MultiDimensional SparseCoded Ambient Backscatter Communication for Massive IoT Networks
Abstract
:1. Introduction
 DirectLink Interference: We consider separated ambient source and reader for longrange AmBC in contrast to the shortrange AmBC with colocated source and reader in [24]. In addition, to effectively cancel DLI, we utilize frequencydomain structure of OFDM carriers from multiple ambient sources, which can be an extension of the works [11,12,25] with single ambient source.
 MultiDimensional Signaling: We extend sparse codes [24] designed for specific cases (i.e., $M\le 8$, ${K}_{1}=2$) in low dimension to generalized cases (i.e., $2\le M<\infty $, $1\le {K}_{1}<\infty $) in multidimensional signal space. Also, to optimize multidimensional signal constellation, we propose a heuristic algorithm which effectively maximizes the minimum Euclidean distance among constellation points. Simulation results demonstrate the feasibility of highorder modulation for the RF tags with small formfactor and BPSK backscatter modulators.
 Reflection Coefficient Projection and Expansion: We modify the low number of projection methods [26,27] considered in active radios with oscillators to support passive ambient backscatter radios implemented via reflection and OFDM carriers from ambient sources. The proposed method can be applied to general AmBC scenarios, extending the previous work [24].
2. System Model
2.1. Notation
2.2. Ambient Source Model
 Data Subcarriers: There are ${L}_{\mathrm{D}}$ data subcarriers with ${M}_{\mathrm{D}}$ary modulation. Symbols in data subcarriers are denoted by ${\rho}_{\mathrm{D}}(l,u,k)\in {\mathcal{A}}_{\mathrm{D}}$, $l\in {\Phi}_{\mathrm{D}}$ where ${\mathcal{A}}_{\mathrm{D}}$ is the alphabet set of data subcarriers with cardinality ${\mathcal{A}}_{\mathrm{D}}={M}_{\mathrm{D}}$ and ${\Phi}_{\mathrm{D}}$ the index set of data subcarriers with cardinality ${\Phi}_{\mathrm{D}}={L}_{\mathrm{D}}$. These subcarriers are used to transmit data intended for legacy receivers but unknown to the reader which has to decode backscattered signals from multiple tags.
 Pilot Subcarriers: There are ${L}_{\mathrm{PI}}$ pilot subcarriers with ${M}_{\mathrm{PI}}$ary modulation. Similar to the above, symbols are denoted by ${\rho}_{\mathrm{PI}}(l,u,k)\in {\mathcal{A}}_{\mathrm{PI}}$, $l\in {\Phi}_{\mathrm{PI}}$ where ${\mathcal{A}}_{\mathrm{PI}}$ is the alphabet set of pilot subcarriers with cardinality ${\mathcal{A}}_{\mathrm{PI}}={M}_{\mathrm{PI}}$ and ${\Phi}_{\mathrm{PI}}$ the index set of pilot subcarriers (e.g., 12, 26, 40, 54 in 802.11g WiFi) with cardinality ${\Phi}_{\mathrm{PI}}={L}_{\mathrm{PI}}$. The pilot subcarriers are required for channel estimation in the legacy OFDM system and generated by known sequences. When the preamble of OFDM frame is received by the reader, the symbols in these subcarriers can be efficiently acquired at the reader. Hence, the pilot subcarriers can act as spreading sequences [11,12,25] enabling reliable AmBC for RF tags.
 Guard Subcarriers: There are ${L}_{\mathrm{G}}$ guard subcarriers with index $l\in {\Phi}_{\mathrm{G}}$ where ${\Phi}_{\mathrm{G}}$ is the index set of guard subcarriers. These subcarriers contain no power to prevent intercarrier interference from adjacent OFDM carriers and not related to the AmBC systems.
2.3. Channel Model
3. Sparse Codes
3.1. Preliminary: Limitations in Conventional Backscatter
 ShortRange Communication: TDMAbased AmBC (TDAmBC) system has a short communication range because the ambient backscatter signals received at the reader are significantly attenuated by both the forward channels and backward channels. By poor propagation properties in the composite forwardbackward channels, or dyadic backscatter channels [6,7], backscatter signals are too weak to be decoded at the reader when the distance among tags and the reader increases. Furthermore, simple repetition coding by increasing the number of time slots K (equivalently, symbol period) [3,5] in the TDAmBC is not sufficient enough to combat channel fading in practice. Besides, in the TDAmBC, only one tag is activated in a time slot for backscatter communication and the rest of $N1$ tags remain idle (or harvesting energy). By adopting orthogonal multiple access (OMA) among tags in the TDAmBC, the duty cycle is limited to $D\le 1/N$. If a massive number of tags $N\gg 1$ are connected to the reader, such as in lowpower wide area network (LPWAN), the duty cycle D goes to zero and the data rate of individual tag diminishes accordingly. Hence, by the channel and connectivity issues, the TDAmBC is limited to shortrange applications, typically less than few meters [1,2,3,5].
 LowRate Communication: In the TDAmBC, the data rates of tags decrease when the channel conditions are bad, or there are multiple concurrent backscatter signals from tags to the reader as we discussed above. In addition, due to hardware limitation of the tags with small formfactor, tags’ data is modulated in loworder modulation schemes, typically ranging from $M=2$ (e.g., onoff keying (OOK) [1,2,5] to $M=16$ (e.g., 16PSK [3], 16QAM [20]). However, to implement highorder backscatter modulators, we should add a series of load impedances and corresponding RF switches to the tags’ circuit, which inevitably increases installation costs and formfactor of the tags. As such, the TDAmBC is limited to lowrate applications in practice.
