Dynamic channel filtering method for multi-mode & multi-service software radio communication systems

Transcription

1 Dynamic channel filtering method for multi-mode & multi-service software radio communication systems Ryo SAWAI, Hiroshi HARADA, Hiroshi SHIRAI, Masayuki FUJISE Chuo University -- Graduate School of Science and Engineering Kasuga, Bunkyo, Tokyo -- Japan Ph.: , ryo@shirai.elect.chuo-u.ac.jp Communications Research Laboratory -- Independent from Administrative Institution Hikarino-oka, Yokosuka, Kanawaga -- Japan Ph.: , Fax: , harada@crl.go.jp ATRACT This paper proposes a dynamic digital channel filtering method based on wavelet packet algorithm to realize a simplified and efficient multi-mode & multi-service software radio receiver which can provide the functions of several systems as well as several services simultaneously via one handset. The method can extract several different bandwidth signals adaptively, for the change of service being offered, by a filtering processing without preparing a set of filters. By applying for the concept of multiple service wireless communications based on Common Frequency Band Radio On Fiber (CFB-ROF) transmission scheme [], it would be possible to be shared the demodulation processing part from an antenna to a channel selection processing for several different service signals. INTRODUCTION Currently, there is a great demand from the public for radio communication services, and the forms of service being offered are becoming more diverse. The future radio communication services, such as the Intelligent Transport Systems (ITS) and 4th-generation mobile telecommunication systems, will provide more intelligent services, like support for automated driving and multimedia communications using motion pictures. In the near future, more flexible and intelligent smart communication systems that will be capable of providing not just one fixed service but a variety of services via a single handset will be heightened. The realization of just such a flexible and intelligent smart communications system was the goal of a number of reported studies made of software radio technology []-[8]. Software radio technology provides the functions of several radio communication systems by changing flexibly the software program used on the Digital Signal Processor (DSP) and the Field Programmable Gate Array (FPGA), in accord with user requests. Moreover, the Multi-mode & Multi-service Software Radio communications (MMSR) system, which provides not only the functions of several systems but also several services simultaneously via one radio terminal, has been proposed [6]. The realization of software radio technology will widely expand the possible offerings of radio communication services to several fields such as the integration of car navigation, the Internet, and TV programs. However, before the implementation of the software radio technology can occur, there are many difficulties to be solved. One of such difficulties involves finding a flexible architecture that provides efficient and speedy radio modulation and demodulation processing in accord with user requests and improvements of device technology. Moreover, an efficient and simple terminal structure must be developed to realize an MMSR system, as this system integrates the components of several different radio communication systems in one radio terminal. In this paper, therefore, a common digital channel filtering method based on wavelet packet algorithm [9], [] is proposed for realizing a simplified and efficient multi-mode & multi-service software radio receiver. The method can extract several different bandwidth signals being offered adaptively by a filtering processing without preparing a set of filters with unequal bandwidths. By combining the concept of multiple service wireless communications based on Common Frequency Band Radio On Fiber (CFB-ROF) transmission scheme [], which is a multiple service transmission techniques by reconverting several different service signals in the same transmission band, it would be possible to be shared the demodulation processing from an antenna to a channel selection processing for several different service signals. As an evaluation of the proposed method, a two-mode multi-mode & multi-service software radio receiver, which are integrated Japanese ETC (Electric Toll Collection system) and PHS (Personal Handy-phone System), is assumed. The magnitude responses of the two system channels in the same transmission band, extracted by the proposed method, are shown here. PROPOSAL OF A DYNAMIC CHANNEL FILTERING METHOD BASED ON WAVELET PACKET ALGORITHM In multiple service wireless communications technique, several different service signals are transmitted in a same transmission band by millimeter wave and optical fiber links as shown in Fig. []. Accordingly, in the receiver side, the demodulation processing such as an antenna and ADC can be shared for several service signals, and the simplicity & efficiency of a MMSR receiver would be realized. However, in the digital signal processing part, a set of filters with unequal bandwidths shown in Fig. must be prepared to extract the channel of interest in accord with services requested by users, but it might be not a desirable configuration method from viewpoints of simplicity and efficiency. Therefore, in this section, a dynamic channel filtering method based on wavelet packet algorithm, which can extract adaptively several different bandwidth signals according to user requests by a common filtering processing, is introduced.

