Communication Bandwidth Research Articles

Ultra-compact quasi-true time delay for boosting wireless channel capacity.

Massive-data connectivity has driven the need for efficient, directed communications through beamforming arrays1-10. Delay elements are critical in any beamforming signal chain. However, these elements impose fundamental limits on size, channel capacity, power efficiency and effective isotropic radiated power11. Although passive phase shifters do not consume DC power, they suffer from narrow bandwidth, poor phase resolution and low power-handling capacity. They introduce a beam squint, in which different frequency components experience different time delays, blurring signals so that they cannot be resolved. This severely limits the data rate of the wireless link, that is, its channel capacity. Although true time delay (TTD) elements12 solve this problem and service a broad bandwidth, they comprise wavelength-scale transmission lines, making them prohibitively area-inefficient for modern semiconductor processes. Here we address this long-standing problem by introducing a quasi-true time delay (Q-TTD) that miniaturizes TTD elements and breaks fundamental channel-capacity limits of these wireless links. We demonstrate this mechanism for a microwave device implemented in a complementary metal-oxide-semiconductor (CMOS) technology. Key to shrinking the footprint is a reflective-type phase-shifting structure with 3D variable TTD reflectors within a sub-wavelength footprint. This achieves ultra-broadband phase tuning by using them to vary the length of the waveguide's path to ground. They produce a delay-to-area ratio that yields a substantially higher on-chip channel capacity compared with existing state-of-the-art methods. This component, when integrated in arrays, enables high-resolution imaging and low-squint beamforming for wideband communication, on-chip radar and other applications.

A traceable and revocable broadcast encryption scheme for preventing malicious encryptors in Medical IoT

Medical data sharing is essential for the advancement of medical research, but the existence of malicious encryptors and malicious users poses a major challenge to the seamless sharing of information among healthcare providers. We proposed a traceable and revocable broadcast encryption scheme for preventing malicious encryptors in the Medical Internet of Things (MIoT). In 2023, Wang et al. first proposed the concept of malicious encryptor for broadcast encryption and trator tracing, and they constructed a scheme for preventing this attack. The malicious encryptor is reasonable in real broadcast encryption systems because the encryptor is not always the same as the digital content provider. When digital content providers for medical institutions want to share sensitive medical data, the employees may implement encryption. Then the employees may plant some trapdoors and sell to the black market, which can be used to construct pirated boxes. To resist malicious encryptor attacks, we rely on the uniform distribution of the output of the secure cryptographic hash function to generate the randomness needed for encryption. To prevent malicious users from selling their private keys to attackers, we set up public tracing and revocation mechanisms. Meanwhile, we enable digital content providers to share common encrypted data to different groups of authorized users protected by privacy, while also sharing individual data to designated consumers of those groups. Under the decision q-parallel BDHE assumption without random oracles, our construction is demonstrated to be adaptive IND-CPA secure. Since we have a compact communication bandwidth, a constant user secret key size, and only three bilinear operations are needed for decryption to recover the encrypted broadcast message, our scheme does not add much overhead and is therefore practical.

Event‐triggered H∞$$ {H}_{\infty } $$ performance state estimation for neural networks with time‐varying delay

The issue of event‐triggered performance state estimation for neural networks with time‐varying delays is addressed in this paper. An innovative event‐triggered approach is presented, designed to strike a harmonious equilibrium between the state estimator's performance and the communication bandwidth of the network. The proposed approach captures the relationship between the time‐varying delay and system states by employing a delay derivative‐dependent integral inequality with matrices that account for delay derivatives. This novel formulation ensures the asymptotic stability of the estimation error system, thereby fulfilling the performance requirement. The desired event‐triggered estimators are then obtained through the use of linear matrix inequalities. The effectiveness of this approach is demonstrated through a numerical example.

