This paper proposes overloaded multiple input multiple output (MIMO) bi-directional communication with physical layer network coding (PLNC) to enhance the transmission speed in heterogeneous wireless multihop networks where the number of antennas on the relay is less than that on the terminals. The proposed overloaded MIMO communication system applies precoding and relay filtering to reduce computational complexity in spite of the transmission speed. An eigenvector-based filter is proposed for the relay filter. Furthermore, we propose a technique to select the best filter among candidates eigenvector-based filters. The performance of the proposed overloaded MIMO bi-directional communication is evaluated by computer simulation in a heterogeneous wireless 2-hop network. The proposed filter selection technique attains a gain of about 1.5dB at the BER of 10-5 in a 2-hop network where 2 antennas and 4 antennas are placed on the relay and the terminal, respectively. This paper shows that 6 stream spatial multiplexing is made possible in the system with 2 antennas on the relay.

In this paper, we describe a wavelength-division multiplexing visible-light communication (VLC) system using two colored light-emitting diodes (LEDs) with similar emission wavelengths. A multi-input multi-output signal-separation method using a neural network is proposed to cancel the optical cross chatter caused by the spectral overlap of LEDs. The experimental results demonstrate that signal separation using neural networks can be achieved in wavelength-multiplexed VLC systems with a bit error rate of less than 3.8×10-3 (forward error correction limit). Furthermore, the simulation results reveal that the carrier-to-noise ratio (CNR) is improved by 2dB for the successive interference canceller (SIC) compared to the zero-forcing method.

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.

This paper presents a deep network based on unrolling the diffusion process with the morphological Laplacian. The diffusion process is an iterative algorithm that can solve the diffusion equation and represents time evolution with Laplacian. The diffusion process is applied to smoothing of images and has been extended with non-linear operators for various image processing tasks. In this study, we introduce the morphological Laplacian to the basic diffusion process and unwrap to deep networks. The morphological filters are non-linear operators with parameters that are referred to as structuring elements. The discrete Laplacian can be approximated with the morphological filters without multiplications. Owing to the non-linearity of the morphological filter with trainable structuring elements, the training uses error back propagation and the network of the morphology can be adapted to specific image processing applications. We introduce two extensions of the morphological Laplacian for deep networks. Since the morphological filters are realized with addition, max, and min, the error caused by the limited bit-length is not amplified. Consequently, the morphological parts of the network are implemented in unsigned 8-bit integer with single instruction multiple data set (SIMD) to achieve fast computation on small devices. We applied the proposed network to image completion and Gaussian denoising. The results and computational time are compared with other denoising algorithm and deep networks.

For massive multiple-input multiple-output (MIMO) communication systems, simple linear detectors such as zero forcing (ZF) and minimum mean square error (MMSE) can achieve near-optimal detection performance with reduced computational complexity. However, such linear detectors always involve complicated matrix inversion, which will suffer from high computational overhead in the practical implementation. Due to the massive parallel-processing and efficient hardware-implementation nature, the neural network has become a promising approach to signal processing for the future wireless communications. In this paper, we first propose an efficient neural network to calculate the pseudo-inverses for any type of matrices based on the improved Newton's method, termed as the PINN. Through detailed analysis and derivation, the linear massive MIMO detectors are mapped on PINNs, which can take full advantage of the research achievements of neural networks in both algorithms and hardwares. Furthermore, an improved limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) quasi-Newton method is studied as the learning algorithm of PINNs to achieve a better performance/complexity trade-off. Simulation results finally validate the efficiency of the proposed scheme.

We present our data-collection and deep neural network (DNN)-training scheme for extracting the optical status from signals received by digital coherent optical receivers in fiber-optic networks. The DNN is trained with unlabeled datasets across multiple administrative network domains by combining federated learning and unsupervised learning. The scheme allows network administrators to train a common DNN-based encoder that extracts optical status in their networks without revealing their private datasets. An early-stage proof of concept was numerically demonstrated by simulation by estimating the optical signal-to-noise ratio and modulation format with 64-GBd 16QAM and quadrature phase-shift keying signals.

