Following Dialog Acquisition, Renesas Goes After Celeno for Wi-Fi 6/6E Boost

Following its $6 billion acquisition of Dialog Semiconductor in August, Renesas recently announced the completion of its acquisition of the Israeli company Celeno Communications. What is Renesas' business strategy in this most recent takeover? 

 

Renesas' Rationale for the Acquisitions

With a broad portfolio of SoC products and microcontrollers, Renesas is perhaps best known for its embedded design solutions. Dialog Semiconductor, on the other hand, provides battery and power management, Wi-Fi, Bluetooth low energy, and industrial edge computing solutions. 

 

The broadband speeds projected worldwide between 2018 and 2023. Image used courtesy of Celeno

 

Celeno also specializes in a wide range of wireless communication solutions, including advanced Wi-Fi chipsets and software solutions for home networks and smart buildings. It seems that the two recent acquisitions by Renesas are aimed at scaling up the company’s capabilities in wireless connectivity and IoT areas. This should complement Renesas’ broad MCU portfolio that spans applications such as IoT, robotics, automation, and automotive.

Acquiring Dialog and Celeno for about $6 billion and $315 million, respectively, Renesas is making aggressive moves to broaden its range of products. Below, we’ll take a brief look at Celeno’s areas of interest.

 

Celeno's Extensive Wi-Fi 6 and 6E Portfolio

Celeno has Wi-Fi 6 and 6E chipsets that, according to Renesas, offer exceptional Wi-Fi network performance with increased security, low latency, and low power consumption.

Wi-Fi 6, also referred to as 802.11ax, is the latest upgrade in Wi-Fi standards. Just like the earlier upgrades of this technology, Wi-Fi 6 attempts to increase maximum data rates and improve security. However, the changes were made with one eye on facilitating capacity, latency, power consumption, coverage, and deployment density (number of devices in a given area).

When applied to IoT applications, traditional Wi-Fi has two main limitations: it cannot efficiently support a large number of devices at the same time and it has a high power consumption. In order to support crowded networks, Wi-Fi 6 employs the orthogonal frequency division multiple access (OFDMA) technique.

With OFDMA, channels are subdivided into up to nine subchannels also called resource units (RUs). These subchannels can be allocated to different devices. Traditional Wi-Fi standards use orthogonal frequency-division multiplexing (OFDM) rather than the OFDMA technique.  The difference between these two methods is illustrated below. 

 

Image used courtesy of Siemens

 

Using the OFDMA technique, a single transmission from the Wi-Fi 6 router can communicate with multiple devices at the same time. This is not the case with previous generations of Wi-Fi, where each device must wait for its turn when the router serves multiple users. The OFDMA-based communication allows Wi-Fi 6 to reduce overhead and latency while increasing capacity. Traditional Wi-Fi has a hard time managing more than a few sensors; however, Wi-Fi 6 can easily support hundreds of devices.

 

Wi-Fi 6/6E and Target Wake Time

What about the power consumption issue? Wi-Fi 6 uses a technique called "target wake time" (TWT) to minimize the power consumption of a battery-powered IoT device. The TWT feature allows the client devices to negotiate a wake-up time schedule with the access point (AP). Therefore, an IoT device that needs to transfer only a few frames in a long time interval can remain in sleep mode and wake up only at the agreed time to send/receive its data packets. 

With traditional Wi-Fi, a device with a small amount of data to transmit could periodically go to sleep and active modes within milliseconds. However, using Wi-Fi 6, a client device can be in sleep mode for minutes, days, or even weeks based on the scheduled wake-up time. This can reduce the client device power consumption by up to 80% and maximize the battery run time.   

 

Image used courtesy of Celeno

 

The TWT allows the access point to have more control over the network. With the transmissions performed in a scheduled manner, the TWT feature helps optimize spectral efficiency and achieve contention-free channel access. 

Wi-Fi 6E is another Wi-Fi standard that, in addition to having all of the advantages of Wi-Fi 6 discussed above, significantly increases data rates by utilizing the 6 GHz frequency band, which provides 1,200 MHz of bandwidth.   

 

What is Wi-Fi Doppler Imaging?

Wi-Fi Doppler imaging is a Wi-Fi solution from Celeno that attempts to take Wi-Fi connectivity to the next level by offering imaging over the Wi-Fi network. This technology turns the ubiquitous Wi-Fi network into a radar sensor that can track the movement and location of people, pets, and objects. It can detect gestures, label objects, and monitor breathing and other vital signs. This technology can also be extended to elderly care and intrusion detection for enhanced home and enterprise security.

The Wi-Fi Doppler imaging technology is based on the Doppler effect (or the Doppler shift), which refers to a change in a wave's frequency in relation to an observer moving relative to the wave source.

 

Image used courtesy of Celeno

 

The Doppler effect is employed in ultrasound machines to create images of structures inside the body and determine the blood flow through the vessels. Radars also use the Doppler effect to determine the velocity of an object.

Wi-Fi Doppler imaging from Celeno creates a Wi-Fi-based radar where Wi-Fi signals are used to track the movement and location of objects. Celeno integrates the radar hardware with the Wi-Fi connectivity hardware to create a Wi-Fi Doppler imaging chip. 

This chip transmits a Wi-Fi signal like a normal Wi-Fi chip; however, it also simultaneously analyzes the reflections of the Wi-Fi signal from the objects in the environment to detect the location and dynamics of the objects.

 

Combining ML With Doppler to Detect Human Motion  

Detecting human movements by means of the Doppler effect is more challenging than detecting the bulk motion of an object such as an airplane. When people move in a certain direction, different parts of the body naturally move at different speeds and directions. As a result, the reflections from the human body create multiple Doppler shifts, which might not be easily recognized by the radar system. 

 

Image used courtesy of Celeno

 

To combat this problem, Celeno uses machine learning and AI techniques to classify and detect the Doppler shift from different human motions such as falling, walking, bending over, sitting down, and standing up.

The above functionalities can also be achieved by installing multiple cameras within a certain space; however, cameras are often perceived as invasive in terms of privacy. Besides, with Celeno's Wi-Fi Doppler imaging technology, a single device is used for both connectivity and imaging.

Since the Doppler imaging solution is free of line-of-sight constraints, it can see through walls and cover a larger space.