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.

This paper proposes the analytical expressions for the IEEE 802.11 string-topology multi-hop networks using Markov-chain model. For achieving that, the proposed analysis procedure includes two proposals, which are: (i) Bianchi's Markov-chain model is modified for considering a relationship between the backoff timer and frame length, and (ii) the interferences, such as hidden node collision and carrier sensing, among network nodes are expressed by merging the proposed Markov-chain model and airtime expression. The analytical expressions are verified by comparisons with simulation results.

IEEE 802.15.7 Visible Light Communication (VLC) networks suffer from performance degradation caused by the hidden device collisions due to the directional transmission with narrow beamwidth. One of the solutions for mitigating the hidden device collisions is to employ a full-duplex transmission technique. As a side effect of the full-duplex transmission in the VLC networks, however, the data-packet discard due to the retransmission limitation occurs frequently in the networks. This paper proposes an improved backoff scheme and its performance analysis to suppress the packet discard. The proposed backoff scheme increases the Backoff Exponent (BE) and the Number of Backoff stage (NB) in IEEE 802.15.7 only when the data packet transmission fails. To evaluate the system performance theoretically, this paper also provides the Markov-chain model for channel access with the proposed scheme. The performance evaluations through simulation and theoretical analysis show the effectiveness of the proposed scheme.

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.

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.