In this paper, a wireless communication network that uses unmanned aerial vehicles (UAVs) in the sky to transmit information between ground users is considered. We highlight a delay-tolerant network, where information is relayed in a store-and-forward fashion by establishing two types of intermittent communication links: between a UAV and a user (UAV-to-user) and between UAVs (UAV-to-UAV). Thus, a flight algorithm that controls the movement of the UAVs is crucial in achieving rapid information transmission. Our study proposes new flight algorithms that simultaneously consider the two types of communication links. In UAV-to-UAV links, the direct information transmission between two UAVs and the indirect transmission through other UAVs are considered separately. The movement of the UAVs is controlled by solving an optimization problem at certain time intervals, with a variable consideration ratio of the two types of links. In addition, we investigate not only the case where all UAVs move cooperatively but also the case where each UAV moves autonomously. Simulation results show that the proposed algorithms are effective. Moreover, they indicate the existence of an optimal consideration ratio of the two types of communication and demonstrate that our approach enables the control of frequencies of establishing the communication links. We conclude that increasing the frequency of indirect communication between UAVs improves network performance.

Existing simple routing protocols (e.g., OSPF, RIP) have some disadvantages of being inflexible and prone to congestion due to the concentration of packets on particular routers. To address these issues, packet routing methods using machine learning have been proposed recently. Compared to these algorithms, machine learning based methods can choose a routing path intelligently by learning efficient routes. However, machine learning based methods have a disadvantage of training time overhead. We thus focus on a lightweight machine learning algorithm, OS-ELM (Online Sequential Extreme Learning Machine), to reduce the training time. Although previous work on reinforcement learning using OS-ELM exists, it has a problem of low learning accuracy. In this paper, we propose OS-ELM QN (Q-Network) with a prioritized experience replay buffer to improve the learning performance. It is compared to a deep reinforcement learning based packet routing method using a network simulator. Experimental results show that introducing the experience replay buffer improves the learning performance. OS-ELM QN achieves a 2.33 times speedup than a DQN (Deep Q-Network) in terms of learning speed. Regarding the packet transfer latency, OS-ELM QN is comparable or slightly inferior to the DQN while they are better than OSPF in most cases since they can distribute congestions.

This brief presents a side-channel attack (SCA) technique on a high-speed asynchronous successive approximation register (SAR) analog-to-digital converter (ADC). The proposed dual neural network based on multiple noise waveforms separately discloses sign and absolute value information of input signals which are hidden by the differential structure and high-speed asynchronous operation. The target SAR ADC and on-chip noise monitors are designed on a single prototype chip for SCA demonstration. Fabricated in 40 nm, the experimental results show the proposed attack on the asynchronous SAR ADC successfully restores the input data with a competitive accuracy within 300 mV rms error.

The complexity and scale of Networks-on-Chip (NoCs) are growing as more processing elements and memory devices are implemented on chips. However, under strict power budgets, it is also critical to lower the power consumption of NoCs for the sake of energy efficiency. In this paper, we therefore present three novel input unit designs for on-chip routers attempting to shrink their power consumption while still conserving the network performance. The key idea behind our designs is to organize buffers in the input units with characteristics of the network traffic in mind; as in our observations, only a small portion of the network traffic are long packets (composed of multiple flits), which means, it is fair to implement hybrid, asymmetric and reconfigurable buffers so that they are mainly targeting at short packets (only having a single flit), hence the smaller power consumption and area overhead. Evaluations show that our hybrid, asymmetric and reconfigurable input unit designs can achieve an average reduction of energy consumption per flit by 45%, 52.3% and 56.2% under 93.6% (for hybrid designs) and 66.3% (for asymmetric and reconfigurable designs) of the original router area, respectively. Meanwhile, we only observe minor degradation in network latency (ranging from 18.4% to 1.5%, on average) with our proposals.

In the current heterogeneous wireless communication system, the sharp rise in energy consumption and the emergence of new service types pose great challenges to nowadays radio access network selection algorithms which do not take care of these new trends. So the proposed energy efficiency based multi-service heterogeneous access network selection algorithm-ESRS (Energy Saving Radio access network Selection) is intended to reduce the energy consumption caused by the traffic in the mobile network system composed of Base Stations (BSs) and Access Points (APs). This algorithm models the access network selection problem as a Multiple-Attribute Decision-Making (MADM) problem. To solve this problem, lots of methods are combined, including analytic Hierarchy Process (AHP), weighted grey relational analysis (GRA), entropy theory, simple additive weight (SAW), and utility function theory. There are two main steps in this algorithm. At first, the proposed algorithm gets the result of the user QoS of each network by dealing with the related QoS parameters, in which entropy theory and AHP are used to determine the QoS comprehensive weight, and the SAW is used to get each network's QoS. In addition to user QoS, parameters including user throughput, energy consumption utility and cost utility are also calculated in this step. In the second step, the fuzzy theory is used to define the weight of decision attributes, and weighted grey relational analysis (GRA) is used to calculate the network score, which determines the final choice. Because the fuzzy weight has a preference for the low energy consumption, the energy consumption of the traffic will be saved by choosing the network with the least energy consumption as much as possible. The simulation parts compared the performance of ESRS, ABE and MSNS algorithms. The numerical results show that ESRS algorithm can select the appropriate network based on the service demands and network parameters. Besides, it can effectively reduce the system energy consumption and overall cost while still maintaining a high overall QoS value and a high system throughput, when compared with the other two algorithms.

