- Open Access
Wireless sensing network transmission system with improved constant modulus algorithm
© Sung; licensee Springer. 2013
- Received: 25 November 2012
- Accepted: 26 February 2013
- Published: 15 April 2013
To solve the instant signal convergence of wireless sensing network transmission system, this study proposes an improved constant modulus algorithm. The improved constant modulus algorithm can quickly find the convergent direction by adding error functions and learning rates based on the reserved original cost functions. After processed by a matched filter, the received consecutive time signal can be sampled in accordance with symbol interval or fraction interval, so that signal channels can be estimated or balanced. At last, this study verifies the raised method with quadrature amplitude modulation on wireless sensing network transmission system and improved constant modulus algorithm experiments which show that the learning rates and the performances of balancers are improved.
- Wireless sensor network
- Transmission system
- Constant modulus algorithm
Signal channel balance is one of the fundamental issues of digital communication technology. The purpose is to overcome interferences among transmitted symbols (ISIs). These interferences are caused by the non-ideal characters of signal channels. When the baud rate is higher than 4,800 bit/s in the wireless sensing network transmission system and higher than 100 bit/s in the short-wave transmission system, a balancer is required. The communication channels may be unknown or changed. Thus, it is required to utilize training sequences to self-adaptively adjust balancers to remove the interferences among symbols. R.W. Lucky raised the earliest self-adaptive balancer and the balancing algorithm of zero forcing. Meanwhile, the minimum mean square error algorithm raised by Widrow and Hoff is used on signal channel balance and widely applied[1, 2].
For most digital communication systems, the characters of signal channel are usually unknown and varied along with time. Thus, to design a correspondent self-adaptive balancer, it is usually required to include the known training sequences in the data frame at the sending end to be transmitted to the receiving end. The purpose is to conduct initial adjustments to the parameters in the balancer in order to guarantee quick convergence in a wide range. However, the balancer based on the training sequences will increase transmission costs and reduce the efficiency of the communication system. For example, in the GSM system, there are 25 symbols for training every 124 symbols. This causes 24% capacity loss. In high-frequency communication systems, due to the serious impacts of the multiple paths and attenuations of the ionosphere, the time used to transmit training signals may occupy 48% of total transmission time[3, 4].
In some important communication applications, it is required to realize balance without the assistance of training signals, so-called unknown balance. For example, in the communication of broadcasting or single-point-to-multiple-point communication, digital high-definition television (HDTV) is a typical example of broadcasting communication. Digital microwave link has serious attenuations impossible to transmit signals reversely. After the receiving signals are temporarily terminated, the receiving machine must be conducted with unknown balance again, as well as the signal interception and reconnaissance systems with military values. All these cannot receive training signals. Thus, the requirements on the applications reveal the importance of unknown balance technology.
Among various algorithms for self-adaptive unknown balance, the constant modulus algorithm (CMA) raised by Treichler is the most famous and simple one[6, 7]. It utilizes the constant modulus characters of sending symbols and the high statistical amount of signal channel output. According to the single sample or multiple samples in a symbol interval, the balancer can be divided into two models: symbol interval balancer and fraction interval balancer. Therefore, the constant modulus algorithm is also divided into two types: symbol interval and fraction interval. The fraction interval balancer can reduce the sensitivity of timing phase. In the 1990s in the twentieth century, the fraction interval balancer of CMA has been used on the unknown channel balance of actual systems, such as digital HDTV system, short-code DS-CDMA system, wireless GMS cellular system, and so on.
However, for a balancer with limited parameters, the constant modulus algorithm sometimes would fall into an unacceptable local minimum point, and the convergent speed is very slow. For QAM-16 signal, SNR = 20 to 35 dB, to achieve convergence, it may require 20,000 to 30,000 data symbols. To improve the performance of CMA, this study raised an improved constant modulus algorithm. Based on the reserved original cost function, error functions and corrective terms of learning rates are added so that the algorithm can quickly find the convergent directions[9, 10].
For the equalizer of the present study, the tap interval is the reciprocal of the symbol rate, i.e., the symbol interval; if the equalizer before the matched filter transmits pulse after a channel distortion, then this tap interval is optimal. When the channel characteristic is unknown, the receiver filter is typically matched to the transmission signal pulses; this method leads to the equalizer performance being very sensitive to the choice of the sampling time.
