- Research Article
- Open Access
Suboptimal Partial Transmit Sequence-Active Interference Cancellation with Particle Swarm Optimization
© Poramate Tarasak et al. 2010
- Received: 31 December 2009
- Accepted: 11 August 2010
- Published: 16 August 2010
Active interference cancellation (AIC) is an effective technique to provide interference avoidance feature for an ultrawideband (UWB) OFDM transmitter. Partial transmit sequence-AIC (PTS-AIC), which was recently proposed as an improvement of AIC, requires high computational complexity by doing the exhaustive search of all possible weighting factors whose number grows exponentially with the number of subblocks used. To reduce the complexity of PTS-AIC, this paper proposes a suboptimal way, called particle swarm optimization (PSO), to choose the weighting factors suboptimally without much performance degradation. Both continuous and discrete versions of PSO have been evaluated, and it has been shown that the discrete PSO is able to reduce the complexity significantly without sacrificing the performance of PTS-AIC in many cases.
- Particle Swarm Optimization
- Particle Swarm Optimization Algorithm
- Interference Power
- PAPR Reduction
- Complementary Cumulative Distribution Function
Ultrawideband (UWB) communication, a spectrum underlay system, has a very small power spectral density that spans over hundreds of megahertz. While a UWB device must endure interference from the primary narrowband devices, the UWB transmission must not cause interference back to them. There are several works studying the impact of interference if the UWB system is to coexist with other narrowband systems, for example, [1–4]. These studies indicate performance degradation as a result of mutual interference between UWB and narrowband systems.
Due to the secondary nature of UWB devices, it is their requirement to avoid causing the interference in the first place. One approach is to enhance the UWB device with the "detect-and-avoid" (DAA)  capability, sensing any ongoing narrowband transmissions and intelligently keeping away from the overlapped spectrum. If the narrowband transmission is found, the UWB device will adjust its transmission such that the effect of the UWB transmission will be negligible at the primary device receiver. DAA has been an interesting research topic on UWB recently. The strong interest of DAA is attributed to widespread usage of wireless applications sharing the same or overlapped part of the spectrum band. For example, current important narrowband systems that share parts of the UWB spectrum are WiMAX at the 3.5 GHz frequency range and IEEE802.11a at the 5 GHz frequency range. In the future, it seems inevitable for the UWB device to have the DAA feature.
This paper focuses on the avoidance part of DAA for the UWB system that employs OFDM transmission such as WiMedia standard . In , tone nulling at the overlapped narrowband spectrum (referred to as an interference band) is suggested as an avoidance technique. Although tone nulling completely removes the interference at the exact center frequency corresponding to the nulled tones, there still exists interference caused by sidelobes of the remaining tones present elsewhere in the interference band . One efficient technique to mitigate sidelobe interference is active interference cancellation (AIC) proposed in . In addition to removing the subcarriers that lie inside the interference band, AIC removes two more subcarriers beside them and replaces the removed tones with the computed "AIC tones". The purpose of placing AIC tones is to generate "negative interference" in order to cancel the sidelobe interference from the remaining subcarriers.
Many extensions and improvements of AIC have been proposed recently. In a subcarrier-based antenna selection system, a new AIC formulation was proposed in . The problem of sidelobe interference coming from a superposition of the transmissions from all antennas necessitates a new AIC for all antennas.
A few AIC algorithms with low complexity were proposed in . The algorithms are based on simplifying matrix computation in the AIC. The complexity saving in  comes at a price of degraded interference cancellation performance although it is claimed that the algorithm is still better than the original AIC in the worst case.
