Beamforming

Beamforming or spatial filtering is a signal processing technique used in sensor arrays for directional signal transmission or reception.^[1] This is achieved by combining elements in an antenna array in such a way that signals at particular angles experience constructive interference while others experience destructive interference. Beamforming can be used at both the transmitting and receiving ends in order to achieve spatial selectivity. The improvement compared with omnidirectional reception/transmission is known as the directivity of the array.

Beamforming can be used for radio or sound waves. It has found numerous applications in radar, sonar, seismology, wireless communications, radio astronomy, acoustics and biomedicine. Adaptive beamforming is used to detect and estimate the signal of interest at the output of a sensor array by means of optimal (e.g. least-squares) spatial filtering and interference rejection.

Pisi

Sonar beamforming requirements

Sonar beamforming utilizes a similar technique to electromagnetic beamforming, but varies considerably in implementation details. Sonar applications vary from 1 Hz to as high as 2 MHz, and array elements may be few and large, or number in the hundreds yet very small. This will shift sonar beamforming design efforts significantly between demands of such system components as the "front end" (transducers, pre-amplifiers and digitizers) and the actual beamformer computational hardware downstream. High frequency, focused beam, multi-element imaging-search sonars and acoustic cameras often implement fifth-order spatial processing that places strains equivalent to Aegis radar demands on the processors.

Many sonar systems, such as on torpedoes, are made up of arrays of up to 100 elements that must accomplish beam steering over a 100 degree field of view and work in both active and passive modes.

Sonar arrays are used both actively and passively in 1-, 2-, and 3-dimensional arrays.

1-dimensional "line" arrays are usually in multi-element passive systems towed behind ships and in single- or multi-element side-scan sonar.
2-dimensional "planar" arrays are common in active/passive ship hull mounted sonars and some side-scan sonar.
3-dimensional spherical and cylindrical arrays are used in 'sonar domes' in the modern submarine and ships.

Sonar differs from radar in that in some applications such as wide-area-search all directions often need to be listened to, and in some applications broadcast to, simultaneously. Thus a multibeam system is needed. In a narrowband sonar receiver the phases for each beam can be manipulated entirely by signal processing software, as compared to present radar systems that use hardware to 'listen' in a single direction at a time.

Sonar also uses beamforming to compensate for the significant problem of the slower propagation speed of sound as compared to that of electromagnetic radiation. In side-look-sonars, the speed of the towing system or vehicle carrying the sonar is moving at sufficient speed to move the sonar out of the field of the returning sound "ping". In addition to focusing algorithms intended to improve reception, many side scan sonars also employ beam steering to look forward and backward to "catch" incoming pulses that would have been missed by a single sidelooking beam.

Schemes

A conventional beamformer can be a simple beamformer also known as delay-and-sum beamformer. All the weights of the antenna elements can have equal magnitudes. The beamformer is steered to a specified direction only by selecting appropriate phases for each antenna. If the noise is uncorrelated and there are no directional interferences, the signal-to-noise ratio of a beamformer with $L$ antennas receiving a signal of power $P$ , (where $\sigma _{n}^{2}$ is Noise variance or Noise power), is: ${\frac {1}{\sigma _{n}^{2}}}P\cdot L$
A null-steering beamformer is optimized to have zero response in the direction of one or more inteferers.
A frequency-domain beamformer treats each frequency bin as a narrowband signal, for which the filters are complex coefficients (that is, gains and phase shifts), separately optimized for each frequency.

Evolved Beamformer

The delay-and-sum beamforming technique uses multiple microphones to localize sound sources. One disadvantage of this technique is that adjustments of the position or of the number of microphones changes the performance of the beamformer nonlinearly. Additionally, due to the number of combinations possible, it is computationally hard to find the best configuration. One of the techniques to solve this problem is the use of genetic algorithms. Such algorithm searches for the microphone array configuration that provides the highest signal-to-noise ratio for each steered orientation. Experiments showed that such algorithm could find the best configuration of a constrained search space comprising ~33 million solutions in a matter of seconds instead of days.^[2]

History in wireless communication standards

Beamforming techniques used in cellular phone standards have advanced through the generations to make use of more complex systems to achieve higher density cells, with higher throughput.