The different sampled points of signal resources between s(k) and s(l) (l ≠ k) are statistically independent.
Signal channel, H(z), is reversible; that is, w p (k) exists to make balancer output y(k) ≈ cs(k − Δ). Here, c and Δ are any constant and integer, where Δ is time delay.
Noise, n(k), is the Gaussian additive noise of zero mean and irrelevant to signal source s(k).
The traditional balancer is based on the training sequence d(k) that both sending and receiving know to adjust w p (k) to complete signal channel balance. The unknown balance is popular because it does not require signal channel input during working.
The digital regulative communication signals have the feature of constant modulus. This feature has been widely applied on many communication applications of self-adaptive algorithm for recovery of unknown signals. The constant modulus algorithm raised by Treichler is the most famous and simple balance algorithm. It utilizes the transmitted symbol constant modulus characters and high-order statistic amount of signal channel output.
For a balancer with limited parameters, the constant modulus algorithm of Equation 5 sometimes would fall into an unacceptable local minimum point, and the convergent speed is slow[15, 16]. In the self-adaptive algorithm, the convergent speed of Newton's method is faster than that of the steepest descent method because Newton's method utilizes the self-relevant messages of receiving data in the self-adaptive learning processes. Inspired by this, this study raised an improved constant modulus algorithm (ICMA), which improves the self-adaptive learning rates. The learning rates are controlled by both receiving data and balancer output. Two modifications are made on Equation 5.
This algorithm is called the improved constant modulus algorithm.
where w T (k) = [w0(k)w1(k) ⋯ w L (k)] is the vector of weight coefficient of the self-adaptive signal channel unknown balancer.
where c j (k) is the convolution of balancer parameter, w j (k), and signal channel parameter, a j .
Comparison between improved constant modulus algorithm and traditional constant modulus algorithm at different signal/noise ratios
Signal/noise ratio (dB)
Convergent iteration time (approximate)
After convergence (ISI/dB)
Convergent iteration time (approximate)
After convergence (ISI/dB)
Receiving sequence x(k) is generated by the model of Equation 5. Noise is the random Gaussian noise whose zero mean square is 0.2. Because of the influence of signal channel and noise, the signal source is seriously damaged. Figure 3b is the output results of the balancer after convergence.
For 4QAM, at the condition that S/N ratio is equal to 20 dB, Figure 2 shows the diagram outputs of traditional CMA and improved CMA as well as the performance curves correspondent to the interferences among symbols. It is obvious that the convergent speed of improved CMA is faster than that of traditional CMA. From the diagram output of traditional CMA (Figure 2), we can see two curves at the complex planes. This means that there are delays of balancer output. In addition, we also found that there are phase rotations in the constellation. This means that the traditional constant modulus algorithm only considers the minimum error margin, instead of phase distortion. Yuksekkaya et al. raised an improved constant modulus algorithm against this. However, the algorithm is too complicate. From the constellation diagram output by improved CMA, the outputs of the balancer have neither delay nor phase rotation. The results are ideal.
The above simulation experiment shows that, compared to traditional CMA, the improved CMA raised in this study is better in terms of the unknown balance performance.
5.1. Interference of learning rate to error and convergent time
From Figure 5, the learning rate of ICMA influences the steady mean square error only a little, varying within the range of about 2 dB. The learning rate obviously influences convergent time and steady residual code. For small learning rate (0.04), the convergent time is about 2,500 iterations. The inter-symbol interference calculated with Equation 11 is about −45 dB. When the learning rate increases to 0.3, the algorithm can converge after 450 iterations; however, the inter-symbol interference increases to about −36 dB.
5.2. Influence of balance length to error and convergent time
From Figure 6, when the balancer length is smaller, the influences to convergent time, steady residual inter-symbol interference, and mean square error are obvious, and the performances are worse. However, when the balancer length is larger than 10, these parameters are gradually stabilized. This explains that the improved CMA is not demanding to the balancer length. The advantage is that the algorithm converges fast. As for CMA and a finite impulse response (FIR) signal channel, a balancer is required for infinitive impulse response. When the balancer length is L = 32, the steady residual ISIs are approximately equal. However, the convergent time of traditional CMA is longer.
