Centralized Versus Distributed Processing in Cell-Free Massive MIMO

A figure from my first paper on Network MIMO, which is nowadays called Cell-Free Massive MIMO.

The new Cell-Free Massive MIMO concept has its roots in the classical Network MIMO concept, and has also been given many other names over the years (e.g., coordinated multipoint). When I started my research on the topic in 2009, the standard assumption was that a set of base stations were jointly transmitting to a set of users by sharing both the data signals and their respective channel state information (CSI). In my first journal paper, we showed that one can get away with only sharing the data signals between the base stations because each one only needs local CSI (between itself and the users) to beamform to the users. The price to pay is that the base stations cannot cancel each others’ interference, so each one should preferably have multiple antennas so it can control how much interference it causes. This was my first well-cited paper but, to be honest, I am still not sure how significant results are.

On the one hand, it is very convenient to only utilize local CSI at every base station, because it can be estimated from uplink pilots in a TDD system, which was a key motivation behind our 2010 paper. The time-critical precoding computation can then be initiated immediately after the pilots have been received, instead of waiting for the CSI to be shared between the base stations. This property was later utilized in the first Cell-Free Massive MIMO papers [Ngo, Nayebi] to alleviate the need for sharing CSI.

On the other hand, CSI is usually a small fraction of the signaling between a base station and the rest of the system in Network MIMO. The majority of the signaling consists of the data signals; for example, if a coherence block with 200 channel uses consists of 20 pilot symbols and 180 data symbols, then there is 180/20 = 9 times more data than CSI. Interestingly, our recent paper “Making Cell-Free Massive MIMO Competitive With MMSE Processing and Centralized Implementation” shows that if Cell-Free Massive MIMO is implemented by sending all CSI to an edge-cloud processor that takes care of all the signal processing, both the communication performance and the signaling load can be greatly improved as compared to the fully distributed approach (which was considered in my 2010 paper and then became the standard assumption in the Cell-Free Massive MIMO literature).

The bottom line is that it is hard to make a distributed network implementation competitive compared to a centralized one. Unless we can find a really clever implementation, there is a risk that we lose too much in communication performance and also raise the fronthaul capacity requirements.

5 thoughts on “Centralized Versus Distributed Processing in Cell-Free Massive MIMO”

  1. Hi,
    Thanks for your useful post.
    Does centralized precoding/combining have better performance than distributed (local) precoding/combining even when we have perfect CSI at each AP? Or is the performance identical in the case of perfect CSI?

    Thanks in advance

    1. It depends on what you mean with “perfect CSI”. Each AP typically only knows the channels from itself to each user (local CSI, which might be perfect), while a central unit can know the channels from all APs to all users (global CSI, which might be perfect). The latter enables more sophisticated precoding/combining, where the APs are cancel out each others’ interference.

      1. Thanks for your reply.
        For example consider the MF or ZF precoder. Now, for the following two cases with the mentioned precoders I get the same SINR.
        case 1) Each AP perfectly knows the channels from itself to each user (distributed case).
        case 2) central unit knows the channels from all APs to all users (centralized case).
        I agree with you that in general case 2 enables more sophisticated precoding, where the APs are cancel out each others’ interference. But , I believe for MF and ZF precoder we have the same performance in both cases. Is that right?
        Thanks in advance.

        1. With MR you will get the same result (if the precodes are normalized in the same way). However, a single AP can only implement a local ZF, which requires every AP to have as many antennas as there are users in the system). In the centralized case, all the APs are operating jointly to perform ZF.

          You can find a lot of details about this in my book “Foundations of User-Centric Cell-Free Massive MIMO”.

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