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Gek Hong Sim

Publications and source records attributed to Gek Hong Sim.

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Sequential Parametric Optimization for Rate-Splitting Precoding in Non-Orthogonal Unicast and Multicast Transmissions

This paper investigates rate-splitting (RS) precoding for non-orthogonal unicast and multicast (NOUM) transmissions using fully-digital and hybrid precoders. We study the nonconvex weighted sum-rate (WSR) maximization problem subject to a multicast requirement. We propose FALCON, an approach based on sequential parametric optimization, to solve the aforementioned problem. We show that FALCON converges to a local optimum without requiring judicious selection of an initial feasible point. Besides, we show through simulations that by leveraging RS, hybrid precoders can attain nearly the same performance as their fully-digital counterparts under certain specific settings.

cs.IT

BEAMWAVE: Cross-Layer Beamforming and Scheduling for Superimposed Transmissions in Industrial IoT mmWave Networks

The omnipresence of IoT devices in Industry 4.0 is expected to foster higher reliability, safety, and efficiency. However, interconnecting a large number of wireless devices without jeopardizing the system performance proves challenging. To address the requirements of future industries, we investigate the cross-layer design of beamforming and scheduling for layered-division multiplexing (LDM) systems in millimeter-wave bands. Scheduling is crucial as the devices in industrial settings are expected to proliferate rapidly. Also, highly performant beamforming is necessary to ensure scalability. By adopting LDM, multiple transmissions can be non-orthogonally superimposed. Specifically, we consider a superior-importance control multicast message required to be ubiquitous to all devices and inferior-importance private unicast messages targeting a subset of scheduled devices. Due to NP-hardness, we propose BEAMWAVE, which decomposes the problem into beamforming and scheduling. Through simulations, we show that BEAMWAVE attains near-optimality and outperforms other competing schemes.

eess.SP

SWAN: Swarm-Based Low-Complexity Scheme for PAPR Reduction

Cyclically shifted partial transmit sequences (CS-PTS) has conventionally been used in SISO systems for PAPR reduction of OFDM signals. Compared to other techniques, CS-PTS attains superior performance. Nevertheless, due to the exhaustive search requirement, it demands excessive computational complexity. In this paper, we adapt CS-PTS to operate in a MIMO framework, where singular value decomposition (SVD) precoding is employed. We also propose SWAN, a novel optimization method based on swarm intelligence to circumvent the exhaustive search. SWAN not only provides a significant reduction in computational complexity, but it also attains a fair balance between optimality and complexity. Through simulations, we show that SWAN achieves near-optimal performance at a much lower complexity than other competing approaches.

eess.SP

HydraWave: Multi-Group Multicast Hybrid Precoding and Low-Latency Scheduling for Ubiquitous Industry 4.0 mmWave Communication

Industry 4.0 anticipates massive interconnectivity of industrial devices (e.g., sensors, actuators) to support factory automation and production. Due to the rigidity of wired connections to harmonize with automation, wireless information transfer has attracted substantial attention. However, existing solutions for the manufacturing sector face critical issues in coping with the key performance demands: ultra-low latency, high throughput, and high reliability. Besides, recent advancements in wireless millimeter-wave technology advocates hybrid precoding with affordable hardware and outstanding spatial multiplexing performance. Thus, we present HYDRAWAVE -- a new paradigm that contemplates the joint design of group scheduling and hybrid precoding for multi-group multicasting to support ubiquitous low-latency communications. Our hybrid precoder, based on semidefinite relaxation and Cholesky matrix factorization, facilitates the robust design of the constant-modulus phase shifts rendering formidable performance at a fraction of the power required by fully-digital precoders. Further, our novel group scheduling formulation minimizes the number of scheduling windows while accounting for the channel correlation of the co-scheduled multicast receivers. Compared to exhaustive search, which renders the optimal scheduling at high overhead, HYDRAWAVE incurs only 9.5% more delay. Notoriously, HYDRAWAVE attains up to 102% gain when compared to the other benchmarked schemes.

eess.SP