5.3. Performance comparison between ICMA and CMA
The convergent time of ICMA is about 300 iterations. The steady residual ISI is about −27 dB. The convergent time of CMA is about 400 iterations. The steady residual ISI is about −13 dB. Obviously, ICMA improves the performance of the balancer in terms of convergent speed and reducing inter-symbol interference. When the balancer length is longer, the steady residual ISIs of both algorithms are approximately equal. However, the convergent speed of ICMA is still faster.
With the above two algorithms for further experiments, it was found that ICMA has better performance than CMA in terms of non-constant modulus 16QAM signals.
In recent years, due to the rapid development of signal processing technologies for wireless sensing network, the signal processing that regulates the balance and estimation of unknown signal channel has become a very important technology to enhance the link. The methods and technologies applied for signal processing study gradually receive attentions and demonstrate their vigorous developmental potentials. Thus, in recent years, many universities started studying in the technical territories relevant to wireless sensing network and the internet of things, such as Central University, Feng Chia University, and Southern Taiwan University of Technology, where the courses or research centers relevant to internet of things have been established in the information technology institute to train the professional studying talents for wireless sensing network, embedded systems, and RFID technologies.
This study wishes to have new contributions and breakthroughs in the signal processing of balance and estimation for regulating unknown signal channel. This study raised the balance and estimation of regulating unknown signal channel with ICMA. This method for signal processing can be applied on various wireless sensing networks and the relay of internet of things.
From the comparison analysis and performance exploration of experimental results, we can obtain that (1) the convergent speed of ICMA is faster than that of traditional CMA, (2) the influence of learning rate to steady mean square error is smaller, varying about within the range of 1 dB, (3) ICMA is not very demanding to balancer length, and (4) ICMA improves balancer performance in terms of convergent speed and reducing inter-symbol interference. We apply the solutions raised by this study to the technologies relevant to internet of things and develop innovations and revolutionary technologies of crossing territories to continuously improve the communication quality and performances of signal processing for wireless sensing network.
If the sampling interval is the symbol interval scores of times, such equalizer is known as the fractionally spaced equalizer, and one of the advantages of the fractionally spaced equalizer is that it is able to reduce the sensitivity of selection of sampling time. Further, for the purposes of the channel, the symbol interval equalizer and a FIR equalizer must have infinite impulse response. However, the fractionally spaced equalizer exceeds or reaches as long as the length of the channel response can be. Compared to the fractionally spaced equalizer in the 1990s, the proposed ICMA will be the next best communications technology solution to wireless sensor networking system and strategy.
This research was supported by the National Science Council of Taiwan under grants NSC 99-2623-E-167-002-ET and NSC 100-2218-E-167-001-. The authors would like to thank the National Chin-Yi University of Technology, Taiwan, for financially supporting this research.
- Passino KM: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst Mag 2002, 22(3):52-67. 10.1109/MCS.2002.1004010MathSciNetView ArticleGoogle Scholar
- Appadwedula S, Veeravalli VV, Jones DL: Energy efficient detection in sensor networks. IEEE J Sel Area Comm 2005, 23(4):693-702.View ArticleGoogle Scholar
- Jagyasi BG, Dey BK, Merchant SN, Desai UB: An MMSE based weighted aggregation scheme for event detection using wireless sensor network. In 14th European Signal Processing Conference, EUSIPCO. Florence; 2006. 4–8 Sep 2006Google Scholar
