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[#]: collector: (lujun9972)
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[#]: translator: (CN-QUAN )
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[#]: reviewer: ( )
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[#]: publisher: ( )
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[#]: url: ( )
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[#]: subject: (NIST aims to make frequency sharing more efficient for wireless networks)
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[#]: via: (https://www.networkworld.com/article/3561618/nist-aims-to-make-frequency-sharing-more-efficient-for-wireless-networks.html)
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[#]: author: (Patrick Nelson https://www.networkworld.com/author/Patrick-Nelson/)
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NIST aims to make frequency sharing more efficient for wireless networks
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======
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Machine-learning formula will help different radio protocols, such as Wi-Fi and LTE, work together more efficiently in the same wireless spectrum.
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Martyn Williams/IDG
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A machine-learning formula developed by the National Institute of Standards and Technology ([NIST][1]) has the potential to significantly improve how [5G][2] and other wireless networks select and share communications frequencies. Compared to trial-and-error methods, NIST's formula could make the process of sharing communications frequencies as much as 5,000 times more efficient, researchers claim.
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The NIST system is based the idea that radio equipment can learn its network environments from experience rather than, as is done now, simply select frequency channels based on trial-and-error.
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"The algorithm learns which channel provides the best outcome" under specific environmental conditions, NIST says in an [article on its website][3].
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**READ MORE:** [How beamforming makes wireless communication faster][4]
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"The formula could be programmed into software on transmitters in many [different] types of real-world networks," the team says.
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Essentially, the computer-modeled algorithm is a formula that maps prior experience in environmental RF conditions. Those conditions can include the number of transmitters operating within a channel (set of adjacent frequencies), for example.
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"… if a transmitter selects a channel that is not occupied, then the probability of a successful transmission rises, leading to a higher data rate," the article says. Likewise, when a transmitter selects a channel that doesn't have much interference on it, the signal is stronger, and you get a better data rate. The transmitter remembers which channel provides the best outcome and learns to choose that spot on the dial when it next needs a clear signal.
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That's different from the way things generally work today. That is, a radio simply tries to find an open frequency and then communicates with like-protocol radios. In sophisticated cases, like Wi-Fi, for example, frequency hopping and [beamforming][4] are used to optimize channels.
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Where NIST's machine-learning technique shines is in the case of shared spectrum, such as sharing Wi-Fi with License Assisted Access (LAA), the researchers explain. LAA is LTE in unlicensed spectrum, known as LTE-U, at 5 GHz. In that combination of Wi-Fi with LAA, on the same frequencies, the protocols are disparate: the radios don't communicate with each other to function in harmony, and chaos could occur the busier the band got—transmissions would bump into other transmissions. But, if all the radios were better at choosing their slot, by learning what works and what doesn't, then things would be better.
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"This could potentially make communications in the unlicensed bands much more efficient," says Jason Coder, a NIST engineer, in the article.
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Indeed, it "could help 5G and other wireless networks select and share communications frequencies about 5,000 times more efficiently than trial-and-error methods," NIST claims.
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The key word here is "share," because in order to increase communications in limited spectrum, more sharing must take place—the users, such as IoT, or media streaming, are all competing for the same metaphorical real estate. Combining unlicensed and licensed bands, as is the case in LAA, will likely become more common as IoT and digital continues to expand. (Unlicensed bands are those not assigned to a specific user, like a mobile network operator; licensed bands are won in auctions and allocated.)
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In the NIST scenario, the competing transmitters "each learn to maximize the total network data rate without communicating with each other." Therefore, multiple protocols and data types, like video or sensor data, or Wi-Fi and mobile networks, can function alongside each other.
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NIST's formula significantly simplifies the process of assigning optimum channels to transmitters, according to the article: "The study found that an exhaustive effort [using trial and error] to identify the best solution would require about 45,600 trials, whereas the formula could select a similar solution by trying only 10 channels, just 0.02 percent of the effort."
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The NIST researchers recently presented their research at [IEEE's 91st Vehicular Technology Conference][5], held virtually this year.
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Join the Network World communities on [Facebook][6] and [LinkedIn][7] to comment on topics that are top of mind.
