Wireless communication basestations, networks, and other systems use power amplifiers to transmit signals to cellular phones, handheld messaging devices, computers, personal electronic assistants, and other devices. A power amplifier increases the average power of the transmitted wireless signal sufficiently to maintain a reliable communication link at any required distance. This is necessary because signal waveforms are used to efficiently convey information between a transmitter and a distant receiver. Since noise and interference are combined with the signal waveform at the receiver, the transmitter must amplify its waveform prior to transmission sufficiently to guarantee that the ratio of received signal energy to noise/interference energy exceeds a specified value; otherwise the receiver's additive noise/interference can overwhelm the signal energy, resulting in loss of information over the data link. This constraint applies to communication systems employing wireless transmission, including radio frequency (RF), optical and audio technologies.
Pre-transmission amplification of the information-bearing signal waveform constitutes one of the major costs associated with modern information transfer. FIG. 1 depicts a typical relationship between amplification cost and the maximum (peak) magnitude of the signal waveform. Package cost generally dominates for low peak-power amplifiers. However, beyond some point, additional peak-power capability results in exponentially-increasing amplifier costs. For this reason, signal processing techniques capable of reducing peak values of the transmitted waveform are greatly valued in modern wireless signal transmission systems.
The transmitted signal's power varies depending on both the modulation type and the data sequence being transmitted, which results in peaks and troughs in the instantaneous power as a function of time. The complexity and cost of an amplifier is highly dependent on the maximum instantaneous power it must accommodate. Consequently, basestation providers and operators and other electronics users seek ways to lower the instantaneous or “peak” power requirements of the relevant system.
To reduce system peak power requirements, a provider may simply limit the maximum amplifier output power by constraining or “clipping” the maximum magnitude of the amplifier's output signal. Clipping the amplifier output effectively reduces the peak power output requirement while still providing ordinary amplification for non-peak signals. Since the cost of a power amplifier rapidly increases as it is required to accommodate higher peak power levels, clipping can significantly reduce system cost. Clipping may be particularly attractive in applications in which large peaks occur only occasionally. For example, a single amplifier often simultaneously amplifies signals for multiple channels. Occasionally, the multiple channel signals constructively combine to generate a relatively high peak. The amplifier must either fully amplify the peak, requiring an expensive high peak-power amplifier, or the output magnitude may be clipped to facilitate the use of a lower peak-power, less expensive amplifier.
In wireless communications and networking, however, clipping is unacceptable. Clipping induces spectral regrowth, creating spectral energy in potentially restricted spectral regions. The electromagnetic spectrum is a finite resource, and it is strictly apportioned by restrictions from various governmental regulating agencies to minimize interference from competing users. The various spectrum users receive permission to transmit within certain bandwidths and are ordinarily prohibited from transmitting outside of the designated bandwidth. Even within the so-called “unlicensed bands”, strict FCC standards regulate spectral emissions to minimize interferences. Because spectral regrowth adds unacceptable frequency components to the signal, spectrum regulations do not permit clipping as a solution for high-power amplifier requirements.
The relationship between signal peaks and amplifier characteristics is of great significance with respect to wireless communications. Efficient power amplifiers exhibit an intrinsically nonlinear relationship between input and output power. The relationship between amplifier input and output power is depicted in the lower curve 240 of FIG. 2. For low levels of input power, the amplifier output signal is essentially a linearly-amplified replica of the input. However, at higher input signal power levels, the amplifier output reaches an upper limit, the amplifier saturation power, which cannot be exceeded. The region of the amplifier curve near the saturation point is nonlinear. Operation of the amplifier near its nonlinear amplification region generates unacceptable nonlinear noise which violates regulatory spectral masks, forcing operation at a lower input power level. Prior art includes numerous techniques which can be used to ‘linearize’ an amplifier, thus mitigating the nonlinear characteristic, and approaching the ideal linear relationship shown in the upper curve 242 in FIG. 2.
Amplifier nonlinearities convert input signal energy into nonlinear spectral energy which may violate regulatory spectral mask constraints. It is therefore necessary to limit the strength of the signal input to the amplifier so that its magnitude only rarely extends beyond the linear region of operation. As FIG. 2 shows, the value of amplifier linearization is that it can greatly extend the upper limits of the amplifier's linear region. After the amplifier has been linearized to the practical limit, generation of unwanted nonlinear spectral components may be further reduced by limiting the likelihood that the signal magnitude extends beyond the amplifier's linear region. This reflects the important fact that generation of unwanted nonlinear components requires that signal peaks extend beyond the amplifier's linear region; both signal and amplifier characteristics are involved, and both must be addressed.