The following works attempt to deepen the notch spectrum obtained with AIC. Extended AIC inserts the so-called extended AIC tones between the usual AIC tones, and they generate better negative interference . However, since the extended AIC tones are placed in between the usual subcarrier positions, orthogonality between the subcarriers is lost and the bit error rate (BER) curve shows an error floor. Reference  proposes three enhancements of the AIC by cyclic shifting, phase shifting, or joint cyclic and phase shifting the data subcarriers. Doing so leads to modification of the spectrum and yields smaller remaining interference after the AIC operation. The algorithms in  increase the cancellation performance of AIC significantly with the drawbacks of high complexity and the requirement of side information. Recently, another technique, so-called partial transmit sequence-AIC (PTS-AIC), was proposed in . It is essentially a novel application of partial transmit sequence that has been applied in OFDM in order to reduce peak-to-average power ratio (PAPR) [13, 14]. Adjacent subblock partitioning and interleaved subblock partitioning were proposed for the PTS-AIC. It was shown that PTS-AIC enhances the performance of AIC and provides more flexibility in parametrizing the algorithm. However, selecting the optimal parameters for PTS-AIC is very complex, especially when a large number of subblocks are used. High complexity comes from the exhaustive search for the optimal weighting factors whose number grows exponentially with the number of subblocks .
Particle swarm optimization (PSO) is a heuristic bio-inspired optimization algorithm that is well suited to solve high-dimensional and multimodal optimization problems [15–18]. The PSO-based CDMA multiuser detection has been reported in [19–21] where exponential complexity in the number of users has been resolved by the PSO. Power allocation problem in CDMA was solved by the PSO in  where a few constraint handlings were investigated and extensive studies on the parameters of PSO were given. PSO was applied in PTS for OFDM PAPR reduction in  to achieve much lower complexity. In , exponential complexity in the number of sensors was solved by the PSO in a sensor scheduling problem which optimizes a group of sensors for target tracking under the performance and cost constraints. More recently, PSO was exploited in determining linear precoding for a linear MMSE multiuser MIMO receiver, and it was shown to outperform the block diagonalization approach .
This paper proposes a PSO as a suboptimal approach to optimize weighting factors for PTS-AIC with the main purpose of reducing complexity. Unlike , both continuous and discrete (binary) version  of PSO are considered. It is shown that PSO can be applied to PTS-AIC effectively and can approach the performance of optimal PTS-AIC in many cases with much lower complexity. The PSO algorithm allows us to enhance the performance of PTS-AIC by using a larger number of subblocks whose complexity is prohibitive for the optimal exhaustive search.
The paper is organized as follows. Section 2 provides the background on AIC. Section 3 reviews the PTS-AIC as well as two types of subblock partitioning proposed in . Section 4 discusses PSO in both continuous and discrete versions as well as their complexity analysis. Section 5 shows the simulation results and discussion. Conclusion is given in Section 6.
We hereby describe the AIC algorithm in a matrix formulation. Detailed description can be found in the original paper .
The PTS-AIC algorithm is characterized by the parameters and . We can adjust both parameters such that the interference cancellation performance meets the target while the complexity is affordable. As either or increases, the performance improves while the complexity increases. The complexity of PTS-AIC is determined by the number of all possible weighting factors to find , which is . Since the complexity grows exponentially with , one cannot increase the number of subblocks to a very large value to improve the performance, as the number of comparisons is prohibitive.
Since PTS-AIC modifies the transmission block by the weighting factors, the receiver must be aware which set of weighting factors is applied so that it can recover the original symbol block. This can be done by sending the index of the optimum weighting factor as side information that amounts to bits. Once the receiver knows the applied weighting factors, multiplication of their complex conjugates to the received signal block after FFT returns the original data block.
Note one major difference between PTS conventionally applied to reduce PAPR and the proposed PTS-AIC. While the conventional PTS measures the PAPR of the (upsampled) time-domain signal after IFFT, the proposed PTS-AIC measures the interference power of the upsampled frequency spectrum before IFFT.
3.1. Performance Measure
where is the upsampled frequency-domain signal with AIC or PTS-AIC processed. is the remaining power of the upsampled spectrum at the interference band after AIC or PTS-AIC being performed on a particular symbol block.
Although the mean of the remaining interference power, , is normally used to compare the algorithms as in , a more complete picture is captured by computing the complementary cumulative distribution function (CCDF), , where is a target remaining interference power. This bears an analogy with the performance measure of a PAPR reduction algorithm of OFDM for which the CCDF is widely used. With a given , the CCDF determines the probability of remaining interference power above the target . We refer to of which as -excess interference power.
