Reciprocity Calibration for Massive MIMO: Proposal, Modeling and Validation

This paper presents a mutual coupling based calibration method for time-division-duplex massive MIMO systems, which enables downlink precoding based on uplink channel estimates. The entire calibration procedure is carried out solely at the base station (BS) side by sounding all BS antenna pairs. An Expectation-Maximization (EM) algorithm is derived, which processes the measured channels in order to estimate calibration coefficients. The EM algorithm outperforms current state-of-the-art narrow-band calibration schemes in a mean squared error (MSE) and sum-rate capacity sense. Like its predecessors, the EM algorithm is general in the sense that it is not only suitable to calibrate a co-located massive MIMO BS, but also very suitable for calibrating multiple BSs in distributed MIMO systems. The proposed method is validated with experimental evidence obtained from a massive MIMO testbed. In addition, we address the estimated narrow-band calibration coefficients as a stochastic process across frequency, and study the subspace of this process based on measurement data. With the insights of this study, we propose an estimator which exploits the structure of the process in order to reduce the calibration error across frequency. A model for the calibration error is also proposed based on the asymptotic properties of the estimator, and is validated with measurement results.Comment: Submitted to IEEE Transactions on Wireless Communications, 21/Feb/201

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

Temporal Analysis of Measured LOS Massive MIMO Channels with Mobility

The first measured results for massive multiple-input, multiple-output (MIMO) performance in a line-of-sight (LOS) scenario with moderate mobility are presented, with 8 users served by a 100 antenna base Station (BS) at 3.7 GHz. When such a large number of channels dynamically change, the inherent propagation and processing delay has a critical relationship with the rate of change, as the use of outdated channel information can result in severe detection and precoding inaccuracies. For the downlink (DL) in particular, a time division duplex (TDD) configuration synonymous with massive MIMO deployments could mean only the uplink (UL) is usable in extreme cases. Therefore, it is of great interest to investigate the impact of mobility on massive MIMO performance and consider ways to combat the potential limitations. In a mobile scenario with moving cars and pedestrians, the correlation of the MIMO channel vector over time is inspected for vehicles moving up to 29 km/h. For a 100 antenna system, it is found that the channel state information (CSI) update rate requirement may increase by 7 times when compared to an 8 antenna system, whilst the power control update rate could be decreased by at least 5 times relative to a single antenna system.Comment: Accepted for presentation at the 85th IEEE Vehicular Technology Conference in Sydney. 5 Pages. arXiv admin note: substantial text overlap with arXiv:1701.0881

Indoor Localization Using Radio, Vision and Audio Sensors: Real-Life Data Validation and Discussion

This paper investigates indoor localization methods using radio, vision, and audio sensors, respectively, in the same environment. The evaluation is based on state-of-the-art algorithms and uses a real-life dataset. More specifically, we evaluate a machine learning algorithm for radio-based localization with massive MIMO technology, an ORB-SLAM3 algorithm for vision-based localization with an RGB-D camera, and an SFS2 algorithm for audio-based localization with microphone arrays. Aspects including localization accuracy, reliability, calibration requirements, and potential system complexity are discussed to analyze the advantages and limitations of using different sensors for indoor localization tasks. The results can serve as a guideline and basis for further development of robust and high-precision multi-sensory localization systems, e.g., through sensor fusion and context and environment-aware adaptation.Comment: 6 pages, 6 figure

Performance characterization of a real-time massive MIMO system with LOS mobile channels

The first measured results for massive MIMO performance in a line-of-sight (LOS) scenario with moderate mobility are presented, with 8 users served in real-time using a 100-antenna base Station (BS) at 3.7 GHz. When such a large number of channels dynamically change, the inherent propagation and processing delay has a critical relationship with the rate of change, as the use of outdated channel information can result in severe detection and precoding inaccuracies. For the downlink (DL) in particular, a time division duplex (TDD) configuration synonymous with massive multiple-input, multiple-output (MIMO) deployments could mean only the uplink (UL) is usable in extreme cases. Therefore, it is of great interest to investigate the impact of mobility on massive MIMO performance and consider ways to combat the potential limitations. In a mobile scenario with moving cars and pedestrians, the massive MIMO channel is sampled across many points in space to build a picture of the overall user orthogonality, and the impact of both azimuth and elevation array configurations are considered. Temporal analysis is also conducted for vehicles moving up to 29km/h and real-time bit error rates (BERs) for both the UL and DL without power control are presented. For a 100-antenna system, it is found that the channel state information (CSI) update rate requirement may increase by 7 times when compared to an 8-antenna system, whilst the power control update rate could be decreased by at least 5 times relative to a single antenna system.Comment: Submitted to the 2017 IEEE JSAC Special Issue on Deployment Issues and Performance Challenges for 5G, IEEE Journal on Selected Areas in Communications, 2017, vol.PP, no.99, pp.1-

