In the following we discuss our method of

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In the following, we discuss our method of interference detection [ 9 ] which is a by-product of LNV-SC. 2.1.4 Method-2: Interference detection with local noise variances From Fig. 5 , it is observed that for K number of interferers, the vector of noise variances σ 2 observes sharp and distinguished rise in magnitude over the regions where noise is higher, i.e., where the narrowband interferers are
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Kumar et al. EURASIP Journal on Wireless Communications and Networking (2019) 2019:224 Page 7 of 21 Fig. 5 LNV estimates corresponding to four ZigBee interferers. Distinguish lobes appear at ZigBee center frequencies due to LNV estimation present compared to the regions where the narrowband interferers are absent. For a given WiFi channel, the over- lapping ZigBee channels’ center frequencies are known a priori as shown in Fig. 1 . Thus the elevated portions in Fig. 5 give a coarse estimate of the presence of the inter- ferers. We combine this knowledge along with a threshold detector to pinpoint the interferers as soon as they appear. Once the interferers appear, the corresponding LNV is estimated, and the LLRs are scaled using LNV-SC. The entire operation of interference detection and LLR scaling is illustrated in Fig. 6 . Our proposed method of interference detection does not add any additional signal processing complexity since it is a by-product of LNV-SC. The key advantage of our approach is that lobes could be obtained even at very low levels of interference. However, our method is effective only when there is an overlap between LTS of WiFi and an ongoing ZigBee transmission as it uses LTS (duration 0.8 μ s) to calculate σ 2 . In order to detect the appearance of ZigBee interference during an ongoing WiFi transmission, pilot subcarriers embedded under every OFDM data sym- bols of WiFi could be used; however, estimation accuracy could be affected. In the following, we discuss our work [ 10 ] which is a multi-antenna extension to LNV-SC. 2.2 Interference mitigation in multi-antenna WiFi receivers The indoor channel, especially inside, home and industries are rich in multipath [ 32 ]. With the appropriate spatial separation between receiver antennas, the inter- ference power on different antennas will be different [ 33 ]. We use this insight for applying multi-antenna diversity techniques along with our previous method of single antenna, i.e., LNV-SC. We start our development by a primer on maximal-ratio combining, but first, we establish the multi-antenna signal model. 2.2.1 Signal model Our signal model consists of a dual-antenna WiFi receiver (WiFi-Rx), a single-antenna WiFi transmitter (WiFi-Tx), and a single-antenna ZigBee transmitter (ZB-Tx) as illus- trated in Fig. 7 . Fig. 6 Flow chart of interference detection and LLR scaling. LLR scaling using LNV (LNV-SC) to be performed only during interference
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Kumar et al. EURASIP Journal on Wireless Communications and Networking (2019) 2019:224 Page 8 of 21 Fig. 7 Signal model: single-antenna WiFi transmitter, single-antenna ZigBee interferer and two-antenna WiFi receiver After FFT, the received signal vector Y on i th subcar- rier of
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