2 ETC 5.8GHz Mobile Network Frequency Integrating Converter Optical Cable (Radio On Fiber) Central Control Station ITS Network Microwave Millimeterwave Vehicle RF/Optical Converter Antenna GHz Digital TV.9GHz PHS Network Local Base Station RF/Optical Converter x g Anaysis Bank h y high y low g' Synthesis Bank h' Fig. 3 Two channels filter bank x' Fig. Concept of the ITS multiple based on CFB-ROF system (Ref. []) Channel Channel Channel 3 π Band pass filter Fig. Example of multi-band filtering x h g hh gh hg gg hhh 3 ghh 3 hgh 3 ggh 3 hhg 3 ghg 3 hgg 3 ggg 3 x ghh 3 gh g Noble identity expression Fig. 4 Example of wavelet packet algorithm and the noble identity expression in analysis part, where gray components show the analysis filters which decompose the channels of interest Wavelet Packet.. Discrete Wavelet Transform (DWT) First, let us describe the DWT algorithm, which composes a basic idea of wavelet packet algorithm, by two channels filter bank as shown in Fig. 3. In this figure, g(z) and h(z) show the low-pass and high-pass filters in the analysis bank, and g (z) and h (z) also show the lowpass and high-pass filters in synthesis bank, respectively. Then, the output x (z) for the input signal x(z) can be expressed as follow: x' ( z) = k ( z) x( z) + k( z) x( z), (.) where, k = [ g' ( z) g( z) + h' ( z) h( z) ], (.) k = [ g' ( z) g( z) + h' ( z) h( z) ] (.3) show the signal and aliasing components, respectively. To perform perfect reconstruction,the aliasing components shown by Eq.(.3) should be canceled out, and output signal x (z) needs to appear as the time delay of input signal x(z). That is, if the conditions of ( L k ) = z and k = can be satisfied in these equations, the system can compose a perfect reconstruction filter bank, since the equation x ' ( n) = x( n ( L ) ) is also satisfied. First, to cancel out the aliasing component ( k = ), the analysis bank and synthesis bank should have a following relation each other: g' ( z) = h( z), h' ( z) = g( z). (.4) Next, to output x (z) as the time delay component for x(z), it should be to satisfy the following equation; ( ) ( ) ( ) ( ) ( L g z h z g z h z = z ). (.5) where, L( z) = g( z) h( z). (.6) Hence, Eq.(.5) can be expressed as L ( z) L( z) = z ( L ). (.7) Equation (.7) shows the condition of perfect reconstruction of two channel filter bank, and then in Eq.(.7), L(z) should be a half-band filter. Here, to generate the orthogonal bases, the equation of B h z = z g z (.8) ( ) ( ) ( ) must be satisfied [], where B show the number of tap in filter g(z)... Wavelet Packet Algorithm Next, let us describe wavelet packet algorithm extended the idea of two channels filter bank in Sec.... Figure 4 shows an example of the algorithm. Similarly for the filtering procedure, a signal is split into a high-pass component and a low-pass component. The high-pass component is then itself split into second-level highpass and low-pass components, and the process is repeated as shown in the left figure of Fig. 3. Here, each component in all levels filtered by the analysis filters (h, g, hh, gh, hg, gg, hhh 3, ), which are generated from a mother wavelet function, can be perfectly reconstructed by the DWT synthesis procedure described in Sec.... Moreover, the filtering procedure can be interchanged to a non-uniform filter bank by noble identity [9], and the simplicity of filtering process can be realized. For instance, when the channel information being offered appears in gray components, the channel information is possible to reconstruct by three synthesis filters corresponded to three analysis filters (g, gh, ghhh 3 ), since the synthesis part can be also expressed by the idea described in Sec... By applying this algorithm, a common channel filtering method for several different bandwidth signals just by setting decimation and interpolation values and several half-band filters given from a mother wavelet function might be realized. In next subsection, a dynamic channel filtering method based on this concept is introduced.

