In modern wireless communication systems, a profound grasp of the channel impulse response (CIR) is pivotal for optimizing the design and functionality of algorithms and systems, especially for multiple-input-multiple-output (MIMO) technology. Conventional methods for gauging and modeling the channels rely on evaluating spatial points sampled discretely, resulting in limitations in acquiring pertinent channel information across a broad region. To overcome these limitations, this study embeds the physical principles of electromagnetic wave propagation into data-driven deep learning models, achieving second-level regional CIR computing efficiency that is hundreds of times faster. The proposed physics-informed deep ray tracing network (PIDRTN) integrates multiple U-shaped network (U-Net) encoder-decoder blocks, capturing radio wave propagation characteristics within a specific region surrounded by buildings, including two equivalent signal propagation directions in a two-dimensional space and a signal intensity correction term. Then, the network employs a parameter-free nonlinear signal transmission module to emulate the physical principles of signal propagation and obtain accurate CIRs from limited anchor locations, which will iteratively generate CIRs for various times within a specified region subjected to enhancement and denoising operations. Furthermore, the PIDRTN-A model, which utilizes anchor data to improve model accuracy, is proposed. A dataset encompassing diverse fading scenarios is constructed using the ray tracing (RT) method. Extensive experiments demonstrate that the proposed models effectively capture directional and reflective properties of signals; using the RT model as a benchmark, normalized root mean squared errors (NRMSEs) of 0.1226 and 0.0969 are obtained for the PIDRTN and PIDRTN-A models, respectively.

With the application and development of the fifth-generation (5G) communications, it is essential to gain insight understanding of the multi-antenna wireless channel characterizations. In particular, their relevant Channel Impulse Response (CIR) so to ensure the effective design of algorithms and systems. However, both the traditional channel measurement and modelling are essentially based on assessment at discretely sampled spatial points without the capability to obtain the relevant channel information over a given surrounding area. To overcome this limitation, this paper proposes to re-assemble the discretely sampled CIRs into equalized video streams. With this basis, a deep learning based video super-resolution method, namely, the Time Decomposition Video Super-Resolution (TDVSR), has been proposed to restore the area channel information for the first time. Moreover, a time decomposition module based on Bidirectional Long Short-Term Memory (BiLSTM) has been designed to decompose the re-assembled CIRs into video form in the time dimension. A retrained video super-resolution model will then process the composited data and output high-resolution frames, which will be reversed to the CIRs at the dense density target area. A data set with various typical fading scenarios has been constructed by Ray Tracing (RT) method. Extensive experiments demonstrate that the proposed TDVSR model successfully learned the nonlinear propagation laws through the data-driven method, which shows satisfied restoration accuracy with significantly increased computation efficiency.

Over-The-Air (OTA) measurement is considered the preferred method for measuring the antenna system and end-to-end performance of Multiple-Input-Multiple-Output (MIMO) devices under test. Spatial correlation has been widely utilized as a key metric for evaluating the accuracy of MIMO OTA measurements. However, there is no guarantee that the standard signal streams convoluted with specified impulse responses will be ideally independent of each other in the implementation of the MIMO OTA testing system. Thus, it is envisaged that the spatial correlation in practical MIMO OTA testing systems may not be exactly equivalent to the expected value of the ideal theoretical model. In this paper, we propose a new evaluation framework for evaluating the spatial correlation performance of MIMO OTA testing system. This evaluation framework provides a novel observation method for spatial correlation, which reflects the non-ideal configuration of the MIMO OTA testing system and can be utilized to predict or cross-validate spatial correlation errors that deviate from the theoretical model. The experimental and simulation results have been verified against the theoretical model, demonstrating good consistency between the theoretical model and the proposed evaluation framework. Furthermore, several test scenarios have been verified with the different varying factors.

This paper characterises the impact of horizontally induced vibration on the antenna's channel response due to fast vehicle manoeuvres or the side push of wind during the overtaking of a large vehicle on a motorway at millimetre wave (mmWave) frequencies. The resultant misalignment perturbs the point of observation, which produces Doppler shifts. A new composite antenna and channel model, including horizontal vibration within the antenna mounting, is developed by measuring the scattering parameters (S21) at discrete intervals of time using a vector network analyser (VNA) that generates a swept tone with a radio frequency (RF) measurement bandwidth of 2GHz centred at 25.5GHz. The S21 over discrete intervals of time represents the channel time-frequency transfer function which is utilised to generate the Doppler variant transfer function and the Doppler power spectrum after post-processing at a vibration frequency below 100Hz. It is proven experimentally that the induced Doppler power is directly proportional to the probability density function (PDF) of the Doppler Shifts. This indicates that Doppler shifts with high probabilities of occurrence have higher Doppler powers. For example, a Doppler shift ≤2000Hz having a PDF = 0.6 or above arrives with a higher power of up to −20dBm as compared to the Doppler shift of ±4000Hz, which has a lower PDF = 0.1 and Doppler powers ≤−25dBm.

