Demodulation Reference Signal Research Articles

5G Enabled Dual Vision and Speech Enhancement Architecture for Multimodal Hearing-Aids

This paper presents the algorithmic framework for a multimodal hearing aid (HA) prototype designed on a Field Programmable Gate Array (FPGA), specifically the RFSOC4*2 AMD FPGA, and evaluates the transmitter performance through simulation studies. The proposed architecture integrates audio and video inputs, processes them using advanced algorithms, and employs the 5G New Radio (NR) communication protocol for uploading the processed signal to the cloud. The core transmission utilizes Orthogonal Frequency Division Multiplexing (OFDM), an algorithm that effectively multiplexes the processed signals onto various orthogonal frequencies, enhancing bandwidth efficiency and reducing interference. The design is divided into different modules such as Sound reference signal (SRS), demodulation reference signal (DMRS), physical broadcast channel (PBCH), and physical uplink shared channel (PUSCH). The modulation algorithm has been optimized for FPGA parallel processing capabilities, making it better suited for the hearing aid requirements for low latency. The optimized algorithm achieves a transmission time of only 4.789 ms and uses fewer hardware resources, enhancing performance in a cost-effective and energy-efficient manner.

Optimized DM-RS Configuration for Improved 5G New Radio Network Capacity and Performance

Network load in mobile networks is continuously growing, putting pressure on mobile operators to deliver target network performance and user experience. This paper focuses on network capacity improvement through increased spectral efficiency, which can be achieved with overhead reduction by optimizing the demodulation reference signal (DM-RS) configuration, which is one of the channels/reference signals with the largest contribution to overhead. In a high-speed scenario, the Doppler effect noticeably influences the temporal nature of the channel such that a channel interpolation process is required within a slot. However, increasing the number of symbols used for DM-RS has a negative impact on capacity. The Doppler effect was analyzed for various 5G NR configurations of operating frequency and subcarrier spacing (SCS), and various use cases were considered using user equipment (UE) speed as the main parameter. For suitable use cases, the DM-RS configuration was optimized in networks with live traffic. The impact of the configuration change on 5G/NR spectral efficiency, user experience and link adaptation performance was assessed through a deep-dive analysis of active measurements, available performance Management (PM) counters and key performance indicators. An optimized DM-RS configuration is proposed, and it is demonstrated to achieve gains of 5–15%, depending on the metric used, use case analyzed, network load, traffic mix and other relevant network characteristics such as topology and clutter type.

Self‐interference cancellation using dmrs‐based power estimation for in‐band full‐duplex transmission

AbstractThis letter presents an improved Self‐Interference Cancellation (SIC) receiver with Demodulation Reference Signal (DMRS)‐based power estimation for In‐band Full‐duplex (IBFD) transmission. The performance of the SIC scheme is highly dependent on the power of transmit signal. The DMRS‐based power estimation can obtain accurate power. Numerical results show that SIC with DMRS‐based power estimation improves the performance of an IBFD system.

Opportunistic channel estimation with LTE signals of limited bandwidth for positioning applications

The positioning problem is interesting in a variety of applications, especially in indoor environments or in urban canyons, where the position information obtainable with traditional Global Navigation Satellite Systems is limited. In this paper, we deal with the problem of estimating, for the purposes of positioning, the time of arrival (TOA) and the angle of arrival (AOA) by processing LTE 3GPP signals, with particular attention to the uplink signals. The main contribution of this paper is the definition of new opportunistic methods to estimate the TOA and the AOA using the upstream demodulation reference signal (DM-RS) instead of the Sounding Reference Signal. We will show that the use of DM-RS and of estimation algorithms such as the Space-Alternating Generalized Expectation-Maximization and the Iterative Adaptive Approach for Amplitude and Phase estimation (IAA-APES) allows an efficient estimate of the parameters, in spite of the small, occupied bandwidth.

ВРЕМЕННАЯ СИНХРОНИЗАЦИЯ В СИСТЕМЕ МОБИЛЬНОЙ РАДИОСВЯЗИ ПЯТОГО ПОКОЛЕНИЯ

In this paper, the issue of time synchronization between the base station and user equipment in a fifth-generation cellular communication system is considered. In order to reduce the complexity of the current method of estimating the start symbol offset, a modification of the algorithm is proposed, in which the cross-correlation is computed in parallel for each DMRS (Demodulation Reference Signal) symbol. Furthermore, calculating the cross-correlation for each DMRS symbol allows for the development of a new method of estimating the sampling frequency offset based on the time interval estimation between two consecutive DMRS signals at the output of the matched filter. It is demonstrated that using a parabolic interpolation of the local regions around the correlation peak at the matched filter outputs yields an accurate estimation of the temporal distance between the received DMRS signals. Numerical simulation results show that the suggested technique can estimate the sampling frequency offset with an accuracy of less than 1 ppm over the entire range of signal-to-noise ratio values.

