Kosuke Sanada

Wireless Brain-Inspired Computing (WiBIC), which implements spiking neural networks (SNNs) in a distributed manner on IoT devices, is gaining attention as a technology enabling serverless, autonomous learning. Since SNN learning relies on precise spike timing information, selecting the wireless communication protocol for transmitting spike signals is critically important. However, protocols like Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA), widely used in IoT, introduce probabilistic delays to avoid collisions, potentially degrading SNN learning performance. This research proposes Asynchronous Pulse Code Multiple Access (APCMA) as a communication method suitable for spike transmission in WiBIC. In actual device experiments, CSMA/CA caused significant delay jitter and packet loss due to communication contention, whereas APCMA maintained nearly constant delay with no observed loss. For XOR learning, the learning success rate using CSMA/CA decreased as communication density increased, while APCMA maintained stable high success rate regardless of communication density.

In in-vehicle wireless communication, each in-vehicle sensor terminal transmits small data in a cycle within which all terminals must transmit data successfully. Clarifying the delay characteristics in such networks is useful for the system design. This paper proposes a maximum delay distribution analysis method for the fixed CW method in CSMA/CA mission-critical periodic communications. The proposed method focuses on the fact that the upper bound of the distribution of the number of collisions and the transition of the number of terminals can be approximated by a binomial distribution. In the first step, the distribution of the total number of slots required for all terminals departure is derived from a two-dimensional Markov-chain model. In the second step, the total slot number distribution is converted into a real-time scale according to the events occurring in a slot, and the upper bound of the maximum delay distribution is derived. This analytical method analytically derives the upper bound of the maximum delay distribution for an arbitrary number of terminals in the network with a fixed CW value while taking interference induced by competition terminals into account. The validity of the proposed method is demonstrated through comparison with computer simulation results.

In OFDM wireless communications, channel estimation performance is compromised in high-speed railway environments owing to extremely fast multipath fading and severe Doppler effect. Recently, a deep learning approach has been employed to improve the channel estimation performance, however it encounters significant challenges due to its high computational complexity. In order to deal with these challenges, this paper proposes channel estimation employing deep learning with one-dimensional convolutional neural network (1D CNN) schemes to enhance conventional least squares (LS) estimation. The first scheme provides better performance compared to conventional LS estimation. However, it is only suitable for OFDM systems with full pilot symbols, leading to decreased transmission efficiency and high complexity. In order to address those problems, the second scheme develops 1D CNN-based channel estimation employing scattered pilot symbols to enhance transmission efficiency and reduce computational complexity. In comparison to conventional LS estimation and deep learning-based channel estimation with bi-gated recurrent unit (bi-GRU), the performance evaluation demonstrates that the proposed 1D CNN-based schemes simultaneously improve channel estimation performance, transmission efficiency, and reduce computational complexity.

Utilizing an analog to digital converter (ADC) with low-resolution is preferred in terms of high sampling rate, low energy consumption and low implementation cost. Against these advantages' utilizing low-resolution ADCs would lead to degrade the signal detection performance. This paper focuses on addressing this issue by introducing a novel receiver that incorporates a new stochastic resonance (SR) processing method, which adaptively adjusts the intentional noise base on the modulation level of the signal, for the detection of multilevel amplitude modulated signals. Through computer simulation, we confirmed the further improvement by applying the proposed adaptive SR. Moreover, we observed that the proposed receiver is more resistant to channel noise.

The fifth-generation (5G) new radio (NR) standard employs ultra-reliable and low-latency communication (URLLC) to provide real-time wireless interactive capability for the internet of things (IoT) applications. To satisfy the stringent latency and reliability demands of URLLC services, grant-free (GF) transmissions with the K-repetition transmission (K-Rep) have been introduced. However, fading fluctuations can negatively impact signal quality at the base station (BS), leading to an increase in the number of repetitions and raising concerns about interference and energy consumption for IoT user equipment (UE). To overcome these challenges, this paper proposes novel adaptive K-Rep control schemes that employ site diversity reception to enhance signal quality and reduce energy consumption. The performance evaluation demonstrates that the proposed adaptive K-Rep control schemes significantly improve communication reliability and reduce transmission energy consumption compared with the conventional K-Rep scheme, and then satisfy the URLLC requirements while reducing energy consumption.

