This study investigates the application of adaptive beamforming techniques within D-band sub-terahertz (sub-THz) communication systems tailored for 6G applications. We present a hardware-in-the-loop transmission system utilizing newly developed prototypes of transmit and receive front ends operating in the frequency range from 150 to 170 GHz. The front ends’ analog beamforming architecture enables the generation of eight distinct beams with 6° separation in azimuth, thus creating a field of view with 48° one dimensional angular scanning range on either side of the link. The system implements dynamic beamforming based on the 5G New Radio (NR) waveform’s design principles for the initial access/beam acquisition procedure. Experimental performance assessments conducted in an indoor environment prove the system’s potential to support mobility and mitigate the effects of signal blockage. Specifically, when encountering a lineof-sight link blockage event, the system successfully transitioned to a reflected propagation path, incurring approximately 4dB of loss. Additionally, a mobile receiver could be tracked while maintaining a 1dB variation in the wireless link budget. These results demonstrate the feasibility of D-band adaptive beamforming.

Multi-agent pathfinding plays a crucial role in various robot applications. Recently, deep reinforcement learning methods have been adopted to solve large-scale planning problems in a decentralized manner. Nonetheless, such approaches pose challenges such as non-stationarity and partial observability. In this paper, we address these challenges by integrating a state representation model into a multi-agent proximal policy optimization framework. To do so, we propose to utilize a state representation model which extracts representation features from the global map and leverages this information to enhance the training process. Our approach involves decoupling the feature extractor from the agent training process, enabling a more accurate representation of the global state that remains unbiased by the actions of the agents. Furthermore, our modularized approach offers the flexibility to replace the representation model with another model or modify tasks within the global map, without the retraining of the agents. We demonstrated the effectiveness of our approach by comparing three multi-agent proximal policy optimization frameworks. Our experimental results demonstrate that our approach improves the average episode reward compared to the other approaches.

This paper reports the development outcomes of the world's first integrated analog beamforming - transmit and receive - wireless front-ends operating in the D-band (a sub-THz band), particularly designed to operate within 150 GHz to 170 GHz frequency range. Front-end hardware development includes monolithic microwave integrated circuit (MMIC) chips of the multichannel power amplifier, multichannel low-noise amplifier, and RF switches. Moreover, the development includes an analog beamforming RF network and a low-profile microstrip 1-by-8 uniform linear antenna array, which were both developed on a resistive silicon substrate. The paper also features a successful long-range (320-meter) line-of-sight unidirectional point-to-point wireless transmission experiment, utilizing a 5G-NR orthogonal frequency division multiplexing (OFDM) waveform over a 160-GHz carrier frequency. The experiment demonstrated a successful transmission of OFDM waveforms using up to a 16-QAM (quadrature amplitude modulation) scheme in the D-band.

This paper investigates reconfigurable intelligent surface (RIS)-aided frequency division duplexing (FDD) communication systems. Since the downlink and uplink signals are simultaneously transmitted in FDD, the phase shifts at the RIS should be designed to support both transmissions. Considering a single-user multiple-input multiple-output system, we formulate a weighted sum-rate maximization problem to jointly maximize the downlink and uplink system performance. To tackle the non-convex optimization problem, we adopt an alternating optimization (AO) algorithm, in which two phase shift optimization techniques are developed to handle the unit-modulus constraints induced by the reflection coefficients at the RIS. The first technique exploits the manifold optimization-based algorithm, while the second uses a lower-complexity AO approach. Numerical results verify that the proposed techniques rapidly converge to local optima and significantly improve the overall system performance compared to existing benchmark schemes.

In this paper, practical utilization of multiple distributed reconfigurable intelligent surfaces (RISs), which are able to conduct group-specific operations, for multi-group multicasting systems is investigated. To tackle the inter-group interference issue in the multi-group multicasting systems, the block diagonalization (BD)-based beamforming is considered first. Without any inter-group interference after the BD operation, the multiple distributed RISs are operated to maximize the minimum rate for each group. Since the computational complexity of the BD-based beamforming can be too high, a multicasting tailored zero-forcing (MTZF) beamforming technique is proposed to efficiently suppress the inter-group interference, and the novel design for the multiple RISs that makes up for the inevitable loss of MTZF beamforming is also described. Effective closed-form solutions for the loss minimizing RIS operations are obtained with basic linear operations, making the proposed MTZF beamforming-based RIS design highly practical. Numerical results show that the BD-based approach has ability to achieve high sum-rate, but it is useful only when the base station deploys large antenna arrays. Even with the small number of antennas, the MTZF beamforming-based approach outperforms the other schemes in terms of the sum-rate while the technique requires low computational complexity. The results also prove that the proposed techniques can work with the minimum rate requirement for each group.

In this paper, we propose a Bayesian channel estimator for intelligent reflecting surface-aided (IRS-aided) millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems with semi-passive elements that can receive the signal in the active sensing mode. Ultimately, our goal is to minimize the channel estimation error using the received signal at the base station and additional information acquired from a small number of active sensors at the IRS. Unlike recent works on channel estimation with semi-passive elements that require both uplink and downlink training signals to estimate the UE-IRS and IRS-BS links, we only use uplink training signals to estimate all the links. To compute the minimum mean squared error (MMSE) estimates of all the links, we propose a novel variational inference-sparse Bayesian learning (VI-SBL) channel estimator that performs approximate posterior inference on the channel using VI with the mean-field approximation under the SBL framework. The simulation results show that VI-SBL outperforms the state-of-the-art baselines for IRS with passive reflecting elements in terms of the channel estimation accuracy and training overhead. Furthermore, VI-SBL with semi-passive elements is shown to be more spectral- and energy-efficient than the baselines with passive reflecting elements.

In this demonstration, we present AB9 SoC system, a single-chip solution for AI application. It provides the reconfigurable and programmable architecture to support the general computations for a variety of neural networks. The AB9 SoC is implemented using TSMC 28-nm process technology with a chip size of 17×23 mm2and 1GHz operating frequency.