This paper proposes a lightweight empirical reinforcement learning-based sliding mode control framework with a hybrid sliding surface, designed to enhance robustness and smoothness of control input under various disturbances and driving conditions. While the conventional Super-Twisting Algorithm (STA) provides strong robustness, its fixed control parameters limit adaptability and generalization. To address this issue, adaptive control parameter tuning is introduced through machine learning techniques such as reinforcement learning. However, such approaches often suffer from high computational cost and instability during the early stages of learning, making them less practical for real-time embedded systems. To overcome these limitations, this study presents a lightweight empirical reinforcement learning method that adjusts control parameters in real time based on system errors and sliding surface dynamics. A hybrid sliding surface structure that dynamically blends linear and nonlinear components is also introduced to allow the control system to adapt its convergence behavior organically according to real-time conditions. This results in an optimal balance between control performance and input smoothness. Experimental evaluations demonstrate that the proposed framework enables flexible and balanced control behavior, making it highly suitable for real-time, robust, and energy-efficient vehicle control applications.

A high input impedance read-out integrated circuit (IC) for DC measurements has been implemented. The proposed read-out IC consists of a capacitively coupled instrumentation amplifier (CCIA) and an incremental delta-sigma ($\Delta \Sigma )$analog-to-digital converter (ADC). An impedance boosting technique, combining the conventional positive-feedback loop with a proposed fine current compensation loop (FCCL), is introduced to enhance the input impedance of the CCIA. Additionally, the proposed structure chops the entire signal chain at a global chopping frequency of 13.2 kHz, which is higher than the 1/$f$corner frequency of the CCIA’s main amplifier. This approach further increases the input impedance of the CCIA and eliminates the need for RRL and an analog low pass filter (LPF), thereby reducing circuit complexity and area. Local auto-zeroing and correlated double sampling techniques are also used to suppress the offset and 1/$f$noise of the read-out IC. The 18-bit ADC operates at a sampling clock of 4 MHz with a third-order cascade of integrators (CoI) digital filter. Implemented in a 0.13$\mu $m CMOS process, the prototype chip occupies an area of 3.808 mm$^{2}$and draws 1.32 mA from a 5 V supply. The input impedance is boosted from 4.4 M$\Omega $to 5.1 G$\Omega $, corresponding to an impedance boosting factor of 1153. The measurement results show an input-referred noise of 2.4$\mu $V$_{\mathrm{RMS}}$with a conversion time of 0.076 ms. The input range of the ROIC is 4.8 V and it achieves the Schreier figure of merit of 164.9 dB.

A high-precision DC measurement read-out integrated circuit (ROIC) is implemented from a low-noise capacitively-coupled chopper instrumentation amplifier (CCIA) followed by a high-resolution incremental discrete- time delta-sigma modulator (DTΔΣM) analog-to-digital converter (ADC). In this paper, a doubled sampling-time (DST) incremental DTΔΣM is proposed to reduce CCIA's bandwidth. Through the proposed technique, the power consumption of the traditionally power-hungry IA is halved, while the desired system specifications such as output data rate (ODR) and effective resolution (ER) are maintained. Implemented in a standard 0.13-μm CMOS process, the ROIC's effective resolution is 21.0 bit at gain 1 and that of 19.8 bit at gain 64. The analog part draws only 114.4 μA from 3-V supply.

The upsurge of mobile traffic volume has led to dense deployment of IEEE 802.11 wireless local area networks (WLANs). To enhance spectral efficiency in dense WLAN deployments, a new 802.11 amendment, namely 802.11ax, introduces overlapping basic service set packet detection-based spatial reuse (OBSS PD-based SR) operation. Although the OBSS PD-based SR operation enables more simultaneous transmissions among OBSSs, whether it improves network performance is highly uncertain and depends on the mutual interference among the simultaneous transmission links. In this paper, we propose a link-aware SR scheme, named LSR, that facilitates simultaneous transmissions among OBSSs considering the mutual interference. LSR efficiently exploits SR transmission opportunities by selecting the appropriate link for the SR transmission while sufficiently protecting the ongoing transmission. Moreover, the proposed method allows BSSs to coordinate with each other in a distributed manner without incurring additional control message exchange. Through extensive ns-3 simulation, we demonstrate that LSR significantly improves the network performance compared with legacy 802.11 and the OBSS PD-based SR operation.