Passive mode: (almost) non-standardized solutions
- Wideband Code Division Multiple Access (WCDMA) supports direction of arrival (DOA) based beamforming

Active mode: mandatory standardized solutions
- 2G – Transmit antenna selection as an elementary beamforming ^{[citation needed]}
- 3G – WCDMA: Transmit antenna array (TxAA) beamforming ^{[citation needed]}
- 3G evolution – LTE/UMB: Multiple-input multiple-output (MIMO) precoding based beamforming with partial Space-Division Multiple Access (SDMA)^{[citation needed]}
- Beyond 3G (4G, 5G, ...) – More advanced beamforming solutions to support Space-division multiple access (SDMA) such as closed loop beamforming and multi-dimensional beamforming are expected

An increasing number of consumer 802.11ac Wi-Fi devices with MIMO capability can support beamforming to boost data communication rates.^[3]

Digital, analog, and hybrid

For receive (but not transmit^{[citation needed]}), there is a distinction between analog and digital beamforming. For example, if there are 100 sensor elements, the "digital beamforming" approach entails that each of the 100 signals passes through an analog-to-digital converter to create 100 digital data streams. Then these data streams are added up digitally, with appropriate scale-factors or phase-shifts, to get the composite signals. By contrast, the "analog beamforming" approach entails taking the 100 analog signals, scaling or phase-shifting them using analog methods, summing them, and then usually digitizing the single output data stream.

Digital beamforming has the advantage that the digital data streams (100 in this example) can be manipulated and combined in many possible ways in parallel, to get many different output signals in parallel. The signals from every direction can be measured simultaneously, and the signals can be integrated for a longer time when studying far-off objects and simultaneously integrated for a shorter time to study fast-moving close objects, and so on.^[4] This cannot be done as effectively for analog beamforming, not only because each parallel signal combination requires its own circuitry, but more fundamentally because digital data can be copied perfectly but analog data cannot. (There is only so much analog power available, and amplification adds noise.) Therefore, if the received analog signal is split up and sent into a large number of different signal combination circuits, it can reduce the signal-to-noise ratio of each.

In MIMO communication systems with large number of antennas, so called massive MIMO systems, the beamforming algorithms executed at the digital baseband can get very complex. In addition, if all beamforming is done at baseband, each antenna needs its own RF feed. At high frequencies and with large number of antenna elements, this can be very costly, and increase loss and complexity in the system. To remedy these issues, hybrid beamforming has been suggested where some of the beamforming is done using analog components and not digital.

There are many possible different functions that can be performed using analog components instead of at the digital baseband.^[5]^[6]^[7]

For speech audio

Beamforming can be used to try to extract sound sources in a room, such as multiple speakers in the cocktail party problem. This requires the locations of the speakers to be known in advance, for example by using the time of arrival from the sources to mics in the array, and inferring the locations from the distances.

Compared to carrier-wave telecommunications, natural audio contains a variety of frequencies. It is advantageous to separate frequency bands prior to beamforming because different frequencies have different optimal beamform filters (and hence can be treated as separate problems, in parallel, and then recombined afterward). Properly isolating these bands involves specialized non-standard filter banks. In contrast, for example, the standard fast Fourier transform (FFT) band-filters implicitly assume that the only frequencies present in the signal are exact harmonics; frequencies which lie between these harmonics will typically activate all of the FFT channels (which is not what is wanted in a beamform analysis). Instead, filters can^{[citation needed]} be designed in which only local frequencies are detected by each channel (while retaining the recombination property to be able to reconstruct the original signal), and these are typically non-orthogonal unlike the FFT basis.

References

^ Van Veen, B. D.; Buckley, K. M. (1988). "Beamforming: A versatile approach to spatial filtering" (PDF). IEEE ASSP Magazine. 5 (2): 4. Bibcode:1988IASSP...5....4V. doi:10.1109/53.665. S2CID 22880273. Archived from the original (PDF) on 2008-11-22.
^ Lashi, Dugagjin; Quevy, Quentin; Lemeire, Jan (November 2018). "Optimizing Microphone Arrays for Delay-and-Sum Beamforming using Genetic Algorithms". 2018 4th International Conference on Cloud Computing Technologies and Applications (Cloudtech). Brussels, Belgium: IEEE: 1–5. doi:10.1109/CloudTech.2018.8713331. ISBN 978-1-7281-1637-2.
^ Geier, Eric. "All about beamforming, the faster Wi-Fi you didn't know you needed". PC World. IDG Consumer & SMB. Retrieved 19 October 2015.
^ Systems Aspects of Digital Beam Forming Ubiquitous Radar, Merrill Skolnik, 2002, [1]
^ Phyo, Zar Chi; Taparugssanagorn, Attaphongse (2016). "Hybrid analog-digital downlink beamforming for massive MIMO system with uniform and non-uniform linear arrays". 2016 13th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). pp. 1–6. doi:10.1109/ECTICon.2016.7561395. ISBN 978-1-4673-9749-0. S2CID 18179878.
^ Zou, Yaning; Rave, Wolfgang; Fettweis, Gerhard (2016). "Analog beamsteering for flexible hybrid beamforming design in mmwave communications". 2016 European Conference on Networks and Communications (EuCNC). pp. 94–99. arXiv:1705.04943. doi:10.1109/EuCNC.2016.7561012. ISBN 978-1-5090-2893-1. S2CID 16543120.
^ Rajashekar, Rakshith; Hanzo, Lajos (2016). "Hybrid Beamforming in mm-Wave MIMO Systems Having a Finite Input Alphabet" (PDF). IEEE Transactions on Communications. 64 (8): 3337–3349. doi:10.1109/TCOMM.2016.2580671. S2CID 31658730.