Design of Self-Matching Photonic Lantern for High-Order Transverse-Mode Laser Systems

High-order transverse-mode lasers have important potential application value in many fields. To address the current issue of the limited controllability of modes in high-order transverse-mode lasers, we have designed a self-matching photonic lantern (SMPL). The SMPL is formed by introducing a few-mode fiber into the input fiber array of the traditional photonic lantern. The parameters of the few-mode fiber match those of the tapered few-mode port of the SMPL; thus, it can transmit high-order modes in a closed loop. The designed SMPL exhibits dual-band multiplexing characteristics at 980/1550 nm, manifesting specifically as high-order mode selectivity excitation at 980 nm and mode preservation at 1550 nm. These characteristics have been validated through simulation and preliminary experiments. The SMPL is designed for constructing all few-mode fiber ring cavity lasers, enabling the pumping of the 980 nm fundamental mode to high-order modes and the transmission of multiple high-order transverse-mode lasers at 1550 nm in a closed loop. The proposed SMPL extends the configuration and functionality of the photonic lantern family, offering a flexible and effective approach to facilitate the generation of multiple high-order transverse-mode lasers. The SMPL combined with fiber laser systems could effectively broaden communication channels and enhance communication bandwidth. It also holds significant value in optical sensing, high-resolution imaging, laser micro-processing, and other fields.

An Analysis of Various Design Pathways Towards Multi-Terabit Photonic On-Interposer Interconnects

In the wake of dwindling Moore’s Law, to address the rapidly increasing complexity and cost of fabricating large-scale, monolithic systems-on-chip (SoCs), the industry has adopted dis-aggregation as a solution, wherein a large monolithic SoC is partitioned into multiple smaller chiplets that are then assembled into a large system-in-package (SiP) using advanced packaging substrates such as silicon interposer. For such interposer-based SiPs, there is a push to realize on-interposer inter-chiplet communication bandwidth of multi-Tb/s and end-to-end communication latency of no more than 10 ns. This push comes as the natural progression from some recent prior works on SiP design, and is driven by the proliferating bandwidth demand of modern data-intensive workloads. To meet this bandwidth and latency goal, prior works have focused on a potential solution of using the silicon photonic interposer (SiPhI) for integrating and interconnecting a large number of chiplets into an SiP. Despite the early promise, the existing designs of on-SiPhI interconnects still have to evolve by leaps and bounds to meet the goal of multi-Tb/s bandwidth. However, the possible design pathways, upon which such an evolution can be achieved, have not been explored in any prior works yet. In this paper, we have identified several design pathways that can help evolve on-SiPhI interconnects to achieve multi-Tb/s aggregate bandwidth. We perform an extensive link-level and system-level analysis in which we explore these design pathways in isolation and in different combinations of each other. From our link-level analysis, we have observed that the design pathways that simultaneously enhance the spectral range and optical power budget available for wavelength multiplexing can render aggregate bandwidth of up to 4 Tb/s per on-SiPhI link. We also show that such high-bandwidth on-SiPhI links can substantially improve the performance and energy-efficiency of the state-of-the-art CPU and GPU chiplets based SiPs.

Completion Time Optimization in UAV-Relaying-Assisted MEC Networks With Moving Users

As one of the most popular consumer electronics, Unmanned Aerial Vehicle (UAV) has the potential to assist User Equipment (UE) in the Internet of Everything. Mobile edge computing (MEC) is also an emerging technique that can provide sufficient computing resources to IoE users. In this paper, we focused on the UAV-assisted MEC problem with mobile UEs, where the UAV serves as an MEC server to assist UEs in computing and acts as a relay to deliver tasks to a ground access point (AP). The objective is to minimize the average time for completing all tasks in the network while jointly optimizing resource allocation such as communication bandwidth, CPU frequency, task division ratio, and UAV’s three-dimensional location deployment. First, we proposed an online MEC network adjustment scheme. Then, we decomposed the formulated non-convex problem into three low-complexity subproblems. Last, we proposed a successive convex approximation-based joint optimization algorithm to solve them. In addition, we presented heuristic conclusions in task allocation rules, UAV flight trajectory prediction, and UAV hovering altitude selection, which can further reduce task completion time and deepen the understanding of the working mode of mobile edge servers. Simulation results show that the algorithm can significantly reduce the completion time.

