Optimized Filter Design for a Filter Bank Based Blocker Detection Concept for LTE Systems Thomas Schlechter Institute of Networked and Embedded Systems, Alpen-Adria Universit¨ at Klagenfurt, Austria
[email protected]
c
2011 Springer. Personal use of this material is permitted. Permission from Springer must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Abstract. For mobile communication systems power efficiency is a very important issue. Especially for mobile user equipments a careful management and efficient use of the limited energy ressources is mandatory. In today’s user equipments quite an amount of energy is wasted. The reason for this is, that analog and digital frontend in communication systems are engineered for extracting the wanted signal from a spectral environment, which is defined in the corresponding communication standards with strict requirements. In a real receiving process those requirements can typically be considered as less critical. Sensing the environmental transmission conditions and adapting the receiver architecture to the actual needs allows to save energy during the receiving process. An efficient architecture being able to fulfill this task for a typical Long Term Evolution scenario has been disussed recently. For the implementation of this architecture, highly efficient filter approaches had to be investigated. This paper gives an overview on the basic properties of those approaches and compares it to well known filter types.
Keywords: spectral sensing, blocker, detection, CR, LTE,
1
Introduction
Recently, research on Cognitive Radio (CR) has gained great interest. The concept of CR, e.g. described in [1], allows the user equipment (UE) to scan its relevant environment with respect to instantaneous spectrum allocation. In the original context of CR this information is used for efficient spectrum usage by different UEs using various radio access technologies. However, this concept can be further extended. Considering a UE providing Long Term Evolution (LTE) functionality, knowledge about the environmental spectral composition is extremely valuable for the design of the receive path [2, 3].
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The main idea is as follows: if the UE detects many interferences to the wanted signal, then both the analog and digital frontend (AFE/DFE) of the receive path have to provide full performance, e.g. highly linear amplifiers, filters of high order, etc. In the remainder of this paper such interferences will be called blockers. Full performance of the AFE and DFE results in high energy consumption of the UE. If, on the other hand, there are only few blockers present, which additionally contain little energy, the receive path does not have to run in full performance mode, which can result in power saving. A concept handling this task for the Universal Mobile Telecommunications System (UMTS) test case has been described in [4], while for the LTE test case different approaches have been given in [5, 6] using methods and results discussed in [7–12]. The main idea is based on spectrally sensing the environment around a UE. The gained information can be used to adopt the AFE and DFE to the actual needs and therefore save energy. For an efficient implementation, filters of extremly low complexity are needed. This paper shows an overview on the basic filter properties and gives a comparison to well known filter types, both finite impulse response (FIR) and infinite impulse response (IIR) filters. Section 2 describes the initial conditions and worst case scenario the UE has to cope with to clarify the motivation of building a spectral sensing filterchain. Section 3 gives an overview on the basic filter properties, while Section 4 provides a complexity comparison of the highly optimized filters to well known filter types, like Butterworth, Chebychev and Elliptic approaches.
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Spectral Environment for an LTE UE
In [5, 13] several blockers are defined for the UE to cope with. These differ between the different allowed channel bandwidths for each LTE UE of 1.4, 3, 5, 10, 15 and 20MHz. As an example, Fig. 1a shows an overview of the blocker scenarios for the 5MHz case in baseband representation defined in the standard. As can be seen, that the wanted LTE signal (black) around DC is surrounded by in several blockers (light and dark grey) of different kind. The peaks in the spectrum refer to continuous wave (CW) blockers or Gaussian Minimum Shift Keying blockers modeled as CW blockers, while the broader blockers represent other LTE users at different channel frequencies. The power levels assigned to the single blockers refer to worst case scenarios defined in the standard. The given power level of around -90dBm/5MHz for the LTE signal is remarkably below the blocker levels, e.g. around -60dBm/5MHz for the adjacent and alternate channels and around -37dBm for the narrowest CW blocker. High filter performance with steep slopes is needed to retrieve the wanted LTE signal in such an environment. However, in most of the cases this scenario will not represent the actual spectral allocation around the UE. A more common scenario could be the one shown in Fig. 1b. Obviously in this scenario the detection of the wanted LTE signal is much more relaxed compared to the previous worst case example. The AFE and DFE, however, are typically designed for the worst case scenario. For the second example, as for many other real communication situations, both frontends
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PSD [dBm/LTE channel bandwidth]
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Fig. 1: Power Spectral Density (PSD) for the defined and the relaxed blocker scenario.
are overengineered. This results in a higher than necessary energy consumption. Therefore, if both the AFE and DFE are reconfigurable and the UE is able to
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gain knowledge about the surrounding spectral situation, energy consumption could be driven to a minimum. For this task highly efficient filter chains containing optimized filters are needed. The latter will be described in the following Section 3.
