Beamforming in mmWave Transmission for 5G Networks
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Hiba Mallouhi
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, Jaspreet Kaur
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, Hasan Tahir Abbas
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Sándor Laki
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* ELTE Eötvös Loránd University, Budapest, Hungary
** University of Glasgow, Glasgow, United Kingdom
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mmWave transmission in 5G
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High propagation loss, signal blockage
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Reduced distance
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Beamforming
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Extended communication range
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Enhanced signal quality
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Many advantages…
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Beamforming direction is challenging
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BS needs to determine the DOA of its users
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Scanning-like approaches has high energy consumption
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Other techniques has high computational complexities
Beamforming
An User-Assisted Approach
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UE reports it’s location
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based on GPS or other sources
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Processing inside the AAN
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Messages handled as control packets
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P4 switch assumption
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Angle computation (or distance, etc.)
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Generate configuration message to be sent to the BS
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BS reconfigures the beam
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According to new angle (and other) information
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UE coordinates:
loc.x, loc.y
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BS coordinates:
bs.x, bs.y
Angle computation is simple
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UE coordinates:
loc.x, loc.y
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BS coordinates:
bs.x, bs.y
Angle computation is simple
…but, in P4?
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Assuming Tofino-like capabilities
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Constrained hardware
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Pipeline packet processing with lookup tables
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Location message from a UE
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Special header format, coordinates and BS id
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BS locations stored in a lookup table
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Mapping BS id to coordinates
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Absolute value of location difference and
the sign of the difference
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The signs determines the quarter
around the BS
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Few properties
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Desired accuracy determines the required TCAM space
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Other parameters like power, signal strength can similarly
be added in the future...
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Customization of the table is also possible
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Signs determine the quadrant
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Implemented on Tofino-1 ASIC
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Theoretical bound on the angle approximation accuracy
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Accuracy vs TCAM space
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Various cell diameters (D): 200-1000 meters
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Various numbers of bins (N): 20-80
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UE moving at a constant speed
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Control cycle
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time from sending a location message to the reconfiguration of the beam at the BS
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P4 switch
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UE moving at a constant speed
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Control cycle
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time from sending a location message to the reconfiguration of the beam at the BS
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P4 switch
Additional error
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UE’s speed: 5-200 kmph
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Control cycle: 5-100ms
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Simulation
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Number of bins (N): 20
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Diameter fixed: 800m
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UE’s speed: 5-200 kmph
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Control cycle: 5-100ms
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Simulation
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Number of bins (N): 80
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Diameter fixed: 800m
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Predictable TCAM size
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And stage number
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Low SRAM usage
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In-network solutions results in low latency replies
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Angle computation is complex but can be approximated
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Bound on the accuracy
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Requires moderate per-stage resources
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Possibility to co-locate with other pipelines like 5G-UPF
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Integration with a realistic radio propagation simulator
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to quantify the effect of our method on channel quality
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Efficient customization of angle prediction table
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E.g., considering BS profiles
Conclusion &
Future plans
Adaptive beamforming plays a vital role in supporting mmWave transmission in 5G networks, addressing challenges like high propagation loss and signal blockage. The use of an innovative user-assisted approach, involving angle computation and configuration messages, enhances the beamforming process for improved signal quality and extended communication range, overcoming complexities associated with determining the direction of arrival for base stations.
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Presentation Transcript
In In- -network network Angle for for Supporting Supporting Adaptive Angle Approximation Approximation Adaptive Beamforming Beamforming Hiba Mallouhi*, Jaspreet Kaur**, Hasan Tahir Abbas**, S ndor Laki* * ELTE E tv s Lor nd University, Budapest, Hungary ** University of Glasgow, Glasgow, United Kingdom
Beamforming mmWave transmission in 5G High propagation loss, signal blockage Reduced distance Beamforming Extended communication range Enhanced signal quality Many advantages Beamforming direction is challenging BS needs to determine the DOA of its users Scanning-like approaches has high energy consumption Other techniques has high computational complexities
An User-Assisted Approach UE reports it s location based on GPS or other sources Processing inside the AAN Messages handled as control packets P4 switch assumption Angle computation (or distance, etc.) Generate configuration message to be sent to the BS BS reconfigures the beam According to new angle (and other) information
Angle computation is simple UE coordinates: loc.x, loc.y BS coordinates: bs.x, bs.y ???.? ??.? ???.? ??.? ???.? ??.? ???.? ??.? ???.? ??.? ???.? ??.? ???.? ??.? ???.? ??.? arctan ???.? > ??.? ??? ???.? > ??.? ? arctan ???.? > ??.? ??? ???.? < ??.? = + arctan ???.? < ??.? ??? ???.? < ??.? 2 arctan ???.? < ??.? ??? ???.? > ??.?