Model Sparsification for Communication-Efficient Multi-Party Learning via Contrastive Distillation in Image Classification

Multi-party learning allows all parties to train a joint model under legal and practical constraints without private data transmission. Related research can perform multi-party learning tasks on homogeneous data through deep networks. However, due to the heterogeneity of data from different parties and the limitation of computational resources and costs, traditional approaches may affect the effectiveness of multi-party learning, and cannot provide a personalized network for each party. In addition, to reduce the computational cost and communication bandwidth of local models, there are still challenges in building an adaptive model from the private data of different parties. To address these challenges, we aim to apply a model sparsification strategy in multi-party learning. Model sparsification can not only reduce the computational overhead in local edge devices and the cost of communication and interaction between multi-party models. It can also develop privatized and personalized networks based on the heterogeneity of local data. We use the contrastive distillation method during training to reduce the distance between local and global models. In addition, we maintain the performance of the aggregation model from heterogeneous data. In brief, we developed an adaptive multi-party learning framework based on contrastive distillation, which can significantly reduce the communication cost in the learning process, improve the effectiveness of the aggregation model for local heterogeneous and unbalanced data, and make it easy to deploy in the limited edge devices. Finally, to verify the effectiveness of this framework, we experimented with the Fshion-MNIST, Cifar-10, and Cifar-100 datasets in different scenarios to verify the effectiveness of this framework.

Quantized iterative learning control of communication-constrained systems with encoding and decoding mechanism

In practical applications, due to the limited communication bandwidth, the network control systems (NCSs) are prone to data dropouts when the load is high. In this paper, the problem of quantized iterative learning control (ILC) based on encoding and decoding mechanism for such communication-constrained systems is studied. By combining the encoding and decoding mechanism with the uniform quantizer, the network burden and the impact of quantization error on the tracking performance of the systems are significantly mitigated. Meanwhile, data dropouts are represented as the Bernoulli random variable model, and an ILC law based on gradient is designed. When data dropouts occur, the signals maintain the value of the previous trial, otherwise the signals are updated. For this kind of learning framework, the asymptotic zero-error tracking performance has been rigorously proven for the uniform quantizer. To validate the proposed design, a joint motion of an industrial robot in the horizontal plane is simulated as an example.

Performance analysis: Securing SIP on multi-threaded/multi-core proxy server using public keys on Diffie-Hellman (DH) in single and multi-server queuing scenarios.

The rapid replacement of PSTN with VOIP networks indicates the definitive phase-out of the PBX/PABX with smartphone-based VOIP technology that uses WLAN connectivity for local communication; however, security remains a key issue, regardless of the communication coverage area. Session initiation protocol (SIP) is one of the most widely adopted VOIP connection establishment protocols but requires added security. On the Internet, different security protocols, such as HTTPS (SSL/TLS), IPSec, and S/MIME, are used to protect SIP communication. These protocols require sophisticated infrastructure and some pose a significant overhead that may deteriorate SIP performance. In this article, we propose the following: i) avoid using Internet bandwidth and complex Internet protocols for local communication within an organization, but harness WLAN connectivity, ii) use multi-threaded or multicore computer systems to handle concurrent calls instead of installing hardware-based SIP servers, and iii) run each thread in a separate core. Cryptography is a key tool for securely transmitting confidential data for long- and short-range communication, and the Diffie-Hellman (DH) protocol has consistently been a popular choice for secret key exchanges. Primarily, used for symmetric key sharing, it has been proven effective in generating public/private key pairs, sharing public keys securely over public channels, and subsequently deriving shared secret keys from private/public keys. This key exchange scheme was proposed to safeguard VOIP communication within WLANs, which rely on the SIP for messaging and multimedia communication. For ensuring an efficient implementation of SIP, the system was rigorously analyzed using the M/M/1 and M/M/c queuing models. We analyze the behavior of SIP servers with queuing models with and without end-to-end security and increase users' trust in SIP security by providing a transparent sense of end-to-end security as they create and manage their private and public keys instead of relying on the underlying SIP technology. This research implements instant messaging, voice conversation, and secret key generation over DH while implementing and observing the role of multi-threading in multiqueue systems that serve incoming calls. By increasing the number of threads from one to two, the SIP response time improved from 20.23809 to 0.08070 min at an arrival rate of 4250 calls/day and a service rate of three calls/min. Similarly, by adding one to seven threads, the queue length was reduced by four calls/min. Implementing secure media streaming and reliable AES-based signaling for session confidentiality and integrity introduces a minor 8-ms tradeoff in SIP service performance. However, the advantages of implementing added security outweigh this limitation.