The need for peak-reduction processing was greatly increased by the relatively recent widespread adoption of so-called ‘multi-channel’ signal waveforms for wireless infrastructure systems. The adoption of multi-channel signaling (MCS) occurred because of the strong economic incentive to combine several independent signal waveforms wherein all of the signals are transmitted in the same spatial direction and all signals can then share a single antenna. Previously, infrastructure basestations separately amplified each waveform, which were then combined using a ‘diplexer’ before sending the composite amplified signal to the antenna. However, since a four-signal high-power diplexer can cost on the order of $10,000, an alternative solution in the form of MCS was developed. In MCS, several independent signal waveforms are generated and combined while still in digital form. The combined signals then share a common frequency translation to RF, a common amplifier and a common antenna. The heavy, bulky, and expensive diplexer is eliminated. The digital channel waveforms remain separated by the inter-channel frequency spacing, typically less than ten megahertz, so that inexpensive (relatively low rate) digital processing can easily generate the composite waveform. FIG. 3 depicts the baseband complex spectra associated with four adjacent cellular signals. Note that the frequency offsets correspond only to the relative transmission frequencies, since the common RF frequency translation will be added to the MCS waveform after it has been converted into analog form. While MCS provides an economically advantageous solution to the diplexer problem associated with earlier transmission systems, MCS greatly aggravates the peak magnitude problem, since the signal peak of an MCS waveform is much higher than that of each of its component signal waveforms. Thus, MCS remains an incomplete solution to the diplexer problem of earlier transmission systems until peak reduction in MCS is effectively addressed.
In addition to the emergence of MCS waveforms with their large peak magnitudes, several important worldwide wireless standards [e.g. 802.11 (WiFi) and 802.16 (WiMAX)] have adopted orthogonal frequency-division multiplexing (OFDM) waveforms which use parallel transmission of many narrowband components. An OFDM signal may be considered as a special case of multi-channel transmission, with no spectral spacing between adjacent channels, and short burst (rather than continuous) transmission. The WiMAX waveform, which has been proposed as a potential worldwide solution for all wireless communication, uses basestation transmissions consisting of OFDM with several hundred channels. These channels are allocated to many users, with modulation types and power levels of those sets of channels sent to each user selected based on the path attenuation for each distinct physical link. The large peak power level variation of the many OFDM channels generates peak-reduction demands similar to those of MCS. OFDM must also satisfy stringent error vector magnitude (EVM) constraints for each set of channels allocated for each individual user, in the face of dynamically-varying channel modulation orders, path losses, and signal power levels. Peak-reduction processing therefore offers economic advantages to modern wireless communication systems, both RF and optical, both MCS and OFDM, as well as any other system in which signal peaks are beneficially reduced based on any standard, requirement or economic factor including, for example, digital radio and television broadcast systems.
Numerous technical papers directed to techniques for peak-reduction processing have been published, and several patents have been awarded, as would be expected for such an economically vital challenge.
One peak-reduction processing approach simply modifies the information stream itself prior to the signal generation (modulation) operation. See, e.g., R. W. Bauml, R. F. H. Fisher, and J. B. Huber, “Reducing the Peak-to-Average Power Ratio of Multi-Carrier Modulation by Selected Mapping,” Electron. Lett., vol. 32, no. 22, October 1996, pp. 2056-2057; R. van Nee and A. de Wild, “Reducing the Peak-to-Average Power Ratio of OFDM,” Proc. IEEE VTC '98, May 1998, pp. 2072-2076. While this technique reduces the peaks, it also significantly degrades the performance of error-correction coding, and has thus failed to find any significant market acceptance.
Other approaches generate/modulate the information stream onto the waveform, then alter that waveform to reduce its peak magnitude. See, e.g., T. May and H. Rohling, “Reducing the Peak-To-Average Power Ratio in OFDM Radio Transmission Systems,” Proc. IEEE VTC '98, May 1998, pp. 2474-78. One such approach applies localized smoothly-varying attenuation to the signal in the vicinity of each peak. Yet another approach avoids generating nonlinear noise by simply subtracting suitably scaled band-limited pulses from the signal to cancel each peak. While these approaches offer improvement, and at least two patents (U.S. Pat. Nos. 6,366,319 and 6,104,761) have been granted for such an approach, they both add excessive noise to the signal. These approaches also do not offer a comprehensive and systematic peak-reduction processing solution when the MCS channels are dynamically varying in relative power levels and when the EVM requirements of each channel also dynamically vary, as is the case with real-world MCS transmission.
