Efficient DSP and Circuit Architectures for Massive MIMO: State-of-the-Art and Future Directions

Massive MIMO is a compelling wireless access concept that relies on the use of an excess number of base-station antennas, relative to the number of active terminals. This technology is a main component of 5G New Radio (NR) and addresses all important requirements of future wireless standards: a great capacity increase, the support of many simultaneous users, and improvement in energy efficiency. Massive MIMO requires the simultaneous processing of signals from many antenna chains, and computational operations on large matrices. The complexity of the digital processing has been viewed as a fundamental obstacle to the feasibility of Massive MIMO in the past. Recent advances on system-algorithm-hardware co-design have led to extremely energy-efficient implementations. These exploit opportunities in deeply-scaled silicon technologies and perform partly distributed processing to cope with the bottlenecks encountered in the interconnection of many signals. For example, prototype ASIC implementations have demonstrated zero-forcing precoding in real time at a 55 mW power consumption (20 MHz bandwidth, 128 antennas, multiplexing of 8 terminals). Coarse and even error-prone digital processing in the antenna paths permits a reduction of consumption with a factor of 2 to 5. This article summarizes the fundamental technical contributions to efficient digital signal processing for Massive MIMO. The opportunities and constraints on operating on low-complexity RF and analog hardware chains are clarified. It illustrates how terminals can benefit from improved energy efficiency. The status of technology and real-life prototypes discussed. Open challenges and directions for future research are suggested.Comment: submitted to IEEE transactions on signal processin

6G Radio Testbeds: Requirements, Trends, and Approaches

The proof of the pudding is in the eating - that is why 6G testbeds are essential in the progress towards the next generation of wireless networks. Theoretical research towards 6G wireless networks is proposing advanced technologies to serve new applications and drastically improve the energy performance of the network. Testbeds are indispensable to validate these new technologies under more realistic conditions. This paper clarifies the requirements for 6G radio testbeds, reveals trends, and introduces approaches towards their development

Review of Recent Trends

This work was partially supported by the European Regional Development Fund (FEDER), through the Regional Operational Programme of Centre (CENTRO 2020) of the Portugal 2020 framework, through projects SOCA (CENTRO-01-0145-FEDER-000010) and ORCIP (CENTRO-01-0145-FEDER-022141). Fernando P. Guiomar acknowledges a fellowship from “la Caixa” Foundation (ID100010434), code LCF/BQ/PR20/11770015. Houda Harkat acknowledges the financial support of the Programmatic Financing of the CTS R&D Unit (UIDP/00066/2020).MIMO-OFDM is a key technology and a strong candidate for 5G telecommunication systems. In the literature, there is no convenient survey study that rounds up all the necessary points to be investigated concerning such systems. The current deeper review paper inspects and interprets the state of the art and addresses several research axes related to MIMO-OFDM systems. Two topics have received special attention: MIMO waveforms and MIMO-OFDM channel estimation. The existing MIMO hardware and software innovations, in addition to the MIMO-OFDM equalization techniques, are discussed concisely. In the literature, only a few authors have discussed the MIMO channel estimation and modeling problems for a variety of MIMO systems. However, to the best of our knowledge, there has been until now no review paper specifically discussing the recent works concerning channel estimation and the equalization process for MIMO-OFDM systems. Hence, the current work focuses on analyzing the recently used algorithms in the field, which could be a rich reference for researchers. Moreover, some research perspectives are identified.publishersversionpublishe

Achievable Rates and Training Overheads for a Measured LOS Massive MIMO Channel

This paper presents achievable uplink (UL) sumrate predictions for a measured line-of-sight (LOS) massive multiple-input, multiple-output (MIMO) (MMIMO) scenario and illustrates the trade-off between spatial multiplexing performance and channel de-coherence rate for an increasing number of base station (BS) antennas. In addition, an orthogonal frequency division multiplexing (OFDM) case study is formed which considers the 90% coherence time to evaluate the impact of MMIMO channel training overheads in high-speed LOS scenarios. It is shown that whilst 25% of the achievable zero-forcing (ZF) sumrate is lost when the resounding interval is increased by a factor of 4, the OFDM training overheads for a 100-antenna MMIMO BS using an LTE-like physical layer could be as low as 2% for a terminal speed of 90m/s.Comment: 4 pages, 5 figure

