Search

This paper introduces a low-complexity ultra-wideband quasi-deterministic ray tracing (QD-RT) method for statistical analysis of wireless channels. This model uses a statistical distribution to model the bistatic radar-cross-section (RCS) of irregular objects such as cars and pedestrians, instead of a deterministic propagation model, i.e., applying the exact values of bistatic RCSs. It is shown that the quasi-deterministic propagation model benefits from a low complexity compared with a deterministic model while keeping the accuracy. The proposed QD-RT method is applied in a realistic street canyon scenario in the millimeter wave frequency band, and the performance of the QD-RT method is verified by the deterministic propagation method, where the second-order statistics including root-mean-square (RMS) delay spread and angular spread and the first-order statistic transfer function yield good agreements. Finally, the application of the QD-RT in stochastic channel modeling is demonstrated by developing a 3GPP-like statistical channel model for street canyon scenarios.

Understanding the evolution of multipath components (MPCs) in real radio channels is crucial to enhancing channel modeling and multipath-assisted positioning. This paper provides an experimental analysis of the behavior of MPCs originating from a standard building facade at millimeter wave (mmWave) frequencies. Utilizing a high-resolution channel parameter estimation method alongside a joint clustering and tracking technique, we identify physical interacting objects and analyze their lifetimes and quantities. The building wall under study is shown to have many distributed distinct backscattering points and reflection contributions due to the windows. Our findings shed light on the reflection and scattering patterns from standard building elements, highlighting the importance of including them in channel models, positioning methods, and ray tracing simulations.

Cellular-connected unmanned aerial vehicle (UAV) has attracted a surge of research interest in both academia and industry. To support aerial user equipment (UEs) in the existing cellular networks, one promising approach is to assign a portion of the system bandwidth exclusively to the UAV-UEs. This is especially favorable for use cases where a large number of UAV-UEs are exploited, e.g., for package delivery close to a warehouse. Although the nearly line-of-sight (LoS) channels can result in higher powers received, UAVs can in turn cause severe interference to each other in the same frequency band. In this contribution, we focus on the uplink communications of massive cellular-connected UAVs. Different power allocation algorithms are proposed to either maximize the minimal spectrum efficiency (SE) or maximize the overall SE to cope with severe interference based on the successive convex approximation (SCA) principle. One of the challenges is that a UAV can affect a large area meaning that many more UAV-UEs must be considered in the optimization problem, which is essentially different from that for terrestrial UEs. The necessity of single-carrier uplink transmission further complicates the problem. Nevertheless, we find that the special property of large coherent bandwidths and coherent times of the propagation channels can be leveraged. The performances of the proposed algorithms are evaluated via extensive simulations in the full-buffer transmission mode and bursty-traffic mode. Results show that the proposed algorithms can effectively enhance the uplink SEs. This work can be considered the first attempt to deal with the interference among massive cellular-connected UAV-UEs with optimized power allocations.

High-precision localization and machine learning (ML) are envisioned to be key technologies in future wireless systems. This paper presents an ML pipeline to solve localization tasks. It consists of multiple parallel processing chains, each trained using a different fingerprint to estimate the position of the user equipment. In this way, ensemble learning can be utilized to fuse all chains to improve localization performance. Nevertheless, a common problem of ML-based techniques is that network training and fine-tuning can be challenging due to the increase in network sizes when applied to (massive) multiple-input multiple-output (MIMO) systems. To address this issue, we utilize a subarray-based approach. We divide the large antenna array into several subarrays, feeding the fingerprints of the subarrays into the pipeline. In our case, such an approach eases the training process while maintaining or even enhancing the performance. We also use the Nyquist sampling theorem to gain insight on how to appropriately sample and average training data. Finally, an indoor measurement campaign is conducted at 3.7 GHz using the Lund University massive MIMO testbed to evaluate the approaches. Localization accuracy at a centimeter level has been reached in this particular measurement campaign.

Accurate characterization of large-scale antenna arrays is growing in importance and complexity for the fifth-generation (5G) and beyond systems, as they feature more antenna elements and require increased overall performance. The full 3D patterns of all antenna elements in the array need to be characterized because they are in general different due to construction inaccuracy, coupling, antenna array's asymmetry, etc. The effective aperture distribution function (EADF) can provide an analytic description of an antenna array based on a full-sphere measurement of the array in an anechoic chamber. However, as the array aperture increases, denser spatial samples are needed for EADF due to large distance offsets of array elements from the reference point in the anechoic chamber, leading to a prohibitive measurement time and increased complexity of EADF. In this paper, we present the EADF applied to large-scale arrays and highlight issues caused by the large array aperture. To overcome the issues, an enhanced EADF is proposed with a low complexity that is intrinsically determined by the characteristic of each array element rather than the array aperture. The enhanced EADF is validated using experimental measurements conducted at 27-30 GHz frequency band with a relatively large planar array.

