# Novel Sparse-Coded Ambient Backscatter Communication for Massive IoT Connectivity
### System Model  
We consider the AmBC network [37] based on access point (AP) such as Wi-Fi as depicted in Figure 1. The AP transmits data to its client as well as energy to nearby RF tags [2; 8; 9; 38], replacing expensive and high-power RFID readers. In terms of deployment cost, utilizing ambient signals is very attractive for backscatter communication as they exist everywhere for providing low-cost Internet connectivity (e.g., TV signal, Wi-Fi, BlueTooth). In the AP's coverage area, there are multiple RF tags utilizing the ambient signals transmitted from the AP to downlink (DL) client. These RF tags can use the ambient signals for RF energy harvesting using a rectifier circuit exempting large-sized batteries. If they harvest sufficient energy for activating backscatter communication, they modulate load impedances connected to micro-controller to transmit \(M\)-ary data instead of large and power-intensive oscillator components [2; 8; 9; 38]. Depending on ambient energy harvesting, tags are only activated in a small fraction of time and mostly remain idle against harvest sufficient energy. The sparsity of ambient backscatter signals motivates us to use encoding data for multi-dimensional complex codewords [20; 32; 33; 39] selected by time-variant reflection coefficients [20; 40] and enables low-complexity decoding even for a massive number of concurrent RF transmissions and higher-order modulation. Consequently, SC-BMA among RF tags can enable NOMA, while providing massive connectivity for low-cost RF tags. Upon the tags' modulation, AP receives low-rate uplink (UL) backscatter signals from the tags while transmitting high-rate DL signals to the client. Assuming a single antenna at AP where transmission and reception occurs simultaneously in full-duplex, these signals are overlapped causing self-interference (SI) [2; 41]. In addition, a high-sampling rate of DL signals significantly reduces sampling period of UL backscatter signals, causing ISI in multipath propagation environments [23]. Moreover, non-orthogonality of sparse code may cause MAI among active tags, degrading detection performance of concurrent RF transmissions [32; 33; 42; 43; 44]. Therefore, the detection of backscattersignals requires mitigation of the three interferences (i.e., SI, ISI and MAI). Based on self-interference cancellation (SIC) [41; 45; 46], strong DL interference can be efficiently removed so that UL backscatter detection can be feasible in practice [2]. Then, D-CEA followed by low-complexity iterative D-MPA is newly implemented to effectively detect data in the presence of the ISI and MAI. Clearly, the ambient backscatter rather exploiting interferences significantly improves spectral efficiency three-fold: full-duplex [2; 41]; NOMA with sparse code [32; 33; 39]; and multipath-diversity combining [22]. To support multiple RF tags, link layer protocol of AmBC is composed of channel estimation and data transmission phase as described in Figure 2. In the channel estimation phase, AP estimates the SI channel using a silent period for RF tags [2]. Then, the tags transmit sparse pseudo random symbols known to AP, and it can efficiently estimate backscatter channels using compressed sensing algorithms [15; 47]. Moreover, as the DBC [12; 13; 18; 19; 20; 21] experiences two-way fading in contrast to the conventional one-way fading models, D-CEA capturing the characteristics of these channels will be described in Section 3. In the data transmission phase, an ambient signal from AP can be sliced into \(K\) time slots, each with \(L\) samples, to spread \(N\geq K\) backscatter signals to increase signal-to-noise ratio (SNR) and reduce circuit-power consumption [2; 5; 6; 7; 48; 49]. As RF tags employ predefined codebooks for data transmissions, active tags can immediately transmit data with low latency without resource allocation and scheduling procedures [34].