Reciprocity Calibration for Massive MIMO
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Directeur de thèse:
Unité de recherche UMR 7102 Laboratoire de recherche d'EURECOM
Domaine: Sciences et technologies de l'information et de la communication
Context Multi-user multiple-input multiple-output (MU-MIMO) systems offer big advantages over conventional point-to-point MIMO: they work with single antenna user terminals and they do not require a rich scattering environment. The number of users that can be served concurrently is less or equal to the number of antennas used. Massive MIMO takes MU-MIMO to the next level by scaling up the number of antennas at the base station by an order of magnitude, providing additional degrees of freedom in the channel. These additional degrees of freedom can be used to design more simple and scalable signal processing algorithms and help focusing energy into small regions of space and thus reducing interference. However, these algorithms require channel state information at the transmitter (CSIT). Due to the large number of antennas, acquisition of CSIT is not feasible through feedback (the channel would be outdated by the time it is measured and fed back). Therefore a time-division duplexing (TDD) approach that exploits the channel reciprocity seems much more feasible. While very attractive in theory, in practice it is not so easy to achieve due to the non-reciprocal nature of the hardware. Eurecom has been working on the topic of channel reciprocity for some years now with some significant results: [4,5,8,11,12,13,16,17,18]. Recently we have also started to develop a massive MIMO platform based on our established OpenAirInterface platform. In its final stage the platform will feature 64 independently controllable RF chains built from 16 ExpressMIMO2 software radio cards. The goal of the platform is to carry out measurement campaigns to validate the feasibility of exploiting channel reciprocity in massive MIMO systems and to demonstrate massive MIMO real-time communication using the OpenAirInterface LTE software modem.
State of the Art in Channel Reciprocity Calibration The symmetry of the electromagnetic propagation channel w.r.t. the exchange of the roles of the transmitter and receiver, or reciprocity, is often cited in the literature as a convenient way to obtain channel state information at the transmitter (CSIT) without requiring a feedback link. Indeed, in systems where the channel is used in both directions using a time-division duplexing scheme, the channel estimates obtained from the received signals can theoretically be used to infer the state of the channel during a subsequent transmission, provided that the channel does not change too fast. However, this symmetry is in practice disturbed by the characteristics of the radio-frequency (RF) circuitry of the transmitter and receiver. Indeed, the channel representation which is used typically by digital signal processors in communication applications is a combination of the characteristics of the digital-to-analog converters and power amplifiers at the transmit side, the antennas on both transmit and receive sides, the electromagnetic channel itself, and the characteristics of the low-noise amplifier and analog-to-digital converters at the receive side. Although the electromagnetic channel between the antennas is demonstrably reciprocal, (see for instance  and references therein), the RF circuits on both sides are usually not identical. This indicates that exchanging the roles of the transmitter and the receiver would actually affect the channel measured by the digital signal processing algorithms. Various solutions to this issue have been proposed. One of them is the calibration of each RF circuit involved , requiring additional hardware. Another alternative, limited to low-power transmission, is to use a specially crafted transceiver where the same op-amp is used for both transmitting and receiving . A third alternative , , termed relative calibration, achieves the same effect as normal calibration without the requirement for extra hardware. Assuming that the impairments due to imperfect RF chains can be modeled as linear time-invariant (LTI) filters, it was shown in  that the channels measured in both directions could be related by a simple convolution operation, thus alleviating the need for hardware-based calibration. A problem encountered in most communication systems are frequency offsets between transmitter and receiver. If perfectly know, this offset can be compensated , but in practice frequency offsets between consecutive channel estimations are unknown. Thereby, authors in  illustrate a calibration scheme which automatically compensates for the unknown offset. In a multi-user system, relative calibration requires signaling only between one Tx and one Rx, but once an node is calibrated no further cooperation from other users is needed . In  the authors observe that relative calibration can even be done within the same antenna array by choosing one array element as a reference. This results in an unknown phase offset at the receiver, which is compensated anyway by the channel compensation. The authors in  apply this concept to the Argos massive MIMO testbed, while the authors in  extend this method to a distributed large-scale MIMO scenario. This calibration method neglects the effects of cross-talk and mutual coupling by assuming that the relative calibration matrix is diagonal. This assumption is investigated experimentally in , where it is shown, that the calibration matrix is indeed not diagonal, but at the same time the effects on some simple beamforming algorithms are negligible. Other practical applications of the relative reciprocity calibration are cognitive overlay systems such as presented in , , , , . There the calibration is used to train a secondary communication system in order to do null-forming towards the primary communication system in order not to interfere with it.
Challenges Massive MIMO relies to a great extent on the exploitation of channel reciprocity to gain channel state information at the transmitter (CSIT). However, while the physical radio channel is reciprocal, the effects of the radio frequency circuits is not and must be calibrated. The calibration itself relies on models of the hardware and the antennas and on the estimation algorithms. Today, no reliable models exist for the selectivity in time or frequency of the calibration factors, or for the mutual coupling and cross talk infor large antenna arrays that model mutual coupling and cross talk exist. They thus need to be developed and tested using measurements. In fact, whereas it is possible to come up with ingredients of these models analytically, reliable models can only be obtained by extracting them from measurement data. Moreover, such new models will require new estimation algorithms that need to be developed too. Last, but not least, another big challenge is the integration of the calibration procedures in a system context, for example in 4G or 5G systems. Expected Results We expect to advance the state of the art in exploiting reciprocity in massive MIMO systems by (i) providing more accurate and experimentally validated models for the non-reciprocal hardware parts, (ii) providing estimation algorithms for the calibration parameters, and (iii) developing calibration procedures that can be integrated in 5G standards. The project will further provide a prototype architecture for a demonstrator that will control 64 active antenna elements from a common baseband engine, providing also calibration mechanisms for beam-forming, capability for digital beamforming and extensions to compliant with the LTE protocol stack. This will allow a two-way experimenting with a massive antenna array, testing and evaluating the different algorithms developed in this project. In order to assess the performance of the developed algorithms we will use the CSIT estimation error. As a baseline reference we will use CSIT estimation error achievable with an LTE Rel 10 FDD system employing transmission mode 9 with an 8 antenna element array and quantized feedback to estimate the CSIT. We expect 1) a significant improvement in terms of CSI accuracy after calibration, 2) and this with minimal calibration overhead by exploiting the proper models, and 32) also in the massive MIMO regime.