Real-Time Object Localization Using a Fuzzy Controller for a Vision-Based Drone

This study proposes a drone system with visual identification and tracking capabilities to address the issue of limited communication bandwidth for drones. This system can lock onto a target during flight and transmit its simple features to the ground station, thereby reducing communication bandwidth demands. RealFlight is used as the simulation environment to validate the proposed drone algorithm. The core components of the system include DeepSORT and MobileNet lightweight models for target tracking. The designed fuzzy controller enables the system to adjust the drone’s motors, gradually moving the locked target to the center of the frame and maintaining continuous tracking. Additionally, this study introduces channel and spatial reliability tracking (CSRT) switching from multi-object to single-object tracking and multithreading technology to enhance the system’s execution speed. The experimental results demonstrate that the system can accurately adjust the target to the frame’s center within approximately 1.5 s, maintaining precision within ±0.5 degrees. On the Jetson Xavier NX embedded platform, the average frame rate (FPS) for the multi-object tracker was only 1.37, with a standard deviation of 1.05. In contrast, the single-object tracker CSRT exhibited a significant improvement, achieving an average FPS of 9.77 with a standard deviation of 1.86. This study provides an effective solution for visual tracking in drone systems that is efficient and conserves communication bandwidth. The validation of the embedded platform highlighted its practicality and performance.

PArtNNer: Platform-Agnostic Adaptive Edge-Cloud DNN Partitioning for Minimizing End-to-End Latency

The last decade has seen the emergence of Deep Neural Networks (DNNs) as the de facto algorithm for various computer vision applications. In intelligent edge devices, sensor data streams acquired by the device are processed by a DNN application running on either the edge device itself or in the cloud. However, “edge-only” and “cloud-only” execution of State-of-the-Art DNNs may not meet an application’s latency requirements due to the limited compute, memory, and energy resources in edge devices, dynamically varying bandwidth of edge-cloud connectivity networks, and temporal variations in the computational load of cloud servers. This work investigates distributed (partitioned) inference across edge devices (mobile/end device) and cloud servers to minimize end-to-end DNN inference latency. We study the impact of temporally varying operating conditions and the underlying compute and communication architecture on the decision of whether to run the inference solely on the edge, entirely in the cloud, or by partitioning the DNN model execution among the two. Leveraging the insights gained from this study and the wide variation in the capabilities of various edge platforms that run DNN inference, we propose PArtNNer , a platform-agnostic adaptive DNN partitioning algorithm that finds the optimal partitioning point in DNNs to minimize inference latency. PArtNNer can adapt to dynamic variations in communication bandwidth and cloud server load without requiring pre-characterization of underlying platforms. Experimental results for six image classification and object detection DNNs on a set of five commercial off-the-shelf compute platforms and three communication standards indicate that PArtNNer results in 10.2× and 3.2× (on average) and up to 21.1× and 6.7× improvements in end-to-end inference latency compared to execution of the DNN entirely on the edge device or entirely on a cloud server, respectively. Compared to pre-characterization-based partitioning approaches, PArtNNer converges to the optimal partitioning point 17.6× faster.