General

Louay M. A. Jalloul and Sam. P. Alex, "Evaluation Methodology and Performance of an IEEE 802.16e System", Presented to the IEEE Communications and Signal Processing Society, Orange County Joint Chapter (ComSig), December 7, 2006. Available at: https://web.archive.org/web/20110414143801/http://chapters.comsoc.org/comsig/meet.html
H. L. Van Trees, Optimum Array Processing, Wiley, NY, 2002.
Jian Li, and Petre Stoica, eds. Robust adaptive beamforming. New Jersey: John Wiley, 2006.
M. Soltanalian. Signal Design for Active Sensing and Communications. Uppsala Dissertations from the Faculty of Science and Technology (printed by Elanders Sverige AB), 2014.
"A Primer on Digital Beamforming" by Toby Haynes, March 26, 1998
"What Is Beamforming?", an introduction to sonar beamforming by Greg Allen.
Krim, H.; Viberg, M. (1996). "Two decades of array signal processing research: The parametric approach". IEEE Signal Processing Magazine. 13 (4): 67–94. Bibcode:1996ISPM...13...67K. doi:10.1109/79.526899.
"Dolph–Chebyshev Weights" antenna-theory.com
A collection of pages providing a simple introduction to microphone array beamforming

External links

Animation of beam steering using phased arrays on YouTube
MU-MIMO Beamforming by Constructive Interference, Wolfram Demonstrations Project

[1] Van Veen, B. D.; Buckley, K. M. (1988). "Beamforming: A versatile approach to spatial filtering" (PDF). IEEE ASSP Magazine. 5 (2): 4. Bibcode:1988IASSP...5....4V. doi:10.1109/53.665. S2CID 22880273. Archived from the original (PDF) on 2008-11-22.

[2] Lashi, Dugagjin; Quevy, Quentin; Lemeire, Jan (November 2018). "Optimizing Microphone Arrays for Delay-and-Sum Beamforming using Genetic Algorithms". 2018 4th International Conference on Cloud Computing Technologies and Applications (Cloudtech). Brussels, Belgium: IEEE: 1–5. doi:10.1109/CloudTech.2018.8713331. ISBN 978-1-7281-1637-2.

[3] Geier, Eric. "All about beamforming, the faster Wi-Fi you didn't know you needed". PC World. IDG Consumer & SMB. Retrieved 19 October 2015.

[4] Systems Aspects of Digital Beam Forming Ubiquitous Radar, Merrill Skolnik, 2002, [1]

[5] Phyo, Zar Chi; Taparugssanagorn, Attaphongse (2016). "Hybrid analog-digital downlink beamforming for massive MIMO system with uniform and non-uniform linear arrays". 2016 13th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). pp. 1–6. doi:10.1109/ECTICon.2016.7561395. ISBN 978-1-4673-9749-0. S2CID 18179878.

[6] Zou, Yaning; Rave, Wolfgang; Fettweis, Gerhard (2016). "Analog beamsteering for flexible hybrid beamforming design in mmwave communications". 2016 European Conference on Networks and Communications (EuCNC). pp. 94–99. arXiv:1705.04943. doi:10.1109/EuCNC.2016.7561012. ISBN 978-1-5090-2893-1. S2CID 16543120.

[7] Rajashekar, Rakshith; Hanzo, Lajos (2016). "Hybrid Beamforming in mm-Wave MIMO Systems Having a Finite Input Alphabet" (PDF). IEEE Transactions on Communications. 64 (8): 3337–3349. doi:10.1109/TCOMM.2016.2580671. S2CID 31658730.

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