Handover is an important property of cellular communication that enables the user to move from one cell to another without losing the connection. It is a very crucial process for the quality of the user’s experience because it may interrupt data transmission. Therefore, good handover management is very important in the current and future cellular systems. Several techniques have been employed to improve the handover performance, usually to increase the probability of a successful handover. One of the techniques is predictive handover which predicts the target cell using some methods other than the traditional measurement-based algorithm, including using machine learning. Several studies have been conducted in the implementation of predictive handover, most of them by modifying the internal algorithm of existing network elements, such as the base station. We implemented a predictive handover algorithm using an intelligent node outside the existing network elements to minimize the modification of the network and to create modularity in the system. Using a recently standardized Open Radio Access Network (O-RAN) Near Realtime Radio Intelligent Controller (Near-RT RIC), we created a modular application that can improve the handover performance by determining the target cell using machine learning techniques. In our previous research, we modified The Near-RT RIC original software that is using vector autoregression to determine the target cell by predicting the throughput of each neighboring cell. We also modified the method using a Multi-Layer Perceptron (MLP) neural network. In this paper, we redesigned the neural network using Long Short-Term Memory (LSTM) that can better handle time series data. We proved that our proposed LSTM-based machine learning algorithms used in Near-RT RIC can improve the handover performance compared to the traditional measurement-based algorithm.

Annealing computation has recently attracted attention as it can efficiently solve combinatorial optimization problems using an Ising spin-glass model. Stochastic cellular automata annealing (SCA) is a promising algorithm that can realize fast spin-update by utilizing its parallel computing capability. However, in SCA, pinning effect control to suppress the spin-flip probability is essential, making escaping from local minima more difficult than serial spin-update algorithms, depending on the problem. This paper proposes a novel approach called APC-SCA (Autonomous Pinning effect Control SCA), where the pinning effect can be controlled autonomously by focusing on individual spin-flip. The evaluation results using max-cut, N-queen, and traveling salesman problems demonstrate that APC-SCA can obtain better solutions than the original SCA that uses pinning effect control pre-optimized by a grid search. Especially in solving traveling salesman problems, we confirm that the tour distance obtained by APC-SCA is up to 56.3% closer to the best-known compared to the conventional approach.

It is now becoming important for mobile cellular networks to accommodate all kinds of Internet of Things (IoT) communications. However, the contention-based random access and radio resource allocation used in traditional cellular networks, which are optimized mainly for human communications, cannot efficiently handle large-scale IoT communications. For this reason, standardization activities have emerged to serve IoT devices such as Cellular-IoT (C-IoT). However, few studies have been directed at evaluating the performance of C-IoT communications with periodic data transmissions, despite this being a common characteristic of many IoT communications. In this paper, we give the performance analysis results of mobile cellular networks supporting periodic C-IoT communications, focusing on the performance differences between LTE and Narrowband-IoT (NB-IoT) networks. To achieve this, we first construct an analysis model for end-to-end performance of both the control plane and data plane, including random access procedures, radio resource allocation, establishing bearers in the Evolved Packet Core network, and user-data transmissions. In addition, we include the impact of the immediate release of the radio resources proposed in 3GPP. Numerical evaluations show that NB-IoT can support more IoT devices than LTE, up to 8.7 times more, but imposes a significant delay in data transmissions. We also confirm that the immediate release of radio resources increases the network capacity by up to 17.7 times.

Reservoir computing (RC) is an attractive alternative to machine learning models owing to its computationally inexpensive training process and simplicity. In this work, we propose EnsembleBloomCA, which utilizes cellular automata (CA) and an ensemble Bloom filter to organize an RC system. In contrast to most existing RC systems, EnsembleBloomCA eliminates all floating-point calculation and integer multiplication. EnsembleBloomCA adopts CA as the reservoir in the RC system because it can be implemented using only binary operations and is thus energy efficient. The rich pattern dynamics created by CA can map the original input into a high-dimensional space and provide more features for the classifier. Utilizing an ensemble Bloom filter as the classifier, the features provided by the reservoir can be effectively memorized. Our experiment revealed that applying the ensemble mechanism to the Bloom filter resulted in a significant reduction in memory cost during the inference phase. In comparison with Bloom WiSARD, one of the state-of-the-art reference work, the EnsembleBloomCA model achieves a 43× reduction in memory cost while maintaining the same accuracy. Our hardware implementation also demonstrated that EnsembleBloomCA achieved over 23× and 8.5× reductions in area and power, respectively.