Massive MIMO Evolution Toward 3GPP Release 18

Since the introduction of fifth-generation new radio (5G-NR) in Third Generation Partnership Project (3GPP) Release 15, swift progress has been made to evolve 5G with 3GPP Release 18 emerging. A critical aspect is the design of massive multiple-input multiple-output (MIMO) technology. In this line, this paper makes several important contributions: We provide a comprehensive overview of the evolution of standardized massive MIMO features from 3GPP Release 15 to 17 for both time/frequency-division duplex operation across frequency range in (FR)-1 and FR 2-1. We analyze the progress on channel state information (CSI) frameworks, beam management frameworks and present enhancements for uplink CSI. We shed light on emerging 3GPP Release 18 problems requiring imminent attention. These include advanced codebook design and sounding reference signal design for coherent joint transmission (CJT) with multiple transmission/reception points (multi TRPs). We discuss advancements in uplink demodulation reference signal design, enhancements for mobility to provide accurate CSI estimates, and unified transmission configuration indicator framework tailored for FR 2-1 bands. For each concept, we provide system level simulation results to highlight their performance benefits. Via field trials in an outdoor environment at Shanghai Jiaotong University, we demonstrate the gains of multi-TRP CJT relative to single TRP at 3.7 GHz.

Demodulation Reference Signal (DM-RS) based channel estimation for non-terrestrial networks to support high mobility

This paper provides an adaptive 5G-NR based solution for non-terrestrial networks (NTN) to support the high mobility of the user equipment (UE). A front-loaded demodulation reference signal (DM-RS) symbol pattern with additional orthogonal symbols is proposed to support UE mobility 500 km/h. We develop a 5G-NR link-level simulator (LLS) to analyze the performance of physical downlink shared channel (PDSCH) under the proposed front-loaded DM-RS symbol pattern, tapped delay line (TDL) channel model, and precoding-based beamforming/MIMO schemes. The simulation results show that the proposed channel estimation method based on front-loaded DM-RS helps to improve the link reliability and achieves the maximum spectral efficiency up to 5.05 bps/Hz under 64-QAM, which can also contribute to the evolution of NTN in 6G networks to support the transonic speed mobility of UE.

AI enlightens wireless communication: Analyses and solutions for DMRS channel estimation

In this paper, a systematic description of the artificial intelligence (AI)-based channel estimation track of the 2nd Wireless Communication AI Competition (WAIC) is provided, which is hosted by IMT-2020(5G) Promotion Group 5G+AI Work Group. Firstly, the system model of demodulation reference signal (DMRS) based channel estimation problem and its corresponding dataset are introduced. Then the potential approaches for enhancing the performance of AI based channel estimation are discussed from the viewpoints of data analysis, pre-processing, key components and backbone network structures. At last, the final competition results composed of different solutions are concluded. It is expected that the AI-based channel estimation track of the 2nd WAIC could provide insightful guidance for both the academia and industry.

Detecting Intelligent Jamming on Physical Broadcast Channel in 5G NR

In fifth generation (5G) new radio (NR), since the synchronization signal blocks are unencrypted during the initial access, an intelligent adversary can detect these signals to obtain the full physical cell identity (PCI) by sniffing, and then use the PCI to attack physical broadcast channel (PBCH) extraction by targeted jamming. Such kind of PBCH intelligent jamming (PBCH-IJ) will cause the failure of the master information block (MIB) decoding, and further result in severe denial of services for those users who want to access this PCI cell. In this letter, we propose to exploit the principal direction of PBCH demodulation reference signal space to detect PBCH-IJ at the user side, which is motivated by the fact that such principal direction under the low mobility scenarios will be significantly impacted if PBCH-IJ happens. Numeric results evaluate and confirm the effectiveness of our detection method.

