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.

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.

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.

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.

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.

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.

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.

Synchronization, as a critical factor of modern wireless communication systems, has attracted close research attention recent years. For the vehicle-to-anything (V2X) communication in 5G new radio, synchronization faces severe challenges due to the extremely low latency and high reliability requirements. In this paper, a terminal aided synchronization scheme is proposed for the vehicle platooning in V2X communication. The shared information, such as NSLIDfrom other cooperative vehicles, are utilized to recover the original transmitted sidelink synchronization signals. The synchronization ID detection probability is therefore improved by 49.6% compared to conventional schemes. Hardware implementation on FPGA Artix-7 AC701 board is performed of the proposed synchronization scheme and the hardware latency is reduced to 67.18 μs compared to 968,654.85 μs in conventional schemes.

Network densification with small cells has been recognized as the key mechanism in addressing the unremitting traffic growth over the next decade. This hierarchical network paradigm is characterized by highly fluctuating traffic demand, vast amount of signaling exchange, as well as progressively intensified resource under-utilization owing to service asymmetry. A flexible backhaul framework is imperative to cope with the above-mentioned challenges. In this paper, we introduce a novel wireless backhaul framework for future small cell networks (SCNs) with a new backhauling component, referred to as Type-A relay. This network entity offers increased architectural flexibility as well as substantial throughput enhancement over the existing LTE relaying-based solutions. Implementation challenges are discussed and potential solutions are provided. Simulation results are presented to demonstrate the strong potential of Type-A relay in future SCNs.

This paper considers the application of compressed sensing (CS) to a wireless sensor network for data measurement communication and reconstruction, where N sensor nodes compete for medium access to a single receiver. Sparsity of the sensor data in three domains due to time correlation, space correlation and multiple access are being utilized. We first provide an in-depth analysis on the CS-based medium access control schemes from a physical layer perspective and reveal the impact of communication signal-to-noise ratio on the reconstruction performance. We show the process of the sensor data converted to the modulated symbols for physical layer transmission and how the modulated symbols being recovered via compressed sensing. This paper further identifies the decision problem of distinguishing between active and inactive transmitters after symbol recovery and shows a comprehensive performance comparison between carrier sense multiple access and the proposed CS-based scheme. Second, a network data recovery scheme that exploits both spatial and temporal correlations is proposed. Simulation results validate the effectiveness of the proposed method in terms of communication throughput and show that enhanced performance can be obtained by utilizing the sensed signal's temporal and spatial correlations.

Maximizing the sum rate of a wireless network with multiple interfering links is an important and challenging problem in communication systems. This difficult non-convex problem has been approached from both an algorithmic perspective to achieve global optimality (e.g., using d.c. programming) and a relaxation perspective to obtain approximate solutions (e.g., high signal-to-noise-ratio approximation, binary power control, network symmetry, game-theoretic reformulation, etc.). It is generally agreed that 1) the global algorithms suffer from scalability issues and are more appropriate for problems of small instances; and 2) the solutions obtained based on maximizing the instantaneous performance are most likely suboptimal in practical fading wireless networks. In this work, we demonstrate that the sum rate can be efficiently optimized using the tool of stochastic geometry. In particular, we show that the average network sum rate can be derived in closed-form, taking into account both the random spatial distribution of the transmitters and the random Nakagami channel fading. An optimal contention density is further derived, which indicates the optimal number of supportable concurrent transmissions that attains the maximal sum rate. We discuss several applications of the derived results in interference-limited wireless systems.

Compressed sensing can be utilized in multiple access to save channel resource and power consumption to realize green communications. This paper considers the application of compressed sensing to a wireless sensor network for data measurement reconstruction collected in a natural environment. We consider a network with N sensor nodes, sharing a wireless channel with M degrees of freedom, and compete for medium access to a single receiver. A multiple access scheme based on multi-dimensional compressed sensing is proposed, which exploits spatial and time correlations that exist in natural signals. The proposed scheme further removes the necessity of conveying identity information, which improves system power efficiency and enhances system security. Simulation results validate the effectiveness of the proposed method in utilizing the sensed signal's time and spatial correlations to obtain enhanced performances.

The problem of distributed power allocation for an interference relay network is considered, where multiple source-relay-destination (S-R-D) links communicate concurrently in the same frequency and interfere over two hops, referred to as a two-hop interference channel. The direct source-destination connections are not considered in this work. Power allocation in this scenario is challenging as it necessitates not only performance tradeoff among multiple links but also power distribution for each link along spatial dimensions. We approach the problem from a game-theoretic perspective and propose a novel implicit-based approach to prove the uniqueness of the Nash equilibrium (NE). This technique complements the existing literature on equilibria analysis by revealing the benefits of expressing the best responses in implicit forms. To benchmark the performance of the NE, we have investigated the non-convex sum-utility maximization problem, and have (1) identified the optimal solution structures; (2) proved that linear pricing is locally optimal in maximizing the sum utilities. A simple and distributed pricing algorithm is then proposed. Simulation results show that for a two-user network, the proposed scheme closely approaches the optimal sum rate, while, for a ten-user network, the improvement is up to 340% compared to the non-cooperative game model without pricing.