Millimeter Wave Antenna Array Calibration and Validation for 5G New Radio Access

This Master thesis work was performed in Ericsson AB, Lund. It studies and validates two classes of algorithms to be used in mm-wave massive MIMO antenna arrays, for their use in the future 5G mobile communication systems. The first class of algorithms relates to the calibration of the transceiver (TRX) chains responses of the antenna array and makes use of the antenna array mutual coupling, which is considered as known by the system. It is used to compensate for inaccuracies in the TRX base-band complex responses, caused by environmental conditions such as temperature, humidity and aging, which are much more critical in mm-wave than conventional bands. After the calibration, the beamforming capabilities of the massive MIMO systems are increased. The second class of algorithms is related to the estimation of antenna array mutual coupling matrix, due to the fact that this information is used in the first method. The antenna array mutual coupling estimation algorithm was proposed by Ericsson's engineers and tested in this work. Since the systems considered will work at mm-wave frequencies, small construction errors can create big differences in antenna array coupling properties, so the coupling matrix must be estimated for each constructed system of the same kind. Several estimation and calibration algorithms were simulated, using Matlab® as a software for simulation, and analyzed. The estimation of TRX chains' complex responses is needed. Two estimation algorithms are used, referred as linear and non-linear least squares estimation. These estimation algorithms need to use the information regarding the antenna array mutual coupling matrix and over-the-air (OTA) self-measurements between pairs of elements in the antenna array. These measurements can be done considering all the possible pairs of elements in the antenna array (full measurements) or just a subset of the closest pairs (neighbour measurements). Firstly, simulations using a generic case were done, and later, simulations considering constraints in an Ericsson radio module proprietary system were done. Internal and external unwanted interference in the radio system were considered, to check for limitations in the estimation algorithms. So as to validate the proposed methods and algorithms, a testbed system using a radio module working at 28 GHz was built and measured. The signal levels and frequencies in the HW components of the testbed were calculated using the data-sheets of the components and later measured using a vector network analyzer and an spectrum analyzer. The last task was to write the code for controlling the radio module, then perform calibration using it and finally measure the performance of the algorithm using an an-echoic chamber. Due to a lack of time, the code needed to do the OTA measurements, the OTA measurements and the validation of the algorithms are left as future work. Observing the results of the simulations, several recommendations are made for future measurement validations. One common conclusion is that it is best to perform the minimum amount of self-measurements available, with those being the ones corresponding to the strongest coupling gains between elements in the antenna array. There were different preferred algorithms for calibration depending on the value of the signal-to-noise ratio (SNR) and the value of the signal-to-interference (SIR) ratio. Regarding the antenna array coupling matrix estimation, the conclusion is that the algorithm proposed by Ericsson's engineers works, but in order to achieve good results, the required post-processing SNR values of the related measurements may be too big. Therefore, it may take too long to perform these measurements. Possibilities to improve this algorithm are recommended and left as future work. Regarding the HW to be used in the testbed, it is recommended to add some extra components in order to improve the quality of the signals in the system, for future measurements.Do you remember a time when you were watching a standard definition (SD) YouTube video or streaming a song on your laptop or PC and the player suddenly stopped? Nowadays you want to watch streaming videos on your laptop or mobile phone in a high-definition (1080p) format and in the future you may even want to stream 4K 3D videos and watch them in your Smart-TV or mobile phone. Mobile services are expected to be fast, reliable and cheap. Apart from this, the number of connected devices is growing exponentially, and three times more devices are expected to be connected in the next 5 years. Consequently, scientists and engineers are working towards developing and installing increasingly sophisticated systems to meet today's and tomorrow's user demands. When it comes to cellular communications, there are several ideas on how to improve the network performance. Two of them are to have base stations with a lot of cooperating antennas and to use higher frequency bandwidths, only available at higher frequencies, in the millimeter wave region. These two concepts seem complicated, and they are! Nevertheless, these novel technologies are two main candidates to be integrated in future fifth-generation (5G) mobile systems, expected to be rolled-out in 2020. When using multiple antennas in a base station (or even in a mobile device), it is desirable to avoid different behavior of the circuits in the system over time caused by temperature, humidity and other environmental factors. This is even more important when many antennas are used, as slight changes in the circuit responses may destroy the performance advantage of using these cooperating antennas. Everything gets even more complicated in higher frequencies, since the environmental effect is stronger. But Maths are really strong, and since all the data in our devices is in fact processed mathematically in a digital form, can't we do something to compensate for this physical (i.e. analog) effects in the mathematical (i.e. digital) domain?. The answer is yes, we can, and this mathematical compensation process is called calibration. For the calibration procedure (or algorithm) to work, it is necessary to know how the many antennas interrelate to one another, for the different frequencies in the frequency band of interest. Hence, some antenna factory characterization must be done beforehand. Since this characterization gets more and more complex with an increasing number of antennas, and the antenna properties may still vary after installation, it is necessary to find more efficient methods of characterization (in particular, efficient estimators of massive MIMO arrays coupling matrix). This Master thesis addresses these two problems associated with environmental changes effects in mm-wave massive MIMO systems and characterization of massive MIMO arrays

