This paper proposes a determination method of the cascaded number for lumped parameter models (LPMs) of the transmission lines. The LPM is used to simulate long-distance transmission lines, and the cascaded number significantly impacts the simulation results. Currently, there is a lack of a system-level determination method of the cascaded number for LPMs. Based on the theoretical analysis and eigenvalue decomposition of network matrix, this paper discusses the error in resonance characteristics between distributed parameter model and LPMs. Moreover, it is deduced that optimal cascaded numbers of the cascaded π-type and T-type LPMs are the same, and the Γ-type LPM has a lowest analog accuracy. The principle that the maximum simulation frequency is less than the first resonance frequency of each segment is presented. According to the principle, optimal cascaded numbers of cascaded π-type, T-type, and Γ-type LPMs are obtained. The effectiveness of the proposed determination method is verified by simulation.

As a hierarchical network framework, clustering aims to divide nodes with similar mobility characteristics into the same cluster to form a more structured hierarchical network, which can effectively solve the problem of high dynamics of the network topology caused by the high-speed movement of nodes in aeronautical ad hoc networks. Based on this goal, we propose a multi-hop distributed clustering algorithm based on link duration. The algorithm is based on the idea of multi-hop clustering, which ensures the coverage and stability of clustering. In the clustering phase, the link duration is used to accurately measure the degree of stability between nodes. At the same time, we also use the link duration threshold to filter out relatively stable links and use the gravity factor to let nodes set conditions for actively creating links based on neighbor distribution. When selecting the cluster head, we select the most stable node as the cluster head node based on the defined node stability weight. The node stability weight comprehensively considers the connectivity degree of nodes and the link duration between nodes. In order to verify the effectiveness of the proposed method, we compare them with the N-hop and K-means algorithms from four indicators: average cluster head duration, average cluster member duration, number of cluster head changes, and average number of intra-cluster link changes. Experiments show that the proposed method can effectively improve the stability of the topology.

The study proposes a personalised session-based recommender system that embeds items by using Word2Vec and sequentially updates the session and user embeddings with the hierarchicalization and aggregation of item embeddings. To process a recommendation request, the system constructs a real-time user embedding that considers users’ general preferences and sequential behaviour to handle short-term changes in user preferences with a low computational cost. The system performance was experimentally evaluated in terms of the accuracy, diversity, and novelty of the ranking of recommended items and the training and prediction times of the system for three different datasets. The results of these evaluations were then compared with those of the five baseline systems. According to the evaluation experiment, the proposed system achieved a relatively high recommendation accuracy compared with baseline systems and the diversity and novelty scores of the proposed system did not fall below 90% for any dataset. Furthermore, the training times of the Word2Vec-based systems, including the proposed system, were shorter than those of FPMC and GRU4Rec. The evaluation results suggest that the proposed recommender system succeeds in keeping the computational cost for training low while maintaining high-level recommendation accuracy, diversity, and novelty.

Information-centric networking (ICN) provides low-latency content delivery with in-network caching, but delivery latency depends on cache distance from consumers. To reduce delivery latency, a scheme to cluster domains and retain the main popular content in each cluster with a cache distribution range has been proposed, which enables consumers to retrieve content from neighboring clusters/caches. However, when the distribution of content popularity changes, all content caches may not be distributed adequately in a cluster, so consumers cannot retrieve them from nearby caches. We therefore propose a dynamic clustering scheme to adjust the cache distribution range in accordance with the change in content popularity and evaluate the effectiveness of the proposed scheme through simulation.

In this paper, we consider distributed decision-making over directed time-varying multi-agent systems. We consider an adversarial bandit problem in which a group of agents chooses an option from among multiple arms to maximize the total reward. In the proposed method, each agent cooperatively searches for the optimal arm with the highest reward by a consensus-based distributed Exp3 policy. To this end, each agent exchanges the estimation of the reward of each arm and the weight for exploitation with the nearby agents on the network. To unify the explored information of arms, each agent mixes the estimation and the weight of the nearby agents with their own values by a consensus dynamics. Then, each agent updates the probability distribution of arms by combining the Hedge algorithm and the uniform search. We show that the sublinearity of a pseudo-regret can be achieved by appropriately setting the parameters of the distributed Exp3 policy.

