CSI-RSRQ

CSI Reference Signal Received Quality

Physical Layer →
Introduced in Rel-15

CSI-RSRQ is a 5G NR channel quality metric representing the ratio of CSI-RS received power to the total received power including interference and noise within a measurement bandwidth.

Category
Physical Layer
Introduced
Rel-15
Where
Radio Access Network › NG-RAN (5G)
Specifications
2 specs
CSI-RSRQ Description Purpose Related Classification Specifications

Description

CSI-RSRQ (CSI Reference Signal Received Quality) is a critical physical layer measurement defined in the 5G New Radio (NR) standards. It quantifies the quality of the received Channel State Information Reference Signal (CSI-RS) by evaluating the ratio of the desired CSI-RS power to the total interference and noise power within the measurement bandwidth. Unlike RSRP (Reference Signal Received Power), which measures signal strength, CSI-RSRQ provides a signal-to-interference-plus-noise ratio (SINR)-like metric specific to the CSI-RS resource, offering a more comprehensive view of the radio link quality in the presence of co-channel interference and noise.

Architecturally, CSI-RSRQ measurement is performed by the User Equipment (UE) in the downlink. The gNodeB configures the UE with specific CSI-RS resources for measurement via RRC signaling. The UE measures the received power of these configured CSI-RS resources, denoted as RSRP_CSI-RS. Simultaneously, the UE measures the total received power (including interference from other cells, noise, and any other signals) within the same frequency bandwidth as the CSI-RS resource. The CSI-RSRQ is then calculated as (N * RSRP_CSI-RS) / (E-UTRA carrier RSSI), where N is the number of resource blocks (RBs) over which the RSSI (Received Signal Strength Indicator) is measured, and the RSSI encompasses the total wideband power. This calculation yields a linear ratio, often reported in dB.

The role of CSI-RSRQ in the network is multifaceted. It is a primary input for Channel State Information (CSI) reporting, which the UE provides to the gNodeB. This reporting is essential for advanced radio resource management functions. Based on CSI-RSRQ, the gNodeB can perform accurate link adaptation, selecting the optimal modulation and coding scheme (MCS) to maximize throughput while maintaining block error rate targets. It is also vital for beam management procedures, helping to identify the best transmit/receive beam pairs by assessing the quality of different CSI-RS beams. Furthermore, CSI-RSRQ measurements support mobility management, including handover decisions and cell reselection, by providing a quality-centric metric alongside RSRP.

Key components involved in CSI-RSRQ operation include the configured CSI-RS resources (with specific time/frequency locations, periodicity, and scrambling), the UE's physical layer measurement circuitry, and the higher-layer filtering and reporting mechanisms defined in 3GPP specifications. The measurement is designed to be flexible, supporting various use cases such as connected-mode mobility, beam failure detection, and radio link monitoring. Its accuracy is critical for the performance of 5G NR, especially in dense deployments and high-frequency bands where interference dynamics are more pronounced.

Purpose & Motivation

CSI-RSRQ was introduced in 5G NR to address the need for a robust, interference-aware channel quality metric specifically tailored for advanced reference signals like CSI-RS. In LTE, RSRQ served a similar purpose for Cell-specific Reference Signals (CRS), but 5G's decoupled architecture, with CSI-RS used for beamformed data transmission and tracking, required a dedicated quality measurement. The primary problem it solves is enabling accurate channel state estimation in interference-limited scenarios, which is essential for the high spectral efficiency targets of 5G.

Historically, relying solely on RSRP (a power-based metric) for link adaptation and mobility could lead to suboptimal decisions in the presence of strong inter-cell interference. A high RSRP value does not guarantee good throughput if the interference level is also high. CSI-RSRQ provides a direct measure of signal quality relative to interference and noise, filling this gap. Its creation was motivated by the evolution towards more complex multi-beam operations and network densification in 5G, where interference management becomes paramount.

The technology addresses limitations of previous approaches by being tightly coupled with the CSI framework. Unlike the LTE RSRQ defined for CRS, CSI-RSRQ is measured on configurable CSI-RS resources, which can be beam-specific and user-specific. This allows for precise quality assessment of the actual beams used for data transmission, rather than a cell-wide average. It enables gNodeBs to make finer-grained scheduling and beam management decisions, directly improving user experience and network capacity in challenging radio conditions.

Classification

Part ofCSI
Related approachesCSI-RSRSRP

Evolution Across Releases

Rel-15 Initial

Introduced as a new measurement for 5G NR, defined for CSI-RS resources. It established the fundamental calculation as (N * RSRP_CSI-RS) / RSSI, with reporting configured via RRC. Supported initial use cases for connected-mode mobility, beam management, and CSI acquisition in both FR1 and FR2 frequency ranges.

Explore further

Broader topics and technologies where CSI-RSRQ plays a role.

Defining Specifications

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

SpecificationTitleRelease
TS 38.214 vj10 NR Physical Layer Procedures for Data Rel-19
TS 38.215 vj10 NR Physical Layer Measurements 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.