- Research Article
- Open Access
Exact Symbol Error Probability of Cross-QAM in AWGN and Fading Channels
© Xi-chun Zhang et al. 2010
- Received: 28 April 2010
- Accepted: 21 November 2010
- Published: 30 November 2010
The exact symbol error probability (SEP) performance of -ary cross quadrature amplitude modulation (QAM) in additive white Gaussian noise (AWGN) channel and fading channels, including Rayleigh, Nakagami-m, Rice, and Nakagami-q (Hoyt) channels, is analyzed. The obtained closed-form SEP expressions contain a finite (in proportion to ) sum of single integrals with finite limits and an integrand composed of elementary (exponential, trigonometric, and/or power) functions, thus readily enabling numerical evaluation. Particularly, Gaussian -function is a special case of these integrals and is included in the SEP expressions. Simple and very precise approximations, which contain only Gaussian -function for AWGN channel and contain three terms of the single integrals mentioned above for fading channels, respectively, are also given. The analytical expressions show excellent agreement with the simulation results, and numerical evaluation with the proposed expressions reveals that cross QAM can obtain at least 1.1 dB gain compared to rectangular QAM when SEP < 0.3 in all the considered channels.
- Fading Channel
- Rayleigh Fading Channel
- Quadrature Amplitude Modulation
- Symbol Error Probability
- Rayleigh Channel
Quadrature amplitude modulation (QAM) has been widely used in digital communication systems due to its high bandwidth efficiency. When the number of bits per symbol is even, transmission can be implemented easily by using square QAM. However, if there is a requirement for the transmission of an odd number of bits per symbol, the rectangular QAM is not a good choice in terms of power efficiency. The issue was overcome by Smith who proposed cross-QAM constellation which is obtained from a square constellation by removing some outer point in each corner and is given the shape of a cross . Smith shows that both the peak and average power can be reduced by using a cross-QAM constellation, and there is at least a 1-dB gain in the average signal-to-noise ratio.
Recently, cross-QAM has been found to be useful in adaptive modulation schemes wherein the constellation size is adjusted depending on the channel quality [2–6]. As the channel quality improves, the constellation size is expected to be increased by incrementing to . If one were to use just square QAM, the increments should be from to (for instance, we need to go from 16 to 64 to 256-QAM ). Using cross-QAM, however, the increment is smoother (16-QAM to cross 32-QAM to 64-QAM ). The steps between consecutive squared constellations are too big, especially for small constellations . An intermediate step (corresponding to odd powers of 2) will make the system to work with more granularity obtaining greater coverage for a determined data rate . As a result, cross-QAMs have been adopted in many practical systems. For example, cross-QAMs with constellations from 5 bits to 15 bits have been used in ADSL and VDSL [8, 9], and cross 32-QAM and cross 128-QAM are adopted in DVB-C . On the other hand, cross-QAMs have special application in blind equalization [11–14].
Despite the immense importance of cross-QAM, the implementation and the calculation of the average symbol error probability (SEP) of cross-QAM are more complicated compared to that of square and rectangular QAMs since the inphase and quadrature components of cross-QAMs cannot be demodulated independently. So, the calculation of SEP of cross-QAM cannot be reduced to a one-dimensional problem by using the independence of the inphase and quadrature components, as can be done for square QAM and rectangular QAM. It can be found the upper bounds of the SEP of cross-QAM in  and [15, Chapter 5]. Recently,  derives the bit error probability (BEP) of cross-QAM constellations with Smith-style Gray coding in additive white Gaussian noise (AWGN) and Rayleigh flat fading. However, the closed-form solutions to BEP for AWGN and Raleigh flat fading channels in  are very complicated, not to mention that  does not consider the SEP of cross-QAM and that it is not straightforward to extend the result of  to SEP calculation. More recently, an exact closed-form SEP expression for 32 cross-QAMs in AWGN has been derived in the form of a finite sum of Gaussian -functions . Note, however, that the decision regions of the corner points for other cross-QAM modulation are more complicated than 32-cross-QAM. In , an infinite double series of the products of Gaussian -functions for the SEP of 128-cross-QAM and 512-cross-QAM in AWGN is derived. The difficulty arises when one tries to find the closed-form exact SEP expression of arbitrary -ary cross-QAM in AWGN, which involves the calculation of the two-dimensional joint Gaussian -function.
In fading channels, on the other hand, the signal-to-noise ratio (SNR) of the received symbol becomes a random variable. As such, the usual method to find the average SEP in fading channels is to first get the conditional SEP as if in an AWGN channel (conditioned on SNR or channel realization) then average the conditional SEP over SNR or channel realization. This approach will, in many cases, result in an integral, usually being two-dimensional integral even three-dimensional integral, that cannot be integrated into a closed form and hence must be integrated numerically. By using the "preaveraging" technique , opposed to the customary and widely adopted "postaveraging" technique, the exact SEP of cross-QAM in Rayleigh fading channels has been derived in , where the closed-form SEP expression obtained contains only elemental functions (trigonometric). However, the "preaveraging" technique involves tedious calculations again, and it would only apply for Nakagami- channels with integer parameter , not to mention whether it can be extended to encompass Rice and Hoyt fading. Perhaps, the best method to get SEP of digital modulation signaling over fading channels is given by Simom and Alouini . They use the moment generating function- (MGF-) based approach to obtain SEP expressions for various digitally modulated signals over fading channels. Their basic technique is to rewrite the Gaussian -function into a preferred form of an integral with finite integration limits (many SEP expressions contain the Gaussian -function), so that the final average SEP expression can be numerically computed with more accuracy. In some special cases, the MGF-based method can even lead to an exact closed-form SEP without undone integrals.