In-band full-duplex (IBFD) has been an attractive technology, which can theoretically double the spectral efficiency. However, when performing IBFD in the dynamic-duplex cellular (DDC) system, inter-user interference (IUI) deteriorates transmission performance in downlink (DL) communication and limits IBFD-applicable area and IBFD application ratio. In this paper, to expand the IBFD-applicable area and improve the IBFD application ratio, we propose an IUI reduction scheme using successive interference cancellation (SIC) for the DDC system. SIC can utilize the power difference and reduce the signal with the higher power. The effectiveness of the proposed scheme is evaluated by the computer simulation. The IUI reducing effect on the IBFD-inapplicable area is confirmed when the received power of the IUI is stronger than that of the desired signal at the user equipment for DL (DL-UE). The IBFD-inapplicable area within 95m from the DL-UE, where the IBFD does not work without the proposed scheme, can reduce by 43.6% from 52.8% to 9.2% by applying the proposed scheme. Moreover, the IBFD application ratio can improve by 24.6% from 69.5% to 94.1%.

Heterogeneous networks (HetNets) are emerging as an inevitable method to tackle the capacity crunch of the cellular networks. Due to the complicated network environment and a large number of configured parameters, coverage and capacity optimization (CCO) is a challenging issue in heterogeneous cellular networks. By combining the self-optimizing algorithm for radio frequency (RF) parameters with the power control mechanism of small cells, the CCO problem of self-organizing network is addressed in this paper. First, the optimization of RF parameters is solved based on reinforcement learning (RL), where the base station is modeled as an agent that can learn effective strategies to control the tunable parameters by interacting with the surrounding environment. Second, the small cell can autonomously change the state of wireless transmission by comparing its distance from the user equipment with the virtual cell size. Simulation results show that the proposed algorithm can achieve better performance on user throughput compared to different conventional methods.

This paper presents linearization technologies for high efficiency power amplifiers of cellular base stations. These technologies are important to actualizing highly efficient power amplifiers that reduce power consumption of the base station equipment and to achieving a sufficient non-linear distortion compensation level. It is well known that it is very difficult for a power amplifier using linearization technologies to achieve simultaneously high efficiency and a sufficient non-linear distortion compensation level. This paper presents two approaches toward addressing this technical issue. The first approach is a feed-forward power amplifier using the Doherty amplifier as the main amplifier. The second approach is a digital predistortion linearizer that compensates for frequency dependent intermodulation distortion components. Experimental results validate these approaches as effective for providing power amplification for base stations.

In the 5th generation mobile communication system (5G), super high frequency (SHF) bands such as 28GHz will be used in many scenarios. In Japan, a local 5G working group has been established to apply advanced 5G technologies to private networks and is working to encourage local companies and municipalities to introduce new services for local needs. Meanwhile, the smaller size of the 28GHz band cells creates the difficulties when establishing deployment areas for homogeneous networks. In general, heterogeneous network approach with the combination of macro-cell and micro-cell have been considered practical and applied by the giant telecommunication operators. However, private network operators have difficulty in deploying both micro- and macro-cells due to the cost issue. Without the assistance of macro-cells, local spot cells with a small service area may not be able to start services while high-speed mobile users are staying in the service area. In this paper, we propose a virtual pre-connection scheme allowing fast connection to local spot cells without the assistance of macro-cells. In addition, we confirm that the proposed scheme can reduce the cell search time required when entering a local spot cell from 100 seconds or more to less than 1 second, and can reduce the loss of connection opportunities to local spot cells for high-speed mobile users.

Two-state number-conserving cellular automaton (NCCA) is a cellular automaton of which cell states are 0 or 1, and the total sum of all the states of cells is kept for any time step. It is a kind of particle-based modeling of physical systems. We introduce a new structure of its value-1 patterns, which we call a “bundle pair” and a “bundle quad”. By employing this structure, we show a relation between the neighborhood size n and n - 2 NCCAs.

This letter proposes a downlink multiple-input multiple-output (MIMO) non-orthogonal multiple access technique that mitigates multi-cell interference (MCI) at cell-edge users, regardless of the number of interfering cells, thereby improving the spectral efficiency. This technique employs specific receive beamforming vectors at the cell-edge users in clusters to minimize the MCI. Based on the receive beamforming vectors adopted by the cell-edge users, the transmit beamforming vectors for a base station (BS) and the receive beamforming vectors for cell-center users are designed to eliminate the inter-cluster interference and maximize the spectral efficiency. As each user can directly obtain its own receive beamforming vector, this technique does not require channel feedback from the users to a BS to design the receive beamforming vectors, thereby reducing the system overhead. We also derive the upper bound of the average sum rate achievable using the proposed technique. Finally, we demonstrate through simulations that the proposed technique achieves a better sum rate performance than the existing schemes and that the derived upper bound is valid.