3 Signal from ADC () Analysis Bank H (z) n H (z) n H M- (z) n M- () Weight Control Adaptive Signal Processing (3) Synthesis Bank Controller n' n' n' M- F (z) F (z) F M'- (z) Programmable Demodulator Table. Simulation model System PHS ETC Freq. Band.9 [GHz] 5.8 [GHz] Symbol-Rate 9,,48, [symbols/sec] Bit-Rate 384,,4, [bits/sec] Modulation Manchester coded Access scheme TDMA-TDD ASK Slotted Aloha TDMA ETC PHS Fig. 5 Flow of a dynamic channel filtering method BPF Frequency Converter AGC LD: Leaser Diode EAM: Electroabsorption Modulator PD: Photo Diode LD EAM Optical Fibre PD (a) Transmitter (Ref. []) Low-IF Conversion (Ref. []) ADC Channel Filtering AM Demodulation Pocessing part Evaluation of the frequency response of channel extracted (Sec.4) (b) Receiver Fig. 6 Integrated transmitter and receiver. Proposed Method In order to adopt the wavelet packet algorithm for the channel filtering processing part in a multi-mode & multiservice receiver, it would be quite important how the filtering procedure can be optimized for the case that the integrated service being offered changes by user request. Hence, the processing needs to reconfigure dynamically for the change of service being offered. Therefore, a channel filtering method, which is composed by a fixed analysis bank and a reconfigurable synthesis bank, is proposed here to satisfy such demand. This method is totally based on the conventional nonuniform filter bank, but it is possible to interchange the synthesis bank adaptively according to services requested from users just by setting decimation and interpolation values and several half-band filters information given from a mother wavelet function. Figure 5 illustrates the flow of the method. Hereafter, let us explain the procedure of the filtering task followed by ()-(3) in this figure. Table. Simulation parameters Channel A B System PHS ETC Local freq. [MHz] [MHz] Sampling rate 5 [M samples/sec] Mother wavelet Meyer wavelet [9] () Firstly, in analysis bank, the signal digitized by an ADC is decomposed by analysis filter (H, H, H,, H M- ), which are generated by a mother wavelet function, and each output is decimated to an adequate sample rate for each system bandwidth. Here, the spectrum resolution should be enough to decompose each channel information for all possible service being offered. () Next, the weight controller normalizes each correlation values by the maximum correlation value. Zero value is then multiplied to the filtering output if the service is then not requested. (3) In synthesis bank, first, the adaptive synthesis processing part detects which channel information is wanted from users, and sets the parameters of synthesis filter and interpolation values to reconstruct only the channel of interest. Also, if the correlation values are lower than a threshold, it is informed to be impossible to connect the target service for users. Finally, the channel information of interest is reconstructed by synthesis filters, and each output is sent to the programmable demodulation processing part. By following this procedure, the method realizes a dynamic channel filtering for several service signals being offered. 3.Simulation Model In order to show the effectiveness of the proposed method, an example is given here. The two systems (ETC and PHS) are integrated as a multi-mode handset. Table shows the specified parameters of each system. The configuration of the multiple service wireless communications based on CFB-ROF transmission technique [] is shown in Fig. 6 (a) and (b). In this transmitter shown by Fig. 6 (a), a channel of interest from a PHS network is first up-converted by frequency converter from.9 GHz band to 5.8 GHz band, and the output is added with another channel of interest from an ETC network. Next, the combined electrical radio signal

4 Channel A Channel B Lowpass Highpass Fig. 7 Amplitude response of the signal inputted to the proposed method Lowpass filter Input signal Channel Exstracted by analysis filter Highpass filter Fig. 8 Amplitude response of the channel extracted by the analysis filter derives Electro-Absorption Modulator (EAM) and the modulated optical signal is delivered to the Local Base Station (L). Then, by using Photo Diode (PD), the optical signal is converted to the radio signal and is transmitted by an antenna. Accordingly, in the receiver side, the analog signal processing parts such as an antenna, analog band-pass filter, Automatic Gain Controller (AGC) and analog down-conversion device for different service signals as shown in Fig. 6(b) can be shared for several service signals, since the two transmitted channels information is in the same transmission band. Each system channel digitalized by an ADC is extracted by the proposed method in accord with user requests. Finally, the outputs are sent to a programmable demodulator, and the transmission data is regenerated by the coherent detection circuit Fig. 9 Amplitude response of the analysis filter bank (Meyer wavelet) this figure, this may be due to the fact that the two system channels are possible to be extracted by the proposed method without spectrum distortion. Moreover, in an environment of Eb/No=, error free was identified. 5.Conclusion In this paper, a dynamic channel filtering method based on wavelet packet algorithm is proposed to realize a simplified and efficient multi-mode & multi-service software radio receiver. Adaptive channel extraction of several different bandwidth signals in the same transmission band could be realized by setting a filtering waveform. By combing the concept of multiple service wireless communications based on CFB-ROF transmission scheme, it would be possible to be shared the demodulation processing from an antenna to a channel selection processing for several different service signals. As an evaluation of the proposed method, a multi-mode & multi-service software radio receiver, which are integrated ETC and PHS, is assumed. Each spectrum of two channels extracted by the proposed method is evaluated, and it was shown that the method could provide the channel selection without spectrum distortion. In this presentation, we would like to show the further evaluation results from the points of theoretical by computer simulation and experimental views when the number of channels requested from users are increasing more. 4.Feasibility Study Let us show a result of the channel filtering processing by the proposed method. As an inputted signal, the ETC and PHS channels down-converted to Low-IF signal in the same transmission band are assumed. The simulation parameters utilized here such as the sampling rate and local frequency for each channel are summarized in Table. Figures 7 and 8 show the amplitude responses in frequency domain of the two system channels digitized by an ADC, and of the channels extracted by the low-pass and high-pass filters in analysis bank, respectively. Meyer wavelet [9] was then utilized as an example, where the amplitude response in frequency domain is shown in Fig. 9. From References [] M. Fujise, K. Sato, H. Harada, and F. Kojima, Millimeter-wave ROF Multiple Service Road- Vehicle Communication Experiments, in the Proceeding of the st Workshop on ITS Telecommunications-ITST, Oct.. [] J. Mitola, The Software Radio Architecture, IEEE Communication Magazine, May 995. [3] European Commission DG XIII-B: in the Proceeding of Software Radio Workshop, May 997. [4] J. Mitola, Technical Challenges in the Globalization of Software Radio, IEEE Communication Magazine, Feb. 999

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