This paper considers the problem of resource allocation for a large-scale wireless network consisting of multiple amplify-and-forward cooperative systems. The traditional competitive design leads to a network collapse where every system obtains almost zero throughput. We resolve this issue by resorting to the novel economic model of coalition formation with externalities by first letting the systems (modeled as players) self-organize into mutually beneficial coalitions, followed by a sequential non-cooperative game with improved player profiles. The results on the two-player game has been generalized to the case of an arbitrary number of players, incorporating additional peak power constraints as well. The uniqueness of the Nash equilibrium is proved analytically. Simulation results show that the proposed method improves the sum rate over 200% relative to the non-cooperative case due to improved frequency reuse.

This paper considers resource allocation for two-hop interference channels where N independent source-destination pairs share the same spectrum band and interfere with each other. The absence of a central coordinating system motivates us to tackle the problem from a non-cooperative game-theoretic perspective. It is shown that when the number of transmission pairs is greater than two, analyzing the properties of the Nash Equilibrium (NE) becomes greatly challenging. With the help of a game scalarization technique, we derive sufficient conditions that guarantee the uniqueness of the NE, as well as the global convergence of the best response dynamics. Numerical results are presented to illustrate the significant rate enhancement over conventional schemes.

We consider the problem of power control for two independent relay-assisted wireless systems that operate in the same spectral band. A general scenario where both systems are allowed to select between amplify-and-forward relaying and decode-and-forward relaying is studied. We adopt a game-theoretic approach and show that once both systems act non-cooperatively to optimize their own rate, they can always reach a unique Nash equilibrium (NE) from an arbitrary starting point in a totally distributed and asynchronous manner. We also provide insights on the impacts of network topology and relaying protocols on the sum rate performance of the NE through extensive numerical results.

We consider frame synchronization for fixed frame length communication system with periodically embedded sync words. In the past few decades, numerous researchers have made significant contributions in various aspects. However, it is almost surprising that a scrutinization over the literature reveals to us an important common assumption: frame synchronization decision-making through single-frame observation. Intuition tells us that an enlarged multiple-frame observation set bears the potential to reduce sync word identification ambiguities and also to combat multi-path fading through ¿frame diversity¿. The objective of this work is thus to characterize the performance improvement of using multiple-frame decision rules rather than the single-frame decision rules considered in the previous work. We select the work of Lui and Tan as our standing point, and have conducted a comparative study in a very comprehensive manner. In particular, we shall show that the benefits of multipleframe based decision rules can be substantial. For example, in many cases, the performance of the correlation rule based on double-frame observation is superior to that of the optimum maximum likelihood rule with single-frame observation. While further enlarging the observation period beyond double frames was found to yield diminishing returns.

This paper considers the multiuser power control problem in Gaussian frequency-flat interference relay channels using a game-theoretic framework. While a lot of attention has been paid to Gaussian interference games, where sufficient conditions for the uniqueness of the Nash equilibrium (NE) have been established, these types of games have not been studied in the context of interference relay channels. We consider here Gaussian interference relay games (GIRGs), where instead of allocating the power budget across a set of sub-channels, each player aims to decide the optimal power control strategy across a set of hops. We show that the GIRG always possesses a unique NE for a two-player version of the game, irrespective of any channel realization or initial system parameters such as power budgets and noise power. Furthermore, we derive explicitly a sufficient condition under which the NE achieves Pareto-optimality. To facilitate decentralized implementation, we propose a distributed and asynchronous algorithm. We also prove that the proposed algorithm always converges to the unique NE from an arbitrary starting point. We then conclude that the distributed game-theoretic approach exhibits great potential in the context of interference relay channels and qualifies as a practically appealing candidate for power control.

We address the power allocation problem for interference relay channels. Due to the competitive nature of the multi-user environment, we model the problem as a strategic non- cooperative game and show that this game always has a unique Nash equilibrium (NE), for any system profile. Two iterative algorithms, based on sequential and simultaneous updating, are proposed to achieve the unique NE in a distributed manner. We shall also prove that both algorithms always converge to the unique NE, from an arbitrary starting point. More importantly, the global optimality in terms of the sum information rate is achieved by the NE, when the interference is relatively low.

In this paper, we propose a joint cooperative multiplexing and user scheduling approach over wireless relay networks. While multiuser diversity gain is achieved in traditional opportunistic scheduling techniques, the proposed method achieves both gains from multiuser diversity and multiplexing simultaneously. Theoretical bounds of the achievable throughput for the proposed method are obtained and verified to approach the simulation results very well. Performance comparison shows that the proposed method significantly outperforms traditional opportunistic techniques in terms of spectral efficiency.