text: Scalability is a major issue for Internet of Things (IoT) as the total amount of traffic data collected and or the number of sensors deployed grow. In some IoT applications such as healthcare, power consumption is also a key design factor for the IoT devices. In this paper, a multi-signal compression and encoding method based on Analog Joint Source Channel Coding (AJSCC) is proposed that works fully in the analog domain without the need for power-hungry Analog-to-Digital Converters (ADCs). Compression is achieved by quantizing all the input signals but one. While saving power, this method can also reduce the number of devices by combining one or more sensing functionalities into a single device (called 'AJSCC device'). Apart from analog encoding, AJSCC devices communicate to an aggregator node (FPMM receiver) using a novel Frequency Position Modulation and Multiplexing (FPMM) technique. Such joint modulation and multiplexing technique presents three mayor advantages&#x2014;it is robust to interference at particular frequency bands, it protects against eavesdropping, and it consumes low power due to a very low Signal-to-Noise Ratio (SNR) operating region at the receiver. Performance of the proposed multi-signal compression method and FPMM technique is evaluated via simulations in terms of Mean Square Error (MSE) and Miss Detection Rate (MDR), respectively.
  @cite_7: A low-complexity and low-power all-analog circuit is proposed to perform efficiently Analog Joint Source Channel Coding (AJSCC). The proposed idea is to adopt Voltage Controlled Voltage Source (VCVS) to realize the rectangular-type mapping in AJSCC. The proposal is verified by Spice simulations as well as via breadboard and Printed Circuit Board (PCB) implementations. Field testing results indicate that the design is feasible for low-complexity and low-power systems such as wireless sensor networks for environmental monitoring.
  @cite_6: Recently, analog joint source-channel coding has been proposed as a means of achieving near-optimum performance for high data rates with a very low complexity. However, no experimental evaluation showing the practical feasibility of this scheme has been performed to date. In this paper, we describe a software-defined radio implementation of an analog joint source-channel coded wireless transmission system. Experimental evaluation carried out in an indoor environment making use of a wireless testbed show that the performance perfectly matches that originally reported by simulations in additive white Gaussian noise channels for signal-to-noise ratio values below 20 dB.
  @cite_23: 
  @cite_5: An analog joint source channel coding (JSCC) system is developed for wireless optical communications. Source symbols are mapped directly onto channel symbols using space filling curves and then a non-linear stretching function is used to reduce distortion. Different from digital systems, the proposed scheme does not require long block lengths to achieve good performance reducing the complexity of the decoder significantly. This paper focuses on intensity-modulated direct-detection (IM DD) optical wireless systems. First, a theoretical analysis of the IM DD wireless optical channel is presented and the prototype communication system designed to transmit data using analog JSCC is introduced. The nonlinearities of the real channel are studied and characterized. A novel technique to mitigate the channel nonlinearities is presented. The performance of the real system follows the simulations and closely approximates the theoretical limits. The proposed system is then used for image transmission by first taking samples of a set of images using compressive sensing and then encoding the measurements using analog JSCC. Both simulation and experimental results are shown.
  @cite_15: We consider the use of spatial diversity to improve the performance of analog joint source-channel coding in wireless fading channels. The communication system analyzed in this paper consists of discrete-time all-analog-processing joint source-channel coding where Maximum Likelihood (ML) and Minimum Mean Square Error (MMSE) detection are employed. By assuming a fast-fading Rayleigh channel, we show that MMSE performs much better than ML at high Channel Signal-to-Noise Ratios (CSNR) in single-antenna wireless systems. However, such performance gap can be significantly reduced by using multiple receive antennas, thus making low complexity ML decoding very attractive in the case of receive diversity. Moreover, we show that the analog scheme can be considerably robust to imperfect channel estimation. In addition, as an alternative to multiple antennas, we also consider spatial diversity through cooperative communications, and show that the application of the Amplify-and-Forward (AF) protocol with single antenna nodes leads to similar results than when two antennas are available at the receiver and Maximal Ratio Combining (MRC) is applied. Finally, we show that the MMSE implementation of the analog scheme performs very close to the unconstrained capacity of digital schemes using scalar quantization, while its complexity is much lower than that of capacity-approaching digital systems.
  @cite_16: We propose a low delay and low complexity sensor system based on the combination of Shannon-Kotel'nikov mapping and compressed sensing (CS). The proposed system uses nonlinear analog mappings on the CS measurements to increase their immunity against channel noise. Numerical results show that the proposed purely-analog system outperforms the state-of-the-art purely CS systems in terms of signal-to-distortion ratio. In addition to sparsity knowledge, we use a statistical characterization of the observed signal to further improve system performance.
  @cite_12: This paper describes a channel estimation scheme which utilizes interlaced transmit pilots to estimate multiple input multiple output (MIMO) channel responses in orthogonal frequency division multiplexing (OFDM) systems. This least square (LS) based channel estimation scheme adopts discrete Fourier transform (DFT) to improve the estimation accuracy. The constraint condition of the estimation scheme is obtained using frequency-domain sampling theorem. Simulation shows that the proposed scheme shows good stability when the constraint condition fails, which is much better than other two commonly-used MIMO-OFDM channel estimation schemes.
  @cite_20: Disclosed is a method, circuit and system for communicating data. A data value to be transmitted from a data source transmitter or transceiver to a downstream receiver or transceiver may be Shannon mapped, by functionally associated processing mapping logic, to a point on a shape within a higher dimensional plane. Different portions of the shape, for example branches of a spiral, may be designated by a portion or branch number. Coordinates of the Shannon mapping, or another descriptors, of the Shannon mapped point may be transmitted using analog transmission methods. A set of data values may be Shannon mapped and transmitted to a downstream receiver transceiver in series. For each set of mapped and transmitted data values, processing logic may calculate a branch ambiguity resolution factor. The branch ambiguity resolution factor for each set of values may be transmitted to the downstream receiver transceiver before, after or with the data values. Decoding logic associated with the downstream receiver transceiver may then use the branch ambiguity resolution factor to convert decode received coordinates associated with the set of values into the data values.
summary: Shannon mapping has been applied in a number of applications such as Software-Defined Radio (SDR) systems @cite_6 , optical digital communications @cite_5 , Compressive Sensing (CS) @cite_16 , and digital video transmissions @cite_20 . All these applications use power-hungry ADCs and other digital components making such implementations unsuitable for healthcare and other low-power IoT solutions. Some works have studied the N:1 spiral-type mapping @cite_15 . The advantage of considering rectangular-type Shannon mapping is that there are existing low-power, all-analog circuit realizations for rectangular-type mapping (our previous work @cite_7 ). Using this approach, sensors can be designed using all-analog circuits that can compress multiple signals into one signal, thereby consuming less power. The signals from multiple sensors are multiplexed at different frequency locations in an interleaved pattern. Similar pattern has been studied for topics of pilot placement @cite_12 @cite_23 .