Deep Learning Assisted Detection for Index Modulation Aided mmWave Systems

Abstract

In this paper, we propose deep learning assisted detection for index modulation millimeter wave (mmWave) systems, where we train a neural network (NN) to jointly detect the transmitted data and index information without relying on explicit channel state information (CSI). As a design example, we first employ multi-set space-time shift keying (MS-STSK) combined with beamforming for transmission over the mmWave channel, where the information is conveyed implicitly using the index of the antennas, the dispersion matrix and the M-ary constellation. Then, we analyze our design when MS-STSK transmission is considered in conjunction with beam index modulation (BIM), where the information is also conveyed by the beam index in addition to the MS-STSK information. In contrast to the MS-STSK's conventional maximum likelihood (ML) detector, our learning-assisted detection dispenses with the channel estimation stage. We demonstrate by simulations that the learning assisted detection outperforms the ML-aided detection in the face of channel impairments with low complexity. Furthermore, we show by simulations that ML-aided detection produces an error floor, when the MS-STSK transmission is coupled with BIM, when realistic channel estimation errors are considered. Additionally, we present qualitative discussions on the receiver complexity in terms of its search space as well as the number of computations required.

Reconfigurable Intelligent Surfaces Relying on Non-Diagonal Phase Shift Matrices

“…, where ρ t is the pilot transmission power and τ p represents the pilot symbols¡ transmission duration [46]. In Fig.…”

Applications of Deep Learning for Millimeter Wave

“…In [27], the authors presented and demonstrated the index modulation millimeter wave (mm wave) systems by the utilization of deep learning assisted detection. Here concurrently detection of transmission and index information are performed by a neural network (NN) that is not reliant on on explicit channel state information (CSI).…”

Machine Learning: A Catalyst for THz Wireless Networks

“…In this direction, several researcher have focused on presenting ML-related solutions for automatic modulation recognition (AMR) (Khan et al, 2016;Li et al, 2018;Wu et al, 2018;Iqbal et al, 2019;Shah et al, 2019;Yang et al, 2019;Bu et al, 2020), channel estimation (Satyanarayana et al, 2019;Zhu et al, 2019;Liu S. et al, 2020;Ma et al, 2020a;Ma et al, 2020b;Moon et al, 2020;Mai et al, 2021;Wang et al, 2021), and signal detection (Jeon et al, 2018;Aoudia and Hoydis, 2019;Samuel et al, 2019;Katla et al, 2020;Satyanarayana et al, 2020). In more detail, AMR has been identified as an important task for several wireless systems, since it enables dynamic spectrum access, interference monitoring, radio fault self-detection as well as other civil, government, and military applications.…”

“…In this sense, in (Samuel et al, 2019), the authors employed a DNN architecture for data detection, whereas, in (Aoudia and Hoydis, 2019), the effectiveness of deep learning for end-to-end signal detection was reported. Similarly, in (Katla et al, 2020), the authors presented a deep learning assisted approach for beam index modulation detection in high-frequency massive MIMO systems. Additionally, in (Satyanarayana et al, 2020), the authors demonstrated the use of deep learning assisted soft-demodulator in multi-set space-time shift keying millimeter wave (mmW) wireless systems.…”

“…Similarly, since an inherent characteristic of THz channels is the high temporal correlation, the ML methodology presented in (Wu et al, 2018) is expected to find application in THz wireless systems. Additionally, despite the fact that the ML algorithms in (Zhu et al, 2019;Ma et al, 2020a;Liu S. et al, 2020;Ma et al, 2020b;Katla et al, 2020;Moon et al, 2020;Satyanarayana et al, 2020;Mai et al, 2021;Wang et al, 2021) were applied to mmW systems and exploit the spatio-temporal and directional characteristics of the channels in this band, the same approach can be used in THz systems that have the similar particularities. Finally, the ML approach presented in Jeon et al (2018) can be applied in any MIMO regardless the operating frequency.…”