**5. Comparison among coherent, non-coherent and hybrid schemes in massive MIMO**

As mentioned before, CDS and NCDS have their benefits and limitations since CDS is suitable for slowly varying and high SNR scenarios, while NCDS is recommendable in the opposite scenarios. Comparatively, CDS can provide high throughput to many users while the NCDS can provide a lower throughput for fewer users, but working in scenarios where the CDS would fail. Consequently, HDS is also proposed in [13], where it is capable of trading-off both CDS and NCDS in order to get the benefits of each scheme, at the expense of a little increment in the channel estimation error. Here we provide a comparison in terms of throughput between the HDS and the CDS for different time and frequency variability. Specifically, we show the percentage improvement in the throughput of the HDS with respect to the CDS for the different required number of pilots in each dimension time (*Np*) and frequency (*Kp*) for 14 OFDM symbols and 12 subcarrier frequencies (**Table 1**).

In **Figure 6a**, a comparison between the coherent (CDS), non-coherent (NCDS), superimposed training (ST, [29]) and hybrid scheme (HDS, [13]) is shown. It can be seen that the HDS outperforms all the other alternatives in fast-varying channels for all SNR ranges. Additionally, we compare the performance of the coherent and the NC massive MIMO for the DL approach including spatial multiplexing proposed in [15]. This approach blindly estimates the channel using reconstructed differential data in the uplink. We can see that the proposed scheme (N) works better than the coherent scheme (C) in case the coherence time *nc* is smaller than 2 times the TDD slot duration. In scenarios where the coherence time is 1.5 times the DL slot duration, even with channel prediction, the coherent scheme performs worse than the proposed scheme. This can be seen in curves C,6,∞,cP and N,6,∞. The reason for this is that the proposed scheme is much more robust than the coherent scheme in these situations.


**Table 1.** *Percentage improvement of the throughput for the HDS with respect to the CDS.*

#### **Figure 6.**

*Throughput comparison of CDS, HDS, ST and NCDS for different constellation sizes, R* ¼ 64*, Kp* ¼ 6 *and Np* ¼ 7 *(left) and (right) SER comparison between classical (C, dashed) and proposed (N, continuous) schemes in the DL, labeled from left to right with the legend written as "technique (N,C), nc (4,6,40) coherence time, SNR (dB) uplink for channel estimation" for R* ¼ 100 *antennas, τ<sup>d</sup>* ¼ 4 *DL time slot, MDL* ¼ 4 *DL constellation size and 2 users. cP refers to the inclusion of channel prediction. (a) Throughput comparison of CDS and NCDS and (b) SER comparison between C and NC.*

We now consider a multi-path time-varying channel and an implementation with OFDM modulation according to the 5G new radio numerology. To obtain these results, the coherence time is calculated as *Tc* ¼ 0*:*15*f* �1 *<sup>D</sup>* , where *f <sup>D</sup>* is the maximum Doppler frequency. We also consider that the duration of an OFDM symbol is the inverse of the separation between subcarriers *Ts* ¼ 1*=*Δ*<sup>f</sup>* . In [13], the coherent scheme employs channel estimation based on zero-forcing with PSAM. The results, which are shown in **Figure 6**, are based on multi-path channels with a delay spread (*στ* <1 μs), resulting in a minimum coherence bandwidth of *Bc*≈1*=*ð Þ¼ 5*στ* 200 kHz. In the NC scheme, differential encoding is performed over the frequency domain [19], and 4 out of 14 OFDM symbols are dedicated to reference signals for each slot, following the 5G

#### **Figure 7.**

*Non-coherent (Mu* ¼ ½ � 4 4 *and β<sup>u</sup>* ¼ ½ � 1 1 *) ([18],Table II) vs. coherent scheme (2 users with regular QPSK) for R=128, for different NCT.*

*Massive MIMO without CSI: When Non-Coherent Communication Meets Many Antennas DOI: http://dx.doi.org/10.5772/intechopen.112053*

#### **Figure 8.**

*CDS, NCDS or HDS depending on channel variability as in Figure 2. Image taken from [13].*

standard. Due to channel estimation overhead, the SNR (*ρ*) for the coherent scheme is penalized as 10*ρ=*14. The NC outperforms the coherent scheme for high *ρ*, except for *NCT* ≥10. Moreover, for all *ρ* values, the NC outperforms the coherent scheme when *NCT* ≤5. In addition, even for large *NCT*, the NC outperforms the coherent counterpart in the low *ρ* regime (**Figures 7** and **8**).

### **6. Conclusions**

This chapter has provided a review of non-coherent massive MIMO based on DMPSK, which leverages the advantage of using an huge number of antennas in the BS either by not using requiring or by obtaining this CSI without transmitting any reference signals. In the case of UL, three different mapping schemes have been proposed for the OFDM. Additionally, a blind channel estimation using reconstructed differentially encoded data has been also proposed. In the case of DL, two proposals are given, one for FDD and the other for TDD. The first one corresponds to a precoding based on either beamforming or codebook selection, while the second one accounts for a precoding based on the channel estimated in the UL. Additionally, we have indicated how the multi-user version of the NC massive MIMO based on DMPSK can be implemented via constellation design. Lastly, a comparison of the coherent, non-coherent and hybrid schemes in terms of performance is provided to demonstrate that the NC alternative is better for the scenarios with a high variability in time and/or frequency, with a low SNR and with many users.

Moreover, it has been observed that the performance of NCDS is highly dependent on the spatial separation of the multiplexed UEs, whether in terms of constellation or space. Hence, scheduling algorithms that optimize a specific performance metric while considering this factor are crucial. While NCDS outperforms CDS in dynamic channel scenarios with moderate SNR and a large number of users, it becomes less advantageous in quasi-static channels, high SNR, or a small number of users. Therefore, hybrid schemes that combine both paradigms, such as the one proposed in [13], are recommended for such scenarios.

Furthermore, the integration of sensing with communication is one of the main goals of 6G mobile communications [3]. In these systems, efficient CSI exploitation under various scenarios will be crucial, and hence, the use of non-coherent techniques to create hybrid systems is expected to be an interesting alternative to increase overall system efficiency. In conclusion, we anticipate that this review of NCDS characteristics, implementation feasibility and performance will inspire new research and advancements in this field.