Mission-critical monitoring services, such as finding criminals with a monitoring camera, require rapid detection of newly updated data, where suppressing delay is desirable. Taking this direction, this paper proposes a network design scheme to minimize this delay for monitoring services that consist of Internet-of-Things (IoT) devices located at terminal endpoints (TEs), databases (DB), and applications (APLs). The proposed scheme determines the allocation of DB and APLs and the selection of the server to which TE belongs. DB and APL are allocated on an optimal server from multiple servers in the network. We formulate the proposed network design scheme as an integer linear programming problem. The delay reduction effect of the proposed scheme is evaluated under two network topologies and a monitoring camera system network. In the two network topologies, the delays of the proposed scheme are 78 and 80 percent, compared to that of the conventional scheme. In the monitoring camera system network, the delay of the proposed scheme is 77 percent compared to that of the conventional scheme. These results indicate that the proposed scheme reduces the delay compared to the conventional scheme where APLs are located near TEs. The computation time of the proposed scheme is acceptable for the design phase before the service is launched. The proposed scheme can contribute to a network design that detects newly added objects quickly in the monitoring services.

This paper proposes a virtual network function (VNF) placement model considering both availability and probabilistic protection for the service delay to minimize the service deployment cost. Both availability and service delay are key requirements of services; a service provider handles the VNF placement problem with the goal of minimizing the service deployment cost while meeting these and other requirements. The previous works do not consider the delay of each route which the service can take when considering both availability and delay in the VNF placement problem; only the maximum delay was considered. We introduce probabilistic protection for service delay to minimize the service deployment cost with availability. The proposed model considers that the probability that the service delay, which consists of networking delay between hosts and processing delay in each VNF, exceeds its threshold is constrained within a given value; it also considers that the availability is constrained within a given value. We develop a two-stage heuristic algorithm to solve the VNF placement problem; it decides primary VNF placement by solving mixed-integer second-order cone programming in the first stage and backup VNF placement in the second stage. We observe that the proposed model reduces the service deployment cost compared to a baseline that considers the maximum delay by up to 12%, and that it obtains a feasible solution while the baseline does not in some examined situations.

Personalized stock recommendations aim to suggest stocks tailored to individual investor needs, significantly aiding the financial decision making of an investor. This study shows the advantages of incorporating context into personalized stock recommendation systems. We embed item contextual information such as technical indicators, fundamental factors, and business activities of individual stocks. Simultaneously, we consider user contextual information such as investors' personality traits, behavioral characteristics, and attributes to create a comprehensive investor profile. Our model incorporating contextual information, validated on novel stock recommendation tasks, demonstrated a notable improvement over baseline models when incorporating these contextual features. Consistent outperformance across various hyperparameters further underscores the robustness and utility of our model in integrating stocks' features and investors' traits into personalized stock recommendations.

Video-based point cloud compression (V-PCC) utilizes video compression technology to efficiently encode dense point clouds providing state-of-the-art compression performance with a relatively small computation burden. V-PCC converts 3-dimensional point cloud data into three types of 2-dimensional frames, i.e., occupancy, geometry, and attribute frames, and encodes them via video compression. On the other hand, the quality of these frames may be degraded due to video compression. This paper proposes an adaptive neural network-based post-processing filter on attribute frames to alleviate the degradation problem. Furthermore, a novel training method using occupancy frames is studied. The experimental results show average BD-rate gains of 3.0%, 29.3% and 22.2% for Y, U and V respectively.

