Proposal 1

PROPOSAL ABSTRACT The interference reduction capability of adaptive beamforming and spectral efficiency enhancement by adaptive modulation gives a significant performance gains in mobile communication systems. In order to make the most of both methods, joint adaptive beamforming and adaptive modulation is considered in this research. Possible solutions for some challenges, problems and drawbacks in combining these two will be investigated. Based on some advantages and disadvantages of each method, performance of different combining approaches in different conditions and environments will be analyzed.

INTRODUCTION Current and future demands in mobile communication for various high speed multimedia data services entail a robust, high data rate transmission system. Increasing numbers of users amid limited spectrum motivate research on technology to expand the capacity and increase spectral efficiency. At the same time, some detrimental effects in randomly varying mobile communication environment like multipath fading, co-channel interference and Doppler effects need to be addressed. Adaptive modulation and adaptive beamforming are part of recent methods that known to offer the solution for the abovementioned problems. Adaptive modulation is a technique that varies some transmission parameters to take advantage of favorable channel conditions. Under bad channel conditions, a robust signal transmission mode will be applied to ensure reliable data exchange. While, in good channel, spectrally efficient mode that offer higher throughput is applied. This mechanism ensures the most efficient mode is always used based on certain criteria and constraints. The varying parameters can be the symbol transmission rate, transmitted power level, constellation size, BER, code rate or scheme, any combination of these parameters [1]. Compared to the fixed modulation system, which was designed specifically for the worst case channel conditions, this adaptive modulation offers higher spectral efficiency, higher throughput and remarkable capacity enhancement without sacrificing BER or wasting power [2]. Research on applications of adaptive antenna arrays have been an interesting subject over past 40 years [3] contributing to the invention of adaptive beamforming method. By taking advantage of the fact that users collocated in frequency domain are typically separated in spatial domain, the beamformer is used to direct the antenna beams toward the desired user while canceling signal from other users [4]. The beamformer electronically steer a phased array by weighting the amplitude and phase of signal at each array element in response to changes in the propagation environment. Capacity improvement is achieved by effective co-channel interference cancellation, intelligent frequency reuse adjustments and dynamic channel allocation (DCA) strategy. Improved SIR at the beamformer output allows the increases in transmission bit rate [4]-[7].

From a few recent works which employed both adaptive beamforming and adaptive modulation [8]-[12], it is proven that the combination of these two techniques give a significant performance gains. In [8], J. Blogh, P. Cherriman, and L. Hanzo did a comparative study on a range of dynamic channel allocation algorithms called distributed control and locally distributed control assisted DCA arrangements under various channel conditions. The performance of locally optimized least interference algorithm (LOLIA), locally optimized most interference algorithm (LOMIA) with a few configurations were simulated and measured in terms of the carried teletraffic, call blocking probability, call dropping probability, probability of low-quality access, and outage probability. Sample matrix inversion (SMI) adaptive beamforming algorithm with geometrically based single bouncei elliptical model (GBSBEM) was used. The applied adaptive modulation aimed to maximize the throughput with a constraint in target Frame Error Rate (FER). From this work, the benefits of using adaptive antenna arrays have been demonstrated, with improved network quality for an increased number of users, for both the (fixed channel allocation) FCA algorithm and the LOLIA. These benefits were then extended through the use of adaptive modulation to further improve call quality while simultaneously increasing the data throughput and the number of users adequately supported by the network. Y.H. Pan, K. Letaief, Z. Cao in [9] proposed a dynamic spatial subchannel allocation algorithm with adaptive beamforming for MIMO/OFDM system. The proposed algorithm use Eigenvector Decomposition Method to generate a number of parallel spatial channels on each subcarrier, adaptively select the eigenvectors associated with the relatively large spatial subchannel eigenvalues to generate the beamforming weights at the mobile and basestations, and then dynamically assigns the corresponding best spatial subchannels to transmit the OFDM block symbols. An optimal bit and power distribution across the subchannels then is derived using low complexity fixed throughput based adaptive modulation algorithm. From the simulation results, the proposed system shown an increase in system’s capacity and bandwidth efficicency as well as improvement in QoS

In [10], J. Blogh and L. Hanzo presented simulation results of adaptive modulation in conjunction with independent up- and down-link adaptive beamforming in UTRA-like FDD/CDMA cellular network under log normal shadow faded environment. The network capacity improvement was shown characterized by the significant reduction in the probability of a dropped call. The work then continued in [11] for UTRA-like TDD/CDMA system. The performance of the adaptive modulation assisted, beam steering aided TDD/CDMA system was analyzed and compared with FDD/UTRA system. It is also concluded that the employment of adaptive beamforming in conjunction with Adaptive Quadrature Amplitude Modulation (AQAM) limited the detrimental effects of co-channel interference and resulted in performance improvements both in terms of achievable call quality and the system’s capacity. Adaptive bit-loading is another terms that is used to associate with adaptive modulation where it characterize the type of modulation based on number of bits/symbol loaded on the channel. In OFDM system, frequency selective fading severely affected certain subcarriers and degraded the overall system performance. In [12], it is demonstrated that combinational scheme based on adaptive beamforming and adaptive loading is very effective in improving the performance of OFDM packet transmission.

