The age of information (AoI) has been studied actively in recent years as a performance measure for systems that require real-time performance, such as remote monitoring systems via communication networks. The theoretical analysis of the AoI is usually formulated based on explicit system modeling, such as a single-server queueing model. However, in general, the behavior of large-scale systems such as communication networks is complex, and it is usually difficult to express the delay using simple queueing models. In this paper, we consider a framework in which the sequence of delays is composed from a non-negative continuous-time stochastic process, called a virtual delay process, as a new modeling approach for the theoretical analysis of the AoI. Under such a framework, we derive an expression for the transient probability distribution of the AoI and further apply the theory of stochastic orders to prove that the high dependence of the sequence of delays leads to the degradation of AoI performance. We further consider a special case in which the sequence of delays is generated from a stationary Gaussian process, and we discuss the sensitivity of the AoI to second-order statistics of the delay process through numerical experiments.

Recent advancements in artificial intelligence (AI) have yielded sophisticated natural language interaction systems, largely driven by Large language models (LLMs). These models generate text auto-regressively by predicting continuations. However, the substantial computational demands of LLM inference typically necessitate reliance on cloud-based services. As demand escalates, organizations are increasingly exploring private cloud deployments to bolster information security and comply with data regulations. Nevertheless, deploying private LLM inference servers entails significant capital expenditure on high-performance Graphics Processing Units (GPUs) and substantial operational costs, including high power consumption, raising environmental concerns. While existing research has explored optimizing individual components of LLM processing, a holistic, system-level perspective on inference efficiency remains underexplored. This paper addresses this gap by formulating private cloud-based LLM inference as a stochastic model. We analyze its performance, focusing on the throughput and processing latency, and specifically investigate the impact of different inference methods and batch sizes on overall efficiency.

In this paper we analyze the distribution of the Age of Information (AoI) of a tagged data stream sharing a processor with a set of other data streams. We do so in the highly general setting in which the interarrival times pertaining to the tagged stream can have any distribution, and also the service times of both the tagged stream and the background stream are generally distributed. The packet arrival times of the background process are assumed to constitute a Poisson process, which is justified by the fact that it typically is a superposition of many relatively homogeneous streams. The first main contribution is that we derive an expression for the Laplace-Stieltjes transform of the AoI in the resulting GI+M/GI+GI/1 model. Second, we use stochastic ordering techniques to identify tight stochastic bounds on the AoI, leading to an explicit lower and upper bound on the mean AoI. In addition, when approximating the tagged stream’s inter-generation times through a phase-type distribution (which can be done at any precision), we present a computational algorithm for the mean AoI. As illustrated through a sequence of numerical experiments, the analysis enables us to assess the impact of background traffic on the AoI of the tagged stream. It turns out that the upper bound on the mean AoI is remarkably close to its true value, which yields an explicit expression (in terms of the model parameters) for an accurate proxy of the AoI-minimizing generation rate.

Deep learning-based joint source-channel coding (Deep JSCC) has attracted attention. Deep JSCC maps information source features directly to IQ symbols using an autoencoder instead of source and channel coding. In image transmission, severe image degradation due to the cliff effect has been a problem in the lower signal-to-noise ratio (SNR) regions. Meanwhile, Deep JSCC provides a better peak signal-to-noise ratio (PSNR) even in low SNR regions. However, a high-resolution image needs to be divided into small patch images to input into Deep JSCC, consideration of the limitation of transmission capacity and delay. The image division causes block noise and PSNR degradation. This paper proposes Deep JSCC with overlapping image division to suppress the block noise. The proposed overlap division overlaps several pixels between patch images when dividing into patch images. After image recomposition by the decoder of Deep JSCC, the receiver removes the overlapping pixel information and combines the edge of the patch images smoothly. This paper conducts the experiments to reveal the effectiveness of block noise reduction owing to the proposed scheme with DIV2K data set. Consequently, we indicate the PSNR is drastically improved in the specific SNR region.

This paper investigated the impact of a quantizer in analog-to-digital and digital-to-analog converters in communication devices on image quality when using deep learning-based joint source-channel coding modulation (JSCCM) for image transmission. In recent years, JSCCM, which efficiently encodes images and videos with low information entropy, has attracted great attention. JSCCM has a structure based on an autoencoder and determines the compression ratios for the image input by adjusting the number of IQ symbol output. The IQ symbol output from the encoder are allocated to symbol constellations with higher degrees of arbitrariness than those in typical square quadrature amplitude modulation and are therefore expected to be strongly affected by the quantization noise. In this paper, we employed quantization to the IQ symbol sequence and investigated its effect. Adjusting the quantizer's clipping ratio and the number of quantization bits, we examined the images' tolerance of the peak signal-to-noise ratio (PSNR). The simulation results showed that by adequately adjusting the clipping ratio, the image quality can be guaranteed to be equivalent to ideal conditions without quantization noise, and the number of required quantization bits that do not degrade the PSNR, was calculated.

