Underwater Acoustic Carrier Aggregation: Achievable Rate and Energy-Efficiency Evaluation

In this work, carrier aggregation in orthogonal frequency division multiplexing (OFDM)-based underwater acoustic cellular networks is proposed, and its achievable communication rate and energy efficiency are evaluated. This technique improves the total throughput of OFDM using an expanded bandwidth, where multiple carriers are aggregated for data transmission from one transmitter. In this paper, a study is presented for a practical communication system consisting of a surface buoy station mounting single transducer and an autonomous underwater vehicle also with single transducer. The results are evaluated in a simulation environment as well as by field experiments. The simulation is first performed on a wide bandwidth up to ${\text{500}}\;{\text{kHz}}$, where the results indicate that carrier aggregation can greatly improve the achievable underwater communication rate for distances up to ${\text{5}}\;{\text{km}}$. It is further found by simulation that the maximum bandwidth configuration does not achieve the optimal energy efficiency; instead, there is an optimal bandwidth that can lead to such optimal energy efficiency. Further measurement results obtained in sea trials with a specifically designed sounding sequence are then presented. The achievable rate and energy efficiency are evaluated by the field measurement data. The measurement results provide insights into the performance of the system under narrow bandwidth settings.

[1]  Dario Pompili,et al.  uwMIMO-HARQ: Hybrid ARQ for Reliable Underwater Acoustic MIMO Communications , 2015, WUWNet.

[2]  Haixin Sun,et al.  Impulsive Noise Mitigation in Underwater Acoustic OFDM Systems , 2016, IEEE Transactions on Vehicular Technology.

[3]  Thierry Chonavel,et al.  Synchronization, Doppler and channel estimation for OFDM underwater acoustic communications , 2014, OCEANS 2014 - TAIPEI.

[4]  Dario Pompili,et al.  AMMCA: Acoustic Massive MIMO with Carrier Aggregation to Boost the Underwater Communication Data Rate , 2015, WUWNet.

[5]  Giovanni Spagnoli,et al.  Preliminary Design of a Trench Cutter System for Deep-Sea Mining Applications Under Hyperbaric Conditions , 2016, IEEE Journal of Oceanic Engineering.

[6]  M. Stojanovic,et al.  Statistical Characterization and Computationally Efficient Modeling of a Class of Underwater Acoustic Communication Channels , 2013, IEEE Journal of Oceanic Engineering.

[7]  Robert W. Heath,et al.  Energy-Efficient Hybrid Analog and Digital Precoding for MmWave MIMO Systems With Large Antenna Arrays , 2015, IEEE Journal on Selected Areas in Communications.

[8]  Changchuan Yin,et al.  Unified view of channel estimation in MIMO-OFDM systems , 2005, Proceedings. 2005 International Conference on Wireless Communications, Networking and Mobile Computing, 2005..

[9]  Dario Pompili,et al.  Energy-efficient OFDM bandwidth selection for underwater acoustic carrier aggregation systems , 2016, 2016 IEEE Third Underwater Communications and Networking Conference (UComms).

[10]  Songzuo Liu,et al.  Superposition Coding for Downlink Underwater Acoustic OFDM , 2017, IEEE Journal of Oceanic Engineering.

[11]  Mohsen Guizani,et al.  Carrier aggregation for LTE-advanced: uplink multiple access and transmission enhancement features , 2013, IEEE Wireless Communications.

[12]  Jintao Wang,et al.  Synchronization and Doppler scale estimation with dual PN padding TDS-OFDM for underwater acoustic communication , 2013, 2013 OCEANS - San Diego.

[13]  Milica Stojanovic,et al.  Adaptive OFDM Modulation for Underwater Acoustic Communications: Design Considerations and Experimental Results , 2014, IEEE Journal of Oceanic Engineering.

[14]  Xiaolin Hou,et al.  Interlaced pilot channel estimation in MIMO-OFDM systems , 2006, 2006 IEEE International Symposium on Circuits and Systems.

[15]  J. Trubuil,et al.  Accurate Doppler estimation for underwater acoustic communications , 2012, 2012 Oceans - Yeosu.

[16]  Hao Zhou,et al.  Adaptive Modulation and Coding for Underwater Acoustic OFDM , 2015 .

[17]  Roger S. Cheng,et al.  A comparative analysis of pilot placement schemes in frequency-selective fast fading MIMO channel , 2007, 2007 Wireless Telecommunications Symposium.

[18]  Hossam S. Hassanein,et al.  Joint Chance-Constrained Predictive Resource Allocation for Energy-Efficient Video Streaming , 2016, IEEE Journal on Selected Areas in Communications.

[19]  Pierre-Jean Bouvet,et al.  Capacity analysis of underwater acoustic MIMO communications , 2010, OCEANS'10 IEEE SYDNEY.

[20]  Milica Stojanovic,et al.  On the relationship between capacity and distance in an underwater acoustic communication channel , 2007, MOCO.

[21]  Roee Diamant,et al.  Clustering Approach for Detection and Time of Arrival Estimation of Hydrocoustic Signals , 2016, IEEE Sensors Journal.

[22]  Li Wei,et al.  A Study on Pulse-Width-Modulation-Based Power Amplification for Underwater Acoustic OFDM , 2016, IEEE Journal of Oceanic Engineering.

[23]  Dario Pompili,et al.  Overview of networking protocols for underwater wireless communications , 2009, IEEE Communications Magazine.

[24]  Dimitris A. Pados,et al.  Software-defined underwater acoustic networks: toward a high-rate real-time reconfigurable modem , 2015, IEEE Communications Magazine.

[25]  Trond Jenserud,et al.  Measurements and Modeling of Effects of Out-of-Plane Reverberation on the Power Delay Profile for Underwater Acoustic Channels , 2015, IEEE Journal of Oceanic Engineering.