Energy and Spectral Efficiencies Trade-off with Filter Optimization in Multiple Access Interference-Aware

This work analyzes the optimized deployment of two resources scarcely available in mobile multiple access systems, i.e., spectrum and energy, as well as the impact of filter optimization in the system performance. Taking in perspective the two conflicting metrics, throughput maximization and power consumption minimization, the distributed energy efficiency (EE) cost function is formulated. Furthermore, the best energy-spectral efficiencies (EE-SE) trade-off is achieved when each node allocates exactly the power necessary to attain the best SINR response, which guarantees the maximal EE. To demonstrate the validity of our analysis, two low-complexity energy-spectral efficient algorithms, based on distributed instantaneous SINR level are developed, and the impact of single and multiuser detection filters on the EE-SE trade-off is analyzed.

[1]  H. Vincent Poor,et al.  An energy-efficient approach to power control and receiver design in wireless data networks , 2005, IEEE Transactions on Communications.

[2]  Taufik Abrão,et al.  Distributed power control algorithm for multiple access systems based on Verhulst model , 2011 .

[3]  H. Vincent Poor,et al.  Joint Receiver and Transmitter Optimization for Energy-Efficient CDMA Communications , 2007, IEEE Journal on Selected Areas in Communications.

[4]  Geoffrey Ye Li,et al.  Low-Complexity Energy-Efficient OFDMA , 2009, 2009 IEEE International Conference on Communications.

[5]  S. Moshavi,et al.  Multi-user detection for DS-CDMA communications , 1996, IEEE Commun. Mag..

[6]  H. Vincent Poor,et al.  A game-theoretic approach to energy-efficient power control in multicarrier CDMA systems , 2006, IEEE Journal on Selected Areas in Communications.

[7]  Candice King,et al.  Fundamentals of wireless communications , 2013, 2013 IEEE Rural Electric Power Conference (REPC).

[8]  Sergio Verdú,et al.  Linear multiuser detectors for synchronous code-division multiple-access channels , 1989, IEEE Trans. Inf. Theory.

[9]  Geoffrey Ye Li,et al.  Fundamental trade-offs on green wireless networks , 2011, IEEE Communications Magazine.

[10]  Lajos Hanzo,et al.  Green radio: radio techniques to enable energy-efficient wireless networks , 2011, IEEE Communications Magazine.

[11]  Geoffrey Ye Li,et al.  Distributed Interference-Aware Energy-Efficient Power Optimization , 2011, IEEE Transactions on Wireless Communications.

[12]  Slawomir Stanczak,et al.  Resource Allocation in Wireless Networks: Theory and Algorithms , 2006, Lecture Notes in Computer Science.

[13]  Cem U. Saraydar,et al.  Efficient power control via pricing in wireless data networks , 2002, IEEE Trans. Commun..

[14]  H. Vincent Poor,et al.  Transmitter Waveform and Widely Linear Receiver Design: Noncooperative Games for Wireless Multiple-Access Networks , 2010, IEEE Transactions on Information Theory.

[15]  Geoffrey Ye Li,et al.  Interference-Aware Energy-Efficient Power Optimization , 2009, 2009 IEEE International Conference on Communications.

[16]  Eduard A. Jorswieck,et al.  Energy-Efficient Power Control and Receiver Design in Relay-Assisted DS/CDMA Wireless Networks via Game Theory , 2011, IEEE Communications Letters.

[17]  Geoffrey Ye Li,et al.  Energy-efficient link adaptation in frequency-selective channels , 2010, IEEE Transactions on Communications.

[18]  Andrea J. Goldsmith,et al.  Variable-rate variable-power MQAM for fading channels , 1997, IEEE Trans. Commun..

[19]  M. Dufwenberg.  Game theory. , 2011, Wiley interdisciplinary reviews. Cognitive science.