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Frequency Status Table
Source publication
This paper presents a solution to resolve the interference problems between the Wi-FiTM and BluetoothTM wireless technologies. A new channel selecting approach is being used to select the frequency channel. The signal strength in a channel is assessed, and that value is used to select the channels to send data without interference. Thus we are tryi...
Context in source publication
Context 1
... selection works as follows. Each Bluetooth receiver will have a Frequency Status Table (FST), where an RSSI value is associated to each frequency channel, as shown in Table (Table 1) below. Frequencies are classified "good" or "bad" depending on whether their RSSI value is 0 or not. ...
Citations
... Overcrowding of WLAN channels results in cochannel interference and adjacent channel interference [1,2]. This may lead to deterioration in the quality of service and drop-in high data transmission rates [3,4]. To overcome the above-mentioned difficulties, a mechanism that switches a user dynamically to a less interference channel is essential. ...
This paper presents a five‐element antennas system formed by integrating one sensing and four reconfigurable communicating antennas on a single substrate for cognitive radio applications in the 2.4 and 5 GHz WLAN bands. The five antennas are termed as A‐1, A‐2, A‐3, A‐4, and A‐5, where A‐1 is designed to sense in the 2.4 (2.4–2.484 GHz) and 5 GHz (5.15–5.875) bands. Antenna A‐2, exhibiting two‐element MIMO configuration with A‐3, is designed to communicate in the 5 GHz band, whereas Antenna A‐4, also exhibiting two‐element MIMO configuration with A‐5, operates in the 2.4 GHz band. Each communicating antenna is incorporated with a varactor diode to accomplish continuous frequency tuning operations. Simulated maximum peak gains of A‐1, A‐2, and A‐4 are 7.07, 2.65, and 2.15 dBi, respectively. A good mutual coupling, of less than −15 dB, is noted between the sensing and communicating antennas. MIMO diversity parameters are found to be ECC ≤ 0.01, TARC ≤ −10 dB, DG ≤ 10 dB, and CCL ≤ 0.4. Overall dimensions of the proposed structure are 80 mm × 100 mm × 1.2 mm. The antenna lumped element circuit models developed in ADS software also present good matching with EM simulation. A prototype of the proposed antenna system is fabricated to validate the simulation results. Because the proposed antenna can sense the WLAN bands using the dual‐band antenna (A‐1) and can be able to communicate at the identified spectrum holes using the reconfigurable NB antennas (A‐2 to A‐5), this antenna system is an appropriate module for the cognitive radio applications in WLAN bands.
... In [22] the authors focus on channel selection considering the coexistence between Wi-Fi and Bluetooth technologies operating in the 2.4 GHz ISM band. The mechanism selects the channel with the least interference as the optimal one, based solely on RSSI measurements. ...
The number of Wi-Fi devices and their requirements for bandwidth keep on increasing, along with their hunger for spectrum. This fact is mostly noticeable in dense urban scenarios where neighbors fight for bandwidth and their networks struggle to deliver the requested information. While the inherent limitations of Wi-Fi technologies cannot be overcome, they can be mitigated through configuration. Optimally selecting a wireless channel is a critical aspect of access point (AP) configuration, and a challenging task due to a broad range of factors affecting the wireless connection performance. This paper addresses the channel selection problem by relying on a time-varying dynamic approach capable of modeling its surrounding wireless networks with respect to their usage patterns, channel utilization and adjacent channel interference. This contextual data is used in a channel selection model, which combines utilization patterns and statistics with a probabilistic mathematical model to accurately estimate the impact of adjacent channel interference on the signal to interference plus noise ratio, hence effectively selecting a wireless channel whose optimal performance is exhibited when the users’ need it. The experimental results demonstrate that the proposed approach outperforms competing methods while closely tracking the simulation models, thus paving the way for smarter APs.
In recent years, in-vehicle infotainment networks (IVINs) have rapidly become one of the most valuable features auto makers have used to promote their flagship models as an advanced competitive marketing weapon. IVINs can provide passengers with multimedia services locally as well as Internet connectivity through a gateway known as a mobile hotspot. The in-vehicle mobile hotspot is embedded in the car and supports cellular connection. Utilizing this system, mobile devices can access the in-vehicle unified infotainment framework to comfortably enjoy streaming services, online games, online commerce, social network services, and so on. However, because of wireless access characteristics, if a significant number of Wi-Fi mobile hotspots are densely located, the throughput of the mobile devices will be tremendously diminished due to the interference among the mobile hotspots of IVINs, as well as with existing fixed office or residential APs along the road. In this article, we discuss the interference problems of Wi-Fi access in IVINs, provide effective solutions to these problems, and present the performance of each proposed approach within typical case studies.