5G RF Planning and Design

We analyze the radio access network to dimension it, determine the minimum cells from coverage criteria and from capacity requirements, then choose the maximum to satisfy both.

Compute the maximum allowable path loss, derive the cell radius from it, and then determine the total number of cells required to serve the service area.

Explore how path loss attenuates signals in both downlink and uplink as they propagate through the wireless channel, reducing power.

Explore 3GPP propagation pathloss models for macro and micro cells across dense urban, urban, suburban, and rural environments, including the You and Me model and the Artemis model.

Differentiate 2D ground distance and 3D distance between the base station and user equipment, incorporating user terminal height, using a Pythagoras-based calculation from 3GPP to sum outside and inside distances.

Explore 3GPP propagation models in rural macro scenarios, comparing line-of-sight and non-line-of-sight paths, break point distance, and baseline path-loss formulas for 5G rf planning.

Explore 3GPP propagation models for urban macro and urban micro, detailing LOS and NLOS scenarios, breakpoint distances, height adjustments, and path loss formulas.

Explore the Okumura-Hata propagation model from Tokyo measurements, including its 150 to 1500 megahertz range, 120 km distance, and base and mobile antenna heights; note its limitations for micro cells.

Analyze the cost-231 and Okumura models for 1.3–2.0 GHz in medium and small cities. Explain the empirical path-loss equation and constants CM, plus limitations for rooftop and micro-cell scenarios.

Explore the Walfisch Ikegami and Sakagami Kuboi models for microcell planning, incorporating Okumura-based insights for base stations below rooftops across 450 MHz to 2000 MHz.

Select suitable path loss models for 5g rf planning based on frequency, range, location, and collector type to minimize the standard deviation between calculated and measured received power.

Learn how to calculate the maximum allowable path loss (MAPL) using a link budget for downlink and uplink, accounting for transmit power, gains, losses, margins, and receive sensitivity.

Explain how to compute receiver sensitivity—the minimum power to decode bits—using thermal noise, noise figure, and cnr threshold, with bandwidth and vendor-specific values shaping user equipment and gmb limits.

Calculate the maximum allowable path loss for uplink and downlink, pick the minimum, then compute the cell radius, area, site distance, and the required number of cells for various layouts.

Explore factors shaping the 5g link budget, including base station transmit power per subcarrier, feeder, penetration, foliage, body block, interference, margins, and downlink vs uplink sensitivity.

Apply 3GPP-based calculations to building penetration loss, combining outdoor loss (DLP), wall material losses, angle of incidence, and frequency effects with indoor propagation in rural and urban macro contexts.

Discusses building penetration loss values across frequencies for 5g rf planning, with example dB values for dense urban, suburban, and rural regions and averaging between 2.1 and 2.6 GHz.

Assess foliage loss between base stations and user equipment in 5g rf planning. Apply vegetation loss values around 17 db, with adjustments for density and higher frequency bands.

Explore rain feed margin for 5g rf planning, detailing how rainfall rate, frequency, drop size, velocity, and water properties influence margin, and how to use prior-year data in link budgets.

Assess body block loss at 28 ghz by distance, number of people, positions, and heights in line-of-sight, using 15 db; in non-line-of-sight, losses drop as signals reach from multiple paths.

Analyze interference margin in the 3.5 gigahertz and 28 gigahertz bands, detailing downlink and uplink interference between neighboring base stations and user equipment.

Explain how shadow fading margin raises the average received power to improve location probability at the cell edge and clarify the difference between cell edge and cell area coverage probabilities.

Raising the shadow operating margin reduces the cell radius as the shared margin grows. This reduction arises with higher allocation and location probabilities and depends on the scheduling standard deviation.

Analyze how shadow fading standard deviation (sigma) in 3GPP models governs LOS and NLOS across rural macro, urban macro, street canyon, and indoor office, via form factor and cell-edge probability.

Boost capacity and coverage with 5G massive MIMO and 3D beamforming. Combine antenna and remote radio unit into an active antenna unit to reduce feeder losses and boost capacity.

Antenna type and array size influence the link budget, and larger 64x64 arrays with beamforming reduce interference, boosting received power, coverage, and capacity.

