Search

With the deployment of 5G networks, standards organizations have started working on the design phase for sixth-generation (6G) networks. 6G networks will be immensely complex, requiring more deployment time, cost and management efforts. On the other hand, mobile network operators demand these networks to be intelligent, self-organizing, and cost-effective to reduce operating expenses (OPEX). Machine learning (ML), a branch of artificial intelligence (AI), is the answer to many of these challenges providing pragmatic solutions, which can entirely change the future of wireless network technologies. By using some case study examples, we briefly examine the most compelling problems, particularly at the physical (PHY) and link layers in cellular networks where ML can bring significant gains. We also review standardization activities in relation to the use of ML in wireless networks and future timeline on readiness of standardization bodies to adapt to these changes. Finally, we highlight major issues in ML use in the wireless technology, and provide potential directions to mitigate some of them in 6G wireless networks., Comment: 6

Artificial intelligence and data-driven networks will be integral part of 6G systems. In this article, we comprehensively discuss implementation challenges and need for architectural changes in 5G radio access networks for integrating machine learning (ML) solutions. As an example use case, 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. In this regard, we study ML-assisted positioning methods and evaluate their performance using system level simulations for an outdoor scenario. The study is based on the use of raytracing tool, a 3GPP 5G NR compliant system level simulator and DL framework to estimate positioning accuracy of the UE. We evaluate and compare performance of various DL models and show mean positioning error in the range of 1-1.5m for a 2-hidden layer DL architecture with appropriate feature-modeling. Building on our performance analysis, we discuss pros and cons of various architectures to implement ML solutions for future networks and draw conclusions on the most suitable architecture., Comment: IEEE Open Journal of Vehicular Technology Society. arXiv admin note: substantial text overlap with arXiv:2001.00977

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., Comment: The final version can be found in IEEE Transactions on Vehicular Technology

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/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., Comment: 17pages. This work has been accepted by IEEE JSAC. Copyright may be transferred without notice, after which this version may no longer be accessible

Delivery of broadcast messages among vehicles for safety purposes, which is known as one of the key ingredients of Intelligent Transportation Systems (ITS), requires an efficient Medium Access Control (MAC) that provides low average delay and high reliability. To this end, a Geo-Location Based Access (GLOC) for vehicles has been proposed for Vehicle-to-Vehicle (V2V) communications, aiming at maximizing the distance of co-channel transmitters while preserving a low latency when accessing the resources. In this paper we analyze, with the aid of stochastic geometry, the delivery of periodic and non-periodic broadcast messages with GLOC, taking into account path loss and fading as well as the random locations of transmitting vehicles. Analytical results include the average interference, average Binary Rate (BR), capture probability, i.e., the probability of successful message transmission, and Energy Efficiency (EE). Mathematical analysis reveals interesting insights about the system performance, which are validated thought extensive Monte Carlo simulations. In particular, it is shown that the capture probability is an increasing function with exponential dependence with respect to the transmit power and it is demonstrated that an arbitrary high capture probability can be achieved, as long as the number of access resources is high enough. Finally, to facilitate the system-level design of GLOC, the optimum transmit power is derived, which leads to a maximal EE subject to a given constraint in the capture probability., Comment: 15 pages and 16 figures. This paper have been submitted for possible publication in IEEE Transactions on Vehicular Technology

This paper compares three candidate large-scale propagation path loss models for use over the entire microwave and millimeter-wave (mmWave) radio spectrum: the alpha-beta-gamma (ABG) model, the close-in (CI) free space reference distance model, and the CI model with a frequency-weighted path loss exponent (CIF). Each of these models have been recently studied for use in standards bodies such as 3GPP, and for use in the design of fifth generation (5G) wireless systems in urban macrocell, urban microcell, and indoor office and shopping mall scenarios. Here we compare the accuracy and sensitivity of these models using measured data from 30 propagation measurement datasets from 2 GHz to 73 GHz over distances ranging from 4 m to 1238 m. A series of sensitivity analyses of the three models show that the physically-based two-parameter CI model and three-parameter CIF model offer computational simplicity, have very similar goodness of fit (i.e., the shadow fading standard deviation), exhibit more stable model parameter behavior across frequencies and distances, and yield smaller prediction error in sensitivity testing across distances and frequencies, when compared to the four-parameter ABG model. Results show the CI model with a 1 m close-in reference distance is suitable for outdoor environments, while the CIF model is more appropriate for indoor modeling. The CI and CIF models are easily implemented in existing 3GPP models by making a very subtle modification -- by replacing a floating non-physically based constant with a frequency-dependent constant that represents free space path loss in the first meter of propagation., Comment: Open access available at: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7434656

