Investigation of Holographic Beamforming via Dynamic Metasurface Antennas in QoS Guaranteed Power Efficient Networks

This work focuses on designing a power-efficient network for Dynamic Metasurface Antennas (DMAs)-aided multiuser multiple-input single output (MISO) antenna systems. The main objective is to minimize total transmitted power by the DMAs while ensuring a guaranteed signal-to-noise-and-interference ratio (SINR) for multiple users in downlink beamforming. Unlike conventional MISO systems, which have well-explored beamforming solutions, DMAs require specialized methods due to their unique physical constraints and wavedomain precoding capabilities. To achieve this, optimization algorithms relying on alternating optimization and semi-definite programming, are developed, including spherical-wave channel modelling of near-field communication. The dynamic reconfigurability and holography-based beamforming of metasurface arrays make DMAs promising candidates for power-efficient networks by reducing the need for power-hungry RF chains. On the other hand, the physical constraints on DMA weights and wave-domain precoding of multiple DMA elements through reduced number of RF suppliers can limit the degrees of freedom (DoF) in beamforming optimizations compared to conventional fully digital (FD) architectures. This paper investigates the optimization of downlink beamforming in DMA-aided networks, focusing on power efficiency and addressing these challenges.

Performance Analysis of OTFS-NOMA System with Fractional Doppler

In this work, we investigate the effect of fractional Doppler on the performance of a system using orthogonal time frequency space (OTFS) modulation and non-orthogonal multiple access (NOMA) where users have different mobility profiles. Fractional Doppler results in inter-Doppler interference (IDI) and degrades the quality of OTFS-modulated signals. We consider a downlink (DL) communication scenario where multiple users are distinguished based on their mobility profiles into a single high-mobility (HM) user and multiple low-mobility (LM) users. OTFS modulation is implemented for the HM user by embedding its information symbols in the delay-Doppler domain, while LM users' symbols are represented in the time-frequency (TF) domain. The LM users' signals are kept orthogonal to each other in the frequency domain by accessing disjoint subcarriers. Further, NOMA spectrum sharing is implemented between the HM user and the KM users to achieve higher spectral efficiency. Performance analysis in terms of DL spectral efficiency and outage probability is conducted for different system parameters. The numerical results show that IDI has a noticeable performance impact on the HM user, depending on the NOMA parameters.

OTFS-NOMA System for MIMO Communication Networks with Spatial Diversity

In this work, we study the use of non-orthogonal multiple access (NOMA) and orthogonal time frequency space (OTFS) modulation in a multiple-input multiple-output (MIMO) communication network where mobile users (MUs) with different mobility profiles are grouped into clusters. We consider a downlink scenario where a base station (BS) communicates with multiple users that have diverse mobility profiles. High-mobility (HM) users' signals are placed in the delay-Doppler (DD) domain using OTFS modulation in order to transform their time-varying channel into a sparse static channel, while low-mobility (LM) users signals are placed in the time-frequency (TF) domain. Precoding is adopted at the BS to direct focused beams towards each cluster of users. Moreover, NOMA spectrum sharing is used in each cluster to allow the coexistence of a single HM user and multiple LM users within the same resource block. LM users access disjoint subchannels to ensure their orthogonality. All users within the same cluster first detect the HM user's signal. Afterward, LM users suppress the interference from the HM user and detect their own signals. Closed-form expressions of the detection signal-to-noise ratios (SNRs) are derived. The numerical results showed that the performance of the proposed system highly depends on the number of LM users, the number of clusters and the power allocation factors between HM and LM users.

Multi-Agent Deep Reinforcement Learning in Vehicular OCC

Optical camera communications (OCC) has emerged as a key enabling technology for the seamless operation of future autonomous vehicles. In this paper, we introduce a spectral efficiency optimization approach in vehicular OCC. Specifically, we aim at optimally adapting the modulation order and the relative speed while respecting bit error rate and latency constraints. As the optimization problem is NP-hard problem, we model the optimization problem as a Markov decision process (MDP) to enable the use of solutions that can be applied online. We then relaxed the constrained problem by employing Lagrange relaxation approach before solving it by multi-agent deep reinforcement learning (DRL). We verify the performance of our proposed scheme through extensive simulations and compare it with various variants of our approach and a random method. The evaluation shows that our system achieves significantly higher sum spectral efficiency compared to schemes under comparison.

Robotic Wireless Energy Transfer in Dynamic Environments: System Design and Experimental Validation

Wireless energy transfer (WET) is a ground-breaking technology for cutting the last wire between mobile sensors and power grids in smart cities. Yet, WET only offers effective transmission of energy over a short distance. Robotic WET is an emerging paradigm that mounts the energy transmitter on a mobile robot and navigates the robot through different regions in a large area to charge remote energy harvesters. However, it is challenging to determine the robotic charging strategy in an unknown and dynamic environment due to the uncertainty of obstacles. This paper proposes a hardware-in-the-loop joint optimization framework that offers three distinctive features: 1) efficient model updates and re-optimization based on the last-round experimental data; 2) iterative refinement of the anchor list for adaptation to different environments; 3) verification of algorithms in a high-fidelity Gazebo simulator and a multi-robot testbed. Experimental results show that the proposed framework significantly saves the WET mission completion time while satisfying collision avoidance and energy harvesting constraints.