Universal Design for Learning (UDL) is considered an approach to include all students in the university. UDL is based on three main principles providing multiple means of Engagement, multiple means of representation, and multiple means of action and expression. It is relevant for teachers and departments to focus on more flexibility for students to get the required information, expressing their thoughts and knowledge, and enhancing their motivation to learn. This project aims to investigate the possibility of implementing the UDL in the teaching style of the Faculty of Engineering (LTH), Lund University. Two main approaches were considered in this project. The first one was to interview an expert at LTH about the implementation of UDL at LTH. The second one was to analyze three self-experienced teaching situations according to the UDL guidelines. The results of this project stated that LTH management is considerably serious about UDL, and they already planned a seminar series for training teachers at LTH on how to implement UDL in their course design. The analyses for the three different teaching situations showed that the UDL guidelines provide options for making these teaching and learning experiences much better for both teachers and students. We concluded that the UDL is a vital tool to improve teaching in the higher education sector, in Sweden and worldwide.

This paper presents an analysis of channel characteristics for an outdoor scenario at the frequency band of 28-30 GHz. A geometry-based clustering algorithm is proposed to group the measured multipath components (MPCs) based on the interaction points on the surrounding walls or objects obtained from Ray Tracing 3-D simulations, which is more physically interpretable than the traditional clustering algorithm. The cluster-level characteristics at a total of 15 positions along a pre-defined route covering both line-of-sight (LoS) and non-LoS (NLoS) scenarios are investigated. Moreover, power contributions of MPCs with multiple bounces (up to 7 bounces in NLoS condition) from the interaction objects (IOs) are also investigated.

As ultra-massive multiple-input multiple-output (UM-MIMO) has emerged as a key technology for millimeter-wave and terahertz communications, the spherical wave propagation should be considered for channel modeling. Therefore, it is critical to identify the locations and evolving behaviors of scatterers, i.e., the sources of the spherical wavefronts. In this contribution, a novel space-alternating generalized expectation-maximization (SAGE) based scatterer localization algorithm is proposed, where a large-scale antenna array is divided into multiple sub-arrays. Due to the decreased aperture of each sub-array, plane wave assumption can be applied to estimate the angles of departure/arrival, delays and amplitudes of multipath components (MPCs). Based on the angle variations of MPCs observed at different sub-arrays, the corresponding scatterers can be located. The proposed algorithm is verified in a simulation using a large-scale uniform circular array (UCA) system. Moreover, we apply this algorithm to an indoor measurement campaign conducted at 27-29\,GHz in a hall scenario. Dominant scatterers are identified, which can be used for the development of further geometry-based stochastic channel models.

Cellular-connected unmanned aerial vehicles (UAVs) have recently attracted a surge of interest in both academia and industry. Understanding the air-to-ground (A2G) propagation channels is essential to enable reliable and/or high-throughput communications for UAVs and protect the ground user equipments (UEs). In this contribution, a recently conducted measurement campaign for the A2G channels is introduced. A uniform circular array (UCA) with 16 antenna elements was employed to collect the downlink signals of two different Long Term Evolution (LTE) networks, at the heights of 0-40m in three different, namely rural, urban and industrial scenarios. The channel impulse responses (CIRs) have been extracted from the received data, and the spatial, including angular, parameters of the multipath components in individual channels were estimated according to a high-resolution-parameter-estimation (HRPE) principle. Based on the HRPE results, clusters of multipath components were further identified. Finally, comprehensive spatial channel characteristics were investigated in the composite and cluster levels at different heights in the three scenarios.

Power control is becoming increasingly essential for the fifth-generation (5G) and beyond systems. An example use-case, among others, is the unmanned-aerial-vehicle (UAV) communications where the nearly line-of-sight (LoS) radio channels may result in very low signal-to-interference-plus-noise ratios (SINRs). The authors in (Chiang et al., 2007) proposed to efficiently and reliably solve this kind of non-convex problem via a series of geometrical programmings (GPs) using condensation approximation. However, it is only applicable for a small-scale network with several communication pairs and practically infeasible with more (e.g., tens of) nodes to be jointly optimized. We therefore in this paper aim to provide new insights into this problem. By properly introducing auxiliary variables, the problem is transformed to an equivalent form which is simpler and more intuitive for condensation. A novel condensation method with linear complexity is also proposed based on the form. The enhancements make the GP-based power control feasible for both small- and especially large-scale networks that are common in 5G and beyond. The algorithm is verified via simulations. A preliminary case study of uplink UAV communications also shows the potential of the algorithm.

Millimeter-Wave (mmWave) massive multiple-input multiple-output (MIMO) has been considered as a key enabler for the fifth-generation (5G) communications. It is essential to design and test mmWave 5G devices under various realisticscenarios since the radio propagation channels pose intrinsic limitations on the performance. This requires emulating realistic dynamic mmWave channels in a reproducible manner in laboratories, which is the goal of this paper. In this contribution, we first illustrate the dominant-cluster(s) concept, where thenon-dominant clusters in the mmWave channels are pruned, for mmWave 5G devices applying massive MIMO beamforming. This demonstrates the importance and necessity to accurately emulate the mmWave channels at a cluster level rather than the composite-channel level. Thus, an over-the-air (OTA) emulationstrategy for dynamic mmWave channels is proposed based on the concept of dominant-cluster(s) in a sectored multiprobe anechoic chamber (SMPAC). The key design parameters including the probe number and the angular spacing of probes are investigated through comprehensive simulations. A cost-effective switch circuit is also designed for this purpose and validated in the simulation. Furthermore, a dynamic mmWave channel measured in an indoor scenario at 28-30 GHz is presented, where the proposed emulation strategy is also validated by reproducing the measured reality.