Pathloss In 5g
Allen Yerke
SIn wireless communications, the pathloss (or large scale fading coefficient) quantifies the lossof signal strength between a transmitter (Tx) and a receiver (Rx) due to large scale effects, such asfree-space propagation loss, and interactions of the radio waves with the obstacles (which blockline-of-sight, like buildings, vehicles, pedestrians), e.g. penetrations, reflections and diffractions.Many present or envisioned applications in wireless communications explicitly rely on theknowledge of the pathloss function, and thus, estimating pathloss is a crucial task. Someexample use cases include: User-cell site association, fingerprint-based localization, physical-layer security, optimal power control, path planning, and activity detection.Deterministic simulation methods such as ray-tracing are well-known to provide very goodestimations of pathloss values. However, their high computational complexity renders themunsuitable for most of the envisioned applications.In the very recent years, many research groups have developed deep learning-based methodswhich achieve a comparable accuracy with respect to ray-tracing, but with orders of magnitudelower computational times, making accurate pathloss estimations available for the applications.In order to foster research and facilitate fair comparisons among the methods, we provide anovel pathloss radio map dataset based on ray-tracing simulations and launch the First PathlossRadio Map Prediction Challenge.In addition to the pathloss prediction task, the challenge also includes coverage classificationas a second independent task, where the locations in a city map should be classified to be aboveor below a given pathloss value.Support on the dataset and the instructions will be provided by the organizing team.
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Many present or envisioned applications in wireless communications explicitly rely on theknowledge of the pathloss function, and thus, estimating pathloss is a crucial task. Someexample use cases include: User-cell site association, fingerprint-based localization, physical-layer security, optimal power control, path planning, and activity detection.
Deterministic simulation methods such as ray-tracing are well-known to provide very goodestimations of pathloss values. However, their high computational complexity renders themunsuitable for most of the envisioned applications.
In the very recent years, many research groups have developed deep learning-based methodswhich achieve a comparable accuracy with respect to ray-tracing, but with orders of magnitudelower computational times, making accurate pathloss estimations available for the applications.
In order to foster research and facilitate fair comparisons among the methods, we provide anovel pathloss radio map dataset based on ray-tracing simulations and launch the First PathlossRadio Map Prediction Challenge.In addition to the pathloss prediction task, the challenge also includes coverage classificationas a second independent task, where the locations in a city map should be classified to be aboveor below a given pathloss value.
The top 5 ranked teams will be invited to submit a 2-page paper and present it at ICASSP2023. The accepted papers will be published in the ICASSP proceedings. The teams that presenttheir work at ICASSP are also invited to submit a full paper about their work to IEEE OpenJournal of Signal Processing.
IMPORTANT NOTE: The intellectual property (IP) of the shared/submitted material (e.g. code) will not be transferred to thechallenge organizers. When such material is made publicly available by a participant, an appropriate license should accompany.
In coverage (or service area) classification, the goal is to classify a region of interest accordingto their pathloss values being above or below some pre-determined pathloss value, given a Txwith known location and the city map. The goal is then to predict the coverage map from the city map and transmitter location.
Path loss, returned as a scalar or as an M-by-N cell array, where each cell contains a row vector of path losses in decibels. M is the number of transmitter sites and N is the number of receiver sites.
For terrain propagation models, the function computes path loss using a terrain elevation profile that it creates from sample locations on the great circle path between the transmitter and receiver. If Map is a siteviewer object with buildings specified, the function adjusts the elevation to include the heights of the buildings.
[1] International Telecommunications Union Radiocommunication Sector. Effects of Building Materials and Structures on Radiowave Propagation Above About 100MHz. Recommendation P.2040. ITU-R, approved August 23, 2023. -REC-P.2040/en.
When calculating path loss using ray tracing models, the pathloss function models materials using the methods and equations in International Telecommunication Union Recommendations (ITU-R) P.2040-3 [1] and ITU-R P.527-5 through ITU-R P.527-6 [2].
Ray tracing propagation models discard propagation paths based on path loss thresholds. By default, when you specify the propmodel input argument as a RayTracing object, the propagation model discards paths that are more than 40 dB weaker than the strongest path.
As a result, the pathloss function can return different values in R2023a compared to previous releases. To avoid discarding propagation paths based on relative path loss thresholds, set the MaxRelativePathLoss property of the ray tracing object to Inf.
Ray tracing models that find propagation paths by using the shooting and bouncing rays (SBR) method correct the results so that the geometric accuracy of each path is exact, using single-precision floating-point computations. In previous releases, the paths have approximate geometric accuracy.
Whether a professional or amateur, one of the first things we learn about propagation is that lower frequencies propagate better. Or propagation loss increases with frequency. In this post I will make the case that Free Space Path Loss really does not exist, and that the real effect we see is independent of frequency.
Let us first understand that the freespace pathloss formula is derived from this basic principle. In the formula above(fig2), we show the transmitted power in the numerator(above the line). In the denominator, we see what is clearly recognisable as the formula for the surface area of a sphere. The intensity of the signal decreases as the surface area of a sphere of radius d(yes, I know radius is normally r, but for some reason RF people use d to equal distance). There is no loss, but the power density, which would have units like Watts per Meter Squared, decreases according to the size of the sphere. This in my opinion really is the fundamental formula we should understand. Its not complicated, and very easy to intuitively understand.
So, why does our FSPL formula differ from this. Well its because of the way we specify antenna gain. For good reasons, we do this in a way that similar similar antennas provide the same gain when scaled for frequency. A standard half-wave dipole will have a gain of 2.15dBi regardless of frequency. Our dipole is tuned to a specific frequency that we can change by making it shorter or longer. If we want to make our antenna work at a higher frequency we make it shorter. The power intensity we calculated in fig2 is in units of W/m^2, ie. Watts per Unit Area, it stands to reason that if the Area is smaller(because our antenna is shorter) there will be less received power.
For my Palmtree Vivaldi antenna, I did a similar calculation. As a planar antenna, its hard to easily see what the aperture is, but given a bit of experimentation I was able to derive a suitable area that worked for my needs. In the diagram below I have drawn a cyan rectangle to represent the assumed aperture for my antenna(45cm^2). Assuming this was fixed, I calculated the calculated the expected gain that this would result in.
In the above plot, we can see that the blue gain(dBi) and red fixed aperture gain plots show very good correlation. There would be very little point in trying to optimise this antenna any further to enhance gain, unless we are willing to try and increase the aperture(and hence size).
I have been careful in this post to only discuss things in freespace(i.e. a vacuum). when we start to add real world materials into the mix, things definitely diverge from this. House bricks definitely do increase the loss of signals that pass through them and this loss is very much dependent upon frequency, although not necessarily in a way that can be easily inserted into such a simple mathematical formula. However, when we start to think about propagation in environments that contain significant other materials, FSPL is probably not the way we want to estimate pathloss.
There are two methods to enter pathloss; and you need to be cautious that only one method is being used at a time. On the tester (through the GUI), you can enter pathloss using Tools > Port Routing > Pathloss. This is NOT recommended when using IQfact+.
This dataset contains pathloss and ToA radio maps generated by the ray-tracing software WinProp from Altair. The dataset allows to develop and test the accuracies of pathloss radio map estimation methods and localization algorithms based on RSS or ToA in realistic urban scenarios. More details on the datasets can be found in the dataset paper:
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