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Advantages of each adaptive mod and adaptive beam – as in intro review type paper and add with some more Advantages of joint AM and AB – highlights works done by others with performance gain achieved

JUSTIFICATION OF STUDY • The rationale of study: not many discussion yet about the problems, challenges, and the effective ways of combining these methods. Highlights some related works -

Adaptive modulation performance highly depends on the perfection of feedback signal used. This problem will be more severe in a fast fading environment where the channel gain changing very fast and the adaptive system are not able to track the changes.  problems in AM  problems in AB  different approaches of AM and AB In adaptive beamforming, nonstationary interferers or jammers raised a problem in systems that use high gain or large antennas. In this kind of system, the directional pattern nulls are very sharp, that cause the nonstationary interferers easily move out from the nulls. The performance will be severely degraded if the beamformer are not able to adapt to the fast interference changing conditions. A few works done to solve this problem includes [] where some modifications were done on conventional adaptive algorithms to enhance their robustness in the said situation. OBJECTIVES • • •

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To study the problems and challenges in joint AB & AM and propose solutions to them To investigate the optimal way of joint AB & AM To measure and compare the performance of different combinations algorithms in different models of environments and conditions. i.e. flat fading, frequency selective fading, fast fading, in present of co-channel interference and adjacent channel interference To propose a novel joint AB & AM algorithm

METHODOLOGY

REFERENCES:

[1]

S.T. Chung, A.J. Goldsmith, “Degrees of Freedom in Adaptive Modulation: A Unified View,” IEEE Trans. Commun., vol. 49, pp. 1561-1571, Sept. 2001

[2]

S. Catreux, V. Erceg, D. Gesbert, and R.W. Health, Jr., “Adaptive Modulation and MIMO Coding for Broadband Wireless Data Networks,” IEEE Commun Mag, pp. 108-115, June 2002

[3]

S. Applebaum, “Adaptive arrays,” Technical Report SPL TR-66-001, Syracuse Univ. Rec., Corp. Report, 1965

[4]

J. Litva, T. K. Lo, “Digital Beamforming in Wireless Communications,” Artech House, Norwood, 1996

[5]

B.D. Van, K. M. Buckley, “Beamforming: A Versatile Approach to Spatial Filtering,”, IEEE ASP Magazine, pp. 4- 24, April 1988

[6]

L. C. Godara, “Applications of Antenna Arrays to Mobile Communications, PartI: Performance Improvement, Feasibility, and System Considerations,” in Proc. IEEE , vol. 85, pp. 1031-1060, July 1997

[7]

L. C. Godara, “Applications of Antenna Arrays to Mobile Communications, PartII: Beam-Forming and Direction-of-Arrival Considerations,” in Proc. IEEE, vol. 85, pp. 1195-1245, August 1997

[8]

J. Blogh, P. Cherriman, L. Hanzo, ”Comparative Study of Adaptive BeamSteering and Adaptive Modulation-Assisted Dynamic Channel Allocation Algorithms,” IEEE Trans. Vehicular Technology, vol. 50, pp. 398-415 March 2001

[9]

Y. H. Pan, K. Lataeif, Z. Cao,”Dynamic Spatial Subchannel Allocation with Adaptive Beamforming for MIMO/OFDM Systems,”, IEEE Trans. Wireless Commun., vol. 3, pp. 2097-2107, Nov. 2004

[10]

J. S. Blogh, L. Hanzo, “Third-Generation Systems and Intelligent Wireless Networking: Smart Antennas and Adaptive Modulation,” John Wiley and IEEE Press, 2002

[11]

Song Ni, Jonathan S. Blogh, Lajos Hanzo, “On the Networking Performance of UTRA-like TDD and FDD CDMA Systems Using Adaptive Modulation and Adaptive Beamforming,” IEEE Semiannual Vehicular Technology Conference, vol. 1, pp. 606-610, 22-25 April 2003

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A. B. Gershman, U. Nickel, J. F. Böhme, “Adaptive Beamforming Algorithms with Robustness Against Jammer Motion,” IEEE Trans. Signal Processing, vol. 45, pp. 1878-1885, July 1997