Skip to main content

LUP Student Papers

LUND UNIVERSITY LIBRARIES

Investigate Redundancy In Sounding Reference Signal Based Channel Estimates

Bhavani Sankar, Nishanth LU and Thakaramkunnath Ajayakumar, Akshay (2019) EITM02 20191
Department of Electrical and Information Technology
Abstract
5G supports enormous increase in data rate. Massive antenna beamforming is expected to play a key role in increasing capacity in case of multi-user MIMO and coverage in case of single-user MIMO. The large number of antennas in massive MIMO system will lead to enormous amount of channel state information being stored in the memory and this necessitates the use of compression techniques for efficient utilization of memory, which is limited.
Sounding Reference Signals (SRS) are transmitted in the uplink to obtain channel estimate. In TDD based systems, by exploiting channel reciprocity channel estimates received in the uplink can be used in downlink as well. The product, we work on at Ericsson, is a TDD based system and uses SRS based... (More)
5G supports enormous increase in data rate. Massive antenna beamforming is expected to play a key role in increasing capacity in case of multi-user MIMO and coverage in case of single-user MIMO. The large number of antennas in massive MIMO system will lead to enormous amount of channel state information being stored in the memory and this necessitates the use of compression techniques for efficient utilization of memory, which is limited.
Sounding Reference Signals (SRS) are transmitted in the uplink to obtain channel estimate. In TDD based systems, by exploiting channel reciprocity channel estimates received in the uplink can be used in downlink as well. The product, we work on at Ericsson, is a TDD based system and uses SRS based channel estimates to compute beamforming weights to facilitate massive antenna beamforming.
SRS based channel state information is represented by 32-bit complex number in this system, which is received per Evolved Node B (eNodeB) antenna, per User Equipment (UE) transmission antenna, and per Physical Resource Block Group (PRBG). This results in a significant amount of data that needs to be stored in the eNodeB. However, memory in the Digital Unit of eNodeB is limited. SRS based estimates occupy a major portion of this memory and therefore limit the capacity of the eNodeB for beamforming.
This thesis focuses on the evaluation and implementation of lossless and lossy compression of SRS based channel estimates to attain space savings in the shared memory of eNodeB. This will help in achieving higher capacity for reciprocity-based beamforming and prolong the lifetime of existing hardware.
Performance of various lossless data compression algorithms was analyzed based on compression ratio, speed and complexity and the optimal one was selected.
Lossy compression of SRS based channel estimates was also implemented for LOS UEs using linear regression by least squares estimate. Impact on performance due to application of lossy compression algorithm was studied. (Less)
Popular Abstract
In order to reliably communicate over the air, the receiver needs to estimate the quality of the wireless link. This is done by the transmission of certain signals named ‘pilots’.
In cellular communications, such as 5G, pilots are sent in both directions, which is from base station to the user and vice versa. To support 5G systems, numerous antennas will be used at transmitter, receiver or both. Such systems are called as massive multiple input multiple output (Massive MIMO) systems. Pilots need to be transmitted for each of these antennas and this will lead to significant amount of data. For efficient and reliable systems, it is important to ensure that the least amount of such information is stored in the base station, which... (More)
In order to reliably communicate over the air, the receiver needs to estimate the quality of the wireless link. This is done by the transmission of certain signals named ‘pilots’.
In cellular communications, such as 5G, pilots are sent in both directions, which is from base station to the user and vice versa. To support 5G systems, numerous antennas will be used at transmitter, receiver or both. Such systems are called as massive multiple input multiple output (Massive MIMO) systems. Pilots need to be transmitted for each of these antennas and this will lead to significant amount of data. For efficient and reliable systems, it is important to ensure that the least amount of such information is stored in the base station, which necessitates the use of data compression techniques.
The link quality values obtained can be compressed by lossy methods which involve loss of some information but higher compression and by lossless methods that have no loss of information but lower compression.
Aim of this thesis is to study about a certain type of pilot, namely Sounding Reference Signal, and compressing the obtained link quality values. Hence, this thesis focuses on analyzing the performance of various compression techniques based on their ability to compress this data, speed of the program and complexity of implementation and selecting the optimal technique based on the analysis.
Implementation of compression of these link quality values will help to prolong the lifetime of the equipment used now and can help in saving costs for Telecom companies. (Less)
Please use this url to cite or link to this publication:
author
Bhavani Sankar, Nishanth LU and Thakaramkunnath Ajayakumar, Akshay
supervisor
organization
course
EITM02 20191
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Sounding Reference Signals, Multiple Input Multiple Output, 5th Generation, Time Division Duplexing, Lossless compression, Lossy Compression
report number
LU/LTH-EIT 2019-725
language
English
id
8994644
date added to LUP
2019-09-12 11:03:55
date last changed
2019-09-12 11:03:55
@misc{8994644,
  abstract     = {{5G supports enormous increase in data rate. Massive antenna beamforming is expected to play a key role in increasing capacity in case of multi-user MIMO and coverage in case of single-user MIMO. The large number of antennas in massive MIMO system will lead to enormous amount of channel state information being stored in the memory and this necessitates the use of compression techniques for efficient utilization of memory, which is limited.
Sounding Reference Signals (SRS) are transmitted in the uplink to obtain channel estimate. In TDD based systems, by exploiting channel reciprocity channel estimates received in the uplink can be used in downlink as well. The product, we work on at Ericsson, is a TDD based system and uses SRS based channel estimates to compute beamforming weights to facilitate massive antenna beamforming.
SRS based channel state information is represented by 32-bit complex number in this system, which is received per Evolved Node B (eNodeB) antenna, per User Equipment (UE) transmission antenna, and per Physical Resource Block Group (PRBG). This results in a significant amount of data that needs to be stored in the eNodeB. However, memory in the Digital Unit of eNodeB is limited. SRS based estimates occupy a major portion of this memory and therefore limit the capacity of the eNodeB for beamforming.
This thesis focuses on the evaluation and implementation of lossless and lossy compression of SRS based channel estimates to attain space savings in the shared memory of eNodeB. This will help in achieving higher capacity for reciprocity-based beamforming and prolong the lifetime of existing hardware.
Performance of various lossless data compression algorithms was analyzed based on compression ratio, speed and complexity and the optimal one was selected.
Lossy compression of SRS based channel estimates was also implemented for LOS UEs using linear regression by least squares estimate. Impact on performance due to application of lossy compression algorithm was studied.}},
  author       = {{Bhavani Sankar, Nishanth and Thakaramkunnath Ajayakumar, Akshay}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Investigate Redundancy In Sounding Reference Signal Based Channel Estimates}},
  year         = {{2019}},
}