Deep joint source-channel coding (JSCC) has been attracting attention for achieving task-oriented communication. It replaces traditional information source coding and channel coding with a deep learning-based autoencoder, directly mapping information sources such as images to IQ symbols. For images, it is claimed to avoid the cliff effect and achieve a higher peak signal noise ratio (PSNR) even in low SNR regions. While related work has assumed various propagation channel models and validated the effectiveness of Deep JSCC, there are few reports confirming its principles through experiments. Specifically, to the best of our knowledge, there are no reported examples of experiments of Deep JSCC in 5G systems. In this paper, we present a proof-of-concept of Deep JSCC in a 5G system. We modified commercially available 5G base stations (gNB) and 5G terminals to enable input and output of IQ data from external devices. We connect the 5G devices using coaxial cables and attenuators, transmit and receive JSCC signals, and evaluate the PSNR. The results demonstrate that even when communicating at power levels lower than the minimum receiver sensitivity specified in the receiver’s datasheet, the image can be successfully restored with less than 1 dB degradation in PSNR compared with the simulation result.

Numerous radio units (RUs) are required to densely compose small cells in the beyond 5G mobile networks. The concept of vehicle-mounted RUs is a promising solution for dynamically deploying small cells in accordance with demand distribution. Wireless relay fronthaul networking is the key enabler for this concept, where forwarding paths of fronthaul streams are computed in real-time by an edge server using the link-state information reported from RUs. Optimization of the reporting interval is a significant issue because of the tradeoff between the freshness of report messages and network load. However, existing works have not investigated the freshness of link information in highly dynamic environments. In this paper, we introduce a new reliability metric called the reliability of information (RoI). The RoI is defined as the joint probability that all nodes in the network maintain correct information. We establish a tractable lower bound of the RoI under a mild assumption on the dynamics of node connectivity, which enables us to design small cells guaranteeing information reliability. We further formulate and solve an optimization problem to find an optimal reporting interval. The usefulness of the proposed scheme is demonstrated through simulation experiments for a highly dynamic vehicular network.

The deep joint source-channel coding and modulation (JSCCM) is a promising technology to realize efficient communication over extreme environments such as underwater area. In previous works, it is shown that deep convolutional neural networks (CNN) can successfully learn JSCCM encoder and decoder, outperforming conventional separation-based coding and modulation schemes in low signal-to-noise ratio settings. This paper proposes a new architecture for deep JSCCM based on the self-attention mechanism. We show that the proposed architecture achieves significant performance improvement compared with the CNN-based schemes while requiring a smaller network size in terms of the number of weight parameters. Furthermore, we present efficient hardware implementation of the proposed JSCCM encoder on a field programmable gate array (FPGA). In particular, we demonstrate that a systolic-array-like structure is effective for FPGA implementation of the proposed JSCCM scheme based on the self-attention mechanism.

A hybrid virtual work model which combines on-site and remote work will contribute for increasing productivity during and after the pandemic. Many workers are interested in collaboration tools among on-site and remote workers for well-being. A significant problem that lies in a hybrid model is that uninterrupted work hours shrank during the work from home period. There have been many worker assistance systems based on focus monitoring technologies. However, existing schemes did not consider groups of workers in hybrid environments. To address this problem, in this paper we propose a real-time interruptive task mediation system among hybrid workers. The goal of the proposed scheme is to improve the total productivity of workers by optimally allocating interruptive tasks based on focus monitoring. The focus state of a worker is considered as a two-state stationary Markov process. The optimum monitoring interval to ensure the target error tolerance is determined using the Age of Information. The feasibility of the proposed scheme was demonstrated via computer simulations and preliminary experimental results.

Underwater networks are expected to be service platforms for broad-sea and deep-sea activities. The significant challenge of underwater communication has been achieving high-speed and long-distance data transmission due to the high-attenuation and time-varying channel state in underwater environments. It is reasonable to get the underwater data above the water surface for establishing underwater-ground networks. However, it is still an unsolved issue to efficiently establish underwater-ground communication channel. To address this problem, we propose an aquatic fronthaul for underwater-ground communication, where floating aquatic relay nodes relay data from underwater drones/sensors to a ground radio unit. We propose a relocation algorithm for aquatic relay nodes to efficiently reconstruct the network according to the distribution of underwater nodes. The advantage of the proposed algorithm is robustness for the uncertainty of underwater node locations due to the difficulty in underwater localization. The performance of the proposed algorithm was evaluated with multi-agent simulations. The feasibility of the aquatic fronthaul network was confirmed via the experimental results with a Wi-Fi mesh network above the water.

