In this work, we investigate user equipment (UE) positioning assisted by deep learning (DL) in 5G and beyond networks. As compared to state of the art positioning algorithms used in today's networks, radio signal fingerprinting and machine learning (ML) assisted positioning requires smaller additional feedback overhead; and the positioning estimates are made directly inside the radio access network (RAN), thereby assisting in radio resource management. The conventional positioning algorithms will be used as back-up for the environments with high variability in conditions; but ML-assisted positioning serves as more efficient and simpler technique to provide better or similar positioning accuracy. In this regard, we study ML-assisted positioning methods and evaluate their performance using system level simulations for an outdoor scenario in Lincoln park Chicago. The study is based on the use of raytracing tools, a 3GPP 5G NR compliant system level simulator and DL framework to estimate positioning accuracy of the UE. The use of raytracing tool and system level simulator helps avoid expensive drive test measurements in practical scenarios. Our proposed mechanism is a first step towards more proactive mobility management in future networks. We evaluate and compare performance of various DL models and show mean positioning error in the range of 1-1.5m for the best DL configuration with appropriate system feature-modeling.

The ability of nodes in a wireless network to be aware of their position continues to become more important for future applications and use cases like the Industrial Internet of Things. Positioning using cellular networks continues to draw attention as a means to realize those future applications. New Radio (NR) provides many opportunities to use cellular networks to incorporate positioning solutions into those networks. In this paper the RAT-dependent solutions expected in NR Rel-16 are discussed in detail. We highlight OTDOA and describe the major roadblocks remaining for achieving higher accuracy positioning. UTDOA is also discussed. Novel solutions to some roadblocks are presented including system level proposals to time synchronization between base stations and the uplink hearability problem. System level simulations are presented which demonstrate the high potential for both regulatory and commercial uses cases of NR positioning. We compare OTDOA with UTDOA and the other positioning solutions that are likely to be standardized for NR Rel-16. We show that OTDOA can meet the FCC E911 requirements and may also meet some commercial use case requirements.

This letter discusses a receiver-side channel estimation algorithm well-suited to turbo equalizers for multiple-input multiple-output (MIMO) systems. The proposed technique is a Kalman-based channel estimator that runs on parallel single-input single-output (SISO) channels. Soft-decision-feedback interference cancellation is utilized to reduce the MIMO channel estimation problem into multiple SISO channel estimation problems. Unlike existing methods, however, the inherent correlation that exists among the output samples of the successive interference canceller is suppressed via careful puncturing of observation samples. The quality of soft decisions and channel estimates are also continuously monitored and incorporated in the Kalman filter update process.

We consider channel estimation specific to turbo equalization for multiple-input multiple-output (MIMO) wireless communication. We develop a soft-decision-driven sequential algorithm geared to the pipelined turbo equalizer architecture operating on orthogonal frequency division multiplexing (OFDM) symbols. One interesting feature of the pipelined turbo equalizer is that multiple soft-decisions become available at various processing stages. A tricky issue is that these multiple decisions from different pipeline stages have varying levels of reliability. This paper establishes an effective strategy for the channel estimator to track the target channel, while dealing with observation sets with different qualities. The resulting algorithm is basically a linear sequential estimation algorithm and, as such, is Kalman-based in nature. The main difference here, however, is that the proposed algorithm employs puncturing on observation samples to effectively deal with the inherent correlation among the multiple demapper/decoder module outputs that cannot easily be removed by the traditional innovations approach. The proposed algorithm continuously monitors the quality of the feedback decisions and incorporates it in the channel estimation process. The proposed channel estimation scheme shows clear performance advantages relative to existing channel estimation techniques.

We consider channel estimation specific to turbo equalization for multiple-input multiple-output (MIMO) wireless communication. We develop soft-decision-driven sequential algorithms geared to a specific pipelined turbo equalizer architecture operating on orthogonal frequency division multiplexing (OFDM) symbols. One interesting feature of the pipelined turbo equalizer is that multiple soft-decisions become available at various processing stages. A tricky issue is the fact that these multiple decisions from different pipeline stages have correlated decision errors as well as varying levels of reliability. This paper establishes an optimization strategy for the channel estimator to track the target channel while dealing with observation sets with different qualities. The resulting algorithm is basically a linear sequential estimation algorithm and, as such, is Kalman-like in nature. The main difference here, however, is that the proposed algorithm must deal with the inherent correlation that exist among the multiple module outputs that cannot easily be removed by the traditional innovation approach. The proposed algorithm continuously monitor the quality of the feedback decisions and incorporate it in the channel estimation process. The proposed channel estimation schemes show certain performance and complexity advantages over existing EM-based algorithms.