Assess capacity dimensions to estimate required sites from busy hour traffic, compute traffic offered by user equipment and average cell throughput, and select the higher capacity or coverage estimate.

Assess the BCR user traffic model for capacity dimensioning by defining uplink and downlink message sizes, transfer intervals, and data rates and deriving average throughput and RRC state metrics.

Compute the average cell throughput with system-level simulations using the journal network configuration and minimal setup, incorporating carrier frequency, channel bandwidth, cell radius, and link-budget margins for uplink and downlink.

Apply overload thresholds to average cell throughput to compute the maximum number of subscribers, then derive required cells and sites, considering uplink and downlink and site layouts for capacity dimensions.

Propagation models rely on regional measurements, so Okumura–Hata based on Tokyo data must be tuned for another city via drive tests, using a planning tool.

Prepare the drive test setup for continuous wave testing with a transmitter (omnidirectional), a power supply, and a GPS-enabled receiver kit to map received power along radial and circumferential routes.

Conduct drive test measurements across dense urban, hotspot, and semi-urban regions, accounting for bridges and elevated heights, while recording longitude, latitude, received power, and transmitter details.

Tune the COST-231 standard propagation model into a semi-empirical form by introducing coefficients for distance, heights, diffraction loss, clutter, and line-of-sight corrections to match drive-test received power.

Explore tuning K1 and K2 coefficients in the propagation model for 5g rf planning, using drive-test data to adjust distance and slope factors while fixing known transmitter and receiver parameters.

Understand how diffraction bends electromagnetic signals at edges and causes diffraction loss, and adjust the K4 coefficient with 3D maps to tune K1, K2, and the propagation model.

Set the clutter loss coefficient k_clutter to one and set the letter loss to zero, so clutter loss does not impact tuning results.

Assess model tuning by comparing drive-test receive power to predicted power, compute mean error and standard deviation, and classify tuning quality from exceptional to fair using dB thresholds.

Tune the Atoll model using drive test data to align predicted and measured received power. Iterate gear values to minimize error and reproduce the 93 dBm at 500 m coverage.

Use nominal planning to run detailed network simulations and determine optimal base station locations for continuous coverage, using radio frequency planning tools and digitally mapped sites tuned by drive testing.

Configure the RF planning pool to compute coverage using frame structure, subcarrier spacing, 5G mode, bands, bandwidth, and synchronization signal block details; import plans and place three-sector sites with antennas.

Learn the inputs and outputs of 5g coverage planning simulation, including site location, antenna parameters, digital map information, and clutter data, then predict received power with beamforming and raytracing.

Utilize the 3D ray tracing model with 3D digital maps to simulate electromagnetic rays and integrate with 5G massive MIMO beamforming for more accurate coverage predictions than non-tuned models.

Explore two maps for 5G planning: 2-D goody maps with building heights, five-meter precision for empirical models, and 3-D maps (3D vectors) for ray tracing, in roster and vector formats.

Explore digital terrain maps, clutter maps, digital height maps, and 3D vector maps, and see how altitude and 3D contour data support ray tracing and 3D building and vegetation attributes.

Use static coverage planning and Monte Carlo simulations with traffic and mobility to assess throughput and blocking; composite and dominance plots reveal coverage gaps and overlaps guiding antenna adjustments.

Learn 3-D coverage prediction in 5G rf planning with the layer method for floor-level results and the facade method for building facades.

Monte Carlo dynamic simulations model network services using traffic maps to generate a user distribution and compute KPIs like signal to interference noise and blocked calls across snapshots with mobility.

Define new services in the planning tool by naming the service, selecting voice or data, and specifying uplink and downlink throughput with activity factors and detailed modulation and coding parameters.

Define user profiles by services and usage patterns, set geographic density and mobility, and use a traffic map to classify environments and calculate user distribution for 5g rf planning.

Initialize Monte Carlo simulations by loading maps, configuration files, and traffic data to create base station and user equipment scenarios, determine coverage, compute signal-to-interference-and-noise ratio, and assess KPIs until convergence.

Monte Carlo simulations generate detailed coverage and capacity maps for 5G RF planning, showing received signal power, signal-to-interference-noise ratio, and throughput for downlink and uplink.