Serving 22 Users in Real-Time with a 128-Antenna Massive MIMO Testbed

This paper presents preliminary results for a novel 128-antenna massive Multiple-Input, Multiple-Output (MIMO) testbed developed through Bristol Is Open in collaboration with National Instruments and Lund University. We believe that the results presented here validate the adoption of massive MIMO as a key enabling technology for 5G and pave the way for further pragmatic research by the massive MIMO community. The testbed operates in real-time with a Long-Term Evolution (LTE)-like PHY in Time Division Duplex (TDD) mode and supports up to 24 spatial streams, providing an excellent basis for comparison with existing standards and complimentary testbeds. Through line-of-sight (LOS) measurements at 3.51 GHz in an indoor atrium environment with 12 user clients, an uncoded system sum-rate of 1.59 Gbps was achieved in real-time using a single 20 MHz LTE band, equating to 79.4 bits/s/Hz. In a subsequent indoor trial, 22 user clients were successfully served, which would equate to 145.6 bits/s/Hz using the same frame schedule. To the best of the author's knowledge, these are the highest spectral efficiencies achieved for any wireless system to date

Massive MIMO: Prototyping, Proof-of-Concept and Implementation

Wireless communication is evolving rapidly with ever more connected devicesand significantly increasing data rates. Since the invention of the smartphoneand the mass introduction of mobile apps, users demand more andmore traffic to stream music, watch high-definition video or to simply browsethe internet. This tremendous growth is more pronounced by the introductionof the Internet of Things (IoT) in which small devices, such as sensors,are interconnected to exchange data for all sorts of applications. One exampleare smart homes in which a user can for instance, check temperature at home,verify if windows are closed or open, or simply turn on and off distributedloud speakers or even light bulbs in order to fake a busy household when onvacation. With all these additional devices demanding connectivity and datarates current standards such as 4G are getting to their limits. From the beginning5G was developed in order to tackle these challenges by offering higherdata rates, better coverage as well as higher energy and spectral efficiencies.Massive Multiple-Input Multiple-Output (MIMO) is a technology offering thebenefits to overcome these challenges. By scaling up the number of antennasat the Base Station (BS) side by the order of hundred or more it allows separationof signals from User Equipments (UEs) not only in time and frequencybut also in space. Exploiting the high spatial degrees-of-freedom it can focusenergy with spotlight precision to the intended UE, thereby not only achievinghigher energy being received per UE but also lowering the interference amongdifferent UEs. Utilizing this precision, massive MIMO may serve a multitudeof UEs within the same time and frequency resource, thereby achieving bothhigher data rates and spectral efficiency. This is a very important feature asspectrum is very crowded and does not allow for much higher band-widths,and more importantly is also very expensive.The promised gains, however, do come at a cost. Due to the significantlyincreased number of BS antennas, signal processing and data distribution atthe BS become a challenging task. Signal processing complexity scales withthe number of antennas, thus requiring to distribute different tasks properlyto still achieve low-latency and energy efficient implementations. The sameholds for data movement among different antennas and central processingunits. Processing blocks have to be distributed in a manner to not exceedhardware limits, especially at points where many antennas do get combinedto perform some kind of centralized processing.The focus of this thesis can be divided into three different aspects, first,building a real-time prototype for massive MIMO, second, conducting measurementcampaigns in order to verify theoretically promised gains, and third,implementing a fully programmable and flexible hardware platform to efficientlyrun software defined massive MIMO algorithms. In order to constructa prototype, challenges such as low-latency signal processing for huge matrixsizes as well as task distribution to lower pressure on the interconnectionnetwork are considered and implemented. By partitioning the overall systemcleverly, it is possible to implement the system fully based on Commercialoff-the-shelf (COTS) Hardware (HW). The working testbed was utilized inseveral measurement campaigns to prove the benefits of massive MIMO, suchas increased spectral efficiency, channel hardening and improved resilienceto power variations. Finally, a fully programmable Application-Specific InstructionProcessor (ASIP) was designed. Extended with a systolic array thisprogrammable platform shows high performance, when mapping a massiveMIMO detection problem utilizing various algorithms, while post-synthesisresults still suggest a relatively low-power consumption. Having the capabilityto be programmed with a high-level language as C, the design is flexibleenough to adapt to upcoming changes in the recently released 5G standard

Building and operating a real-time massive MIMO testbed - Lessons learned

Massive multiple-input multiple-output (MIMO) is one of the key candidates for the upcoming 5G wireless generation. It offers a multitude of advantages over traditional techniques, such as reduced latency, reduced interference among user equipments (UEs) and increased spectrum and energy efficiencies. However, to verify the theoretically promised gains in real-life, prototype systems are inevitable. This paper discusses the many experiences gathered during designing, building and operating the Lund University Massive MIMO (LuMaMi) testbed. We discuss the six main lessons learned including practical issues, such as the mechanical setup or driver issues but also implementation challenges, such as increasing operation count compared to traditional wireless systems, complicated data shuffling and low-processing latency and detail their specific requirements

An Application Specific Vector Processor for Efficient Massive MIMO Processing

This paper presents an implementation for a baseband massive multiple-input multiple-output (MIMO) application-specific instruction set processor (ASIP). The ASIP is geared with vector processing capabilities in the form of single instruction multiple data (SIMD), and furthermore exploits instruction level parallelism by employing a very large instruction word (VLIW) architecture. Additionally, a systolic array is built into the pipeline which is tuned to speed up matrix calculations. A parallel memory subsystem and stand-alone accelerators are integrated into the ASIP architecture in order to meet the processing requirement. The processor is synthesized in 22FD-SOI technology running at a clock frequency of 800 . The system achieves a maximum detection throughput of 0.75 Gb/s/mm $^2$ for a $128\times 8$ massive MIMO system