In this paper, we consider a distributed stochastic nonconvex optimization problem for multiagent systems. We propose a distributed stochastic gradient-tracking method with event-triggered communication. A group of agents cooperatively finds a critical point of the sum of local cost functions, which are smooth but not necessarily convex. We show that the proposed algorithm achieves a sublinear convergence rate by appropriately tuning the step size and the trigger threshold. Moreover, we show that agents can effectively solve a nonconvex optimization problem by the proposed event-triggered algorithm with less communication than by the existing time-triggered gradient-tracking algorithm. We confirm the validity of the proposed method by numerical experiments.

Distributed computing is one of the powerful solutions for computational tasks that need the massive size of dataset. Lagrange coded computing (LCC), proposed by Yu et al. [15], realizes private and secure distributed computing under the existence of stragglers, malicious workers, and colluding workers by using an encoding polynomial. Since the encoding polynomial depends on a dataset, it must be updated every arrival of new dataset. Therefore, it is necessary to employ efficient algorithm to construct the encoding polynomial. In this paper, we propose Newton coded computing (NCC) which is based on Newton interpolation to construct the encoding polynomial. Let K, L, and T be the number of data, the length of each data, and the number of colluding workers, respectively. Then, the computational complexity for construction of an encoding polynomial is improved from O(L(K+T)log 2(K+T)log log (K+T)) for LCC to O(L(K+T)log (K+T)) for the proposed method. Furthermore, by applying the proposed method, the computational complexity for updating the encoding polynomial is improved from O(L(K+T)log 2(K+T)log log (K+T)) for LCC to O(L) for the proposed method.

This paper proposes a network design model, considering data consistency for a delay-sensitive distributed processing system. The data consistency is determined by collating the own state and the states of slave servers. If the state is mismatched with other servers, the rollback process is initiated to modify the state to guarantee data consistency. In the proposed model, the selected servers and the master-slave server pairs are determined to minimize the end-to-end delay and the delay for data consistency. We formulate the proposed model as an integer linear programming problem. We evaluate the delay performance and computation time. We evaluate the proposed model in two network models with two, three, and four slave servers. The proposed model reduces the delay for data consistency by up to 31 percent compared to that of a typical model that collates the status of all servers at one master server. The computation time is a few seconds, which is an acceptable time for network design before service launch. These results indicate that the proposed model is effective for delay-sensitive applications.

Due to the increase in the volume of data and intensified concurrent requests, distributed caching is commonly used to manage high-concurrency requests and alleviate pressure on databases. However, there is limited research on distributed record mapping caching, and traditional caching algorithms have suboptimal resolution performance for mapping records that typically follow a long-tail distribution. To address the aforementioned issue, in this paper, we propose a recommendation-based adaptive auxiliary caching method, AC-REC, which delivers the primary cache record along with a list of additional cache records. The method uses request correlations as a basis for recommendations, customizes the number of additional cache entries provided, and dynamically adjusts the time-to-live. We conducted evaluations to compare the performance of our method against various benchmark strategies. The results show that our proposed method, as compared to the conventional LCE method, increased the cache hit ratio by an average of 20%, Moreover, this improvement is achieved while effectively utilizing the cache space. We believe that our strategy will contribute an effective solution to the related studies in both traditional network architecture and caching in paradigms like ICN.

When mission-critical applications are provided over a network, high availability is required in addition to a low delay. This paper proposes a multi-homing network design model, named MHND, that achieves low delay, high availability, and the order guarantee of events. MHND maintains the event occurrence order with a multi-homing configuration using conservative synchronization. We formulate MHND as an integer linear programming problem to minimize the delay. We prove that the distributed server allocation problem with MHND is NP-complete. Numerical results indicate that, as a multi-homing number, which is the number of servers to which each user belongs, increases, the availability increases while increasing the delay. Noteworthy, two or more multi-homing can achieve approximately an order of magnitude higher availability compared to that of conventional single-homing at the expense of a delay increase up to two times. By using MHND, flexible network design is achieved based on the acceptable delay in service and the required availability.