Explore how 5G NR beam planning uses 3D beams to improve capacity and coverage from rural to city centers, with sharper beams and more antennas steered to high-rise coverage.

classify 5g beams into static and dynamic types. static beams include broadcast beams carrying the physical broadcast channel and synchronization sequences; control beams carry the physical downlink control channel.

Explore static beams in 5g rf planning, including broadcast beam cycling in bowling mode and uplink sounding reference signals guiding the downlink control beam selection with the strongest received signal.

Dynamic beams carry data to user equipment and adjust shape and direction according to user movement. At the cell edge, beams sharpen to maximize signal to interference noise for coverage.

Examine 5g rf beam scenarios by comparing horizontal and vertical widths, from scenario one (110°/6°) for large areas to scenario five (5°/6°) for small buildings, and 12–16 for high-rise coverage.

Calculate horizontal and vertical beam angles for a broadcast scenario. Use building width, distance, base station height, and vertical coverage height to select a suitable scenario from one to sixteen.

Examine how 5G down-tilt planning shapes antenna gain, using the down tilt angle to balance cell coverage and interference, while noting 5G beams separate traffic, broadcast, and control channels.

Examine mechanical tilt, using a pole mount to tilt the beam downward, with drawbacks like raised back and side lobes and interference within minus 20 to 20 degrees.

Explore electrical tilt by adjusting signal phases across antenna elements to shape beams, examine preset electrical tilt built into antennas, and compare mechanical tilt for physical beam steering.

Define the total 5G beam downtilt as the sum of mechanical and electrical downtilt, including the digital dome, for traffic, broadcast, and control channels.

Ensure that in a given area, a user equipment using the ssb beam and the csi-rsrp beam yields the same best serving gnb.

Explore 5G RF downtilt planning principles to optimize downlink coverage for traffic, control, and broadcast beams, prioritizing adjustable electrical down tilt before mechanical and digital down tilt optimization.

Learn PCI planning in 5G RF design: assign physical cell IDs in the SSP block, configure SS and SD values, and plan neighbors and tracking areas for collision-free operation.

Apply the collision free principle in 5g rf planning by ensuring neighboring cells do not share the same BCI value, preventing BCI collisions in overlapping zones.

Apply the PCI confusion-free principle by ensuring neighboring cells do not share the same PCI value, preventing handover ambiguity when a user equipment moves between cells; reuse after a distance.

Minimize the PCI planning impact on network performance by reducing interference on the primary synchronization signal and the DMRS, and by coordinating the sounding reference signal in uplink and downlink.

Stagger PCI mod 3 values across neighboring cells to avoid identical BCA modulo 3 results, preventing delays in cell acquisition and misestimation.

Apply pci mod 4 staggering to dmrs on the pbch by varying the fourth subcarrier based on bca mode, preventing identical bca values in neighboring cells and reducing interference.

Explore PCI mod 30 staggering to reduce uplink DMRS interference across PUSCH, PUCCH, and SRS by using ZC sequences from root sequences with cyclic shifts and BCI values.

Discover how the physical random access channel enables user equipment via uplink preambles, cyclic prefix, and delay measurement for synchronization, with preambles formed by shifts of Z Sea Route sequences.

Examine long preamble sequences for large fr1 cells with 1.25 or 5 khz spacing. Examine short preamble sequences for small cells, fr1 and fr2, with 15–30 and 60–120 khz spacing.

Explore the steps in prach planning by selecting a bridge format, calculating cyclic shifts in the root sequence index, and determining preambles and root sequence index groups per cell.

Determine PRACH format by evaluating uplink band, duplex mode, downlink-uplink subframe ratio, and cell radius; select between format two with 15 kilohertz SCS or format zero with 1.25 kilohertz.

Calculate the number of cyclic shifts Ncs from the cell radius by applying sampling time and delay spread formulas for short and long preambles, subcarrier spacing, and round-trip delay.

Calculate the number of preambles per road sequence index using long and short preamble formats, dividing RSI length by cyclic shifts and rounding down, e.g., 39 by 34 equals 4.

Calculate the number of RSIs per cell and RSI groups by dividing required preambles by preambles per route sequence index, with a short format example yielding 8 RSIs per cell.