interest. We first formulate the problem as a finite-horizon sampled-time Markov decision problem and obtain the optimal policy by value iterations. To conquer the complexity issue, we propose the balanced-task-assignment (BETA) policy which is proved optimal and has a clear structure: it always assigns the task with the minimum number of replicas. Moreover, a tight closed-form performance upper bound for the BETA policy is derived, which indicates that the deadline violation probability follows the Rayleigh distribution approximately. Applying the vehicle speed-density relationship in the traffic flow theory, we find that vehicle mobility benefits VCC systems more compared with road traffic systems, by showing that the optimum vehicle speed to minimize the deadline violation probability is larger than the critical vehicle speed in traffic theory which maximizes traffic flow efficiency.

To quickly verify and fix vulnerabilities, it is necessary to judge the exploitability of the massive crash generated by the automated vulnerability mining tool. While the current manual analysis of the crash process is inefficient and time-consuming, the existing automated tools can only handle execute exceptions and some write exceptions but cannot handle common read exceptions. To address this problem, we propose a method of determining the exploitability based on the exception type suppression. This method enables the program to continue to execute until an exploitable exception is triggered. The method performs a symbolic replay of the crash sample, constructing and reusing data gadget, to bypass the complex exception, thereby improving the efficiency and accuracy of vulnerability exploitability analysis. The testing of typical CGC/RHG binary software shows that this method can automatically convert a crash that cannot be judged by existing analysis tools into a different crash type and judge the exploitability successfully.

In beam-based massive multiple-input multiple-output systems, signals are processed spatially in the radio-frequency (RF) front end and thereby the number of RF chains can be reduced to save hardware cost, power consumptions, and pilot overhead. Most existing work focuses on how to select or design analog beams to achieve performance close to full digital systems. However, since beams are strongly correlated (directed) to certain users, the selection of beams and scheduling of users should be jointly considered. In this paper, we formulate the joint user scheduling and beam selection problem based on the Lyapunov-drift optimization framework and obtain the optimal scheduling policy in a closed form. For reduced overhead and computational cost, the proposed scheduling schemes are based only upon statistical channel state information. Towards this end, asymptotic expressions of the downlink broadcast channel capacity are derived. To address the weighted sum rate maximization problem in the Lyapunov optimization, an algorithm based on block coordinated update is proposed and proved to converge to the optimum of the relaxed problem. To further reduce the complexity, an incremental greedy scheduling algorithm is also proposed, whose performance is proved to be bounded within a constant multiplicative factor. Simulation results based on widely-used spatial channel models are given. It is shown that the proposed schemes are close to optimal and outperform several state-of-the-art schemes.

In this paper, the joint user and beam scheduling problem in beam-based massive multiple-input multiple-output (MIMO) systems is formulated based on the Lyapunov-drift optimization framework and the optimal scheduling policy is given in a closed-form. To address the weighted sum rate maximization problem (mixed integer programming) arisen in the Lyapunov-drift maximization, an algorithm based on the block coordinated update is proposed and proved to converge to the global optimum of the relaxed convex problem. In order to make the scheduling decisions based only upon statistical channel state information (CSI), asymptotic expressions of the downlink broadcast channel capacity are derived. Simulation results based on widely-adopted spatial channel models are given, which show that the proposed scheme is close to the optimal scheduling scheme, and outperforms the state-of-the-art beam selection schemes.

In this paper, we propose a learning-based low-overhead channel estimation method for coordinated beamforming in ultra-dense networks. We first show through simulation that the channel state information (CSI) of geographically separated base stations (BSs) exhibits strong non-linear correlations in terms of mutual information. This finding enables us to adopt a novel learning-based approach to remotely infer the quality of different beamforming patterns at a dense-layer BS based on the CSI of an umbrella control-layer BS. The proposed scheme can reduce channel acquisition overhead by replacing pilot-aided channel estimation with the online inference from an artificial neural network, which is fitted offline. Moreover, we propose to use more anchor points and more candidate beam patterns to obtain better performance. Simulation results based on stochastic ray-tracing channel models show that the proposed scheme can reach an accuracy of 99.74\% in settings with 20 beamforming patterns.

Multiuser multiple-input multiple-output (MIMO) systems suffer from a huge overhead of channel estimation for the application of the Internet of things (IoT), which demands the systems to support massive connectivity of users. An intermittent estimation scheme is proposed to ease the burden of channel acquisition. In the scheme, we exploit the temporal correlation of MIMO channels and analyze the influence of the age of CSI on the downlink transmission rate using linear precoders. We show the CSI updating interval of each user should follow a quasi-periodic distribution. The CSI updating frequency is optimized to balance between the accuracy of CSI estimation and the overhead of CSI acquisition.

Energy harvesting is an emerging technology providing clean energy for wireless communication systems. Due to the randomness in energy arrivals, wireless service process needs to be matched with energy provision to avoid energy waste or shortage. Other than passively adjusting energy usage according to traffic and energy profiles, a framework, namely, GreenDelivery has been proposed to proactively push popular contents to users in advance, such that harvested energy can be utilized more efficiently. In this paper, a heterogeneous network with multiple GreenDelivery small cells is considered. Due to spatial proximity, adjacent small cells might conflict with each other due to simultaneous transmissions or repeated pushes of identical contents to the same user, which calls for a more sophisticated design of push scheme in a multi-cell scenario. To tackle the interference, small base station (SBS) scheduling is proposed, exploiting the intermittent nature of renewable energy, to temporally separate the transmission of adjacent small cells. Heuristic push schemes are then proposed to further reduce the user requests handled by macro base stations (MBS) with centralized and distributed realizations. Simulations show that the proposed push schemes outperform the baseline scheme in which contents are pushed in the descending order of their popularities, especially when content popularity is more uniformly distributed.