The number of IEEE 802.11 hotspots is increasing due to popularity and low price, resulting in a dense deployment of wireless local area networks (WLANs). To increase per station (STA) throughput in a densely deployed environment, a new amendment to the WLAN standard, namely, IEEE 802.11ax introduces orthogonal frequency-division multiple access (OFDMA). Especially, uplink OFDMA-based random access (UORA) enables multiple STAs to have simultaneous random access (RA) to an access point (AP) by using different subchannels. However, due to the nature of RA, UORA suffers from data collisions with the number of contending STAs. Moreover, the bandwidth underutilization problem degrades performance of UORA. In this paper, we propose a scheme, named CRUI, that substantially reduces data collisions and improves bandwidth utilization by using an extra backoff stage and opportunistic subchannel hopping. Through simulation, we demonstrate that our proposed scheme significantly improves UORA performance with lowered collision probability and improved bandwidth utilization while maintaining fairness.

The explosion of mobile traffic volume has led to dense deployment of IEEE 802.11 WLANs. As a consequence, periodic beacon transmissions can overwhelm the air-time, leading to significant air-time depletion for data transmissions. In this work, we develop an opportunistic air-time sharing scheme, named BeaconRider, that facilitates simultaneous data and beacon transmissions aimed at improving spectrum efficiency in dense network environments. The proposed method works for downlink communication and allows access points (APs) to coordinate with each other in a distributed manner to exploit opportunities provided by the capture effect. Our protocol is backward compatible with legacy 802.11 APs. Through experiments with a prototype implementation using off-the-shelf IEEE 802.11n dongles as well as extensive ns-3 simulation, we show that the proposed method achieves substantial performance gains that increase with the number of APs.

In time-division duplex (TDD) massive multiple-input multiple-output (MIMO) systems, the spatial multiplexing gain cannot be fully achieved since available pilot resources in each channel coherence time interval are limited. In this paper, we propose OpSAC, an Opportunistic user Scheduling algorithm that uses Aged Channel state information (CSI) to increase the spatial multiplexing gain without incurring additional pilot overhead. Assuming the base station (BS) employs precoders of maximum ratio transmission (MRT) and zero-forcing (ZF), we first derive closed- form lower bounds on the achievable sum-rate under channel aging. According to the analysis results, we develop a heuristic solution which opportunistically schedules additional users by exploiting aged CSI to enhance spectral efficiency. Through simulation, we confirm that our analysis is accurate, and show the impact of channel aging on the performance of massive MIMO systems. In addition, it is shown that OpSAC achieves near-optimal performance and considerably outperforms the conventional user scheduling algorithm that only uses current CSI.

In wireless networks, frame retransmission is used to increase reliability with the use of additional wireless resources. Recently, various approaches have been proposed to reduce the overhead for retransmission. However, they still suffer from performance degradation due to unpredictable wireless channel variation during a frame transmission in fast-fading environments. In this paper, we propose an in-frame rate adaptation (InFRA) scheme as a solution for mitigating the retransmission overhead. In InFRA, a receiver estimates the channel SNR of incoming symbols and feeds it back to the sender. According to the feedback information, the sender adaptively changes its symbol transmission rate during a frame transmission. In this way, InFRA significantly increases the reliability of frame transmission even when the channel state changes within a frame transmission. To evaluate the effectiveness of InFRA, we mathematically analyze its performance and compare it with the existing schemes. Finally, our simulation results show that InFRA achieves significant throughput improvements over the other competitive schemes in fast fading channel environments.

A phase-dependent gain-transition correlated double-sampling technique is proposed and applied to a 10-bit 75-MS/s pipelined analog-to-digital converter. This reduces the accumulation of predictive error of each multiplying digital-to-analog converter stage due to the finite gain of the operational amplifiers, without the need for additional capacitors and switches at the input. With a 10-MHz sinusoidal input, a prototype fabricated in a 0.13- μm CMOS process has a 56.90 dB signal-to-noise plus distortion ratio (SNDR) and a 64.57 dB spurious-free dynamic range (SFDR) at 75 MS/s. For a 37 MHz input at full sampling rate, the SNDR and SFDR are 55.01 and 60.77 dB, respectively. The IC has an active area of 0.65 mm2and consumes 32 mW with a 1.2 V supply.