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--------------------------------------------------------------------------------
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via: https://www.networkworld.com/article/3561618/nist-aims-to-make-frequency-sharing-more-efficient-for-wireless-networks.html
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作者：[Patrick Nelson][a]
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选题：[lujun9972][b]
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译者：[译者ID](https://github.com/译者ID)
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校对：[校对者ID](https://github.com/校对者ID)
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本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
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[a]: https://www.networkworld.com/author/Patrick-Nelson/
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[b]: https://github.com/lujun9972
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[1]: https://www.nist.gov/
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[2]: https://www.networkworld.com/article/3330603/5g-versus-4g-how-speed-latency-and-application-support-differ.html
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[3]: https://www.nist.gov/news-events/news/2020/05/nist-formula-may-help-5g-wireless-networks-efficiently-share-communications
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[4]: https://www.networkworld.com/article/3445039/beamforming-explained-how-it-makes-wireless-communication-faster.html
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[5]: https://events.vtsociety.org/vtc2020-spring/conference-sessions/program/
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[6]: https://www.facebook.com/NetworkWorld/
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[7]: https://www.linkedin.com/company/network-world
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@ -0,0 +1,65 @@
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[#]: collector: (lujun9972)
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[#]: translator: (CN-QUAN )
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[#]: reviewer: ( )
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[#]: publisher: ( )
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[#]: url: ( )
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[#]: subject: (NIST aims to make frequency sharing more efficient for wireless networks)
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[#]: via: (https://www.networkworld.com/article/3561618/nist-aims-to-make-frequency-sharing-more-efficient-for-wireless-networks.html)
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[#]: author: (Patrick Nelson https://www.networkworld.com/author/Patrick-Nelson/)
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NIST的目标是使无线网络的频率共享更加有效
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======
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机器学习公式将帮助不同的无线电协议，如Wi-Fi和LTE，在同一的无线频谱中更有效地协同工作。
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马丁·威廉姆斯/IDG
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美国国家标准与技术研究所([NIST][1])开发的机器学习公式有可能显著改善[5G][2]和其他无线网络选择和共享通信频率的方式。研究人员声称，与试错法相比，NIST的公式可以使共享通信频率的过程效率提高多达5000倍。
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NIST系统的理念是，无线电设备可以从经验中学习其网络环境，而不是像现在这样，简单地根据试错法选择频率信道。
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NIST在[其网站上的一篇文章][3]中说，在特定的环境条件下，“该算法可以学习哪个信道提供最好的结果”。
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**阅读更多内容：**[波束成形如何提高无线通信速度][4]
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该团队说:“这个公式可以被编程到现实世界中许多[不同]类型网络的发射机的软件中。”
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从本质上讲，计算机模拟的算法是一个公式，它映射了先前在环境射频条件下的经验。例如，这些条件可以包括在一个信道(一组相邻的频率)内运行的发射机的数量。
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文章说：“…如果发射机选择了一个未被占用的信道，那么成功传输的概率就会上升，从而导致更高的数据速率。”同样地，当发射机选择一个没有太多干扰的信道时，信号会更强，你也会得到更好的数据速率。发射机记住哪个信道提供最佳结果， 并学会在下次需要清晰信号时学会选择刻度盘上的那个位置。
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这与今天的工作方式不同。也就是说，无线电只是试图找到一个开放频率，然后与类似协议的无线电进行通信。在复杂的情况下，例如Wi-Fi，跳频和[波束成形][4]被用来优化信道。
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研究人员解释说，NIST的机器学习技术的亮点在于共享频谱，比如通过授权频谱辅助接入(LAA)共享Wi-Fi。LAA是未经许可的LTE频谱，称为LTE-U，频率为5GHz。在相同频率下的Wi-Fi与LAA的组合中，协议是不同的：无线电之间不能相互通信以协调工作，而且频带越繁忙就可能出现混乱——传输会遇到其他传输。但是，如果所有的无线电接收机都能更好地选择他们的时段，通过学习哪些有效，哪些无效，那么这将会更好。
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NIST工程师杰森·科德（Jason Coder）在文章中说：“这可能会使未经许可的频段的通信更加高效。”
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事实上，NIST声称，它“可以帮助5G和其他无线网络选择和共享通信频率，其效率大约是试错法的5000倍。”
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这里的关键词是“共享”，因为为了在有限的频谱内增加通信，必须进行更多的共享——物联网或媒体流媒体等用户都在争夺同样的隐喻地产。随着物联网和数字技术的不断发展，无授权和授权频段的结合，就像LAA中的情况一样，可能会变得更加普遍。(未授权的频段是指那些没有分配给特定用户的频段，比如移动网络运营商；授权的频段是在拍卖中中标并分配的。)
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在NIST场景中，相互竞争的发射机“都学习在不相互通信的情况下最大化网络数据速率”。因此，多种协议和数据类型，如视频或传感器数据，或Wi-Fi和移动网络，可以相互协作。
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NIST的公式大大简化了为发射机分配最佳信道的过程，根据这篇文章研究发现，穷尽努力[使用试错法]来确定最佳解决方案需要大约45600次试验，而这个公式只需要尝试10个渠道就可以选择类似的解决方案，仅仅0.02%的努力。”
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NIST的研究人员最近在今年举行的IEEE第91届车辆技术会议上展示了他们的研究成果。
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--------------------------------------------------------------------------------
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via: https://www.networkworld.com/article/3561618/nist-aims-to-make-frequency-sharing-more-efficient-for-wireless-networks.html
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作者：[Patrick Nelson][a]
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选题：[lujun9972][b]
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译者：[CN-QUAN](https://github.com/CN-QUAN)
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校对：[校对者ID](https://github.com/校对者ID)
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本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
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[a]: https://www.networkworld.com/author/Patrick-Nelson/
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[b]: https://github.com/lujun9972
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[1]: https://www.nist.gov/
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[2]: https://www.networkworld.com/article/3330603/5g-versus-4g-how-speed-latency-and-application-support-differ.html
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[3]: https://www.nist.gov/news-events/news/2020/05/nist-formula-may-help-5g-wireless-networks-efficiently-share-communications
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[4]: https://www.networkworld.com/article/3445039/beamforming-explained-how-it-makes-wireless-communication-faster.html
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[5]: https://events.vtsociety.org/vtc2020-spring/conference-sessions/program/
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[6]: https://www.facebook.com/NetworkWorld/
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[7]: https://www.linkedin.com/company/network-world
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