Wireless powered communication network (WPCN) is a novel structure of the network that integrates wireless information communication (WIT) and wireless energy transfer(WET). The scheduling design is a key factor for performance improvement for WPCN. As an effective scheduling design approach, “Harvest-then-Access” has been proposed. This approach requires optimizing control parameters in the scheduling. Therefore, some approaches, such as machine learning or simulation, have been used. This paper proposes a Markov-chain model-based analytical model of Harvest-then-Access scheduling for WPCN. We propose a new Markov-chain model, which includes the essential operation in WPCN with Harvest-then-Access. From the analytical model, we derive the optimal time interval of WET in the network with Harvest-then-Access, which simultaneously provides throughput improvement and fairness among devices. The validity of the proposed analytical model is demonstrated by comparing the analytical and simulation result. Then, we evaluate the performance of Harvest-then-Access scheduling through comparison to the other scheduling.

A wireless powered communication network (WPCN) is a novel network architecture that integrates wireless information transfer (WIT) and wireless energy transfer (WET). To improve the performance of this network, it is necessary to design a scheduling method that provides appropriate opportunities for WIT as well as WET throughout the network. The Harvest-then-access protocol for the WPCN consists of a simple frame structure with a WET harvesting duration preceding the random access duration. This frame structure reduces considerable overhead and complexity and is suitable for WPCN. However, no analytical model of the Harvest-then-Access protocol for WPCN has been proposed so far. In this paper, we extend the analytical model based on the energy queueing model and propose a performance analysis of the Harvest-then-Access protocol for WPCN. By deriving the appropriate frame size from the analytical model, we provide an appropriate opportunity for energy harvesting by WET and information transmission to the entire network, which contributes to throughput improvement. The validity of the proposed model is demonstrated by comparison with analytical and simulation results. The proposed model also clarifies the characteristics of the Harvest-then-Access protocol in terms of throughput and energy efficiency.

In broadband communication systems, an analog-to-digital converter (ADC) with few-bit quantization is a promising component in a RF front-end to ensure higher sampling rate. To mitigate the nonlinear behavior in the few-bit ADC, a stochastic resonance (SR), which improves a symbol error rate (SER) in the presence of the noise, has been focused. The SER degrades in a high signal-to-noise ratio (SNR) region. In this paper, we introduce the addition of the intentional noise in the SR-based receiver. To add the intentional noise appropriately, we propose simple estimation methods for the channel noise-power in the receiver with the few-bit ADC. To obtain the closed-form for a error function, we employ a sigmoid approximation. Numerical examples show that the proposed receiver can estimate the noise-power in a specific range of SNR. Low SER can be achieved for higher SNR by the addition of the intentional noise.

In IoT networks, mitigation of channel access contention is one of the crucial requirements due to a massive number of devices connecting to the shared communication channel. In order to satisfy this requirement, IEEE 802.11ah introduces the restricted access window (RAW) mechanism which divides wireless stations (STAs) into several STA groups and allocates a dedicated RAW slot to each STA group. Since STA grouping plays an important role in this mechanism, several grouping methods have been proposed. However, the IoT networks have various network non-uniformity such as in STA deployment. Although the existing method cannot handle this non-uniform characteristic enough to maintain fairness among STAs. We take up non-uniform STA deployment, heterogeneous traffic patterns and multiple data rates at STAs as the potential network non-uniformity. This paper proposes a sector-based grouping method with adjustable central angles, which can handle all three factors of the network non-uniformity. The performance for the proposed method is evaluated through computer simulation to show that the proposed method can improve the network performance even when STAs with various traffic patterns and data rates are deployed non-uniformly.