How do devices share channel resources?
The first suggestion that WiFi devices share channel resources equally has not been confirmed by experiments (fig. 1).

Often, building an authentication system requires some sort of deployment of IoT devices. At worst, every user needs a RFID device (similar to a key) to authenticate themselves. The idea proposed in this paper (Shi et al.) is to use present Wi-Fi infrastructure, which is very widely available (e.g. your local library has it, even the local candle shop likely has Wi-Fi).

How do WiFi channels affect each other? And what can we do about it?
An interesting approach to this problem was published in this article.

It´s becoming clear that work from home is not just an exceptional situation in times of high Covid 19 incidence anymore. It's accepted now as a working model by many companies outside of the global pandemic context.

I recommend checking it out just because its design is stunning. Of course, the topic is also very interesting - CPEs and Internet of Things (IoT) devices often have very low energy requirements. On the one hand, such devices are inherently small and require less energy.

The Auto-channel means that a Wi-Fi router selects channels automatically based on the vendor’s algorithms. In this case, only a single device is considered and this can increase interference for the whole system.

Repeating computation efforts are very annoying. But even more annoying is it if they take a lot of time. Because that can get nasty quickly. But this is where caches come to save us from a lot of wasted time.
What is a cache?

There are many different Full Band Capture (FBC) impairments, one of the interesting impairments is adjacency because it is often so pronounced on the spectrum. The figures below shows a small adjacency: The channels suddenly jump slightly in their dB level.

As mentioned in the NQI post, data is extremely important for HFC networks. In this post, we will be taking a look at a very interesting paper published in one of the top high impact journals in CS, …

Since a large set of posts on this blog deal with Machine Learning, I decided to write up a cursory look on the Machine Learning (ML) field. ML is the process of training a model using a training dataset and an optimiziation algorithm such that a certain task is fulfilled.

In their paper "Performance Monitoring Challenges in HFC Networks", Milan et al. say that collecting big data and processing it is integral to calculating the health of the HFC network of operators. Their KPIs include some interesting ones such as Flap List and SNR - let's take a closer look at them.

The goal of this exercise is to find a smart way to group the SNR data over time of many channels belonging to a single cable modem dynamically to make visualization better: for example, have 24 lines appear as roughly 5-8 grouped lines that can be drilled down on.

An integral part of FBC impairments detection is channel detection. For instance, the adjacency impairment looks at neighboring channels when deciding for an impairment.

In the modern world, most Internet devices rely on APIs to function properly. But what actually is an API?

Proactively catching and fixing issues in the network instead of reacting when customers start piling up on your support centres has become the main modus operandi for service providers.

Filtering is an extremely useful method in DOCSIS cable networks. There are many use cases - for instance one could disable data channels to alleviate other impairments and thus make the connection stable or even just to match the level of service the customer is paying for.

The wave impairment is one of many impairments that can be detected on a DOCSIS FBC (full band capture) downstream spectrum. In the following, we take a cursory look at an implementation of the wave impairment detection algorithm.

Axiros recently unveiled AXESS 3.8.1, an important update to the company’s flagship and industry-leading software.