In this paper, using the alternate representation of the two-dimensional joint Gaussian -function and the MGF-based method, the exact SEP expressions of arbitrary -ary cross-QAM in AWGN and fading channels, including Rayleigh, Nakagami-m, Rice, and Nakagami-q (Hoyt) channels, have been derived. The closed-form SEP expressions obtained contain Gaussian -functions and a finite (in proportion to ) sum of single integrals with finite limits and an integrand composed of elementary (exponential, trigonometric, and power) functions, thus readily enabling numerical evaluation.
The remainder of this paper is organized as follows. First, Section 2 briefly introduces the problem background. Next, Sections 3 and 4 derive the average SEP expressions for cross-QAM in AWGN channels and fading channels, respectively. And then, Section 5 gives the simulation and numerical results. Finally, Section 6 concludes the contributions of this work.
2.1. Construction of Cross-QAM
2.2. Decision Boundaries for Symbols in Cross-QAM
2.3. System Parameters for -ary Cross-QAM
Note that, in addition to the advantage of having finite integration limits, the form in (4) has the argument contained in the integrand rather than in the integration limits as is the case in (3), and it also has an integrand that is exponential in the argument , so that it can be numerically evaluated with more accuracy. Moreover, the form in (4) has some interesting implications with regard to simplifying the evaluation of performance results related to communication problems, for example, as seen later to the SEP performance evaluation over fading channels, wherein the argument of the -function is dependent on random system parameters and, thus, requires averaging over the statistics of these parameters.
which denotes the normalized least distance (in noise standard deviation) from a signal point to a decision boundary.
So, we can also leave the SEP expression in terms of as mentioned above.
2.4. Overview of SEP Approximations in AWGN
where is the average number of nearest neighbors for a symbol in the constellation, and for cross-QAM (In , the expression for appears erroneously as .) when . Note that, as increases, increases and approaches . In fact, the principle behind the above two approximate expressions is very intuitive since they can be interpreted as the sum of the probabilities that a given point is mistaken for its neighbors. At the same time, since the sum has recalculate some error regions, both the above two expressions overestimate the actual SEP as shown later.
While the SEP expressions for the interior symbols and edge symbols can be deduced easily from the SEP expressions for square QAM in [15, pages 265 and 278], the SEP of corner symbols needs to be derived separately.
Although it is difficult to express the function in a closed form without integration, it is a one-dimensional integral with finite limits, and its integrand only composes of elementary (exponential and trigonometric) functions, while the function actually is a two-dimensional integral of exponential function with infinite limits according to (A.2) in Appendix. With (4), in fact, can be easily and accurately evaluated numerically (see, e.g., using MATLAB). Note that the numerical calculation of (4) is much simpler and more precise than that of the infinite double series of the products of Gaussian -functions of equation (3) in .
Note that, when , (21) is also the exact SEP. On the other hand, the first term of (21) is just the average number of nearest neighbors or the Gilbert approximation. At SNR = 0 (i.e., ), especially, Gilbert approximation is , which is much larger than one, while (21) yields , which is the exact SEP.
Note that the relationships and for , mentioned above, are applied in the expression (24). Particularly, the approximate average SEP is only considering the first three terms in (24).
where for the sake of notational convenience.
Using the above four integral expressions, the average SEP (24) over fading channels can be conveniently evaluated through numerical integration since these formulae are single integrals with finite limits and an integrand composed of elementary (exponential, trigonometric, and/or power) functions. Note that (27) and (28) with integer can also be evaluated in closed form using equation (5A.24) in [21, page 155]. Furthermore, the author in  has given a simpler closed-form expression for the SEP of cross-QAM in Rayleigh channel.
In this paper, the exact SEP expressions of cross-QAM in AWGN channel and fading channels have been derived. The obtained closed-form SEP expressions contain a finite sum of single integrals with finite limits and an integrand composed of elementary (exponential, trigonometric, and/or power) functions, which can be easily and accurately evaluated numerically. Simple and very precise approximations, which contain only Gaussian -function for AWGN channel and contain three terms of the single integrals mentioned above for fading channels, respectively, are also given. The analytical expressions show excellent agreement with the simulation results, and the numerical evaluation with the proposed expressions reveals that cross-QAM can obtain at least 1.1 dB gain compared to rectangular QAM when SEP <0.3 in all the considered channels. The obtained exact SEP expressions will provide valuable insight into the design of wireless systems. In particular, the exact SEP performance of cross-QAM in AWGN channel will be very useful in adaptive modulation wherein the constellation size is adjusted depending on the channel quality through the SEP performance of the adopted modulations with AWGN.
This work was supported by the National Natural Science Foundation of China (no. 61071212), Chinese Universities Scientific Fund of SCUT (nos. 2009ZM0248 and 2009ZM0271), and Nature Science Funds of Guangdong province, China (nos. 07006488 and 9351064101000003).
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