Angle computation is simple UE coordinates: loc.x, loc.y BS coordinates: bs.x, bs.y but, in P4? ???.? ??.? ???.? ??.? ???.? ??.? ???.? ??.? ???.? ??.? ???.? ??.? ???.? ??.? ???.? ??.? arctan ???.? > ??.? ??? ???.? > ??.? ? arctan ???.? > ??.? ??? ???.? < ??.? = + arctan ???.? < ??.? ??? ???.? < ??.? 2 arctan ???.? < ??.? ??? ???.? > ??.?
In P4? Preparation Preparation Assuming Tofino-like capabilities Constrained hardware Pipeline packet processing with lookup tables Location message from a UE Special header format, coordinates and BS id BS locations stored in a lookup table Mapping BS id to coordinates Absolute value of location difference and the sign of the difference The signs determines the quarter around the BS
In P4? Computing Computing arctan arctan ?.? ?.? Goal: ?????? Grid-based approximation with exponential binning The grid points on both axes are the same: 0, ?1, ?2, . . . , ? ?? ?= ? ?, where ? is the base number, ? is the granularity factor (the number of bins on the axes) while ? is the cell diameter (the maximum range) Approx. based on the centroid point of the containing grid-cell
In P4? Computing Computing arctan arctan Few properties Desired accuracy determines the required TCAM space Other parameters like power, signal strength can similarly be added in the future... Customization of the table is also possible ?.? ?.? Goal: ?????? Grid-based approximation with exponential binning The grid points on both axes are the same: 0, ?1, ?2, . . . , ? ?? ?= ? ?, where ? is the base number, ? is the granularity factor (the number of bins on the axes) while ? is the cell diameter (the maximum range) Approx. based on the centroid point of the containing grid-cell
In P4? Final Final angle angle transformation transformation Signs determine the quadrant ???.? ??.? ???.? ??.? ???.? ??.? ???.? ??.? ???.? ??.? ???.? ??.? ???.? ??.? ???.? ??.? arctan ???.? > ??.? ??? ???.? > ??.? ? arctan ???.? > ??.? ??? ???.? < ??.? = + arctan ???.? < ??.? ??? ???.? < ??.? 2 arctan ???.? < ??.? ??? ???.? > ??.?
In P4? The The whole whole pipeline pipeline Implemented on Tofino-1 ASIC Theoretical bound on the angle approximation accuracy Accuracy vs TCAM space
Evaluation Approximation Approximation Error Error in in static static scenarios scenarios Various cell diameters (D): 200-1000 meters Various numbers of bins (N): 20-80
Evaluation Moving Moving UE and UE and control control latency latency UE moving at a constant speed Control cycle time from sending a location message to the reconfiguration of the beam at the BS Loc msg P4 switch
Evaluation Moving Moving UE and UE and control control latency latency UE moving at a constant speed Control cycle time from sending a location message to the reconfiguration of the beam at the BS Additional error P4 switch
Evaluation Moving Moving UE and UE and control control latency latency UE s speed: 5-200 kmph Control cycle: 5-100ms Simulation Number of bins (N): 20 Diameter fixed: 800m
Evaluation Moving Moving UE and UE and control control latency latency UE s speed: 5-200 kmph Control cycle: 5-100ms Simulation Number of bins (N): 80 Diameter fixed: 800m
Evaluation Cost of Cost of accuracy accuracy Predictable TCAM size And stage number Low SRAM usage
Conclusion & Future plans In-network solutions results in low latency replies Angle computation is complex but can be approximated Bound on the accuracy Requires moderate per-stage resources Possibility to co-locate with other pipelines like 5G-UPF Integration with a realistic radio propagation simulator to quantify the effect of our method on channel quality Efficient customization of angle prediction table E.g., considering BS profiles