A Simple and Low-Cost Technique for 5G Conservative Human Exposure Assessment

The purpose of this paper is to introduce a simple, low-cost methodology for estimating a conservative value of the maximum field level that can be radiated by a 5G base station useful for human exposure assessment. The method is based on a Maximum Power Extrapolation (MPE) approach and requires the measurement of a reference quantity associated with the SS-PBCH, such as Primary Synchronization Signal (PSS), Secondary Synchronization Signal (SSS), Physical Broadcast CHannel (PBCH), or PBCH Demodulation Reference Signal (PBCH-DMRS). This step requires a simple spectrum analyzer and allows one to obtain the Resource Element (RE) power of a signal transmitted through broadcast beams. In the second phase, the RE power of the signal transmitted through the traffic beam is estimated using the Cumulative Distribution Function (CDF) of the antenna boost factor obtained from the broadcast and the traffic envelope radiation patterns made available by the base station vendor. The use of the CDF allows us to mitigate the problems related to the exact estimation of the direction of the measurement point with respect to the beam of the 5G antenna. The method is applied to a real 5G communication system, and the result is compared with the value given by other MPE methods proposed in the literature.

PEACH: Proactive and Environment-Aware Channel State Information Prediction with Depth Images

Up-to-date and accurate prediction of Channel State Information (CSI) is of paramount importance in Ultra-Reliable Low-Latency Communications (URLLC), specifically in dynamic environments where unpredictable mobility is inherent. CSI can be meticulously tracked by means of frequent pilot transmissions, which on the downside lead to an increase in metadata (overhead signaling) and latency, which are both detrimental for URLLC. To overcome these issues, in this paper, we take a fundamentally different approach and propose PEACH, a machine learning system which utilizes environmental information with depth images to predict CSI amplitude in beyond 5G systems, without requiring metadata radio resources, such as pilot overheads or any feedback mechanism. PEACH exploits depth images by employing a convolutional neural network to predict the current and the next 100 ms CSI amplitudes. The proposed system is experimentally validated with extensive measurements conducted in an indoor environment, involving two static receivers and two transmitters, one of which is placed on top of a mobile robot. We prove that environmental information can be instrumental towards proactive CSI amplitude acquisition of both static and mobile users on base stations, while providing an almost similar performance as pilot-based methods, and completely avoiding the dependency on feedback and pilot transmission for both downlink and uplink CSI information. Furthermore, compared to demodulation reference signal based traditional pilot estimation in ideal conditions without interference, our experimental results show that PEACH yields the same performance in terms of average bit error rate when channel conditions are poor (using low order modulation), while not being much worse when using higher modulation orders, like 16-QAM or 64-QAM. More importantly, in the realistic cases with interference taken into account, our experiments demonstrate considerable improvements introduced by PEACH in terms of normalized mean square error of CSI amplitude estimation, up to 6 dB, when compared to traditional approaches.

Sequential Anomaly Detection Against Demodulation Reference Signal Spoofing in 5G NR

In fifth generation (5G) new radio (NR), the demodulation reference signal (DMRS) is employed for channel estimation as part of coherent demodulation of the physical uplink shared channel. However, DMRS spoofing poses a serious threat to 5G NR since inaccurate channel estimation will severely degrade the decoding performance. In this correspondence, we propose to exploit the spatial sparsity structure of the channel to detect the DMRS spoofing, which is motivated by the fact that the spatial sparsity structure of the channel will be significantly impacted if the DMRS spoofing happens. We first extract the spatial sparsity structure of the channel by solving a sparse feature retrieval problem, then propose a sequential sparsity structure anomaly detection method to detect DMRS spoofing. In simulation experiments, we exploit clustered delay line based channel model from 3GPP standards for verifications. Numerical results show that our method outperforms both the subspace dimension based and energy detector based methods.

A Digital Self-Interference Cancellation Scheme for In-Band Full-Duplex-Applied 5G System and Its Software-Defined Radio Implementation

The development of mobile communication technology toward 6G is difficult because the required spectrum resources have already been allocated to existing systems. Therefore, new communication systems must have high spectral efficiency to effectively utilize the available resources. This article proposes a digital self-interference (SI) canceller that can be applied to full-duplex cellular systems based on the current 5G signal format (5G-FDC) to introduce in-band full-duplex systems beyond 5G. Specifically, we reorganize the 5G demodulation reference signal (DMRS) and define a novel DMRS configuration, in which the DMRSs for uplink and downlink communications do not interfere with each other. The proposed DMRS configuration effectively improves the accuracy of the SI channel estimation. Moreover, we propose a channel extrapolation scheme that suppresses the channel estimation degradation, which deteriorates the block error rate performance. Additionally, we propose a noise estimation method to improve the low-density parity check decoding performance of the desired signal in 5G-FDC systems. The effectiveness of the proposed method is demonstrated through computer simulations and experimentally by using our developed and implemented software-defined radio-based 5G-FDC prototype. Digital SI cancellation performances of 50 dB and 30.6 dB are observed in the simulation and experimental evaluations, respectively.