3
Basic Filter Properties
The filters to be used need to include some special properties, which have already been discussed in detail in [5]. In contrast to well known standard filter approaches, we look at a class of IIR filters, which can be implemented in hardware at much lower cost. In this class we ran an exhaustive search based on computer simulations. During this search, those filters giving a good compromise between performance and sparsity of filter coefficients have been selected. The latter has to be considered twofold. First, the filter coefficients shall be sparse in the twos-complement representation, meaning they can be implemented by very few shift-and-add operations without using any multiplier. Therefore, only coefficients of the type ci =
−1 X
ak 2k with ak ∈ {0, 1}
(1)
ˆ k=−k
and typically kˆ ∈ {1, 2, ..., 8}, are used. Second, the filter is designed such, that the number of non-zero coefficients is minimum. The latter results in the choice of half band (HB) filters where naturally every second coefficient is equal to zero. This is a well known fact for FIR filters, while for the investigated class of IIR filters this is also true. Additionally, all of the possible filter implementations need to fulfill the prerequisites given in Tab. 1. Those requirements are defined by the specific usecase the filter is used in later on. One filter resulting from the optimization process and contained in the final filter bank is given as a reference in the following Section 4.
Table 1: Prerequisites for the given filter approaches. max passband ripple Apass 0.1dB min stopband attenuation Astop -22dB passband frequency fpass 25 MHz stopband frequency fstop 27.6 MHz
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Comparison of Different Filter Implementations
Now different filter approaches can be compared to the highly optimized ones. In Fig. 2 a 7th order IIR filter of the proposed class described in Section 3 is
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given as a reference. Its performance is compared to well known standard filter types. All filters need to fulfill the prerequisites given in Tab. 1. Note, that the reference filter is a HB filter and therefore consists of three coefficients only, while the others are all equal to zero. Furthermore, in contrast to all the other non-optimized filters given in Fig. 2, no multipliers are present in the whole filter structure. As can be seen from Fig. 2, this low complex filter fulfills the given prerequisites at much lower complexity compared to the standard type filters. This fact allows to build an efficient filter bank being able to fulfill the tasks described in [6]. In a more general way, this result is also true for filters of higher
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Fig. 2: Complexity comparison of different filter types.
order taken from the investigated class of IIR filters. However, the exhaustive search method is no longer suitable for orders greater than eleven, as the simulation time exceeds acceptable limits. For those approaches filter optimization methods like described in [8] are suggested to be used instead.
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Conclusions
For a recently given filter chain approach a comparison of the used filters has been given. As has been shown, the optimized filter approach reaches the system relevant performance of the well known filter types given in Fig. 2 at much lower computational cost. This allows to build a highly efficient filter being able to fulfill the tasks described in [6].
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Acknowledgment This work was funded by the COMET K2 ”Austrian Center of Competence in Mechatronics (ACCM)”. The COMET Program is funded by the Austrian Federal government, the Federal State Upper Austria, and the Scientific Partners of ACCM.
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12. T. Schlechter, “Estimating Complexity in Multi Rate Systems,” in Proceedings of the 17th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2010, Athens, Greece, pp. 728–731 (2010) 13. TS 36.101 Evolved Universal Terrestrial Radio Access (E-UTRA); User Equipment (UE) radio transmission and reception, 3rd Generation Partnership Project (3GPP) Std., Rev. 9.3.0, (2010). [Online]. Available: http://www.3gpp.org/ftp/Specs/ archive/36\ series/36.101/36101-930.zip 14. D. G. Manolakis, V. K. Ingle, and S. M. Kogon, Statistical and Adaptive Signal Processing. 685 Canton Street, Norwood, MA 02062: Artech House, Inc. (2005) 15. F. C. A. Fernandes, I. W. Selesnick, R. L. Spaendock, and C. S. Burrus, “Complex Wavelet Transform with Alpass Filters,” Signal Processing, vol. 8, pp. 1689–1706 (2003)
