UL-SRS

Uplink Sounding Reference Signal

Physical Layer →
Introduced in Rel-16

UL-SRS is an uplink reference signal transmitted by the UE to enable the gNB to estimate uplink channel quality for scheduling, link adaptation, and, in TDD, downlink beamforming.

Category
Physical Layer
Introduced
Rel-16
Where
Radio Access Network › NG-RAN (5G)
Specifications
4 specs
UL-SRS Description Purpose Related Classification Specifications

Description

The Uplink Sounding Reference Signal (UL-SRS) is a known sequence transmitted by the UE that allows the gNodeB (gNB) to perform uplink channel estimation. Unlike demodulation reference signals (DM-RS) which are tied to a specific PUSCH/PUCCH transmission, SRS is transmitted independently, often on a configured, periodic, semi-persistent, or aperiodic basis. The gNB configures the SRS transmission parameters via RRC signaling and can trigger aperiodic SRS via DCI. Key parameters include bandwidth, time/frequency position, transmission comb, cyclic shift, and sequence ID, allowing for flexible and UE-specific sounding.

The primary function is channel sounding. By analyzing the received SRS, the gNB can estimate the uplink channel state information (CSI), including the channel frequency response across the sounded bandwidth. This estimation is crucial for several network functions. First, it enables frequency-selective scheduling for the uplink UL-SCH. The scheduler can allocate PUSCH resources in frequency regions where the UE has a strong channel, thereby maximizing throughput and spectral efficiency. Second, it facilitates uplink link adaptation by providing the information needed to select the appropriate modulation and coding scheme (MCS) for a given UE's PUSCH transmission, balancing data rate and error probability.

In Time Division Duplex (TDD) systems, where uplink and downlink share the same frequency band, the channel reciprocity principle can be applied. The uplink channel estimate derived from SRS can be used to infer the downlink channel conditions. This is a cornerstone for massive MIMO and beamforming. The gNB can use the SRS-based channel knowledge to calculate precoding weights for downlink transmissions, effectively steering energy towards the UE and creating narrow, focused beams. This improves downlink signal strength, reduces interference to other users, and increases overall network capacity. Furthermore, SRS can be configured with antenna switching, allowing a UE with multiple transmit antennas to sound the channel from different antennas sequentially, enabling the gNB to acquire channel knowledge for all UE transmit antennas, which is vital for uplink MIMO and downlink beamforming based on reciprocity.

Purpose & Motivation

UL-SRS was introduced to provide the network with a dedicated and flexible mechanism for obtaining uplink channel state information, a capability that became increasingly critical with the evolution of LTE and the advent of 5G NR. Early LTE releases focused on downlink CSI feedback (CQI, PMI, RI) reported by the UE. However, for optimal uplink performance and to fully exploit the potential of advanced antenna systems, the network needed direct, wideband knowledge of the uplink radio channel.

The motivation stems from several limitations of relying solely on downlink measurements or demodulation references. Demodulation reference signals (DM-RS) are only present during a PUSCH transmission and cover only the scheduled bandwidth, providing no information about channel quality on other frequencies. SRS solves this by allowing the network to proactively sound the channel over a configurable, potentially system-wide, bandwidth even when the UE has no data to send. This enables proactive scheduling decisions.

With the push towards massive MIMO and beamforming, especially in TDD deployments, channel reciprocity became a highly efficient alternative to explicit CSI feedback. UL-SRS is the enabling signal for this approach. By sounding the uplink, the gNB, equipped with a large antenna array, can estimate the spatial characteristics of the channel and derive accurate beamforming vectors for downlink transmission. This is far more scalable and provides more timely information than having each UE measure and report downlink CSI for dozens of antenna ports. Thus, UL-SRS is a key enabler for the high spectral efficiency and capacity targets of 5G networks.

Classification

Part ofDM-RS
Related approachesPUSCHTDD

Release Timeline

Evolution Across Releases

Rel-16 Initial

Introduced in NR with enhanced flexibility compared to LTE SRS. Key initial features included support for wider bandwidths aligned with NR carrier bandwidths, flexible placement within a slot, and robust support for aperiodic SRS triggering. It was designed from the ground up to support massive MIMO and beamforming, with explicit configurations for spatial relation info and usage parameters (e.g., 'codebook', 'nonCodebook', 'antennaSwitching', 'beamManagement').

Enhanced SRS capabilities for improved performance and efficiency. Introduced SRS switching gaps to allow a single RF chain to sound multiple panels/antennas. Enhanced support for multi-panel and multi-TRP (Transmission Reception Point) operation, allowing more sophisticated beam management and coordination between multiple network transmission points.

Explore further

Broader topics and technologies where UL-SRS plays a role.

Defining Specifications

3GPP specifications that define or reference UL-SRS, with the latest known release. Sourced from the 3GPP document catalog — see methodology.

SpecificationTitleRelease
TS 37.571 vj00 UE Conformance for Positioning Rel-19
TS 38.305 vj00 NG-RAN UE Positioning Stage 2 Rel-19
TS 38.455 vj10 NR Positioning Protocol A (NRPPa) Rel-19
TS 38.473 vj10 5G F1 Application Protocol (F1AP) Rel-19
Patrick Zandl

About the author: Patrick Zandl (b. 1974)

Telecommunications specialist, technology journalist (founder of the Mobil server), and developer who has been running since 2025 — the largest Czech-language resource on AI-assisted programming. Formerly Chief Wizard Architect at Prusa3D and head of development for Turris at CZ.NIC; currently a consultant and instructor on AI implementation in companies.