Essay: Non-orthogonal Multiple access and FULL Duplex in Acoustic Underwater Communications

1 Introduction
Underwater technologies has recently gained an increasing research attention. This is mainly due to the recent increase in exploitation of natural resources underwater along with the ongoing trend on collecting big data from underwater enviroments through Internet of Underwater things (IOUT) [kiko,Gussen,Jiang]. Also, such technologies would support multiple ocean applications such as: oceanographic data collection, seismic waves monitoring, sea water pollution measurement, assessment of water quality, supporting Unmanned Underwater Vehicle (UUV) missions, biological monitoring, and security [kiko,Gussen]. Traditionally, sensors would be sent to the water, record data, and then recovered to extract the data from them on the surface. However, this methodology inhibited the capabilities of underwater technologies as it prevents online data extraction , online data processing, bi-directional commuication and immediate identification of faults and failures[kiko]. In order to overcome such drawbacks, the need for an effecient underwater communication system became crucial .
Underwater wireless communication has been distinct from its terrestial counterpart. The underwater enviroment suffers from the reflections, dispersion and variations due to salt concerntation, pressure ,temperataure, wind speed and other enviromental factors. There has been three major technologies. One technology is radio-frequency (RF) communication, which features high data throughput at short range and suffers from mild Doppler effect. Other technology is optical transmission but requires line-of-sight positioning. Another technology, which is the most employed one, is acoustic communication. This latter technology is the one that allows the longest range of communication, but achieves low throughput.
The reason Acoustic Underwater communication is the most deployed is that it allows long range of commui. For that purpose, this paper would attempt to combat the main draw back of acoustic communication, which is low data rate. In order to do that, the paper would investigate having full duplex along with Nonorthogonal division multiple access in Underwater acoustic communications. Besides, it would derive an optimization model that would optimize the power used in order to provide for the highest capacity.
Full duplex (FD) technology underwater have been investigated in []. Full duplex allow the system to transmit and receive signals at the time. FD can double the system’s throughput theoretically with respect to conventional half duplex (HD) counterpart, which threw light upon the employment of inband full duplex in UnderWater Acoustic applications. Previous works [ICIBFD] provided different schemes to help interference cancellation in underwater full duplex enviroment.
Non orthogonal Multiple Access (NOMA) based systems, the users are served simultanuosly at the same frequency but with different power level assignment(POWER NOMA) or different code assignment (COde NOMA). The power level assignemnet would depend on the channel strength. NOMA was shown to provide better spectral effeciency than Orthogonal multiple access systems. Code NOMA in underwater systems was discussed in paper [] while Power NOMA in acoustic underwater was discussed in [].
There was research done r on combining Full duplex and NOMA in the terrestial domain. In this research, FD and NOMA would be applied in underwater domain. The first aim of this paper is to derive the analytical model of full duplex NOMA in underwater system. Beside, this research aims to maximize the capacity of underwater links while optimizing the use of the power. The following sections would be divided as follows, Section II would describe the system model that would be used in this research. Section III, the optimization capacity with power is section III, and results would be shown in section IV.
2 System Model and protocol description
The system scenario assumed for this research would be a hierarchial underwater system. The system consists of a bouy, a relay , two robotic arms and two sensors. S1 and S2 would be sending their communiucation to the Relay which in turn would send that data to the buoy. At the same time the buoy would be sending data to the Relay which would forward that to the robotic arm. The relay and the bouy would be acting as full duplex nodes. There would be three uplink channel, Relay(R) to Buoy(B) , Sensor 1(S1) to relay and Sensor 2(S2) to relay. Beside, there would be three downlink channel, Buoy to Relay , Relay to Robotic arm1(RA1) beside Relay to Robotic arm 2(RA2). The downlik channels would act as one NOMA group and the uplink channel would act as one NOMA group. All operate on same bandwidth at three different depth levels . The water depth would be 100m. The buoy would be on the surface, while relay would be at 40 m depth and 60 m depth. The distance between each node and the other is shown in the below table.
The distances between the nodes has been chosen so that the buoy and relay communication would have the best link in their group whether up link or downlink. The next best would be the channel between S1 and Relay besides RA1 and Relay. Those would be followed by the channels between S2 and Relay and channels between RA2 and Relay. The link between Ss and RAs are considered weak , while the weakest signal is the one between the buoy and the Ss and the buoys and the RAs. The distances and the frequency used were chosen to match the MACE experiment (????).
There have been several mathematical models for the underwater channels. The most representive would be the one cited in [] . The reason this one is chosen because it includes most of the needed factors including water depth, frequency of signal and distance. The simulation software for this channel model is in []. This software is used to generate the respective gain for the channel in the scenario given. A table of the inputs provided to the simulation is provdided in table ???. For the sake of simplicity all the NOMA SIC was considered to have the same value q . Self interference at buoy wuold be considered I and at the relay would be considered as I2.
The calculation of I is done based on [] as per the equation???. It is assumed that lamda at the buoy is better than lamda at the Relay by +0.1. Beta and theta would be of values [] and []. The noise was calculated based on []. Given noise due to s would be insignificant in this band it s would be considered 0. While noise (Nw) is the most significant type of noise so the wind speed would be considered as w=10m/s.
Based on these above the following SINR equations for the links were derived, as such the capacity of the links would be calculated as below
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3 Optimization Capacity with power
In order to maximize the capacity of the system with respect to power some measures should be taken. The transmit power of any sensor should not exceed a certain power limit of 70 dB []. Also, the power from the relay on all its channels and the buoy should not exceed 750 mw. In addition, in order to guarantee all links has data there should be a minimum data rate set for all links, that was set to 1 Mbps. Also, the data sent from Buoy to relay should be less than the data sent by the relay to the robotic arms. Besides, The data sent from the relay to the buoy should be less than the data received from the sensors. Dynamic programming methods were used to maximize the capacity equation as in equation ??? .
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4 Results
First , the system was applied to MATLAB 2017 at all nodes taking the maximum power level was used. Q would be fixed at 0.5 and lamda1 would be changed or vice versa in order to measure the effect of change of lamda or q on the system. If lamda is changed the relation between lamda 1 and lamda 2 remains constant. It was shown that as the capability of the system to eliminate interference increase the capcity increase.
The next step is to apply the optimization. It can be shown that the optimized capacity is much higher than the capacity on the non optimized, and required less power.
In addition to that, q and lamda were exploited further. so all lamdas and qs were plotted against capacity. It can be shown that as the interference capability of the system increase the Capacity of the system increase.