Carrier Diversity Incorporation to Low-Complexity Near-ML Detection for Multicarrier Systems over V2V Radio Channel  
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1 Facultad de Ingenieria, Universidad Panamericana, [PERSON] 49, Zapopan, Jalisco 45010, Mexico; [EMAIL_ADDRESS]  
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[PERSON]  
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[PERSON]  
1 Facultad de Ingenieria, Universidad Panamericana, Alvaro [PERSON] 49, Zapopan, Jalisco 45010, Mexico; [EMAIL_ADDRESS]  
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Footnote 1: email: {j.j. Approaches [6; 7; 8] make a substantial reduction in the computational complexity required in the data detection. This is achieved by approximating the original signal model using a reduced signal model in the frequency domain, based on the band equalization, where the channel matrix is truncated to keeping only a small number of bands. The approximate observation model described in [6; 7; 8] does not include the channel frequency diversity, which causes the linear detection the achieving of a lower performance compared to the detection carried out in the complete observation model. Furthermore, the approximate model described in [6; 7; 8] is not compatible with non-linear detectors when including the channel frequency diversity. Works [9; 10] presents systems with data estimators suitable to counteract the distortions produced by the ICI, achieving better performance than conventional receivers. However, they modify the physical layer of the 802.11p standard in order to introduce additional training sequences, which decreases the spectral efficiency of the system and represents an incompatibility problem with the 802.11p standard [11]. They also use channel estimators with an observation window that covers a large number of OFDM symbols, increasing the memory required for their implementation and the system's latency. Since data detection is the process with the greatest computational complexity in the receiver, the practical feasibility of any system designed to work in real-time in V2V channels depends mainly on the order of complexity required in the detection task. The computational complexity of the optimal detector of maximum likelihood (ML) is \(\mathcal{O}(N_{D}\Omega^{N_{D}})\)[12], being \(N_{D}\) number of data subcarriers and \(\Omega\) the constellation size. This complexity makes the real-time implementation to be less viable in comparison with linear detectors whose complexity is bounded in \(N_{D}^{3}\). Recently, a series of detectors have been proposed, which have lower complexity than the ML detector while maintaining similar performance. The spherical detector (SD) [13], which is based on tree-search algorithms, has been applied to multicarrier systems [14], yielding lower complexity than the ML detection [15], as it was expected. Some non-linear detection schemes are based on the M-algorithm, and the QR decomposition of the channel matrix [16; 17]. Ordered successive iterative cancellation (OSIC) has been proposed recently in [18]. However, all the aforementioned non-linear detectors are applied to a system model that does not include the channel frequency diversity, as well as not exploiting an approximation of the band channel matrix during its QR decomposition. However, all the aforementioned non-linear detectors are applied to a system model that does not include the channel frequency diversity, as well as not exploiting an approximation of the band channel matrix during its QR decomposition. An important factor that impacts the multicarrier system performance is the frequency selectivity of the channel. This characteristic of the broadband channel makes it difficult to recover some signals degraded by a deep fading of the channel. Because the non-linear data detection is carried out consecutively, the channel's selectivity causes degradation in performance of the non-linear detection, even in conditions of high signal-to-noise ratio (SNR). The works mentioned above [3; 4] tackle this problem through the inclusion of a stage for channel coding, with the consequent loss of spectral efficiency.