You are right that the overhead required to implemented the pilot power optimization should be accounted for. This is a concern if you change the power allocation often, based on the current small-scale fading realizations. If the power is instead selected only based on the channel statistics, which are fixed, we only need to optimize the system once and then we can use the solution “forever”. This is the case in the pilot power optimization problems that I referred to, so the overhead is negligible in this case.

]]>As you mentioned, the estimation error results in poor channel hardening effect in MRC and detection error. I simulated the BER performance of receiver with MRC for few and huge number of antennas in presence of LMMSE estimation in case of i.i.d Rayleigh channels. What I observed was that when the number of antenna goes large, the BER significantly decreases even in case of very poor MSE e.g. in case of an LMMSE estimator when channels are assumed i.i.d . If my simulation result is valid then it means that even in case of poor MSE, the channel hardening appears strong enough to mitigate the interference. This question hit my mind that while implementing huge number of antennas in massive MIMO could significantly reduce the BER without being in need of improving the MSE, then maybe it is unnecessary to incorporate the pilot adaptation schemes in massive MIMO case regarding its resultant system overhead for informing BS with the pilot values through dedicated control channels which is a price to be paid in return of improving the MSE. Pilot adaptation might become even unreasonable in massive MIMO since the number of users is huge and the resulted system overhead might become severe and degrade the overall system performance. The question is: “DOES PILOT ADAPTATION SCHEME always RESULT IN IMPROVEMENT IN MASSIVE MIMO SYSTEM PERFORMANCE?”

In order to answer to this question, one needs to take the resulted overhead in to account in the optimization objective function. As far as I have seen, (of course, my knowledge of literature is not sufficient) papers I have reviewed do not include this overhead in their optimization objective functions which is equivalent to assuming that there exists always adequate vacant spectral resources left to be used for the control channels (the objective function is sum SE in which the reduction in users bandwidth because of the control channels is not taken in to account). It would be much more realistic to assume the available spectrum fixed and consider the required spectral resources by control channels as a portion of this fixed spectrum. This portion must be taken in to account in the objective function which might give totally different results. The results might give an upper bound on the number of users per cell for which the pilot adaptation scheme results in overall system improvements. ]]>

As you say, the base station will typically be involved in the selection of pilot power.

]]>Thank you

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{1}. In “massive MIMO network” book pp.250, the effective pilot SNR and pilot processing gain is introduced. Regarding eq. 10 pp. 90 of book “5G mobile communications”, inverted effective SNR appears in the denominator of MSE equation in addition to the pilot contamination term. What I guess is that in most cases the effective pilot SNR is high and its inverse could be ignored in presence of the pilot contamination term in the denominator therefore what I understand is that changing pilot power may not have significant impact on MSE.

I understand that in the block-type, the pilots are assigned to one full OFDM symbol (all the subcarriers at certain transmission time) with the same power of the data symbols. Whereas in the comb-type, each OFDM symbol may contain both pilot and data (allocated along the available subcarriers) with same or different power allocation. If this is true, is PAPR problem resulting in the same OFDM symbol or in OFDM frame (successive OFDM symbols)? ]]>