Youngchul Sung

Recent advancements in offline reinforcement learning (RL) have showcased the potential for leveraging static datasets to train optimal policies. However, real-world applications often face challenges due to missing or incomplete state information caused by imperfect sensor performance or intentional interlaces. We propose the Dimension-Wise Drop Decision Transformer (D3T), a novel framework designed to address dimension-wise data loss in sensor observations, enhancing the robustness of RL algorithms in real-world scenarios. D3T innovatively incorporates dimension-wise drop information embeddings within the Transformer architecture, facilitating effective decision-making even with incomplete observations. Our evaluation in the D4RL MuJoCo domain demonstrates that D3T significantly outperforms existing methods such as the Decision Transformer, particularly with substantial dimension-wise drops of observations. These results confirm D3T’s capability in managing real-world imperfections in state observations and illustrate its potential to substantially expand the applicability of RL in more complex and dynamic environments.

Reconfigurable intelligent surface (RIS)-assisted millimeter wave (mmWave) communication is a promising technology for enlarging the coverage area of millimeter wave systems. Unfortunately, realizing the full potential of these systems requires addressing numerous challenges in channel estimation. In this paper, channel estimation for RIS-assisted mmWave communications is considered. Under the assumption that the array manifolds of the base station antennas and the RIS reflecting elements are given by uniform arrays, an efficient two-stage channel estimation method based on array signal processing techniques is proposed. In the proposed algorithm, the direct and indirect channels are jointly estimated by space-time processing that exploits the sparsity in RIS-assisted mmWave channels and the features associated with uniform arrays. Then, several practical issues, including detection of the number of channel paths, imperfect RIS hardware, and complexity, are addressed. Extensions to the cases of uniform planar array-based RIS, wideband communication, and multiple users are also discussed. Numerical results validate the effectiveness of the proposed method.

In this paper, the problem of training signal design for intelligent reflecting surface (IRS)-assisted millimeter-wave (mmWave) communication under a sparse channel model is considered. The problem is approached based on the Cramér-Rao lower bound (CRB) on the mean-square error (MSE) of channel estimation. By exploiting the sparse structure of mmWave channels, the CRB for the channel parameter composed of path gains and path angles is derived in closed form under Bayesian and hybrid parameter assumptions. Based on the derivation and analysis, an IRS reflection pattern design method is proposed by minimizing the CRB as a function of design variables under constant modulus constraint on reflection coefficients. Extensions of the proposed design to a multi-antenna transceiver, a uniform planar array (UPA)-based IRS, and multi-user case are discussed. Numerical results validate the effectiveness of the proposed design method for sparse mmWave channel estimation.

In this paper, a new method of fast beam search and refinement is proposed for millimeter-wave hybrid beamforming systems. The proposed method is based on a two-level phased array approach in which the first-level phased array is formed by actual analog-domain subarrays, and the second-level phased array is virtually formed by the outputs of the first-level subarray phased combiners. Exploiting the fact that the overall beam pattern of the two-level array is the product of the beam patterns of the two levels, the proposed method searches angle-of-arrivals by sweeping coarse-resolution analog training beams at the first-level phased array and matching the first-level subarray outputs with fine-resolution digital training beams by parallel fast Fourier transform (FFT) filtering. In this way, the overall beam search resolution of the proposed method is given by the fine resolution of the second-level virtual array only with the overhead of coarse beam sweeping at the first-level subarrays. Hence, the proposed method provides a very efficient way of beam training and refinement. The performance of the proposed method including the directional ambiguity, beam search latency, and computational complexity is analyzed. Numerical results show the effectiveness of the proposed method.

In this paper, the outage behavior and diversity order of the mixture transceiver architecture for multiple-input single-output broadcast channels are analyzed. The mixture scheme groups users with closely-aligned channels and applies superposition coding and successive interference cancellation decoding to each group composed of users with closely-aligned channels, while applying zero-forcing beamforming across semi-orthogonal user groups. In order to enable such analysis, closed-form lower bounds on the achievable rates of a general multiple-input single-output broadcast channel with superposition coding and successive interference cancellation are newly derived. By employing channel-adaptive user grouping and proper power allocation, which ensures that the channel subspaces of user groups have angle larger than a certain threshold, it is shown that the mixture transceiver architecture achieves full diversity order in multiple-input single-output broadcast channels and opportunistically increases the multiplexing gain while achieving full diversity order. Furthermore, the achieved full diversity order is the same as that of the single-user maximum ratio transmit beamforming. Hence, the mixture scheme can provide reliable communication under channel fading for ultra-reliable low latency communication. Numerical results validate our analysis and show the outage superiority of the mixture scheme over conventional transceiver designs for multiple-input single-output broadcast channels.

In this paper, the problem of transmit beam design and user scheduling is investigated for multiuser (MU) multiple-input single-output (MISO) nonorthogonal multiple access (NOMA) downlink. First, Pareto-optimal beam design is solved for two-user MISO broadcast channels (BCs) with successive interference cancelation (SIC), and certain properties of Pareto-optimal beam vectors are obtained. Based on this result, a beam design and user scheduling method is proposed for MU-MISO NOMA. In the proposed method, simultaneously served users are grouped into multiple clusters with two users in each cluster, and a hierarchical beam structure composed of zero forcing intercluster beamforming and two-user Pareto-optimal intracluster beamforming is applied. The proposed method has the ability to control the rates of strong and weak users and hence provides great flexibility to MU-MISO NOMA operation. In addition, the problem of Pareto-optimal beam design for general M-user MISO BCs with SIC is solved.

In this paper, a new two-phase-feedback-based user-scheduling-and-beamforming method is proposed for multi-user multiple-input multiple-output (MU-MIMO) downlink in the context of two-stage beamforming. The key ideas of the proposed method are 1) to use a set of orthogonal reference beams and construct a cone around each reference beam to select ‘nearly-optimal’ semi-orthogonal users based only on channel quality indicator (CQI) feedback and 2) to apply post-user-selection beam design with zero-forcing beamforming (ZFBF) based on channel state information (CSI) feedback only from the selected users. It is proven that the proposed scheduling-and-beamforming method is asymptotically optimal as the number of users increases. Furthermore, the proposed scheduling-and-beamforming method almost achieves the performance of the existing semi-orthogonal user selection with ZFBF (SUS-ZFBF) that requires full CSI for all users, with a significantly reduced amount of required channel information which is even less than that required by random beamforming (RBF).

In this paper, analog-and-digital hybrid beamformer design is considered for millimeter-wave (mmWave) wideband multi-user (MU) multiple-input single-output (MISO)-OFDM downlink systems, where a single common analog beamformer is used for all subcarriers while a separate digital beamformer is applied to each subcarrier. By applying a signal-to-leakage-plus-noise ratio (SLNR) approach, an efficient beamformer design algorithm based on a trace quotient is derived for joint design of the common analog beamformer and the separate digital beamformers.