Joint physical and link layer error control analysis for nanonetworks in the Terahertz band

Abstract

Nanonetworks consist of nano-sized communicating devices which are able to perform simple tasks at the nanoscale. The limited capabilities of individual nanomachines and the Terahertz (THz) band channel behavior lead to error-prone wireless links. In this paper, a cross-layer analysis of error-control strategies for nanonetworks in the THz band is presented. A mathematical framework is developed and used to analyze the tradeoffs between Bit Error Rate, Packet Error Rate, energy consumption and latency, for five different error-control strategies, namely, Automatic Repeat reQuest (ARQ), Forward Error Correction (FEC), two types of Error Prevention Codes (EPC) and a hybrid EPC. The cross-layer effects between the physical and the link layers as well as the impact of the nanomachine capabilities in both layers are taken into account. At the physical layer, nanomachines are considered to communicate by following a time-spread on-off keying modulation based on the transmission of femtosecond-long pulses. At the link layer, nanomachines are considered to access the channel in an uncoordinated fashion, by leveraging the possibility to interleave pulse-based transmissions from different nodes. Throughout the analysis, accurate path loss, noise and multi-user interference models, validated by means of electromagnetic simulation, are utilized. In addition, the energy consumption and latency introduced by a hardware implementation of each error control technique, as well as, the additional constraints imposed by the use of energy-harvesting mechanisms to power the nanomachines, are taken into account. The results show that, despite their simplicity, EPCs outperform traditional ARQ and FEC schemes, in terms of error correcting capabilities, which results in further energy savings and reduced latency.

Simple and Energy Efficient Image Compression for Pulse-Based Communication in THz Band

“…The computation energy can be reduced by factor 100 using JPEG dedicated hardware [26]. As mentioned before, the computation in a nano-device consumes 0.1 aJ [9] per instruction. So, JPEG hardware compression consumes 100 fJ.…”

“…In micro scale (nanonetworks), computation energy in nano-devices remains unknown, since nano-processor using graphene-based nano-transistor is under development [8]. But initial models of energy consumption in nano-devices give a ratio between communication and computation of 10:1 [9], e.g. energy to transmit a pulse is 1 aJ and energy to execute 1 instruction is 0.1 aJ.…”

Joint Parameter Optimization for Perpetual Nanonetworks and Maximum Network Capacity

“…The total noise in the THz Band is contributed by the background atmospheric noise and the self-induced noise, which can be obtained as follows [29] …”

“…In order to guarantee the perpetual data transmission, the spreading factor should be satisfied by the constraint in (29). Moreover, the transmission distance, pulse probability and the number of nanonodes are also required to be comprehensively manipulated to maximize the achievable network capacity.…”

Modelling of the terahertz communication channel for in-vivo nano-networks in the presence of noise

“…Furthermore, the noise model was updated in [7] and it had been pointed out that the molecular absorption of the transmitted signal should be an additional noise source in the THz band. Then, such models were further investigated and applied in [8][9][10]. The different physical causes and mechanisms behind the absorption and emission were also studied in [11].…”