Jia-Ning Guo

Visible light communication (VLC) integrates illumination and communication, offering significant potential for 6 G mobile communications. In 6 G communications, reliability and low latency are critical. To meet these demands, finite blocklength codewords are employed for data transmission. For indoor VLC scenarios, multiple light-emitting diodes (LEDs) serve as transmitters, with each transmitter operating under distinct optical power constraints tailored to user requirements. In this paper, a multidimensional constellation design based on geometric shaping is proposed for multi-input single-output (MISO) VLC with per-LED optical power constraints. This design employs truncated cubic shaping to design the equivalent transmitted signal and decomposes it into transmitted signals using a partition-based greedy decomposition. The proposed scheme designs signals in the finite blocklength regime while adhering to per-LED optical power constraints. Simulation results for indoor MISO VLC systems demonstrate the advantages of the proposed scheme over benchmark schemes and quantify the performance gains as well as the gap to the Shannon limit.

Due to the high data rates, license-free operations and inherent security, visible light communication (VLC) is highly valued in industrial Internet of Things (IIoT) applications. Short packet communication, which offers ultra-low latency and efficient resource utilization, is suitable for the low-latency and massive connectivity demand of the IIoT system. It adopts finite blocklength codewords for data transmissions. In this paper, we present a general framework of designing geometrically shaped constellations in short-packet VLC with peak and average intensity constraints for IIoT applications. By leveraging tools from large deviation theory, we first characterize the second-order asymptotics of the optimal constellation shaping region under aforementioned intensity constraints, which serves as a good performance metric for the best geometric shaping in finite blocklength. To further incorporate a sufficiently large coding gain and a nearly maximum shaping gain, we construct multidimensional constellations by the nested structure of Construction B lattices, where the constellation shaping is implemented by controlling the boundary of the embedded sublattice, i.e., a strategy called coarsely shaping and finely coding. Fast algorithms for constellation mapping and demodulation are presented as well. As an illustrative example, we present an energy-efficient 24-dimensional constellation design based on the Leech lattice, whose superiority over existing constellation designs is verified by numerical results.

Geometric constellation shaping for multiple-input single-output optical wireless communication is considered in this paper. We first characterize the optimal shaping region, whose shaping gain may serve as a performance baseline for multidimensional constellation designs. Then, to approach this maximum shaping gain, we propose a novel constellation design based on modified truncated cubic shaping and existing signaling algorithms. Numerical results show that the gap between the maximum shaping gain and the shaping gain of the modified truncated cubic shaping is small, and significant performance gains over the benchmark schemes can be obtained.

In this paper, we investigate the capacity of a multiple-input multiple-output (MIMO) optical intensity channel (OIC) with peak- and average-intensity constraints individually imposed on each transmitter. We first consider the case where the average input intensities are required to be equal to preassigned constants due to the requirement of illumination quality and color temperature. For the MIMO OIC with a strongly connected channel graph, we prove that its strongest eigen-subchannel must have positive channel gains, which reveals the underlying relationship between the MIMO OIC and the multiple-input single-output OIC. In the general case, we derive various capacity bounds by standard information theoretic tools and investigate several transceivers with certain architectures. In the special case where the channel rank is one less than the number of transmitters, we derive an equivalent capacity expression from the perspective of convex geometry, and new capacity lower bounds are derived based on this equivalent expression. Finally, the developed results are extended to another type of channels, where the average input intensities are required to be no larger than preassigned constants. These derived bounds are numerically verified to approach the capacity in the low or high signal-to-noise ratio regime.

As a wireless communication which combines illumination and communication, visible light communication (VLC) has attracted great attention. In indoor VLC systems, multiple illumination devices are commonly employed, forming a natural multi-input multi-output (MIMO) communication system. In this paper, a spatial constellation design based on the optimal geometric shaping is proposed for the MIMO-VLC system with the peak-power and total average-power constraints. Firstly, we derive the optimal geometric shaping region for the MIMO-VLC system under the aforementioned power constraints. Subsequently, we intersect the scaled integer lattice with the optimal geometric shaping region and combine it with minimum-energy mapping to obtain a spatial constellation. Simulation results for indoor MIMO-VLC systems verify the superiority of our approach compared to conventional methods.

In this paper, we present a general framework of designing geometrically shaped constellations for short-packet visible light communications with a peak- and an average-intensity constraints. By leveraging tools from large deviation theory, we first characterize the second-order asymptotics of the optimal constellation shaping region under aforementioned intensity constraints, which serves as a good performance measure for the best geometric shaping in finite blocklength. To further incorporate a sufficiently large coding gain and a nearly-maximum shaping gain, we construct multidimensional constellations by the nested structure of Construction B lattices, where the constellation shaping is implemented by controlling the boundary of the embedded sublattice, i.e., a strategy called coarsely shaping and finely coding. Fast algorithms for constellation mapping and demodulation are presented as well. As an illustrative example, we present an energy-efficient $24$-dimensional constellation design based on the Leech lattice, whose superiority over existing constellation designs is verified by numerical results.

In this paper, we investigate the capacity of a multiple-input multiple-output (MIMO) optical intensity channel (OIC) under per-antenna peak- and average-intensity constraints. We first consider the case where the average intensities of input are required to be equal to preassigned constants due to the requirement of illumination quality and color temperature. When the channel graph of the MIMO OIC is strongly connected, we prove that the strongest eigen-subchannel must have positive channel gains, which simplifies the capacity analysis. Then we derive various capacity bounds by utilizing linear precoding, generalized entropy power inequality, and QR decomposition, etc. These bounds are numerically verified to approach the capacity in the low or high signal-to-noise ratio regime. Specifically, when the channel rank is one less than the number of transmit antennas, we derive an equivalent capacity expression from the perspective of convex geometry, and new lower bounds are derived based on this equivalent expression. Finally, the developed results are extended to the more general case where the average intensities of input are required to be no larger than preassigned constants.

As an emerging technology, reconfigurable intelligent surface (RIS) has attracted increasing attention due to the ability to configure the wireless propagation environment. Parallel multiple-input multiple-output (MIMO) visible light communication (VLC) is suitable for some scenarios, e.g., multi-user time-division multiple access for indoor VLCs. The application of RIS in VLC is a promising trend in the future, and this paper investigates the employment of RIS in a parallel MIMO VLC system with asymptotic capacity maximization. To this end, the RIS-aided parallel MIMO VLC channel model is derived in this paper under the point source assumption, and then, as for the non-convex optimization problem, instead of searching exhaustively for the optimal solution, we propose a low-complexity near-optimal relaxing particle swarm optimization (RPSO) algorithm to optimize the RIS parameters. Meanwhile, we discover that the convex relaxation is an effective method for the optimization problems. Finally, numerical results indicate the effectiveness of the proposed design of RIS, and meanwhile, the asymptotic capacity can be improved to a great degree by adopting RIS in the parallel MIMO VLC system.