PSO algorithm was described in analogy with an activity of bird flocking or fish schooling . Imagine a group of birds trying to locate a position in the field with the highest concentration of food. Each bird flies over the field and detects the concentration of food at its location. Each bird has an ability to remember its own best location and is aware of the group's best location. Its flying path depends on the previously observed location and is influenced by its own best location and the group's best location. Each bird has a chance to "fly over" the best location previously found and therefore observes the surrounding for a possibly better location. As time passes, most birds will be crowded at the best location they found as a group.
In the optimization problem, each bird is called a particle and its location represents an -dimension solution candidate in the -dimensional solution space. In the context of the PTS-AIC, a particle represents a vector of weighting factors . The location of the particle is reflected in the elements of . The concentration of food corresponds to the remaining interference power of PTS-AIC which is an objective value of the objective function in (6). The best location corresponds to the particle whose objective value is the minimum among other particles.
where the time duration (supposed to be multiplied with the velocity) is assumed to be one. This completes the task for one particle in one iteration. The algorithm is repeated from the point of evaluation of the objective function for all particles and for iterations.
The original PSO algorithm was designed for a problem with continuous parameters. Since the optimization parameters in PTS-AIC are the weighting factors which are discrete, the location of a particle has to be quantized to the nearest point in the constellation set before the objective function is evaluated. This is done after the location update. The number of dimensions of each location in PTS-AIC (for the binary case) is . PSO for PTS-AIC with (binary case) can be described by Algorithm 1.
Algorithm 1: PSO PTS-AIC.
Another way to tackle discrete parameters is to apply the PSO algorithm modified for binary parameters proposed in . The idea is to work with dummy continuous parameters, transform them to have the range from 0 to 1, and consider the results as probabilities of the optimization parameters taking value 0 or 1. Therefore, there is no need to round off the parameters as in the previous algorithm. The transformation function proposed in  is a sigmoid limiting function, , whose domain is and whose range is . is the probability of equal to one, and the location is updated by generating a random number uniformly distributed over and comparing it with . We refer to this discrete version as discrete PSO (DPSO). The only change from Algorithm 1 is at Step ( ), which should be replaced by the location update algorithm according to Algorithm 2.
Algorithm 2: Location update for DPSO.
where each component is generated from the distribution
To handle nonbinary parameters for both PSO and DPSO, we can simply extend the dimension of location and velocity vectors and convert the binary tuples into symbols on the constellation. For example, for , the dimension of each vector will be and two dimensions in the location are converted into one QPSK symbol.
4.1. Complexity Analysis
Random QPSK symbols are generated to form OFDM data blocks. Each block contains 128 symbols corresponding to the 128-point FFT/IFFT. The subcarrier indices from the 85th to 87th are assumed to be the interference tones. From the AIC algorithm, the 84th to 88th subcarriers are removed and replaced by the AIC tones. The frequency-domain upsampling factor is four. To compute CCDF of the remaining interference power, , 10,000 data blocks are simulated in each case.
The parameters of PSO are set as , and resulting in equal influence from its own particle and from the group. This choice of parameters was recommended in an early work on PSO .
CPU time of Optimal, random and PSO/DPSO PTS-AIC.
CPU time (ms)
We propose a suboptimal algorithm, called particle swarm optimization (PSO), for partial transmit sequence active interference cancellation (PTS-AIC) used for interference avoidance feature for UWB OFDM transmitter, to reduce the computational complexity significantly. The PTS-AIC with PSO becomes more attractive when the number of subblocks and/or the constellation set for the weighting factors are large for PTS-AIC. The discrete version of PSO is a better choice for the suboptimal algorithm compared to the continuous version and is able to approach the performance of the optimal PTS-AIC in many cases at much lower complexity. The benefit that is, brought from PSO to PTS-AIC becomes more attractive when the number of subblocks for PTS-AIC is large and makes PTS-AIC implementable in hardware.
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