Unveiling the Power of MIMO 5G: What You Need to Know About This Revolutionary Technology

The rollout of 5G technology is reshaping how we connect, making communication faster and more reliable than ever before. At the heart of this revolution is MIMO (Multiple Input Multiple Output). This technology is and remains a game changer, significantly boosting data speeds, enhancing capacity, and improving the overall reliability of networks.

This article will dive into mMIMO (massive MIMO) in 5G, examining how it functions, the challenges involved, and practical considerations.

Especially for the technical part of this article i would suggest to visit my previous post for massive MIMO (mMIMO ) design consideration .

Considerations for mMIMO deployment

When discussing 5G deployment, many think of mMIMO, which can create the misconception that widespread mMIMO rollout is essential for efficiency. Here are some practical considerations.

Performance can usually be differentiated through the choice of the MIMO solutions or the conventional MIMO solution, however, as a most cost-effective deployment consideration, would be to alternate with both solutions based on traffic demand and performance requirement or service requirements, for example :

• In Dense areas With high traffic demand, we may require a mMIMO solution, in terms of feature design consideration, MU-MIMO could be an efficient solution on maximizing spectral efficiency and satisfying the high demand, usually users are more distributed horizontally however a product that may include vertical beamforming would be also a good option to maximize coverage and/or capacity on higher building areas.

  
  

• In suburban area with low traffic demand, we may want to focus on cost-effective solutions while satisfying low traffic demand and extending coverage ( large inter-site distance ) so low-order MIMO or conventional MIMO solutions could be used with low antenna chain .

  
  

Depending on the vertical beamforming requirements for MU-MIMO, either a 64T64R or 32T32R antenna configuration may be selected to meet coverage and capacity targets

CSI/SSB/SRS design implementation consideration

SSB 

in 5G SA deployments, SRS will define the coverage for the UE in idle mode.

A higher SSB beam will provide more granularity and finer SSB refinement resulting in better coverage.

Usually, all beamformed SSBs are grouped to sweep the target sector coverage area, resulting in better indoor coverage, eventually, with the cost of SSB overhead (a more suitable strategy on deep indoor coverage requirement ) , a maximum of 8 SSBs possible for FR1 deployment. But usually 6 SSBs should be used to sweep an entire sector coverage area as a good tradeoff with overhead or less, depending on SSB beamwidth ( controlled through beamforming gain ).

CSI RS

• In CSI RS, each CSI can be produced using a beamforming coefficient applied to one or both polarizations. If the antenna has a single TRX row, beamforming is limited to the azimuth direction, creating a horizontal row of beams. However, with multiple rows, beamforming expands to both azimuth and elevation directions, allowing for a two-dimensional beam pattern ( for dense urban area coverage requirement use case for example)

• Note that the the following figure 1 is just as example, less CSI RS could be used per SSB, and even one-to-one mapping could be used ( 1SSB to 1 CSI ).

Figure 1

Usually for SU-MIMO implementation, , M CSI-RS resources called CRI of N port ( max 32 port ) CSI-RS beamformed to be mutually orthogonal. Each CSI-RS resource is identified by a CSI-RS resource indicator (CRI). The UE (limited by its M capability) is then expected to report the CRI of its best preferred CSI-RS resource along with CQI, PMI, and RI, which are used for PDSCH precoding procedure is summarized in figure 2

PMI is derived based on 3GPP-defined codebooks. Type I Codebook provides Simplicity & moderate spatial resolution where Type II Codebook Provided enhanced spatial resolution with the cost of overhead and complexity ,

In release 16, Enhanced Type II Codebook is also defined, which is designed for Wideband massive MIMO systems

CSI in MIMO precoding

despite the several enhancements on the PMI codebook, the precoding still suffer from very high quantization error as the combination used for PDSCH precoding would be limited by the codebook entry ( difference between the actual channel state and its discrete representation), however, this solution still benefits in FDD due to no channel reciprocity

Figure 2

SRS or sounding reference signal

The base station estimates the SRS channel response from the user N antenna at M antenna ports, however, the uplink transmit chain would be limited, especially in 5G NSA implementation ( as the transmit chain would be shared between LTE & NR)

A workaround to this problem would be through the antenna switching, where channel response could be collected through connecting the device RF chain to one Rx antenna at a time, so in case of N switch antennas at the receiver (N × M), the channel matrix could be derived by exploiting orthogonality

In DL SU-MIMO, the beamforming weight for each layer is derived from the PMI or SRS response to assign multiple layers to a single user. whereas in MU-MIMO, Multiple users could be simultaneously allocated the same Resource Blocks if those users are spatially separated (multiple simultaneous beams transmit the same Resource

Blocks with different payloads in different directions.