A Physical-Layer Watermarking Scheme Based on 5G NR

Based on the existing 5G NR system, a physical-layer watermarking scheme is proposed to enhance the physical-layer security in 5G communication systems. A new scheme for watermark generation is proposed to improve the robustness of the authentication. The watermark signal is embedded in the phase of the demodulation reference signal (DMRS), and the influence of the watermark on the demodulation reference signal is reduced by designing the encoder of the watermark. Simulation results show that the watermarking scheme proposed in this paper has good anti-noise and anti-frequency-offset performance, and has good feasibility both in the Gaussian channel and Rayleigh fading channel.

Performance Analysis of NB-IoT Uplink in Low Earth Orbit Non-Terrestrial Networks.

The 3rd Generation Partnership Project (3GPP) narrowband Internet of Things (NB-IoT) over non-terrestrial networks (NTN) is the most promising candidate technology supporting 5G massive machine-type communication. Compared to geostationary earth orbit, low earth orbit (LEO) satellite communication has the advantage of low propagation loss, but suffers from high Doppler shift. The 3GPP proposes Doppler shift pre-compensation for each beam region of the satellite. However, user equipment farther from the beam center has significant residual Doppler shifts even after pre-compensation, which degrades link performance. This study proposes residual Doppler shift compensation by adding demodulation reference signal symbols and reducing satellite beam coverage. The block error rate (BLER) data are obtained using link-level simulation with the proposed technique. Since the communication time provided by a single LEO satellite moving fast is short, many LEO satellites are necessary for seamless 24-h communication. Therefore, with the BLER data, we analyze the link budget for actual three-dimensional orbits with a maximum of 162 LEO satellites. We finally investigate the effect of the proposed technique on performance metrics such as the per-day total service time and maximum persistent service time, considering the number of satellites and the satellite spacing. The results show that a more prolonged and continuous communication service is possible with significantly fewer satellites using the proposed technique.

5G NR V2X에서 데이터 처리량 기준 QoS 유지를 위한 적응 수비학-DMRS 기법

원격 주행과 같은 V2X 서비스에서 안정적인 데이터 처리량 유지는 매우 중요한 요구사항 중의 하나이다. UE(User Equipment)의 이동 속도가 높은 경우 채널은 특성이 빠르게 변하기 때문에 시변(time varying) 주파수 선택적 페이딩 환경에서의 신뢰성 있는 통신이 이루어지도록 해야 한다. LTE나 5G NR 표준에서는 복조 참조 신호(Demodulation Reference Signal: DMRS)를 전송하여 수신측에서 채널 추정에 이용하도록 하고 있다. 5G NR에서는 수비학(numerology)을 가변적으로 설정할 수 있게 되어 있어서 부반송파 간격(sub-carrier spacing: SCS)이 가변적이며, 또한 슬롯당 DMRS 심볼 수도 가변적으로 적용할 수 있다. 본 논문에서는 5G NR 기반의 V2X에서 채널 환경을 인지하여 수비학 파라미터와 슬롯당 DMRS 심볼 수를 적응적으로 설정함으로써 채널 환경이 변동하는 조건에서도 목표 데이터 처리량이 유지되도록 하는 기법을 제안한다. 본 알고리즘의 특징은 DMRS 심볼을 복조에 이용하는 것 외에 채널의 지연확산 정도를 추정하는 데 이용하는 것이다. 제안된 적응 수비학-DMRS 시스템의 성능을 평가하기 위하여 링크 레벨 모의실험을 수행하였다. UE가 여러 속도로 이동하는 중에 채널의 지연확산이 변화하는 복합적인 환경에서 목표 데이터 처리량 유지가 얻어지는지 분석하였으며, 제안된 시스템의 성능을 고정 파라미터를 사용하는 시스템과 비교 평가하였다.