It is becoming clear that 5G wireless systems will encompass frequencies from around 500 MHz all the way to around 100 GHz. To adequately assess the performance of 5G systems in these different bands, path loss (PL) models will need to be developed across this wide frequency range. The PL models can roughly be broken into two categories, ones that have some anchor in physics, and ones that curve-match only over the data set without any physical anchor. In this paper we use both real-world measurements from 2 to 28 GHz and ray-tracing studies from 2 to 73.5 GHz, both in an urban-macro environment, to assess the prediction performance of the two PL modeling techniques. In other words, we look at how the two different PL modeling techniques perform when the PL model is applied to a prediction set which is different in distance, frequency, or environment from a measurement set where the parameters of the respective models are determined. We show that a PL model with a physical anchor point can be a better predictor of PL performance in the prediction sets while also providing a parameterization which is more stable over a substantial number of different measurement sets., Comment: 5 pages, 8 figures,in 2016 IEEE 83rd Vehicular Technology Conference (Spring VTC-2016), May 2016

This paper presents key parameters including the line-of-sight (LOS) probability, large-scale path loss, and shadow fading models for the design of future fifth generation (5G) wireless communication systems in urban macro-cellular (UMa) scenarios, using the data obtained from propagation measurements at 38 GHz in Austin, US, and at 2, 10, 18, and 28 GHz in Aalborg, Denmark. A comparison of different LOS probability models is performed for the Aalborg environment. Alpha-betagamma and close-in reference distance path loss models are studied in depth to show their value in channel modeling. Additionally, both single-slope and dual-slope omnidirectional path loss models are investigated to analyze and contrast their root-mean-square (RMS) errors on measured path loss values. While the results show that the dual-slope large-scale path loss model can slightly reduce RMS errors compared to its singleslope counterpart in non-line-of-sight (NLOS) conditions, the improvement is not significant enough to warrant adopting the dual-slope path loss model. Furthermore, the shadow fading magnitude versus distance is explored, showing a slight increasing trend in LOS and a decreasing trend in NLOS based on the Aalborg data, but more measurements are necessary to gain a better knowledge of the UMa channels at centimeter- and millimeter-wave frequency bands., Comment: to appear in proceedings of IEEE Global Communications Conference Workshop, Dec. 2015

This paper presents and compares two candidate large-scale propagation path loss models, the alpha-beta-gamma (ABG) model and the close-in (CI) free space reference distance model, for the design of fifth generation (5G) wireless communication systems in urban micro- and macro-cellular scenarios. Comparisons are made using the data obtained from 20 propagation measurement campaigns or ray-tracing studies from 2 GHz to 73.5 GHz over distances ranging from 5 m to 1429 m. The results show that the one-parameter CI model has a very similar goodness of fit (i.e., the shadow fading standard deviation) in both line-of-sight and non-line-of-sight environments, while offering substantial simplicity and more stable behavior across frequencies and distances, as compared to the three-parameter ABG model. Additionally, the CI model needs only one very subtle and simple modification to the existing 3GPP floating-intercept path loss model (replacing a constant with a close-in free space reference value) in order to provide greater simulation accuracy, more simplicity, better repeatability across experiments, and higher stability across a vast range of frequencies., Comment: in 2016 IEEE 83rd Vehicular Technology Conference (VTC2016-Spring), May 2016, Nanjing, China