Information and communication technologies have improved the quality of intelligent transportation systems (ITS). By estimating from cellular floating vehicle data (CFVD) is more cost-effective, and easier to acquire than traditional ways. This study proposes a cell probe (CP)-based method to analyse the cellular network signals (e.g., call arrival, handoff, and location update), and regression models are trained for vehicle speed estimation. In experiments, this study compares the practical traffic information of vehicle detector (VD) with the estimated traffic information by the proposed methods. The experiment results show that the accuracy of vehicle speed estimation by CP-based method is 97.63%. Therefore, the CP-based method can be used to estimate vehicle speed from CFVD for ITS.

In this paper, we explore how to enhance the physical layer security performance in downlink cellular networks through cooperative jamming technology. Idle user equipments (UE) are used to cooperatively transmit jamming signal to confuse eavesdroppers (Eve). We propose a threshold-based jammer selection scheme to decide which idle UE should participate in the transmission of jamming signal. Threshold conditions are carefully designed to decrease interference to legitimate channel, while maintain the interference to the Eves. Moreover, fewer UE are activated, which is helpful for saving energy consumptions of cooperative UEs. Analytical expressions of the connection and secrecy performances are derived, which are validated through Monte Carlo simulations. Theoretical and simulation results reveal that our proposed scheme can improve connection performance, while approaches the secrecy performance of [12]. Furthermore, only 43% idle UEs of [12] are used for cooperative jamming, which helps to decrease energy consumption of network.

This letter presents a non-orthogonal multiple access (NOMA) technique for a two-cell multiple-input multiple-output (MIMO) system that exploits the alignments of inter-cell interference channels and signal channels within a cluster in a cell. The proposed technique finds combiner vectors for users that align the inter-cell interference channels and the signal channels simultaneously. This technique utilizes the aligned interference and signal channels to obtain precoder matrices for base stations through which each data stream modulated by NOMA can be transmitted to the intended cluster without interference. In addition, we derive the sufficient condition for transmit and receive antenna configurations in the MIMO NOMA systems to eliminate inter-cell interference and inter-cluster interference simultaneously. Because the proposed technique effectively suppresses the inter-cell interference, it achieves a higher degree of freedom than the existing techniques relying on an avoidance of inter-cell interference, thereby obtaining a better sum rate performance in high SNR regions. Furthermore, we present the hybrid MIMO NOMA technique, which combines the MIMO NOMA technique exploiting channel alignment with the existing techniques boosting the received signal powers. Using the benefits from these techniques, the proposed hybrid technique achieves a good performance within all SNR regions. The simulation results successfully demonstrate the effectiveness of the proposed techniques on the sum rate performance.

We studied the detection of the incongruence between the two eyes' retinal images from occlusion perception. We previously analyzed the evasion action caused by occlusion by using green-red equiluminance, which is processed by parvocellular cells. Here we analyzed this action by using yellow-blue equiluminance, which is said to be treated by koniocellular cells and parvocellular cells. We observed that there were the cases in which the subject could perceive incongruence by the occlusion and other cases in which the subject could not perceive it. Significant differences were not seen in all conditions. Because a difference was seen in an evasion action at the time of the rim occlusion gaze when we compare the result for the yellow-blue equiluminance with the green-red equiluminance, it is suggested that the response for each equiluminance is different. We were able to clarify the characteristic difference between parvocellular cells and koniocellular cells from an occlusion experiment.

This paper investigates data uploading in cellular networks with the consideration of device-to-device (D2D) communications. A generalized data uploading scheme is proposed by leveraging D2D cooperation among the devices to reduce the data uploading time. In this scheme, we extend the conventional schemes on cooperative D2D data uploading for cellular networks to a more general case, which considers D2D cooperation among both the devices with or without uploading data. To motivate D2D cooperation among all available devices, we organize the devices within communication range by offering them rewards to construct multi-hop D2D chains for data uploading. Specifically, we formulate the problem of chain formation among the devices for data uploading as a coalitional game. Based on merge-and-split rules, we develop a coalition formation algorithm to obtain the solution for the formulated coalitional game with convergence on a stable coalitional structure. Finally, extensive numerical results show the effectiveness of our proposed scheme in reducing the average data uploading time.