Communication in extreme environments is an important research topic for various use cases including environmental monitoring. A typical example is underwater acoustic communication for 6G mobile networks. The major challenges in such environments are extremely high-latency and high-error rate. They make real-time image transmission difficult using existing communication protocols. This is partly because frequent retransmission in noisy networks increases latency and leads to serious deterioration of real-timeness. To address this problem, this paper proposes a stochastic image transmission with Constrained Application Protocol (CoAP) for extreme environments. The goal of the proposed idea is to achieve approximate real-time image transmission without retransmission using CoAP over UDP. To this end, an image is divided into blocks, and value is assigned for each block based on the requirement. By the stochastic transmission of blocks, the reception probability is guaranteed without retransmission even when packets are lost in networks. We implemented the proposed scheme using Raspberry Pi 4 to demonstrate the feasibility. The performance of the proposed image transmission was confirmed from the experimental results.

A key aspect to realize Internet of things applications such as industry automation and smart agriculture is to enable realtime and networked automatic monitoring via cloud computing and computer vision. However, to design a networked monitoring system, it is necessary to realize a balance between the monitoring accuracy and monitoring cost, for instance, between the network traffic to transmit images and the computation load. Although the monitoring cost can be decreased by increasing the sampling interval of cameras, it becomes more likely that informative images cannot be obtained; in other words, the monitoring accuracy decreases with a reduction in the amount of data. Moreover, although on-device image processing can decrease the network traffic, a large computation delay may be incurred, limiting the sampling rate of the monitoring system. The objective of this study was to examine the balance between the monitoring accuracy and cost and to develop a joint optimization technique for the sampling interval and computation offloading to minimize the monitoring cost in a networked monitoring system while ensuring a high monitoring accuracy. The main contributions of this paper are that we prove the joint optimization problem can be solved explicitly and to develop an algorithm to obtain the solution of the joint optimization problem. The simulation results demonstrated that the proposed algorithm can reduce the monitoring cost by 24-48% while maximizing the number of nodes ensured to achieve high monitoring accuracy.

In recent years, advances in deep learning (DL) technology have greatly improved research and services related to artificial intelligence (AI). In particular, real-time object recognition has become an important technology in smart cities. To achieve this, low-cost network deployment and low-latency data transfer are the key technologies. In this paper, we focus on Time- and Wavelength-Division Multiplexed Passive Optical Network (TWDM-PON) based inference systems to deploy cost-efficient networks that accommodate many network cameras. A significant issue for a GPU-based inference system via TWDM-PON is optimally allocating upstream wavelength and bandwidth to enable real-time inference. However, it has not been considered to increase the batch size of arrival data at edge servers ensuring low-latency transmission. Therefore, this paper proposes a concept of an inference system in which a large number of cameras periodically upload image data to a GPU-based server via TWDM-PONe We also propose a cooperative wavelength and bandwidth allocation algorithm to ensure low-latency and time-synchronized data arrival at the edge. The performance of the proposed scheme is verified with computer simulation.

Underwater communication is a promising technology to provide ubiquitous network connectivity, where acoustic waves are used as the primary carrier for long-range communication. It has been a challenging research topic to efficiently transmit images with under-water acoustic communication (UAC), due to its inherently narrow bandwidth, strong signal attenuation, time-varying multipath propagation, and low propagation speed. In this paper, we present a new approach to addressing these limitations in UAC, namely the joint source-channel coding and modulation (JSCCM) based on a deep neural network (DNN). We develop a training method of DNN-based encoder and decoder, which directly encode/decode image-pixel values to modulated symbols, unlike conventional separation-based source and channel coding and modulation. Through numerical simulations, the deep JSCCM is confirmed to achieve significantly higher data-rate than conventional schemes.

This paper proposes a space-time-domain successive interference cancellation-based adaptive equalizer (STD-SIC-AE) for underwater acoustic communication (UAC). The demand for high-capacity real-time video transmission underwater has increased for exploring ocean resources and marine research. UAC is capable of long-haul transmission and is the promising means for deep-sea exploration. However, the transmission capacity is limited because of the reflected wave from the sea surface and seafloor. It causes a multipath interference with a long propagation delay that is not easy to remove by the conventional space-time-domain equalizer. This is because that the finite impulse response (FIR) filter requires impractically huge taps. In this paper, by taking advantage of the fact that the interference (delay) wave is a direct wave that has already been received, a replica is generated by the received direct wave and the SIC is operated in the time domain. The numerical simulation verifies that our proposed STD-SIC-AE can significantly improve BER performance in terms of SNR and SIR even under higher-order modulation such as 16QAM.