RT-Ranked: Towards Network Resiliency by Anticipating Demand in TSCH/RPL Communication Environments

Time-slotted Channel Hopping (TSCH) Media Access Control (MAC) was specified to target the Industrial Internet of Things needs. This MAC balances energy, bandwidth, and latency for deterministic communications in unreliable wireless environments. Building a distributed or autonomous TSCH schedule is arduous because the node negotiates cells with its neighbours based on queue occupancy, latency, and consumption metrics. The Minimal TSCH Configuration defined by RFC 8180 was specified for bootstrapping a 6TiSCH network and detailed configurations necessary to be supported. In particular, it adopts Routing Protocol for Low Power and Lossy networks (RPL) Non-Storing mode, which reduces the node’s network awareness. Dealing with unpredicted traffic far from the forwarding node is difficult due to limited network information. Anticipating this unexpected flow from multiple network regions is essential because it can turn the forwarding node into a network bottleneck leading to high latency, packet discard or disconnection rates, forcing RPL to change the topology. To cope with that, this work proposes a new mechanism that implements an RPL control message option for passing forward the node’s cell demand, allowing the node to anticipate the proper cell allocation for supporting the traffic originating by nodes far from the forwarding point embedded in Destination-Oriented Directed Acyclic Graph (DODAG) Information Object (DIO) and Destination Advertisement Object (DAO) RPL control messages. Implementing this mechanism in a distributed TSCH Scheduling developed in Contiki-NG yielded promising results in supporting unforeseen traffic bursts and has the potential to significantly improve the performance and reliability of TSCH schedules in challenging network environments.

CuXCMP: CUDA-Accelerated Private Comparison Based on Homomorphic Encryption

Private comparison schemes constructed on homomorphic encryption offer the noninteractive and parallelizable features, and have advantages in communication bandwidth and performance. In this work, we propose cuXCMP, an extension of the privacy comparison scheme XCMP (AsiaCCS 2018). We address the relatively small input domain and the incompletely expressible output of XCMP, by modifying the encoding method and devising a constant term extraction (CTX) approach. Then, we describe a method for constructing privacy-preserving decision tree (PPDT) using this scheme. Considering the high computational overhead of CTX, we exploit the massive parallelism of the GPU to accelerate this function. Based on the results of the kernel profiling, we utilize several optimization techniques to improve the performance, including using multiple CUDA streams, reducing the grid dimension, kernel fusion, etc. By accelerating this function, we boost the execution time of the scheme and demonstrate 130× and 1.9× speedups for CTX and cuXCMP, respectively, as well as a 35% reduction in the evaluation time of PPDT.

Human somatosensory systems based on sensor-memory-integrated technology.

As a representative artificial neural network (ANN) for incorporating sensing functions and memory functions into one system to achieve highly miniaturized and highly integrated devices or systems, artificial sensory systems (ASSs) can have a far-reaching influence on precise instrumentation, sensing, and automation engineering. Artificial sensory systems have enjoyed considerable progress in recent years, from low degree integrations to highly advanced sophisticated integrations, from single-modal perceptions to multimode-fused perceptions. However, there are issues around the large hardware area, power consumption, and communication bandwidth needed during the processes where multimodal sensing signals are converted into a digital mode before they can be processed by a digital processor. Therefore, deepening the research into sensory integration is of great importance. In this review, we briefly introduce fundamental knowledge about the memristor mechanism, describe some representative human somatosensory systems, and elucidate the relationship between the properties of memristor devices and the structure. The electronic character of the sensors, future prospects, and key challenges surrounding sensor-memory integrated technologies are also discussed.

Quantized Primal-Dual Algorithms for Network Optimization With Linear Convergence

This note investigates a network optimization problem in which a group of agents cooperate to minimize a global function under the practical constraint of finite bandwidth communication. We propose an adaptive encoding-decoding scheme to handle the quantization communication between agents. Based on this scheme, we develop a continuous-time quantized distributed primal-dual (QDPD) algorithm for the network optimization problem. Our algorithm achieves linear convergence to an exact optimal solution. Furthermore, we obtain the relationship between the communication bandwidth and the convergence rate. Finally, we use a distributed logistic regression problem to illustrate the effectiveness of our methods.

