7.1 Introduction


As we noted in Chapter 1, spread spectrum, in the form of direct sequence (DS) or frequency hopping (FH), is one of the most common signaling schemes in current and emerging wireless services. Such services include both second-generation (IS-95) and third-generation (WCDMA, cdma2000) cellular telephony [130, 329], piconets (Bluetooth) [41], and wireless LANs (IEEE802.11 and HiperLAN) [41]. Among the reasons that spread spectrum is so useful in wireless channels are its use as a countermeasure to frequency-selective fading caused by multipath and its favorable performance in shared channels. In this chapter we are concerned with the latter aspect of spread spectrum, which includes several particular advantages, including flexibility in the allocation of channels and the ability to operate asynchronously in multiuser systems, frequency reuse in cellular systems, increased capacity in bursty or fading channels, and the ability to share bandwidth with narrowband communication systems without undue degradation of either system's performance. More particularly, this chapter is concerned with this last aspect of spread spectrum, specifically with the suppression of narrowband interference (NBI) from the spread-spectrum partner of such shared-access systems.

NBI arises in a number of types of practical spread-spectrum systems. A classical and still important instance of this is narrowband jamming in tactical spread-spectrum communications; and of course, the antijamming capabilities of spread spectrum was an early motivator for its development as a military communications technique. A further situation in which NBI can be a significant factor in spread-spectrum systems is in systems deployed in unregulated bands, such as the industrial, scientific, and medical (ISM) bands, in which wireless LANs, cordless phones, and Bluetooth piconets operate as spread-spectrum systems. Similarly, shared access gives rise to NBI in military very high frequency (VHF) systems that must contend with civilian VHF traffic. An example of this arises in littoral sonobuoy networks that must contend with onshore commercial VHF systems, such as dispatch systems. Yet another situation of interest is that in which traffic with multiple signaling rates is generated by heterogeneous users sharing the same CDMA network. Finally, in some parts of the world the spectrum for third-generation (3G) systems is being allocated in bands not yet vacated by existing narrowband services, creating NBI with which 3G spread-spectrum systems must contend. In all but the first of these examples, the spread-spectrum systems are operating as overlay systems , in which the combination of wideband signaling, low spectral energy density, and natural immunity to NBI of spread-spectrum systems, are being exploited to make more efficient use of a slice of the radio spectrum. These advantages are so compelling that we can expect the use of such systems to continue to rise in the future. Thus, the issue of NBI in spread-spectrum overlay systems is one that is of increasing importance in the development of future advanced wideband telecommunications systems [58, 229, 230, 234, 331, 332, 358, 368, 400, 401, 466, 528 “534, 562, 566].

The ability of spread-spectrum systems to coexist with narrowband systems can easily be explained with the help of Fig. 7.1. We first note that spreading the spread-spectrum data signal over a wide bandwidth gives it a low spectral density that assures that it will cause little damage to the narrowband signal beyond that already caused by the ambient wideband noise in the channel. On the other hand, although the narrowband signal has very high spectral density, this energy is concentrated near one frequency and is of very narrow bandwidth. The despreading operation of the spread-spectrum receiver has the effect of spreading this narrowband energy over a wide bandwidth while it collapses the energy of the originally spread data signal down to its original data bandwidth. So, after despreading, the situation is reversed between the original narrowband interferer (now wideband) and the originally spread data signal (now narrowband). A bandpass filter can be employed so that only the interferer power that falls within the bandwidth of the despread signal causes any interference. This will be only a fraction (the inverse of the spreading gain) of the original NBI that could have occupied this same bandwidth before despreading.

Figure 7.1. Spectral characteristics of a narrowband interference (NBI) signal and spread-spectrum (SS) signal before and after despreading.

graphics/07fig01.gif

Although spread-spectrum systems are naturally resistant to narrowband interference, it has been known for decades that active methods of NBI suppression can significantly improve the performance of such systems. Not only does active suppression of NBI improve error-rate performance [37], it also leads to increased CDMA cellular system capacity [373], improved acquisition capability [328], and so on. Existing active NBI suppression techniques can be grouped into three basic types: frequency-domain techniques, predictive techniques, and code-aided techniques. To illustrate these three types, let us consider a basic received waveform