In this study, an overloaded large-scale distributed antenna network is considered, for which the number of active users is larger than that of antennas distributed in a base station coverage area (called a cell). To avoid overload, users in each cell are divided into multiple user groups, and, to reduce the computational complexity required for multi-user multiple-input and multiple-output (MU-MIMO), users in each user group are grouped into multiple user clusters so that cluster-wise distributed MU-MIMO can be performed in parallel in each user group. However, as the network size increases, conventional computational methods may not be able to solve combinatorial optimization problems, such as user scheduling and user clustering, which are required for performing cluster-wise distributed MU-MIMO in a finite amount of time. In this study, we apply quantum computing to solve the combinatorial optimization problems of user scheduling and clustering for an overloaded distributed antenna network and propose a quantum computing-based user scheduling and clustering method. The results of computer simulations indicate that as the technology of quantum computers and their related algorithms evolves in the future, the proposed method can realize large-scale dense wireless systems and realize real-time optimization with a short optimization execution cycle.

This paper presents a compact fully-differential distributed amplifier using a coupled inductor. Differential distributed amplifiers are widely required in optical communication systems. Most of the distributed amplifiers reported in the past are single-ended or pseudo-differential topologies. In addition, the differential distributed amplifiers require many inductors, which increases the silicon cost. In this study, we use differentially coupled inductors to reduce the chip area to less than half and eliminate the difficulties in layout design. The challenge in using coupled inductors is the capacitive parasitic coupling that degrades the flatness of frequency response. To address this challenge, the odd-mode image parameters of a differential artificial transmission line are derived using a simple loss-less model. Based on the analytical results, we optimize the dimensions of the inductor with the gradient descent algorithm to achieve accurate impedance matching and phase matching. The amplifier was fabricated in 0.18-µm CMOS technology. The core area of the amplifier is 0.27 mm2, which is 57% smaller than the previous work. Besides, we demonstrated a small group delay variation of ±2.7 ps thanks to the optimization. the amplifier successfully performed 30-Gbps NRZ and PAM4 transmissions with superior jitter performance. The proposed technique will promote the high-density integration of differential traveling wave devices.

The maximal independent set (MIS) problem is one of the most fundamental problems in the field of distributed computing. This paper focuses on the MIS problem with unreliable communication between processes in the system. We propose a relaxed notion of MIS, named almost MIS (ALMIS), and show that the loosely-stabilizing algorithm proposed in our previous work can achieve exponentially long holding time with logarithmic convergence time and space complexity regarding ALMIS, which cannot be achieved at the same time regarding MIS in our previous work.

This paper presents a quantized gradient descent algorithm for distributed nonconvex optimization in multiagent systems that takes into account the bandwidth limitation of communication channels. Each agent encodes its estimation variable using a zoom-in parameter and sends the quantized intermediate variable to the neighboring agents. Then, each agent updates the estimation by decoding the received information. In this paper, we show that all agents achieve consensus and their estimated variables converge to a critical point in the optimization problem. A numerical example of a nonconvex logistic regression shows that there is a trade-off between the convergence rate of the estimation and the communication bandwidth.

To cope with ever growing mobile data traffic, we recently proposed a concept of cellular ultra-dense radio access network (RAN). In the cellular ultra-dense RAN, a number of distributed antennas are deployed in the base station (BS) coverage area (cell) and user-clusters are formed to perform small-scale distributed multiuser multi-input multi-output (MU-MIMO) transmission and reception in each user-cluster in parallel using the same frequency resource. We also proposed a decentralized interference coordination (IC) framework to effectively mitigate both intra-cell and inter-cell interferences caused in the cellular ultra-dense RAN. The inter-cell IC adopted in this framework is the fractional frequency reuse (FFR), realized by applying the channel segregation (CS) algorithm, and is called CS-FFR in this paper. CS-FFR divides the available bandwidth into several sub-bands and allocates multiple sub-bands to different cells. In this paper, focusing on the optimization of the CS-FFR, we find by computer simulation the optimum bandwidth division number and the sub-band allocation ratio to maximize the link capacity. We also discuss the convergence speed of CS-FFR in a cellular ultra-dense RAN.