Still another technique is the classic clip-and-filter approach, which simply passes the waveform through a “clipper” (i.e. hard-limiter), then filters the clipped to ensure compliance with regulatory spectral constraints. This approach is very commonly used for peak-reduction of OFDM signals. e.g., R. O'Neill and L. Lopes, “Envelope Variations and Spectral Splatter in Clipped Multi-carrier Signals,” Proceedings of the PMRC '95, September 1995, pp. 71-75; J. Armstrong, “New OFDM Peak-to-Average Power Reduction Scheme,” IEEE VTC 2001, May 2001, Rhodes, Greece; J. Armstrong, “Peak-to-Average Power Reduction in Digital Television Transmitters,” DICTA2002 Conference, Melbourne, January 2002, pp. 19-24; J. Armstrong, “Peak-to-Average Power Reduction for OFDM by Repeated Clipping and Frequency Domain Filtering,” Electronics Letters. vol. 38, No. 5, February 2002, pp. 246-47; U.S. Patent Publication Nos. 2004/0266372, 2004/0266369; H. A. Suraweera, K. Panta, M. Feramez and J. Armstrong, “OFDM Peak-to-Average Power Reduction Scheme With Spectral Masking,” Int'l Symposium on Comm. Systems Networks and Digital Processing (2004). The prior art in this area does nothing more than filter away out-of-band (OOB) energy. However, hard-limiting in this manner introduces passband nonlinear interference which cannot be removed by out-of-band filtering, and even out-of-band DFT filtering distorts the signal.
A conceptually-related peak reduction technique involves determining the ‘excursion’ (the portion of the signal exceeding a defined magnitude threshold), then filtering, scaling and time-aligning the excursion prior to subtracting it from a suitably delayed version of the original signal. This ‘filtered excursion’ approach eliminates signal distortion by applying filtering only to the excursion. The advantage is that spectral constraints are met without generating signal distortion, and peaks can be reduced by the maximum amount permitted by spectral constraints. The only prior art description of the filtered excursion approach, J. Armstrong, “PCC-OFDM with Reduced Peak-to-Average Power Ratio,” in IEEE 3Gwireless 2001, May 30-Jun. 2, 2001, San Francisco, pp. 386-391, is limited to a non-standard variant of OFDM that involves overlapped symbols. The author has notably described clip-and-filter as the preferred peak-reduction approach for standard OFDM signals in all subsequent publications.
This ‘filtered excursion’ approach forms the theoretical basis for the present invention as described and claimed below, but the present invention goes beyond prior approaches in several significant respects. The prior art relating to the filtered excursion approach to peak-reduction processing properly recognized the need for interpolation prior to forming the excursion signal, although claiming, incorrectly, that over-sampling by a factor of only two was required. An increased sampling rate prevents nonlinear spectral components associated with the excursion from aliasing back into the spectrum occupied by the original signal. This is important because once such nonlinear components occur, they cannot be removed by filtering. However, the prior art failed to recognize several critical factors involved in achieving optimal peak reduction. For example, the prior art did not recognize the need to vary the attenuation-versus-frequency characteristic of the excursion filtering across the signal passband in order to properly protect the weaker signal components. The prior art described only static frequency-dependent attenuation of the out-of-band excursion spectral components, and pointedly instructed to “distort the in-band (i.e. passband) component of the difference (excursion) as little as possible.” However, the nonlinearity represented by excursion formation generates relatively uniform spectral nonlinearity noise across the signal bandwidth. Ensuring that all portions of the signal satisfy a minimal signal-to-noise ratio (SNR) constraint thus requires that extra attenuation be applied to the excursion in those spectral regions of weaker signal spectral energy. Even more critically, since the relative spectral energy of different signals varies dynamically, any such signal-responsive filtering must be dynamically adapted over time. Finally, each portion of a multi-channel signal must independently satisfy the error vector magnitude (EVM) constraint, which limits each distinct channel's SNR to one of a set of defined values, depending on that channel's modulation type. The cited prior art failed to recognize the need to dynamically adapt the signal passband ‘filtering’ in order to satisfy this critical specification. Finally, the prior art failed to grasp the critical importance of applying dynamic scaling to different portions of the excursion prior to filtering in order to achieve significantly enhanced peak-reduction. An object of the present invention is thus to provide gain and other control strategies for optimizing peak reduction subject to noise level (for example EVM) constraints, signal dynamics and residual linear and nonlinear distortion energy considerations.