Autonomous vehicles require vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. Fifth-generation (5G) and beyond wireless networks enable such infrastructure through beamforming techniques at millimetre-wave (mm-wave) frequencies. This paper characterises the impact of antenna beamwidth on the Doppler shift, cross-polarisation ratio (XPR) and the channel attenuation for the 26GHz mm-wave V2I line-of-sight (LoS) channel. It is proven that the received Doppler power spectrum is proportional to the probability density function (PDF) of the Doppler Shift. This indicates that Doppler power reduces with the Doppler shift. It has also been established that a wider antenna half power beamwidth (HPBW) of 20° instead of 17° reduces the XPR by 20dB whilst increasing the channel attenuation by 10dB in a mm-wave LoS channel with dynamic blockage. Therefore, a trade-off between the XPR and channel attenuation can be introduced by employing different antenna HPBWs.

This paper investigates the effects of antenna vibration occurring due to suspension variation during the movement for vehicle-to-infrastructure (V2I) communication by developing a two dimensional (2D) model. With highly directional antennas fitted on a car's rear bumper and at a base station (BS), different beamwidths from 10° to 50° are investigated for a moving vehicle at 70mph. The gain sensitivity of the radiation pattern and the micro-Doppler shift are modelled for suspension variations between 5cm and 20cm. The analysis has been performed based on the observed angle of arrivals (AoA) at the receiver antenna in the elevation plane. The gain sensitivity represents the BSs’ main lobes' misalignments and receiver antennas’ radiation patterns at 26GHz. The results reveal a significant impact on gain sensitivity during the first 200m of movement away from the BS. Furthermore, the total Doppler shift increases due to the micro-Doppler with vertical displacements from the suspension variation. However, this can be mitigated if the beamwidth increases relative to the link budget or the BS height increases. The 2D model is validated by utilising accelerometer data recorded whilst the vehicle is moving at 70mph on a motorway, by extracting the displacements and computing the gain sensitivity.

Real-world wireless communication channels are typically highly underspread: their coherence time is much greater than their delay spread. In such situations, it is common to assume that, with sufficiently high bandwidth, the capacity without channel state information (CSI) at the receiver (termed the noncoherent channel capacity) is approximately equal to the capacity with perfect CSI at the receiver (termed the coherent channel capacity). In this paper, we propose a lower bound on the noncoherent capacity of highly underspread fading channels, which assumes only that the delay spread and coherence time are known. Furthermore, our lower bound can be calculated recursively, with each increment corresponding to a step increase in bandwidth. These properties, we contend, make our lower bound an excellent candidate as a simple method to verify that the noncoherent capacity is indeed approximately equal to the coherent capacity for typical wireless communication applications. We precede the derivation of the aforementioned lower bound on the information capacity with a rigorous justification of the mathematical representation of the channel. Furthermore, we also provide a numerical example for an actual wireless communication channel and demonstrate that our lower bound does indeed approximately equal the coherent channel capacity.

To deploy effective communication systems in vehicle cavities, it is critical to understand the time variation of the in-vehicle channel. Initially, rapid channel variation is addressed, which is characterized in the frequency domain as a Doppler spread. It is then shown that, for typical Doppler spreads, the in-vehicle channel is underspread, and therefore, the information capacity approaches the capacity achieved with perfect receiver channel state information in the infinite bandwidth limit. Measurements are performed for a number of channel variation scenarios (e.g., absorptive motion, reflective motion, one antenna moving, and both antennas moving) at a number of carrier frequencies and for a number of cavity loading scenarios. It is found that the Doppler spread increases with carrier frequency; however, the type of channel variation and loading appear to have little effect. Channel variation over a longer time period is also measured to characterize the slower channel variation. Channel variation is a function of the cavity occupant motion, which is difficult to model theoretically; therefore, an empirical model for the slow channel variation is proposed, which leads to an improved estimate of the channel state.

Interest is increasing in the use of Wireless Sensor Networks (WSNs) for intra-vehicle network applications owing to the desire of manufacturers to deliver advanced in-car services. Operating WSNs in the unlicensed 2.4 GHz frequency band that is shared with other wireless technologies, e.g., Bluetooth and WiFi, brings additional challenges owing to interference and multipath fading. Therefore, a comprehensive understanding of the intra-vehicle wireless channel is essential for Radio Frequency (RF) resource allocation management, and to establish if various diversity techniques will assist in mitigating the effects of interference and fading in this environment. In this paper, we present our preliminary results in measuring the wireless channel for different scenarios within a car and also assessing the performance benefits that are potentially available from the use of Frequency Diversity (FD) for Line-of-Sight (LOS) and Non Line-of-Sight (NLOS) cases. A cross correlation based analysis of the results shows that FD offers a route to improve Intra-Vehicle Communication (IVC) quality by the use of smart frequency agile MAC protocols.