In this paper, we investigate an artificial intelligence and machine learning (AI/ML) aided channel state information (CSI) feedback scheme for beamforming transmission in next generation wireless local area networks (WLANs). We exploit the deep neural network autoencoder (DNN-AE) with an objective of minimizing CSI feedback overhead while maintaining packet error rate (PER) performance. The key component of the proposed DNN-AE is a pre-processor with a mathematical closed-form, which enables a single generalized DNN-AE applicable universally to various combinations of CSI types, bandwidth, transmit and receive antenna numbers. The generalized DNN-AE has benefits of not only reducing complexity required for hardware implementation but also alleviating the engineering efforts to train DNN-AE. Simulation results show the generalized DNN-AE can provide an average of 50% CSI feedback overhead reduction with almost same packet error rate (PER) performance, compared with existing Extremely High Throughput (EHT) IEEE 802.11be WLANs. This leads to throughput increase by 30%, which can satisfy the goal of next generation Ultra High Reliability (UHR) IEEE 802.11bn WLANs.

To support higher throughput in next-generation Wi-Fi systems, efficient compression and feedback of channel state information (CSI) from a station (STA) to an access point (AP) is essential. This paper proposes a deep learning (DL)-based CSI feedback framework tailored for Wi-Fi systems. The framework employs encoder and decoder networks to compress and reconstruct CSI angle parameters, with a trainable vector quantization (VQ) module enabling efficient finite-bit representation through end-to-end training. To further enhance performance, we introduce an angle-difference feedback strategy that exploits the temporal correlation of the angle parameters by feeding back the difference between the current and previous values. This is complemented by preprocessing that handles the periodicity of angles and tailored VQ modules that compensate for residual quantization errors. Additionally, we present a DL-based CSI refinement module at the AP, which improves reconstruction by jointly using current and prior feedback. Simulation results show that the proposed framework outperforms both standard Wi-Fi feedback and existing DL-based feedback methods, with notable gains from both angle-difference feedback and CSI refinement.

Channel smoothing is widely adopted in wireless local area network (WLAN) systems to improve channel estimation, but the combination of channel smoothing and beamforming has been still a challenging work so far. The difficulty of this combination is due to the existence of discontinuities in the beamforming matrices across subcarriers. We first propose a receiver design for channel state information (CSI) feedback with a smooth beamforming matrix in an optimal way. This design is based on the optimization to maximize the cross-correlation between adjacent beamforming matrices at a cost of slightly increased feedback overhead. Then, a sub-optimal design is also proposed which has the same amount of feedback overhead as current WLANs. To further reduce feedback overhead with minimum loss of performance, machine learning (ML) technique is adopted in which the CSI quantization level is determined dynamically based on real-time channel frequency selectivity. The simulation through the IEEE 802.11be link-level simulator shows that the proposed schemes can achieve channel smoothing gain in the beamforming, improving the throughput significantly with reduced feedback overhead.

Givens rotation based channel state information (CSI) feedback has been adopted as a limited feedback technique for beamforming in the wireless local area networks (WLANs). On the other hand, the long term evolution (LTE) systems utilize the predefined codebook for the CSI feedback. In this paper, we propose a dual CSI feedback technique for next generation WLANs, which combines the codebook and Givens rotation to derive the benefits of both techniques. Machine learning (ML) technique is adopted for the improved codebook design. The extensive simulation is carried out via IEEE 802.11be link-level simulator to verify the performance of the proposed scheme. It shows that the proposed scheme can reduce feedback overhead by more than 50% compared to the scheme adopted in current WLANs, and enhances the throughput significantly.

In this paper, we propose a new stream power allocation method for single-user beamforming for BICM MIMO-OFDM systems. To this end, we first employ a mean mutual information per bit (MMIB) for power allocation problem to accurately estimate the error rate performance. Also, a simple stream SINR for maximum likelihood detection is derived to calculate the MMIB. Then, the optimal power allocation with a diagonal matrix is derived from the approximated function of the MMIB. Also, we extend the diagonal matrix power allocation to a rotational matrix power allocation, which can provide more diversity gain thanks to combined sub-channel process. Finally, we propose an adaptive power allocation algorithm between the diagonal matrix and the rotational matrix, since the rotation matrix incurs inter-stream interference due to off-diagonal terms so that there is a performance trade-off. From the link level simulation on IEEE WLAN, we verify that our proposed stream power allocation method is effective to improve the packet error rate performance.

This paper investigates the combination of beamforming and channel smoothing for wireless local area network (WLAN) systems. Although channel smoothing is widely adopted in WLAN systems to improve the initial channel estimates, the combination of channel smoothing and beamforming has not been successful up to date. The difficulty of this combination is due to the existence of discontinuities in the beam-steering matrix across subcarriers. It destroys the frequency correlation of the beamformed channel matrix, which is unsuitable for the channel smoothing. In this paper, we propose a joint transceiver design of the beamformer and beamformee to provide a smooth beam-steering matrix. The simulation is performed extensively based on the IEEE 802.11ac link-level simulator, which shows that significant gains from channel smoothing are possible due to the proposed transceiver design.

In this paper, we present a low-complexity singular value decomposition (SVD) algorithm to generate a transmit beamforming matrix for multiple-input multiple-output (MIMO) systems. By introducing divide and conquer concept, we divide original channel matrix to sub-block channel matrices with a half of size. By using this form, we formulate eigenvalue problems according to the number of transmitted data streams and provide closed-form solutions. The proposed algorithm offers a good tradeoff between complexity and performance compared to the conventional iterative SVD algorithm. Simulation results demonstrate that the proposed SVD algorithm achieves almost the same performance of conventional iterative SVD algorithm with much reduced computational complexity.

In this paper, we propose an adaptive beamforming matrix feedback scheme, which guarantees the best packet error rate (PER) by selecting the beamforming matrix between singular value decomposition (SVD) and geometric mean decomposition (GMD) based beamforming matrices. The physical layer (PHY)-abstraction is utilized for joint PER prediction and modulation and coding scheme (MCS) level selection. In addition, a low complexity GMD is proposed where the iterative GMD process is converted to 2×2 block-wise GMD without iteration. Each 2×2 GMD is further simplified to remove the square-root and division operators for easy hardware implementation. The simulation is performed extensively via a 802.11ax link-level simulator. The results show that the proposed adaptive feedback scheme provides a significant performance gain in comparison to the conventional SVD- or GMD-based beamforming matrix feedback scheme. Also, the proposed GMD has an advantage in both complexity and PER performance when the number receive antenna is more than two.