We consider a device-to-device (D2D) underlaid cellular network where D2D communications are allowed to share the same radio spectrum with cellular uplink communications for improving spectral efficiency. However, to protect the cellular uplink communications, the interference level received at a base station (BS) from the D2D communications needs to be carefully maintained below a certain threshold, and thus the BS coordinates the transmit power of the D2D links. In this paper, we investigate on-off power control for the D2D links, which is known as a simple but effective technique due to its low signaling overhead. We first investigate the optimal on-off power control algorithm to maximize the sum-rate of the D2D links, while satisfying the interference constraint imposed by the BS. The computational complexity of the optimal algorithm drastically increases with D2D link number. Thus, we also propose an on-off power control algorithm to significantly reduce the computational complexity, compared to the optimal on-off power control algorithm. Extensive simulations validate that the proposed algorithm significantly reduces the computational complexity with a marginal sum-rate offset from the optimal algorithm.

Stereoscopic vision technology is applied in a wide range of fields, from 3D movies to medical care. Stereoscopic vision makes it possible to observe images in parallax between both eyes. However, parallax images cannot be used all the time due to a situation called “occlusion”, in which an object is hidden in the depths by another object. In this case, different images are projected on the right and left retina. Here, we propose a psychology experiment to elucidate the function of parvocellular cells in the LGN of the visual cortex of the brain using occlusion perception. As a new psychology experiment to clarify whether parvocellular cells in the LGN of the visual cortex, said to process chromatic and luminance information, can detect a disagreement between the retinal images produced by each eye, we measured convergence eye movement when gazing at the rim of a column under occlusion using an equiluminance random dot pattern. When eye movement prevented the disagreement of the retinal images caused by occlusion, we thought that convergence eye movement to move both eyes in front of the rim or divergence eye movement to move them behind the rim would occur. In other words, we thought that we could clarify whether there was parvocellular system process agreement or disagreement between the right and left retinal images under equiluminance. Therefore, we examined whether a system to detect disagreement between the retinal images exists in the brain when gazing at the rim of a column onto which an equiluminance random dot texture was mapped. Results suggested that the mechanism to avoid disagreement between the retinal images of the eyes caused by occlusion occurs in the parvocellular cells, which mainly process color information, as well as in the magnocellular cells, which process binocular disparity.

An efficient handover measurement technique is proposed for millimeter-wave (mm-wave) cellular systems with directional antenna beams. As the beam synchronization signal (BSS) carries the cell ID and the beam ID in a hierarchal manner, handover events (interbeam handover and intercell handover) are distinguished at the physical layer. The proposed signal metrics are shown to be effective in detecting the beam boundaries and cell boundaries in mm-wave cellular systems, which allows to distinguish interbeam handover from intercell handover. The proposed handover measurement technique is shown to reduce the processing time significantly using the proposed signal metrics produced by the BSS.

In cellular network environments, where users are not evenly distributed across cells, overloaded base stations handling many users have difficulties in providing effective and fair services with their limited resources. Additionally, users at the cell edge may suffer from the potential problems resulting from low signal-to-interference ratio owing to the incessant interference from adjacent cells. In this paper, we propose a relay-assisted load balancing scheme to resolve these traffic imbalance. The proposed scheme can improve the performance of the overall network by utilizing relay stations to divert heavy traffic to other cells, and by adopting a partial frequency-reuse scheme to mitigate inter-cell interference. Each user and relay station calculates its own utility influence in the neighboring candidates for reassociation and decides whether to stay or move to another cell presenting the maximum total network utility increment. Simulation results show that the proposed scheme improves the overall network fairness to users by improving the performance of cell boundary users without degrading the total network throughput. We achieve a system performance gain of 16 ∼ 35% when compared with conventional schemes, while ensuring fairness among users.

Internet of Things (IoT) devices deployed in urban areas are seen as data sources for urban sensing IoT applications. Since installing cellular interfaces on a huge number of IoT devices is expensive, we propose to use a user equipment (UE) device with a local wireless interface as a mobile IoT gateway for fixed IoT devices. In this paper, we design a new mobile architecture based on cellular networks to accommodate non-cellular fixed IoT devices by UE devices working as IoT gateways. One key feature is that our architecture leverages proximity services (ProSe) to discover relay UE devices with low overhead in terms of discovery messages. Through simulation studies, we clarify the feasibility of our architecture including the relay UE discovery mechanism in urban areas.