Abstract
Machine-to-machine (M2M) communication is one of the vertical sectors that will benefit from 5G communication systems, but today, these systems are still dominated by technologies such as ZigBee and WiFi. An M2M scenario will experience dense deployment of ZigBee and WiFi nodes in order to route the data from one end to the other. In the 2.4 GHz industrial, scientific, and medical (ISM) band, both of the technologies perform co-channel overlapped operation and hence face severe cross technology co-channel interference (CCI). In contrast to cellular systems, which solve the CCI by centralized coordination through the base station, addressing CCI in the ISM band is non-trivial due to heterogeneous wireless technologies and the lack of centralized coordination. In this work, we first present interference mitigating receiver architectures for OFDM-based WiFi using single and multiple antennas. Our single antenna work is based on the localized estimation of excess noise caused by single and multiple co-channel narrowband interferers and scaling the log-likelihood ratios (LLRs) of the affected WiFi subcarriers. The simulation shows our method achieves a significant gain in SNR compared to the conventional method for a given packet error rate (PER) criterion. Next, we discuss maximal ratio combiner with LLR scaling (MLSC), which is a multi-antenna extension to our previous work. The simulation shows MLSC achieves diversity gain apart from the gain in SNR. Further, we propose soft-bit maximal ratio combiner with LLR scaling (SB-MLSC). SB-MLSC is an easy to implement version of MLSC. However, diversity combining in SB-MLSC is performed by combining the LLRs. Nonetheless, simulations show equivalence in performance by SB-MLSC and MLSC. Finally, as a significant part of this work, we implemented all our methods using a software-defined radio (SDR) and performed over-the-air (OTA) testing in the 2.4-GHz ISM band using standard WiFi and ZigBee frames. Results of OTA tests fall in complete agreement with our simulations indicating the practical applicability of our methods. Our methods apply to all the standards and related radio transmission techniques which are based on OFDM and face narrowband co-channel interference. Additionally, since our work focuses only on receiver side modifications, they can be integrated with the existing infrastructure with minimal modifications.
Highlights
The rapid increase in low-cost heterogeneous wireless devices and scarcity of radio-frequency (RF) spectrum is causing cross-technology co-channel interference (CTCCI)
We observe that Local noise variance LLR scaling (LNV-SC) (LLR scaling with Local noise variance (LNV) estimates) achieves 10% packet error rate (PER) mark at a lower WiFi Transmit power (TXP) compared to the conventional Log-likelihood ratios (LLR) scaling (Conv-SC) for both the WiFi
For the case when the interference is caused by two ZigBee interferers, we observe that LNV-SC fails to provide any gain over ConvSC
Summary
The rapid increase in low-cost heterogeneous wireless devices and scarcity of radio-frequency (RF) spectrum is causing cross-technology co-channel interference (CTCCI). Our application scenarios are smart homes and modern automated factories where there is dense deployment of wireless sensors and machine-to-machine communications play a key role in routing the sensory data to the processing centers. These wireless sensors predominantly use wireless local area networks (WLAN; based on IEEE 802.11) such as IEEE 802.11 a/b/g/n/ah, and wireless personal area networks (WPAN) such as IEEE 802.15.4 and IEEE 802.15.1. Our frequency of interest is 2.4-GHz ISM band which has a usable bandwidth of 80 MHz which is shared by several heterogeneous wireless standards such as IEEE 802.11a/b/g and IEEE 802.15.4, IEEE 802.15.1, etc. The extent of degradation depends on the received power levels (RXP) and the degree of time/frequency overlap of the interfering signals
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