- Jagyasi BG, Dey BK, Merchant SN, Desai UB: Weighted aggregation scheme with lifetime-accuracy tradeoff in wireless sensor network. In Proceedings of the 4th International Conference on Intelligent Sensing and Information Processing 2006, ICISIP. Bangalore; 2006. 15 Oct–18 Dec 2006Google Scholar
- del Valle Y, Venayagamoorthy GK, Mohagheghi S, Hernandez JC, Harley R: Particle swarm optimization: basic concepts, variants and applications in power systems. IEEE Trans Evol Comput 2008, 12(2):171-195.View ArticleGoogle Scholar
- Blum R, Kassam S, Poor HV: Distributed detection with multiple sensors: part ii - advanced topics. Proceedings of the IEEE 1997, 85(1):64-79. 10.1109/5.554209View ArticleGoogle Scholar
- Heinzelmal W, Chandrakasam A, Balakrishnan H: An application-specific protocol architecture for wireless micro sensor networks. IEEE Trans Wirel Commun 2002, 1(4):660-670. 10.1109/TWC.2002.804190View ArticleGoogle Scholar
- Chair Z, Varshney PK: Optimal data fusion in multiple sensor detection systems. IEEE Trans Aerosp Electro Syst 1986, AES-22: 98-101.View ArticleGoogle Scholar
- Chair Z, Varshney PK: Distributed Bayesian hypothesis testing with distributed data fusion. IEEE Trans Syst Man Cybern B Cybern 1988, 18(5):695-699. 10.1109/21.21597View ArticleGoogle Scholar
- Chen B, Jiang R, Kasetkasam T, Varshney PK: Channel aware decision fusion in wireless sensor networks. IEEE Trans Signal Process 2004, 52(12):3454-3458. 10.1109/TSP.2004.837404MathSciNetView ArticleGoogle Scholar
- Xiao J-J, Cui S, Luo Z-Q, Goldsmith AJ: Joint estimation in sensor networks under energy constraints. In Proceedings of the First Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, 2004 (IEEE SECON 2004). Santa Clara; 4–7 Oct 2004Google Scholar
- Akyildiz IF, Su W, Sankarasubramaniam Y, Cayirci E: A survey on sensor networks. IEEE Communications Magazine 2002, 40(8):102-114. 10.1109/MCOM.2002.1024422View ArticleGoogle Scholar
- Wen-Tsai S: Employed BPN to multi-sensors data fusion for environment monitoring services. In Autonomic and Trusted Computing Edited by: González Nieto J, Reif W, Wang G, Indulska J. 149-163. Lecture Notes in Computer Science, vol. 5586 (Springer, Berlin Heidelberg, 2009)Google Scholar
- Wen-Tsai S, Hung-Yuan C: Design an Innovative Localization Engines into WSN via ZigBee and SO. In 2008 CACS International Automatic Control Conference. Tainan; 14–22 Nov 2008Google Scholar
- Wen-Tsai S: Determine global energy minimum solution via Lyapunov stability theorem. IJICIC 2009, 5(7):2011-2030.Google Scholar
- Boukerche A, Oliveira HAB, Nakamura EF, Loureiro AAF: Localization systems for wireless sensor networks. IEEE Wireless Commun Mag 2007, 14(6):6-12.View ArticleGoogle Scholar
- Romer K, Mattern F: The design space of wireless sensor networks. IEEE Trans Wireless Commun 2004, 11(6):54-61. 10.1109/MWC.2004.1368897View ArticleGoogle Scholar
- Wimalajeewa T, Jayaweera SK: Optimal power scheduling for correlated data fusion in wireless sensor networks via constrained PSO. IEEE Trans. Wireless Commun. 2008, 7(9):3608-3618.View ArticleGoogle Scholar
- Nicules D, Nath B: Ad hoc positioning system (APS) using AoA. In Proceedings of the IEEE INFOCOM. San Francisco; 2003. 30 Mar–3 Apr, pp. 1734–1743Google Scholar
- Klein LA: A Boolean algebra approach to multiple sensor voting fusion. IEEE Trans Aerosp Electron Syst 1993, 29(2):317-327. 10.1109/7.210070View ArticleGoogle Scholar
- Sun T, Chen LJ, Han CC, Gerla M: Reliable sensor networks for planet exploration. In Proceedings of the IEEE International Conference on Networking, Sensing and Control. Tucson; 2005. 19–22 Mar, pp. 816–821Google Scholar
- Chia-Hung L, Ying-Wen B, Ming-Bo L: Remote-controllable power outlet system for home power management. IEEE Trans Consum Electron 2007, 53(4):1634-1641.View ArticleGoogle Scholar
- Erdem H, Üner A: A multi-channel remote controller for home and office appliances. IEEE Trans Consum Electron 2009, 55(4):2184-2189.View ArticleGoogle Scholar
- Yuksekkaya B, Kayalar AA, Tosun MB, Ozcan MK, Alkar AZ: A GSM, internet and speech controlled wireless interactive home automation system. IEEE Trans Consum Electron 2006, 52(3):837-843. 10.1109/TCE.2006.1706478View ArticleGoogle Scholar
- Vernon S, Joshi SS: Brain–muscle–computer interface: mobile-phone prototype development and testing. IEEE Trans Inf Technol Biomed 2011, 15(4):531-538.View ArticleGoogle Scholar
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