Generalized Superimposed Training Scheme in IRS-Assisted Cell-Free Massive MIMO Systems

Abstract

In this paper, for a cell-free massive multi-input multi-output (MIMO) system assisted with intelligent reflective surface (IRS) panels, a generalized superimposed pilot (GSP) training scheme is proposed, where the available number of pilots are equal to the coherence time slots, and the transmitting data symbols are spread over the coherence time with the help of simple precoding. Further, in order to keep the system scalable, a low complexity approach is employed for processing, and the corresponding rate components are analyzed. It is shown that with careful design of precoding and number of data symbols, the GSP symbols can provide much better channel estimation and data detection performance, as compared to the regular pilot scheme and the conventional superimposed scheme. These results, verified via simulations, shows that the centralized processing improves the data detection performance than localized processing. The pilot contamination effect, is significantly reduced due to the availability of larger number of pilots, as compared to the regular pilots transmission. For four IRS panels in the system, the proposed scheme is shown to reduce the channel estimation errors by 74% approximately.

Impact of Pilot Contamination Between Operators With Interfering Reconfigurable Intelligent Surfaces

“…for j, k ∈ {1, 2} and j ̸ = k. Φk denotes the RIS configuration computed according to (16) with ĝk = g k +b k . At high SNR, the MSE in ( 21) can be rewritten as…”

“…Active RISs, on the other hand, use amplifiers to overcome the large path loss but have the traditional issues of relays: increased power consumption, higher cost, and additional noise [14], [15]. While the aforementioned problems caused by the RIS have been recognized [16], pilot contamination caused by the presence of multiple RISs has not been studied in the literature.…”

Generalized superimposed training for RIS-aided cell-free massive MIMO-OFDM networks

“…Hence, data estimates can be calculated locally and then sent to the CPU. We do not need to transmit channel estimates from APs to the CPU and then carry out the data estimation [1] and [35].…”

Exact Analysis of MIMO Channel Estimation Based on Superimposed Training

“…In recent years, channel estimation based on superimposed training (ST) has attracted much interest among researchers dealing with wireless communication systems, such as unmanned aerial vehicle-assisted cellular communication systems [1], massive multiple-input multiple-output networks [2], [3], orthogonal frequency division multiplexing systems [4], cyclic block filtered multi-tone [5], and multiple access relay networks [6]. In an ST scheme, a periodic training sequence is arithmetically added to the information sequence prior to transmission.…”