Immediately after the occurrence of a natural disaster, communication infrastructures used in daily life become temporarily unavailable. Under such a situation, intermittently connected mobile ad hoc networks (MANETs) play an important role in providing post-disaster networking. While previous studies on such networks have mainly focused on one-to-one messaging applications, the importance of monitoring applications has become increasingly important in recent years. For monitoring applications, the key performance measure is given by the freshness of the information, rather than the traditional delay characteristics. In this paper, we present a mathematical analysis of the age of information (AoI) for intermittently connected MANETs, which captures the information freshness of monitoring applications. We further investigate basic principles in the network design based on the analytical results obtained. In particular, we discuss the AoI-energy tradeoff from different perspectives of source and relay nodes.

Optical wireless communication is a promising technology for underwater broadband access networks, which are particularly important for high-resolution environmental monitoring applications. This paper focuses on a deep-sea monitoring system, where an underwater optical wireless network is deployed on the seafloor. We model such an optical wireless network as a general queueing network and formulate an optimal relay placement problem, whose objective is to maximize the stability region of the whole system, i.e., the supremum of the traffic volume that the network is capable of accommodating. The formulated optimization problem is further shown to be non-convex, so that its global optimization is non-trivial. In this paper, we develop a global optimization method for this problem and we provide an efficient algorithm to compute an optimal solution. Through numerical evaluations, we show that a significant performance gain can be obtained by using the derived optimal solution.

The concept of vehicle-mounted crowdsourced radio units (CRUs) for a smart city has been proposed to utilize the power of citizens in the deployment of small cells of the centralized radio access network (C-RAN) architecture. Wireless relay fronthaul networking is a promising solution for efficient utilization of vehicle-mounted small cells. However, there have been no routing schemes that can satisfy the strict delay requirements of mobile fronthaul coping with the high dynamicity of vehicles. Thus, this paper proposes a real-time routing scheme for establishing wireless relay fronthaul with vehicle-mounted CRUs. The route optimization is formulated as a boolean satisfiability problem (SAT), and an FPGA-based SAT solver is employed for the fast computation. It can dynamically optimize the forwarding paths in real-time with the constraints of delay requirements. The performance of the proposed routing scheme is confirmed via computer simulations.

This paper considers the stationary distribution of the age of information (AoI) in information update systems. We first derive a general formula for the stationary distribution of the AoI, which holds for a wide class of information update systems. The formula indicates that the stationary distribution of the AoI is given in terms of the stationary distributions of the system delay and the peak AoI. To demonstrate its applicability and usefulness, we analyze the AoI in single-server queues with four different service disciplines: first-come first-served (FCFS), preemptive last-come first-served (LCFS), and two variants of non-preemptive LCFS service disciplines. For the FCFS and the preemptive LCFS service disciplines, the GI/GI/1, M/GI/1, and GI/M/1 queues are considered, and for the non-preemptive LCFS service disciplines, the M/GI/1 and GI/M/1 queues are considered. With these results, we further show comparison results for the mean AoI’s in the M/GI/1 and GI/M/1 queues under those service disciplines.

This paper considers the age of information (AoI) in a stationary information update system with packet deadline and infinite buffer capacity which is modeled as an M/G/1+G queue, where the last symbol represents the probability distribution of deadlines. We first characterize the distribution of the AoI in the general case where service times and deadlines are both generally distributed. We next show that this result is dramatically simplified in the M/M/1+G queue. Finally, we consider the M/M/1+D queue, and provide an explicit formula for the mean AoI.

We consider the stationary distributions of the age of information (AoI) and the peak AoI in information update systems. We first derive an invariant relation among the distributions of the AoI, the peak AoI, and the system delay, which holds for a wide class of information update systems. Based on this result, we next obtain several formulas for the stationary distributions of the AoI and the peak AoI in the first-come first-served (FCFS) GI/GI/1 queue, which is a general model of FCFS information update systems. Finally, we derive explicit formulas for the Laplace-Stieltjes transforms of the stationary distributions of the AoI and the peak AoI in FCFS M/GI/1 and GI/M/1 queues.

A software system for measurement of two-dimensional traffic flow using moving picture processing has been developed. The proposed system is characterized by the functions classifying passing vehicles into one of three classes: motorcycle, small-and large-sized vehicles and detecting their two-dimensional trajectories on a roadway. Experiments to test the system performance have been performed and all of passing vehicles were detected by the system and were classified correctly except for only one large-sized vehicle.