Soft-output deep LAS detection for coded MIMO systems: a learning-aided LLR approximation

The multiple-input-multiple-output-orthogonal frequency division multiplexing (MIMO-OFDM) receiver aims to softly decode the transmitted information from the observed received signal. However, soft-output detection requires additional computation and leads to higher overall detection complexity in high-dimensional MIMO and higher-order modulation. Therefore, accurate and low-complexity soft-output detection is a challenging task in such systems. The conventional likelihood ascent search (LAS) detectors perform well in large antenna setups, however, multiple symbol updates and the soft-output estimation using brute force search further boost its complexity. In this paper, we propose a model-based soft-output LAS detector to jointly detect and precisely estimate the soft output in MIMO-OFDM systems. Furthermore, we propose a data-driven deep-LAS (Deep LAS) architecture for MIMO detection which is a multi-layer perception (MLP), and a gated recurrent unit (GRU)-based hybrid trainable learning framework to unfold the proposed two-stage LAS algorithm by directly learning the soft output from the received equalized signals. Numerical results demonstrate that the proposed two-stage soft-output LAS detector precisely computes the log-likelihood ratio (LLR) and provides better performance than the conventional LAS detector. Alternatively, the proposed Deep LAS efficiently estimates the LLR values by achieving a performance gain of 2.55 dB and 3 dB compared to the conventional LAS algorithm. Furthermore, the proposed Deep LAS outperforms the counterpart model-based and standalone data-driven learning schemes and provides a comparable signal-to-noise ratio (SNR) gap of 0.4 dB and 1.2 dB with the optimal soft output sphere decoding (SD) to achieve a BER of 10-5 for 4-QAM and 16-QAM, respectively

Full-duplex multi-user MIMO communication systems performance optimization using leakage-based precoding

Abstract The spectral efficiency (SE) can approximately double when using full-duplex (FD) multiuser MIMO communications. However, there are difficulties because of multiuser interferences, self-interference (SI), and co-channel interference (CCI). To improve the SE of the downlink (DL), this paper proposes CCI-aware enhancement to SLNR (signal-to-leakage-and-noise-ratio) signal-to-leakage-and-noise-ratio (SLNR). It considers a suppressing filter at the receiver to cancel the interferences again designing a beamformer based on CCI-plus-noise covariance matrices for every user at the transmitting side. Additionally, we propose an improvement in the SLNR method by using SI-plus-noise covariance matrices to design uplink (UL) beamformers. Unlike zero-forcing and block-diagonalization, the SLNR approach serves numerous antennas at users and BS (base station). The total SE of the communication yielded using the optimized precoder, i.e., obtained from the SLNR-based precoding. To achieve maximum energy efficiency (EE), we use a power consumption model. Simulation results confirm that full-duplex performs well compared to half-duplex (HD) when the number of antennas at every user in uplink as well downlink channels grow, for all Rician factors, for slight powers of the CCI and SI, and a limited number of antennas at the BS. With the proposed scheme for given transmit power and circuit power, we demonstrate that FD has a higher EE than HD

A novel joint transmit beamforming and receive time switching strategy for MISO SWIPT system

Abstract In multi-antenna simultaneous wireless information and power transfer (SWIPT) system, beamforming strategy has been widely analyzed due to the increasing signal strength. It can not only increase the signal strength in the direction of the antenna array but also reduce the interference strength, which is a good option for SWIPT system to achieve directional transmission of information and energy. However, the traditional beamforming strategy only uses single beamforming vector, and it does not consider the differences between information and energy in SWIPT system. Actually, interference can also be collected as energy. Based on the traditional beamforming strategy, the resources in SWIPT system are not properly utilized. Therefore, this paper proposes a joint beamforming strategy in multi-input and single-output (MISO) SWIPT system. We extend the traditional single beamforming vector into two beamforming vectors to realize independent control of information and energy based on time switching (TS) receiving mode. In information receiving phase, information beamforming vector is used to carry specific user’s information for information alignment. Since there is an orthogonal relationship between information beamforming vector and channel gain vector, we can achieve to eliminate interference and realize error-free information transmission. In energy receiving phase, energy beamforming vector is used to carry user’s energy. Energy beamforming vector and channel gain vector do not require the orthogonality so that the interference can also be collected as energy. In this paper, we model it as a transmission power optimization (TPO) problem, which is a complex non-convex problem. We firstly transform it into a convex problem, and then, it can be solved using CVX toolbox. Simulation results show that the proposed strategy could increase the energy collection at the same transmission power and also decrease the transmission power at the same energy collection

Unsupervised Indoor Positioning System Based on Environmental Signatures

Mobile sensors are widely used in indoor positioning in recent years, but most methods require cumbersome calibration for precise positioning results, thus the paper proposes a new unsupervised indoor positioning (UIP) without cumbersome calibration. UIP takes advantage of environment features in indoor environments, as some indoor locations have their signatures. UIP considers these signatures as the landmarks, and combines dead reckoning with them in a simultaneous localization and mapping (SLAM) frame to reduce positioning errors and convergence time. The test results prove that the system can achieve accurate indoor positioning, which highlights its prospect as an unconventional method of indoor positioning