Reconfigurable intelligent surfaces (RISs) are envisioned to play a pivotal role in future wireless systems with the capability of enhancing propagation environments by intelligently reflecting the signals toward the target receivers. However, the optimal tuning of the phase shifters at the RIS is a challenging task due to the passive nature of reflective elements and the high complexity of acquiring channel state information (CSI). Conventionally, wireless systems rely on pre-defined reflection beamforming codebooks for both initial access and data transmission. However, these existing pre-defined codebooks are commonly not adaptive to the environments. Moreover, identifying the best beam is typically performed using an exhaustive search that leads to high beam training overhead. To address these issues, this paper develops a multi-agent deep reinforcement learning framework that learns how to jointly optimize the active beamforming from the BS and the RIS-reflection beam codebook relying only on the received power measurements. To accelerate learning convergence and reduce the search space, the proposed model divides the RIS into multiple partitions and associates beam patterns to the surrounding environments with low computational complexity. Simulation results show that the proposed learning framework can learn optimized active BS beamforming and RIS reflection codebook. For instance, the proposed MA-DRL approach with only 6 beams outperforms a 256-beam discrete Fourier transform (DFT) codebook with a 97% beam training overhead reduction.

Deep Reinforcement Learning Based Beamforming Codebook Design for RIS-aided mmWave Systems

Speech enhancement methods employing a priori information of speech and noise as trained codebooks of speech and noise spectral shapes parametrized by, e.g., linear predictive (LP) coefficients have shown to perform well in non-stationary noise conditions even in single channel mode. Generally, speaker independent (SI) codebooks are employed but for applications such as mobile communication, speaker dependent (SD) code-books are more effective. However, large amount of training data required for generating such SD models is not available in practical application. One way to overcome this limitation is to adapt available SI model to a specific speaker data using smaller amounts of training data incrementally as and when available. In this paper, we investigate the adaption of SI codebook of spectral representation of speech data to a specific target speaker using Vector Quantization Maximum a posteriori (VQ-MAP) algorithm and study its effect on speech enhancement performance. The experimental results indicate that VQ-MAP leads to adapted codebooks which are closer representation of a speaker than SI codebooks and enable better speech enhancement compared to SI models in codebook-based speech enhancement technique.

Speaker Adapted Codebooks for Speech Enhancement

High data rate coverage, security, and energy efficiency will play a key role in the continued performance scaling of next-generation mobile systems. Dense, small mobile cells based on a novel network architecture are part of the answer. Motivated by the recent mounting interest in free-space optical (FSO) technologies, this paper addresses a novel mobile fronthaul network architecture based on FSO, spread spectrum codes, and graphene modulators for the creation of dense small cells. The network uses an energy-efficient graphene modulator to send data bits to be coded with spread codes for achieving higher security before their transmission to remote units via high-speed FSO transmitters. Analytical results show the new fronthaul mobile network can accommodate up to 32 remote antennas under error-free transmissions with forward error correction. Furthermore, the modulator is optimized to provide maximum efficiency in terms of energy consumption per bit. The optimization procedure is carried out by optimizing both the amount of graphene used on the ring resonator and the modulator’s design. The optimized graphene modulator is used in the new fronthaul network and requires as low as 4.6 fJ/bit while enabling high-speed performance up to 42.6 GHz and remarkably using one-quarter of graphene only.

/pdf/beyond-5g-fronthaul-based-on-fso-using-spread-spectrum-codes-2nyw67p5.pdf

Beyond 5G Fronthaul Based on FSO Using Spread Spectrum Codes and Graphene Modulators

More connectivity, higher data rates, more reliability, massive network capacity, higher performance and fewer delays are required in the fifth generation (5G) of cellular networks. The last ten years have contained explosive growth in mobile data traffic due to the rapid proliferation of Internet-connected smart devices. For 5G mobile and wireless networks, one of the challenges is to discover how to solve the dilemma between capacity requirements and spectrum shortage. Millimeter-wave communication is therefore a key enabler for 5G technologies. Due to the high path and penetration losses at millimeter wavelengths, antenna beamforming assumes a pivotal role in establishing and maintaining a robust communication link. Recently, codebook-based beamforming has been proposed to achieve a fair balance between complexity and performance and to eliminate the overheads. In this paper, we track the techniques of codebook-based beamforming for millimeter-wave communications in the context of the distinct requirements for low-mobility channel and high-mobility channel scenarios. Subsequently, we will provide a comparison of existing codebook-based beamforming techniques in terms of their respective benefits and shortcomings. Finally, some open directions of research are discussed, and challenges that need to be met are pointed out.

https://ieeexplore.ieee.org/ielx7/6287639/9668973/09720956.pdf

Semah Mabrouki

Papers

Codebook Designs for Millimeter-Wave Communication Systems in Both Low- and High-Mobility: Achievements and Challenges