Design of frequency-difference stabilizing system for two-cavity dual-frequency Nd:YAG laser using quadrature-demodulated Pound–Drever–Hall method

To stabilize the frequency-difference of the two-cavity dual-frequency Nd:YAG laser at 1064 nm, a quadrature-demodulated Pound–Drever–Hall (QD-PDH) frequency-difference stabilizing system has been designed, which is composed of two sets of QD-PDH frequency stabilizing subsystems sharing the same Fabry–Perot cavity as the frequency reference. Both phase modulators are driven by the signals with the same frequency of 10 MHz generated by a single direct digital synthesizer (DDS), and the DDS also outputs the other two orthogonal signals as the demodulation reference signals of both frequency stabilizing subsystems. A QD-PDH frequency-difference stabilizing system for a two-cavity dual-frequency Nd:YAG laser with a frequency-difference of ∼24 GHz at 1064 nm has been established and investigated experimentally. The experimental results have indicated that during a period of 1 h, the laser frequency stabilities of the linear and right-angle cavities are estimated by Allan variance to be better than 2.3 × 10 − 11 and 2.7 × 10 − 11, respectively, corresponding to frequency-difference stability of better than 4.2 × 10 − 7. Such a frequency-difference-stabilized two-cavity dual-frequency Nd:YAG laser can be used as an ideal light source for the synthetic-wave absolute-distance interferometric system.

Downlink transmission and channel estimation for cell-free massive MIMO-OFDM with DSDs

In a cell-free massive MIMO system, multiple users arrive at multiple access points at separate times, while in an OFDM system, different delays can be equivalent to delay spread differences (DSDs). Since DSDs are not all the same, in the downlink transmission process, it is necessary to consider its impact on transmission techniques, such as channel estimation and downlink precoding. In this paper, aiming at the performance loss caused by DSDs in cell-free massive MIMO-OFDM system, we propose a multi-RB precoding optimization algorithm that maximizes the downlink sum rate. We derive the sum rate maximization problem into an iterative second-order cone programming form to achieve convex approximation. Then, considering the impact of DSDs on the accuracy of cell-free massive MIMO-OFDM channel estimation, we propose a downlink channel estimation method, which jointly uses channel state information reference signal and demodulation reference signal. The simulation results show that the proposed multi-RB optimal precoding can effectively improve the downlink sum rate, and the proposed downlink channel estimation can obtain accurate multi-RB frequency-domain channel parameters.

End-to-End Learning for OFDM: From Neural Receivers to Pilotless Communication

The benefits of end-to-end learning has been demonstrated over AWGN channels but has not yet been quantified over realistic wireless channel models. This work aims to fill this gap by exploring the gains of end-to-end learning over a frequency- and time-selective fading channel using OFDM. With imperfect channel knowledge at the receiver, the shaping gains observed on AWGN channels vanish. Nonetheless, we identify two other sources of performance improvements. The first comes from a neural network-based receiver operating over a large number of subcarriers and OFDM symbols which allows to reduce the number of orthogonal pilots without loss of BER. The second comes from entirely eliminating orthogonal pilots by jointly learning a neural receiver together with either superimposed pilots (SIPs), combined with conventional QAM, or an optimized constellation. The learned constellation works for a wide range of signal-to-noise ratios, Doppler and delay spreads, has zero mean and does hence not contain any form of SIP. Both schemes achieve the same BER as the pilot-based baseline with 7% higher throughput. Thus, we believe that a jointly learned transmitter and receiver are a very interesting component for beyond-5G communication systems which could remove the need and associated overhead for demodulation reference signals.

Effective PSCCH Searching for 5G-NR V2X Sidelink Communications

Cooperative Intelligent Transport Systems (C-ITS) are essential for increasing road safety and to make road transport more efficient, sustainable, and environmentally friendly. The implementation of C-ITS technology is only possible through the connectivity of Vehicle-to-Everything (V2X), which allows the interconnection of vehicles in a network and with road support infrastructure. However, real-time systems require efficient signal processing in order to respond within the necessary time. Some of this processing is related to searching the Physical Sidelink Control Channel (PSCCH), where a blind algorithm is commonly used. However, this algorithm is quite inefficient to searching the PSCCH, since all the processing should be completed several times before successful decoding it. Therefore, the aim of this paper is to design a more efficient algorithm to search/decode the PSCCH. In the proposed algorithm, we firstly compute all the correlations between the received signal and the Demodulation Reference Signal (DMRS), and the remaining conventional processing to decode the PSCCH is only performed over the subchannels with higher correlation, which leads to a strong complexity reduction. The proposed algorithm is evaluated and compared with the conventional blind algorithm. The results have shown a significant performance improvement in terms of runtime.