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Millimeter wave (mmWave) multi-path channel models based on a Saleh-Valenzuela (SV) model are fundamental for simple link-level simulations in mmWave communication systems. However, parameter extractions of the model incur developer-dependent and sometimes manual procedures, which hinders from not only confirming the validity of the existing parameters but also correcting cumulative knowledge about parameters for versatile environments. This study develops an end-to-end software to automatically perform channel modeling and simulation with statistical validity just by inputting measured power profiles. The developed software involves comprehensive algorithms to reach channel parameter extraction, such as multi-path component (MPC) extraction, MPC clustering, and maximum-likelihood estimations for respective parameters; thereby serving as a tutorial to reach the objective. Based on the developed software, we highlight that channel parameters reported by the IEEE 802.15.3c task group (TG) do not necessarily fit the original power profiles, highlighting the need to examine the validity of the existing parameters by leveraging the developed software. Moreover, we propose improved parameters for residential environments, which are reported in the TG, and demonstrate the feasibility of the developed software generating more resemble channel characteristics to the measured channel response relative to the reported parameters by the IEEE 802.15.3c TG.

This study proposes a 3GPP-compatible channel generation framework for ultra-wideband and indoor short-range communication realized by the fifth-generation (5G) new radio operating in the frequency range (FR) of 52.6–71.0 GHz. The addition of this band, coined “FR2–2,” to the 5G specification allows foreseeing the use cases of shorter-range communications with ultra-wide bandwidth. However, the current focus of the 3GPP channel model on longer-range communication hinders link-level simulations of this use case. To fill this void, we develop a channel generation framework compatible with the 3GPP channel model and applicable to the FR2-2 indoor short-range use case based on real channel measurements. We highlight that even with appropriate parameters for this use case, the 3GPP channel model does not necessarily reproduce similar characteristics of the measured channel impulse response (CIR) owing to the lack of uniformity of cluster angle of arrival (AoA) and power dispersion in intra-cluster subpaths. Based on this finding, an amendment is proposed, where cluster AoA is uniformly distributed, and the intra-cluster power dispersion term is introduced. We demonstrate the feasibility of the proposed framework for reproducing the real CIR characteristic by showcasing an agreement of root-mean-square delay and angular spreads with the measured CIR.

In this study, we experimentally validated the possibility of estimating the angle of departure (AoD) using multiple signal classification (MUSIC) with only WiFi control frames for beamforming feedback (BFF), defined in IEEE 802.11ac/ax. The examined BFF-based MUSIC is a model-driven algorithm that does not require a pre-obtained database. This is in contrast with most existing BFF-based sensing techniques, which are data-driven and require a pre-obtained database. Moreover, BFF-based MUSIC affords an alternative AoD estimation method without requiring access to the channel state information (CSI). Extensive experimental and numerical evaluations demonstrate that BFF-based MUSIC can successfully estimate the AoDs for multiple propagation paths. Moreover, the evaluations performed in this study reveal that BFF-based MUSIC, where BFF is a highly compressed version of CSI in IEEE 802.11ac/ax, achieves an error of AoD estimation that is comparable to that of CSI-based MUSIC.

As a step towards establishing reliable broadcast wireless local area networks (WLANs), this paper proposes acknowledgement (ACK)-less rate adaptation to alleviate reception failures at broadcast recipient stations (STAs) using distributional reinforcement learning (RL). The key point of this study is that the algorithms for learning the strategy of ACK-less rate adaptation are evaluated in terms of the broadcast performance, which is composed of the data rate of the broadcast access point (AP) and the reception success rate at the recipient STAs. ACK-less rate adaptation framework was realized using the received signal strength (RSS) of the uplink frames transmitted by the non-broadcast STAs to the non-broadcast APs, which correlated with the broadcast performance with a confounding effect from the deployment of the broadcast recipient STAs. However, this rate adaptation framework has the problem that it incurs the reception failures at a part of the broadcast recipient STAs, because deep Q-learning used in the previous framework cannot deal with the wide distribution of the broadcast performance. To address this challenge, this paper further discusses the rate adaptation using distributional RL, which approximates the entire distribution of the broadcast performance. The simulations confirmed the following: 1) Using the expected broadcast performance learned by deep Q-learning improved the performance in terms of the Pareto efficiency. 2) Learning the entire distribution of the broadcast performance enabled the broadcast AP to determine the tail of the distribution using risk measure, and to alleviate reception failures while implementing the rate adaptation in the same way as the method that learns only expected broadcast performance.

The distributed inference (DI) framework has gained traction as a technique for real-time applications empowered by cutting-edge deep machine learning (ML) on resource-constrained Internet of things (IoT) devices. In DI, computational tasks are offloaded from the IoT device to the edge server via lossy IoT networks. However, generally, there is a communication system-level trade-off between communication latency and reliability; thus, to provide accurate DI results, a reliable and high-latency communication system is required to be adapted, which results in non-negligible end-to-end latency of the DI. This motivated us to improve the trade-off between the communication latency and accuracy by efforts on ML techniques. Specifically, we have proposed a communication-oriented model tuning (COMtune), which aims to achieve highly accurate DI with low-latency but unreliable communication links. In COMtune, the key idea is to fine-tune the ML model by emulating the effect of unreliable communication links through the application of the dropout technique. This enables the DI system to obtain robustness against unreliable communication links. Our ML experiments revealed that COMtune enables accurate predictions with low latency and under lossy networks.

In the field of WiFi sensing, as an alternative sensing source of the channel state information (CSI) matrix, the use of a beamforming feedback matrix (BFM) that is a right singular matrix of the CSI matrix has attracted significant interest owing to its wide availability regarding the underlying WiFi systems. In the IEEE 802.11ac/ax standard, the station (STA) transmits a BFM to an access point (AP), which uses the BFM for precoded multiple-input and multiple-output communications. In addition, in the same way, the AP transmits a BFM to the STA, and the STA uses the received BFM. Regarding BFM-based sensing, extensive real-world experiments were conducted as part of this study, and two key insights were reported: Firstly, this report identified a potential issue related to accuracy in existing uni-directional BFM-based sensing frameworks that leverage only BFMs transmitted for the AP or STA. Such uni-directionality introduces accuracy concerns when there is a sensing capability gap between the uni-directional BFMs for the AP and STA. Thus, this report experimentally evaluates the sensing ability disparity between the uni-directional BFMs, and shows that the BFMs transmitted for an AP achieve higher sensing accuracy compared to the BFMs transmitted from the STA when the sensing target values are estimated depending on the angle of departure of the AP. Secondly, to complement the sensing gap, this paper proposes a bi-directional sensing framework, which simultaneously leverages the BFMs transmitted from the AP and STA. The experimental evaluations reveal that bi-directional sensing achieves higher accuracy than uni-directional sensing in terms of the human localization task.