In this article, a new Beamforming Network (BFN) realized in Broadside Coupled Stripline (BCS) is proposed to feed 1×4 and 2×2 arrays antenna at 28 GHZ-Band. The new BFN is composed only of couplers and phase shifters. It doesn't require any crossover compared to the conventional Butler Matrix (BM) which requires two crossovers. The tight coupling and low loss characteristics of the BCS allow a design of a compact and wideband BFN. The new BFN produces the phase differences of (±90°) and (±45°, ±135°) respectively in x- and y-directions. Its integration with a 1×4 linear array antenna reduces the array area by 70% with an improvement of the gain performance compared with the conventional array. The integration with a 2×2 array allows the realization of a full 2-D beam scanning. The proposed concept has been verified experimentally by measuring the fabricated prototypes of the BFN, the 1-D and 2-D patch arrays antennas. The measured 11.5 dBi and 11.3 dBi maximum gains are realized in θ0 = 14° and (θ0, φ0) = (45°,345°) directions respectively for the 1-D and 2-D patch arrays. The physical area of the fabricated BFN is only (0.37λ0×0.3λ0×0.08λ0), while the 1-D array and 2-D array antennas areas without feeding transmission lines are respectively (0.5λ0×2.15λ0×0.08λ0) and (0.9λ0×0.8λ0×0.08λ0).

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.

With a rapidly escalating number of sophisticated cyber-attacks, protecting Internet of Things (IoT) networks against unauthorized activity is a major concern. The detection of malicious attack traffic is thus crucial for IoT security to prevent unwanted traffic. However, existing traditional malicious traffic detection systems which relied on supervised machine learning approach need a considerable number of benign and malware traffic samples to train the machine learning models. Moreover, in the cases of zero-day attacks, only a few labeled traffic samples are accessible for analysis. To deal with this, we propose a few-shot malicious IoT traffic detection system with a prototypical graph neural network. The proposed approach does not require prior knowledge of network payload binaries or network traffic signatures. The model is trained on labeled traffic data and tested to evaluate its ability to detect new types of attacks when only a few labeled traffic samples are available. The proposed detection system first categorizes the network traffic as a bidirectional flow and visualizes the binary traffic flow as a color image. A neural network is then applied to the visualized traffic to extract important features. After that, using the proposed few-shot graph neural network approach, the model is trained on different few-shot tasks to generalize it to new unseen attacks. The proposed model is evaluated on a network traffic dataset consisting of benign traffic and traffic corresponding to six types of attacks. The results revealed that our proposed model achieved an F1 score of 0.91 and 0.94 in 5-shot and 10-shot classification, respectively, and outperformed the baseline models.

A fault-tolerant aggregate signature (FT-AS) scheme is a variant of an aggregate signature scheme with the additional functionality to trace signers that create invalid signatures in case an aggregate signature is invalid. Several FT-AS schemes have been proposed so far, and some of them trace such rogue signers in multi-rounds, i.e., the setting where the signers repeatedly send their individual signatures. However, it has been overlooked that there exists a potential attack on the efficiency of bandwidth consumption in a multi-round FT-AS scheme. Since one of the merits of aggregate signature schemes is the efficiency of bandwidth consumption, such an attack might be critical for multi-round FT-AS schemes. In this paper, we propose a new multi-round FT-AS scheme that is tolerant of such an attack. We implement our scheme and experimentally show that it is more efficient than the existing multi-round FT-AS scheme if rogue signers randomly create invalid signatures with low probability, which for example captures spontaneous failures of devices in IoT systems.

Considering the growth of the IoT network, there is a demand for a decentralized solution. Incorporating the blockchain technology will eliminate the challenges faced in centralized solutions, such as i) high infrastructure, ii) maintenance cost, iii) lack of transparency, iv) privacy, and v) data tampering. Blockchain-based IoT network allows businesses to access and share the IoT data within their organization without a central authority. Data in the blockchain are stored as blocks, which should be validated and added to the chain, for this consensus mechanism plays a significant role. However, existing methods are not designed for IoT applications and lack features like i) decentralization, ii) scalability, iii) throughput, iv) faster convergence, and v) network overhead. Moreover, current blockchain frameworks failed to support resource-constrained IoT applications. In this paper, we proposed a new consensus method (WoG) and a lightweight blockchain framework (iLEDGER), mainly for resource-constrained IoT applications in a permissioned environment. The proposed work is tested in an application that tracks the assets using IoT devices (Raspberry Pi 4 and RFID). Furthermore, the proposed consensus method is analyzed against benign failures, and performance parameters such as CPU usage, memory usage, throughput, transaction execution time, and block generation time are compared with state-of-the-art methods.