GaN-Based Freestanding Micro-LEDs With GHz Bandwidth and Low Efficiency Droop for Visible Light Communication

GaN-Based Freestanding Micro-LEDs With GHz Bandwidth and Low Efficiency Droop for Visible Light Communication

Integrated Deep-Ultraviolet Micro-LED Array with Ultralow Contact Resistance and Ultrahigh Bandwidth for Intermixed Solar-Blind Optical Wireless Communication

Integrated Deep-Ultraviolet Micro-LED Array with Ultralow Contact Resistance and Ultrahigh Bandwidth for Intermixed Solar-Blind Optical Wireless Communication

Wideband Near-Field Integrated Sensing and Communication With Sparse Transceiver Design

Wideband Near-Field Integrated Sensing and Communication With Sparse Transceiver Design

A COMPACT 2 × 2 UWB-MIMO ANTENNA WITH FUNNEL SHAPED STUB FOR ISOLATION ENHANCEMENT

The growth of wireless communication technologies demands advancement in designing antennas. The essential need for efficient and low-profile antennas are required for wireless communication. The development of antenna structures(compact) with performances is considerably enhanced over traditional antenna structures and methodologies. A compact printed and planar multiple-input multiple-output (MIMO) for ultra wideband (UWB) communications is presented. Two circular disc monopole antenna elements constitute the proposed UWB-MIMO antenna, operating over the frequency band of 4-10 GHz. The required antenna performance in terms of frequency response, gain, S-parameters, and voltage standing wave ratio (VSWR) can be achieved in the range of 3-7 GHz gain with minimum attenuation. The proposed antenna is simulated using computer simulation technology (CST) microwave studio software, and the designed antenna will operate for wireless communication application.

A Game Theoretic Approach to Computation Offloading Strategy Optimization for Non-cooperative Users in Mobile Edge Computing

Computation offloading from a user equipment (UE, also called mobile user, mobile subscriber, or mobile device) to a mobile edge cloud (MEC) provides an effective way to virtualize an ordinary smart mobile device (e.g., smartphone, tablet, handheld computer, wearable device, and personal digital assistant) into a formidable equipment, which is able to provide more and stronger functionalities than that of a laptop or a desktop computer. It is conceivable that there can be several MECs with different processing capabilities in a geographic area, and each MEC many serve many UEs with endless sequences of computation tasks, various application characteristics, and diversified communication requirements and bandwidths. Furthermore, the mobile users are competitive and selfish, which means that computation offloading strategy optimization needs to be carried out for each individual mobile user to optimize the performance of only his applications. In this paper, we conduct a mathematical study of computation offloading strategy optimization for non-cooperative users in mobile edge computing by using a game theoretic approach. The main contributions of this paper can be summarized as follows. We establish an M/G/1 queueing model to characterize multiple heterogeneous UEs and MECs, so that the average response time of all offloadable and non-offloadable tasks generated on a UE can be calculated analytically and the optimal computation offloading strategy of a UE can be defined rigorously. We construct a non-cooperative game framework for a mobile edge computing environment, in which each player (i.e., a UE) can selfishly minimize his payoff by choosing an appropriate strategy in his strategy space. We prove the existence of the Nash equilibrium of the above game. We develop algorithms to find the Nash equilibrium, including an algorithm to find the best response of a mobile user and an iterative algorithm to find the Nash equilibrium. We demonstrate numerical examples and data of our game, including numerical data for the Nash equilibrium and numerical data for the convergence of the Nash equilibrium. To the best of the author's knowledge, this is the first paper that effectively investigates computation offloading strategy optimization for multiple, heterogeneous, and competitive mobile users and multiple heterogeneous mobile edge clouds by using a non-cooperative game approach. Hence, the paper makes noticeable contributions towards the understanding of a competing mobile edge computing environment and its stabilization.