Equation 7.1

graphics/07equ001.gif


consisting of the useful (wideband) data signal { S ( t )}, NBI signal { I ( t )}, and wideband ambient noise { N ( t )}. As the name implies, frequency-domain techniques operate by transforming the received signal { r ( t )} into the frequency domain, masking frequency bands in which the NBI { I ( t )} is dominant and then passing the signal off for subsequent despreading and demodulation. This process is illustrated in Fig. 7.2. Alternatively, predictive systems operate in the time domain. The basic idea of such systems is to exploit the discrepancy in predictability of narrowband signals and wideband signals to form an accurate replica of the NBI that can be subtracted from the received signal to suppress the NBI. In particular, the received signal { r ( t )} consists of the wideband component { S ( t ) + N ( t )} and the narrowband component { I ( t )}. If one generates a prediction (e.g., a linear prediction) of { r ( t )}, the values predicted will consist primarily of a prediction of { I ( t )} since the wideband parts of the signal are largely unpredictable (without making explicit use of the structure of { S ( t )}). Thus, such a prediction forms a replica of the NBI, which can then be suppressed from the received signal. That is, if we form a residual signal

graphics/387equ01.gif


Figure 7.2. Transform domain NBI suppression.

graphics/07fig02.gif

where graphics/rcap.gif ( t ) is a prediction of r ( t ) from past observations, the effect of the subtraction is to significantly reduce the narrowband component of { r ( t )}. The prediction residual is then passed on for despreading and demodulation. Interpolators can also be used to produce the replica in such a scheme, with somewhat better performance and with less distortion of the useful data signal. Prediction-based methods take advantage of the difference in bandwidths of the spread-spectrum signal and the NBI without making use of any knowledge of the specific structure of the spread-spectrum data signal. Figure 7.3 illustrates this process, which is described in more detail in Sections 7.2 and 7.3. Code-aided techniques get further performance improvement by making explicit use of the structure of the useful data signal and, where possible, of the NBI. To date, these methods have made use primarily of techniques from linear multiuser detection, such as those described in Chapter 2.

Figure 7.3. Predictive method of NBI suppression.

graphics/07fig03.gif

Progress in the area of NBI suppression for spread-spectrum systems up until the late 1980s is reviewed in [327]. The principal techniques of that era were frequency-domain techniques and predictive or interpolative techniques based on linear predictors or interpolators. In the past decade or so there have been a number of developments in this field, the main thrust of which has been to take further advantage of the signaling structure. This has led to techniques that improve the performance of predictive and interpolative methods, and more recently to the more powerful code-aided techniques mentioned above. In this chapter we discuss these latter developments. Since these results have been concerned primarily with direct-sequence spread-spectrum systems (exceptions are found in [211], which considers frequency-hopping systems, and [423], which considers multicarrier systems), we restrict our attention to such systems throughout most of this chapter. We also focus here on predictive and code-aided techniques; for discussions of frequency-domain and other transform-domain techniques (including time-frequency methods), the reader is referred to [95, 140, 152, 225, 251, 317, 327, 330, 402, 418, 430, 432, 433, 590, 611]. We also refer the reader to a recent survey paper [57], which discusses a number of aspects of code-aided NBI suppression.

The remainder of this chapter is organized as follows . In Sections 7.2, 7.3, and 7.4 we discuss, respectively, linear predictive techniques, nonlinear predictive techniques, and code-aided techniques for NBI suppression. In Section 7.5 we present performance comparisons of the foregoing three families of NBI suppression techniques. In Section 7.6 we discuss the near “far resistance of the linear MMSE detector to both NBI and MAI. In Section 7.7 we present the adaptive linear MMSE NBI suppression algorithm. In Section 7.8 we discuss briefly a maximum- likelihood code-aided NBI suppression method. Finally, some mathematical derivations are collected in Section 7.9.

The following is a list of the algorithms appearing in this chapter.

  • Algorithm 7.1: Kalman “Bucy prediction-based NBI suppression

  • Algorithm 7.2: LMS linear prediction-based NBI suppression

  • Algorithm 7.3: ACM-filter-based NBI suppression

  • Algorithm 7.4: LMS nonlinear prediction-based NBI suppression



Wireless Communication Systems
Wireless Communication Systems: Advanced Techniques for Signal Reception (paperback)
ISBN: 0137020805
EAN: 2147483647
Year: 2003
Pages: 91

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