Dimensionality loss is defined as the channel estimation overhead, which results in a loss of time-frequency resources in pilot-assisted wireless systems. In this paper, the scaling result of dimensionality loss, i.e., the scaling factor, in frequency-division-duplex (FDD) massive multiple-input-multiple-output(MIMO) downlinks is derived. The scaling factor determines the amount of channel estimation overhead, and thus is vital to understand the downlink throughput in FDD massive MIMO systems. Moreover, the transmit diversity of the downlink channel is also derived. In the simulations, we adopt a geometry-based stochastic channel model to validate our analysis. The impact of several assumptions made in our analysis is also investigated.

User scheduling in multiuser multiple-input-multiple-output (MU-MIMO) systems is fundamentally different with single-user systems1, in the sense that without spatial multiplexing, users in single-user systems are sharing the time-frequency degree-of-freedoms (DoFs), whereas in MU-MIMO systems, due to the fact that the number of spatial DoFs scales with the number of users (assuming sufficient base station (BS) antennas), users are not sharing the DoFs, but rather creating additional DoFs for their own use. However, instead of limited by the available DoFs, the number of simultaneous users are limited by the channel state information (CSI) acquisition overhead in pilot-assisted MU-MIMO systems. In this paper, we investigate the user scheduling scheme in pilot-assisted time-division-duplex (TDD) MU-MIMO systems. Leveraging the Lyapunov optimization techniques, we derive the throughput-optimal scheduling policy which serves as a performance bound due to its non-causality and high complexity. We then propose a heuristic scheme, which is causal and substantially decreases the complexity. Moreover, it performs fairly close to the optimum.

It is well known that the performance of frequency-division-duplex (FDD) massive MIMO systems with i.i.d. channels is disappointing compared with that of time-division-duplex (TDD) systems, due to the prohibitively large overhead for acquiring channel state information at the transmitter (CSIT). In this paper, we investigate the achievable rates of FDD massive MIMO systems with spatially correlated channels, considering the CSIT acquisition dimensionality loss, the imperfection of CSIT and the regularized-zero-forcing linear precoder. The achievable rates are optimized by judiciously designing the downlink channel training sequences and user CSIT feedback codebooks, exploiting the multiuser spatial channel correlation. We compare our achievable rates with TDD massive MIMO systems, i.i.d. FDD systems, and the joint spatial division and multiplexing (JSDM) scheme, by deriving the deterministic equivalents of the achievable rates, based on the one-ring model and the Laplacian model. It is shown that, based on the proposed eigenspace channel estimation schemes, the rate-gap between FDD systems and TDD systems is significantly narrowed, even approached under moderate number of base station antennas. Compared to the JSDM scheme, our proposal achieves dimensionality-reduction channel estimation without channel pre-projection, and higher throughput for moderate number of antennas and moderate to large channel coherence block length, though at higher computational complexity.

Multiuser multiple-input-multiple-output (MU-MIMO) systems are known to be hindered by dimensionality loss due to channel state information (CSI) acquisition overhead. In this paper, we investigate user-scheduling in MU-MIMO systems on account of CSI acquisition overhead, where a base station dynamically acquires user channels to avoid choking the system with CSI overhead. The genie-aided optimization problem (GAP) is first formulated to maximize the Lyapunov-drift every scheduling step, incorporating user queue information and taking channel fluctuations into consideration. The scheduling scheme based on GAP, namely the GAP-rule, is proved to be throughput-optimal but practically infeasible, and thus serves as a performance bound. In view of the implementation overhead and delay unfairness of the GAP-rule, the T-frame dynamic channel acquisition scheme and the power-law DCA scheme are further proposed to mitigate the implementation overhead and delay unfairness, respectively. Both schemes are based on the GAP-rule and proved throughput-optimal. To make the schemes practically feasible, we then propose the heuristic schemes, queue-based quantized-block-length user scheduling scheme (QQS), T-frame QQS, and power-law QQS, which are the practical versions of the aforementioned GAP-based schemes, respectively. The QQS-based schemes substantially decrease the complexity, and also perform fairly close to the optimum. Numerical results evaluate the proposed schemes under various system parameters.

In this paper we consider the minimum base station (BS) power consumption given the sum rate requirement in large-scale multiple-input-multiple-output (MIMO) systems. A single cell with an Mtot-antenna BS and N single-antenna users is considered. The BS power consumption consists of two parts: The part accounting for the total transmit power and the part proportional to the number of active antennas. Specifically, closed-form approximations (CFAs) of the optimal transmit power and optimal number of active antennas are derived when the sum rate requirement is high. A CFA of the ergodic sum capacity upper bound for the downlink broadcast channel is also given.

To fully understand the capacity of clustered network-MIMO systems and analyze the system performance (throughput, energy-efficiency or quality of service), one must have an analytical expression of the system capacity or capacity bounds. In this paper, the impact of cluster size on the downlink network-MIMO system capacity is analyzed considering inter-cluster interference (ICLI) based on the 1-dimensional Wyner model. For the nonfading channels, the lower and upper bounds of the per-cell capacity with ICLI are derived. The per-cell capacity with ICLI demonstrates a linear growth versus the cluster size in the interference-limited regime due to ICLI. The lower and upper bounds are generalized to a 2-dimensional cellular system. The ICLI turns out to have a great impact on the system capacity when the cluster size is small. Introducing Rayleigh fading channels, assuming the number of users in each cell is sufficiently large, a lower bound of the per-cell ergodic capacity with ICLI is derived.