3.2. Characteristics of Sparse Codes
 MultiDimensional Signalling: In contrast to the TDAmBC with duty cycle $D\le \frac{1}{N}$, sparsecoded AmBC can prolong the duty cycle of RF tags to be $D\ge \frac{1}{N}$ by allowing nonorthogonal transmissions among tags. When sparse codes are applied to tags, a codeword encoded by each tag spans for $K\le N$ compressed time slots by utilizing sparsity and there exist $2\le {K}_{1}\le K$ nonzero elements and $K{K}_{1}$ zero elements contained in each codeword. By the definition of the duty cycle $D=\frac{{K}_{1}}{K}\ge \frac{1}{N}$, sparse codes can prolong the duty cycle of RF tags effectively. Especially, the parameter ${K}_{1}$ denotes the dimension of signal constellation which is related to the duty cycle D. Therefore, based on the sparsity of codewords, ambient backscatter signals can be represented in higher dimension for achieving diversity gain [38] by increasing the duty cycle of RF tags. As a result, the multidimensional signalling can support longrange AmBC of few tens of meters, enabling the vision of LPWAN [14].
 Feasibility of HighOrder Modulation: Instead of manufacturing the customized Mary backscatter modulators [3,20,21,22] in the conventional TDAmBC, sparse codes can implement Mary modulation at commercial RF tags with small formfactor by employing binary PSK (BPSK) backscatter modulator. Based on the principle of the low number of projection method [26,27], Mary constellation points can be projected onto $\tilde{M}=2$ reflection coefficients at the tags, and then decoded at the reader by a lowcomplexity iterative message passing algorithm (MPA). Theoretically, any highorder modulation (e.g., $M\ge 32$) can be implemented in AmBC as long as the dimension ${K}_{1}$ is large enough to span $M\le {2}^{{K}_{1}}$ constellation points. Thus, sparse codes can also achieve highrate AmBC with reasonable installation costs for massive IoT.
3.3. Design of Constellation Mapping Function
 In the ${k}_{1}=1,\cdots ,V$th column, a vector ${\overrightarrow{\mathbf{e}}}_{{k}_{1}}=\overrightarrow{\mathbf{b}}\otimes {\mathbf{1}}_{{2}^{{k}_{1}1}}$ is repeated ${M}_{0}=\frac{M}{{2}^{{k}_{1}}}$ times.
 For ${k}_{1}=V+1,\cdots ,{K}_{1}$, the ${k}_{1}$th column is equal to the ${k}_{0}$th column where ${k}_{0}=\mathrm{mod}({k}_{1}1,V)+1$.
Algorithm 1 Constellation Mapping Matrix Optimization 

3.4. Design of Factor Graph
3.5. DutyCycling Operation
4. TwoStage Detection
4.1. Stage1: Utilizing OFDM Structure
4.2. Stage2: Utilizing Sparse Code Structure
5. Simulation Results
5.1. Comparison of AmBC Systems: TDAmBC and MSCAmBC
5.2. Practical Implementation of AmBC
5.3. Simulation 1: Effect of OFDM Carriers
5.4. Simulation 2: Effect of DutyCycling Operation
5.5. Simulation 3: Effect of Signal Constellation
5.6. Simulation 4: Effect of Sparse Codes
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
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Component  TDAmBC  MSCAmBC 

(1) Modulation  [3,17,18,19,20,21,22]  
Signal constellation  2dimensional PSK/QAM  ${K}_{1}\ge 2$ dimensional lattice 
Load impedance  $M\ge 2$ impedances  $\tilde{M}=2$ impedances only ($\tilde{M}\le M$) 
Symbol mapping  directmapping (MtoM)  reflection coefficient projection (Mto$\tilde{M}$) 
(2) Coding  [3,10,32]  
Multiple access  OMA using TDMA  NOMA using sparse code 
Duty cycle  limited to $D\le \frac{1}{N}$  extended to $D\le \frac{{K}_{1}}{K}$ where $K\le N$, ${K}_{1}\ge 2$ 
Codeword  orthogonal (or diagonal)  nonorthogonal and interfering codes 
(3) Detection  [3,11,25]  
Diversity combining  N parallel MRC blocks  integrated MPA with $\tilde{K}$ FNs and N VNs 
ML detection  hypothesis test in 2 dimension  projection & expansion in ${K}_{1}$ dimension 
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Kim, T.Y.; Kim, D.I. MultiDimensional SparseCoded Ambient Backscatter Communication for Massive IoT Networks. Energies 2018, 11, 2855. https://doi.org/10.3390/en11102855
Kim TY, Kim DI. MultiDimensional SparseCoded Ambient Backscatter Communication for Massive IoT Networks. Energies. 2018; 11(10):2855. https://doi.org/10.3390/en11102855
Chicago/Turabian StyleKim, Tae Yeong, and Dong In Kim. 2018. "MultiDimensional SparseCoded Ambient Backscatter Communication for Massive IoT Networks" Energies 11, no. 10: 2855. https://doi.org/10.3390/en11102855