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High Spectrum Efficiency Technologies

Description:

OBJECTIVE: Develop innovative technologies, tools and related infrastructure that can demonstrate>25X impact to spectrum efficiency of wireless communications networks over current SISO QPSK technology for defense communication applications and platforms. DESCRIPTION: Techniques and tools are sought that change spectrum efficiency from the typical principles of one waveform on one frequency at one time within a large communication interference footprint, to the ability of large numbers of communication networks simultaneously operating in the same amount of frequency in the same area. Spectrum efficiency is measured as bits/s/hz or alternatively as bits/s/hz/sqKm for all nodes of a network. This effort seeks to find economical and practical means to increase efficiency metrics by factors greater than 25X relative to current SISO QPSK rate 1/2 TDMA system examples. Particular attention should be paid to innovative methods to use the same spectrum with other heterogeneous legacy systems while minimizing cross network interference. Many new technologies have been proposed at every layer of communication network protocol that can have important impact on spectrum efficiency. Proposed techniques at the physical layer include MIMO, Collaborative MIMO, MUD, Polarimetrics, DSA, OSA, Smart Antennas, Interference Alignment and overlay/underlay [1-8]. Header compression, adaptive scheduling, adaptive FEC, adaptive power control, and prioritized queuing are common additional efficiency considerations [9-10]. Techniques have also been proposed to enhance efficiency of modulation, MAC, Link, network and application layers. Awareness techniques such as local capacity analysis have been proposed enabling optimization for current spectrum activity and network traffic requirements. Database structures and other tools have also been proposed as supporting infrastructure to enhance spectrum efficiency, including: trunking, pooling, and radio environment maps. In addition, circuit techniques have been proposed to enhance linearity and dynamic range to enable previously impractical methods for simultaneous transmission and reception (STAR) [11]. The focus of this SBIR topic is to create demonstration quality combinations of techniques leading to much higher systems levels of spectrum efficiency than any single technique would be able to achieve. Design adaptively should locally optimize current spectrum activity and traffic load conditions. It is desirable that system designs consider cost effectiveness, network performance, and practical deployment for various platforms. Deployment platforms of interest range in scale, power and performance over the range of ships, aircraft, vehicles, robots, soldiers, and sensors. Performers must clearly demonstrate the achievable level of spectrum efficiency of their completed architecture with real world implementation considerations of channel impairments, platform motion, interference sources, non-linearity of circuit elements, antenna properties, dense deployment fields of radio networks, and variation in traffic load, resulting in useful communication networks. PHASE I: Study, simulate, analyze and fully validate proposed performance levels. Results of the Phase 1 study must be quantitative for all layers of the integrated communication network protocols, referencing a standard communication network as a baseline performance of spectrum efficiency, showing system level spectrum efficiency improvement factors (TRL3-4). PHASE II: Develop and demonstrate all essential elements of the proposed architecture to an integrated system level capable of demonstrating achieved network spectrum efficiency adapting to current spectrum and network traffic. This phase has a target Transition Readiness Level (TRL) of 5 while demonstrating the potential of progressing to TRL 6 during a possible Phase III. PHASE III: Commercial telecommunication, public safety, and broadcast systems can all benefit from enhanced spectrum efficiency techniques by reducing cost of spectrum access to maintain the grade of service for their business. This phase should support transition to production ready deployment level for defense communication systems. REFERENCES: 1) Chen, B, Gans,M.,"MIMO communications in ad hoc networks", IEEE Trans Signal Processing, July 2006 2) Yoon, Y.C.,Kohno, R.,"Optimum multi-user detection in ultra-wideband (UWB) multiple-access communication systems", IEEE Intnl Conf on Communications, 2002 3) Pratt, T., Tapse, H., Fette, B., Baxley, R., Walkenhorst, B., Acosta-Marum, G.,"Polarization-based zero forcing suppression with multiple degrees of freedom", IEEE Military Communications Conference 2011 4) Marshall, P.,"Dynamic Spectrum Access as a m\Mechanism for Transition to Interference Tolerant Systems", IEEE Symposium on New Frontiers in Dynamic Spectrum", 6-9 April 2010. 5) Zhao, Q.,"Decentralized cognitive MAC for Opportunisitic Spectrum Access in ad hoc networks: A POMDP framework", IEEE J on Selected Areas in Comm, April 2007. 6) Godara, L.C., Smart Antennas, CRC Press, 2004. 7) Peters, S.W., Heath, R.W.,"Interference alignment via alternating minimization", IEEE Intnl Conf on ASSP, 2009. 8) Chakravarthy, V.D., Wu, Z., Shaw, A., Temple, M.A., Kannan, R., Garber, F.,"A general overlay / underlay analytic expression representing cognitive radio waveform", IEEE Intnl Waveform diversity and Design Conf, 2007. 9) Hoang,A.T., Liang,Y.C.,"Adaptive Scheduling of Spectrum Sensing Periods in Cognitive Radio Networks", IEEE Globecom Nov 2007. 10) Youping, Z., Reed, J.H., Mao, S. Bae, K.K.,"Overhead Analysis for Radio Environment Map enabled Cognitive Radio Networks", IEEE Workshop on Networking technologies for Software Defined Radio Networks, Sept 2006. 11) 11) Bliss, D.,Parker, P.A., Margetts, A.R.,"Simultaneous Transmission and Reception for Improved Wireless Network Performance", IEEE Workshop on Statistical Signal Processing, 2007.
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