This paper first takes IoT as an example to provide the motivation for eliminating the single point of trust (SPOT) in a CA-based private PKI. It then describes a distributed public key certificate-issuing infrastructure that eliminates the SPOT and its limitation derived from generating signing keys. Finally, it proposes a method to address its limitation by all certificate issuers.

In this paper, user set selection in the allocation sequences of round-robin (RR) scheduling for distributed antenna transmission with block diagonalization (BD) pre-coding is proposed. In prior research, the initial phase selection of user equipment allocation sequences in RR scheduling has been investigated. The performance of the proposed RR scheduling is inferior to that of proportional fair (PF) scheduling under severe intra-cell interference. In this paper, the multi-input multi-output technology with BD pre-coding is applied. Furthermore, the user equipment (UE) sets in the allocation sequences are eliminated with reinforcement learning. After the modification of a RR allocation sequence, no estimated throughput calculation for UE set selection is required. Numerical results obtained through computer simulation show that the maximum selection, one of the criteria for initial phase selection, outperforms the weighted PF scheduling in a restricted realm in terms of the computational complexity, fairness, and throughput.

In a power network, it is important to detect a cyber attack. In this paper, we propose a method for detecting false data injection (FDI) attacks in distributed state estimation. An FDI attack is well known as one of the typical cyber attacks in a power network. As a method of FDI attack detection, we consider calculating the residual (i.e., the difference between the observed and estimated values). In the proposed detection method, the tentative residual (estimated error) in ADMM (Alternating Direction Method of Multipliers), which is one of the powerful methods in distributed optimization, is applied. First, the effect of an FDI attack is analyzed. Next, based on the analysis result, a detection parameter is introduced based on the residual. A detection method using this parameter is then proposed. Finally, the proposed method is demonstrated through a numerical example on the IEEE 14-bus system.

In this letter, we consider the problem of joint selection of transmitters and receivers in a distributed multi-input multi-output radar network for localization. Different from previous works, we consider a more mathematically challenging but generalized situation that the transmitting signals are not perfectly orthogonal. Taking Cramér Rao lower bound as performance metric, we propose a scheme of joint selection of transmitters and receivers (JSTR) aiming at optimizing the localization performance under limited number of nodes. We propose a bi-convex relaxation to replace the resultant NP hard non-convex problem. Using the bi-convexity, the surrogate problem can be efficiently resolved by nonlinear alternating direction method of multipliers. Simulation results reveal that the proposed algorithm has very close performance compared with the computationally intensive but global optimal exhaustive search method.

High gain antennas with narrow-beamforming are required to compensate for the high propagation loss expected in high frequency bands such as the millimeter wave and sub-terahertz wave bands, which are promising for achieving extremely high speeds and capacity. However using narrow-beamforming for initial access (IA) beam search in all directions incurs an excessive overhead. Using wide-beamforming can reduce the overhead for IA but it also shrinks the coverage area due to the lower beamforming gain. Here, it is assumed that there are some situations in which the required coverage distance differs depending on the direction from the antenna. For example, the distance to an floor for a ceiling-mounted antenna varies depending on the direction, and the distance to the obstruction becomes the required coverage distance for an antenna installation design that assumes line-of-sight. In this paper, we propose a novel IA beam search scheme with adaptive beam width control based on the distance to shield obstacles in each direction. Simulations and experiments show that the proposed method reduces the overhead by 20%-50% without shrinking the coverage area in shield environments compared to exhaustive beam search with narrow-beamforming.