Massive MIMO for Dependable Communication

Cellular communication is constantly evolving; currently 5G systems are being deployed and research towards 6G is ongoing. Three use cases have been discussed as enhanced mobile broadband (eMBB), massive machine-type communication (mMTC), and ultra-reliable low-latency communication (URLLC). To fulfill the requirements of these use cases, new technologies are needed and one enabler is massive multiple-input multiple-output (MIMO). By increasing the number of antennas at the base station side, data rates can be increased, more users can be served simultaneously, and there is a potential to improve reliability. In addition, it is possible to achieve better coverage, improved energy efficiency, and low-complex user devices. The performance of any wireless system is limited by the underlying channels. Massive MIMO channels have shown several beneficial properties: the array gain stemming from the combining of the signals from the many antennas, improved user separation due to favourable propagation -- where the user channels become pair-wise orthogonal -- and the channel hardening effect, where the variations of channel gain decreases as the number of antennas increases. Previous theoretical works have commonly assumed independent and identically distributed (i.i.d.) complex Gaussian channels. However, in the first studies on massive MIMO channels, it was shown that common outdoor and indoor environments are not that rich in scattering, but that the channels are rather spatially correlated. To enable the above use cases, investigations are needed for the targeted environments. This thesis focuses on the benefits of deploying massive MIMO systems to achieve dependable communication in a number of scenarios related to the use cases. The first main area is the study of an industrial environment and aims at characterizing and modeling massive MIMO channels to assess the possibility of achieving the requirements of URLLC in a factory context. For example, a unique fully distributed array is deployed with the aim to further exploit spatial diversity. The other main area concerns massive MIMO at sub-GHz, a previously unexplored area. The channel characteristics when deploying a physically very large array for IoT networks are explored. To conclude, massive MIMO can indeed bring great advantages when trying to achieve dependable communication. Although channels in regular indoor environments are not i.i.d. complex Gaussian, the model can be justified in rich scattering industrial environments. Due to massive MIMO, the small-scale fading effects are reduced and when deploying a distributed array also the large-scale fading effects are reduced. In the Internet-of-Things (IoT) scenario, the channel is not as rich scattering. In this use case one can benefit from the array gain to extend coverage and improved energy efficiency, and diversity is gained due to the physically large array