Erik Larsson co-authored the textbook on Massive MIMO, which included an opinion that FDD MM would never be practical. MM requires the phone/UE to constantly report back metrics to the cell. TDD can do that efficiently; Larsson believes FDD cannot.
Verizon disagrees and is already deploying. Ericsson, Huawei, and ZTE have done successful testing with telcos. Not long ago, a senior engineer at a large telco told me the company is confident their FDD cells will work just fine.
Larssen and a team from Lund went out to the university parking lot. They used a 7 meter test rig with 128 elements. They tested at 2.6 GHz with a bandwidth of 50 MHz,
They report results from several different proposed methods of FDD.
One of the vendors promised me data to check and I'm asking the others.
I also have several academic papers that propose methods to solve the problem. The abstract for one, Non-uniform Directional Dictionary-Based Limited Feedback for Massive MIMO Systems
The answers will be clear when we have more data from the field.
Fundamentals of Massive MIMO by Tom Marzetta, Larsson, Hong Yang, and Hien Quoc Ngo is the primary source by the people who invented it. Whether or not they have TDD/FDD right, every engineer should have a copy. Amazon sells the ebook for $51 and the print for $78.
Massive MIMO Performance—TDD Versus FDD: What Do Measurements Say?
Jose Flordelis, Student Member, IEEE, Fredrik Rusek, Member, IEEE, Fredrik Tufvesson, Fellow, IEEE, Erik G. Larsson, Fellow, IEEE, and Ove Edfors, Senior Member, IEEE
VI. CONCLUSIONS Using measured channels at 2.6 GHz, we have compared the performance of five techniques for DL beamforming in Massive MIMO, namely, fully-digital reciprocity-based (TDD) beamforming, and four flavors of FDD beamforming based on feedback of CSI (D-GOB, H-GOB, D-SUB, and H-SUB). The central result is that, while FDD beamforming with predetermined beams may achieve a hefty share of the DL sum-rate of TDD beamforming, performance depends critically on the existence of advantageous propagation conditions, namely, LOS with high Ricean factors. In other considered scenarios, the performance loss is significant for the non reciprocity-based beamforming solutions. Therefore, if robust operation across a wide variety of propagation conditions is required, reciprocity based TDD beamforming is the only feasible alternative.
Non-uniform Directional Dictionary-Based Limited
Feedback for Massive MIMO Systems
Panos N. Alevizos⋆, Xiao Fu†, Nicholas Sidiropoulos†, Ye Yang+, and Aggelos Bletsas⋆
⋆School of Electrical and Computer Engineering, Technical University of Crete
†Dept. of Electrical and Computer Engineering, University of Minnesota
+Physical Layer & RRM IC Algorithm Dept., WN Huawei Co., Ltd.
Abstract—This work proposes a new limited feedback channel
estimation framework. The proposed approach exploits a sparse
representation of the double directional wireless channel model
involving an overcomplete dictionary that accounts for the
antenna directivity patterns at both base station (BS) and user
equipment (UE). Under this sparse representation, a computationally efficient limited feedback algorithm that is based on
single-bit compressive sensing is proposed to effectively estimate
the downlink channel. The algorithm is lightweight in terms
of computation, and suitable for real-time implementation in
practical systems. More importantly, under our design, using
a small number of feedback bits, very satisfactory channel
estimation accuracy is achieved even when the number of BS
antennas is very large, which makes the proposed scheme ideal
for massive MIMO 5G cellular networks. Judiciously designed
simulations reveal that the proposed algorithm outperforms a
number of popular feedback schemes in terms of beamforming
gain for subsequent downlink transmission, and reduces feedback
overhead substantially when the BS has a large number of