Massive MIMO Map Feb 2018 450

Dark blue: Building actively: China Mobile, Softbank Japan, Bharti India, Jio India, Vodafone India, Singtel, Globe Phillippines, Sprint USA

Medium: Announced: DT, FT/Orange, BT, Qatar, Verizon USA, T-Mobile Netherlands, Telekom South Africa  

Light blue: Talking: Vodafone England, Vodafone Turkey,
Safaricom Kenya, 

Lund testing

Prof on Massive MIMO: "TDD beamforming is the only feasible alternative"!?

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

 Here are the abstract and conclusions

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.



Channel state information (CSI) feedback is a challenging issue in frequency division duplexing (FDD) massive MIMO systems. This paper studies a cooperative feedback scheme, where the users first exchange their CSI with each other through device-to-device (D2D) communications, then compute the precoder by themselves, and feedback the precoder to the base station (BS). Analytical results are derived to show that the cooperative precoder feedback is more efficient than the CSI feedback in terms of interference mitigation. To reduce the delays for CSI exchange, we develop an adaptive CSI exchange strategy based on signal subspace projection and optimal bit partition. Numerical results demonstrate that the proposed cooperative precoder feedback scheme with adaptive CSI exchange significantly outperforms the CSI feedback scheme, even under moderate delays for CSI exchange via D2D.

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

The Site for Massive 230
dave ask

Massive MIMO is rapidly deploying across the world; Soon, I'll be adding many more countries to the Massive MIMO map. On average, adding 64 or 128 antennas triples the performance of the cell site at moderate cost. Ericsson, Huawei, and ZTE are shipping by the thousands.

Being a reporter is a great job for a geek. I'm not an engineer but I've learned from some of the best, including the primary inventors of DSL, cable modems, MIMO, Massive MIMO, and now 5G mmWave. Since 1999, I've done my best to get closer to the truth about broadband.

Send questions and news to Dave Burstein, Editor.