Wearable and Neuronic Antennas for Medical and Wireless Applications. Группа авторов

Wearable and Neuronic Antennas for Medical and Wireless Applications - Группа авторов


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famous technique in the MC as it is easy to implement. However, the OFDM has poor spectral efficiency due to limited filtering options available. Thus, to enhance spectral efficiency, an alternative to OFDM called Filter bank multicarrier (FBMC) communication was introduced, which has more freedom of filtering options. On the other hand, the FBMC preserves only real orthogonality for the waveforms, resulting in imaginary interference. Hence, the equalization in FBMC has to deal with this additional interference which becomes challenging in multiuser communication. In this chapter, the aim is to deal with this challenge.

      Keywords: Multiuser communications, multicarrier communications, OFDM, FBMC, 5G, equalization, machine learning, MMSE

      Improved bandwidth, efficient power utilization, and better handling of selective fading are prominent advantages of the MC [1]. The CP-OFDM is a simpler MC solution. However, it has poor spectral efficiency due to limited filtering options available.

      Another candidate considered for multiple radio access in 5G is the Filter Bank Multi-Carrier (FBMC) [2]. The FBMC is a modified version of OFDM, where the rectangular transmit and receive waveforms are replaced by any other more frequency localized pulse shapes, which offer a more confined spectrum, relevant for spectrum-sharing scenarios.

      In the FBMC with Offset Quadrature Amplitude Modulation (OQAM) system, Channel equalization is challenging compared to the standard CP-OFDM. This is because the real and the imaginary symbols in the FBMC-OQAM system are transmitted with a time offset, which results in loss of orthogonality in the imaginary part [2]. Thus, the received signal suffers from the additional imaginary part, which is termed as Imaginary interference. This interference degrades the performance of the channel estimator. As a result, channel equalization also degrades as it requires the knowledge of channel estimates. In conventional equalization methods for the FBMC-OQAM, the channel estimates are used to cancel the phase of the received signal and extract its real part. However, due to errors in channel estimates, the phase cancellation is not completely achieved. Thus, this conventional approach does not work in this scenario. Alternatively, people have proposed direct equalization methods, which still need improvements. Therefore, there is a need to design an efficient equalization method to combat the imaginary interference and inter-symbol interference (ISI), and inter-carrier interference (ICI) in the FBMC-OQAM system.

      The FBMC modulation is a recent type of MC technique developed to improve the performance of the conventional OFDM by minimizing ISI and ICI [1, 2].

      The simplest equalization method for the FBMC system is the one-tap equalizer which has shown better performance in the absence of ISI and ICI [7]. However, its performance severely degrades in the presence of selective channels, particularly for high Signal-to-Noise Ratio (SNR) scenarios.

      The existing solutions for equalization in the FBMC system commonly assumed that the channel is time-invariant [8–12]. The equalization method in [8] developed a parallel architecture using Fast Fourier Transform (FFT) blocks for the equalization. Another method based on larger FFT was proposed in [10]. In [11], a Minimum Mean Squared Error (MMSE) criteria-based equalization technique was developed, which was later modified in [12] to the MIMO scenario. In [13], a spatio-temporal structure based n-tap MMSE equalizer was introduced for the FBMC systems.

      The authors in [14] have given evidence that the MMSE equalizer for the FBMC has robustness property in the presence of a time-varying channel.

      In [15], the authors have reformulated the multicarrier equalization problem as single carrier QAM and derived relevant MMSE expression.

      In the FBMC-OQAM transmission, various processes are involved, as shown in Figure 1.1.

Schematic illustration of FBMC-OQAM transmission system.

      Figure 1.1 FBMC-OQAM transmission system.

Schematic illustration of FBMC-OQAM reception system.

      Figure 1.2 FBMC-OQAM reception system.

      The transmitted basis pulse for the lth sub-carrier and kth symbol gl, k (t) is defined as:

      (1.1) Image

      Where p (t) is the prototype filter, F is the subcarrier spacing, and T is symbol spacing in time. The sampling rate is evaluated by fs = 1/∆t = FNFFT, where NFFT ≥ L. Here, NFFT is used to show the FFT size. The sampled basis pulse gl,k(t), can be expressed as vector gl, kCN × 1, given by

      (1.2) Image

      with

      Next, by combining all the vectors in a matrix GCN × LK, given by,

      (1.3) Image

      (1.4) Image

      and by representing the combined data vector for all the transmitted symbols as vector xCLK × 1,

      (1.5) Image

      (1.6) Image

      (1.7) Image

      The impulse response under multipath fading in a time-variant channel can be expressed as matrix HCN × N, whose entries are given by [2],

      (1.8) Image

      Finally, the received symbol is projected using received waveform to obtain

      (1.9) Image

      where n represents the additive noise term which is usually modeled as Gaussian random variable.

      1.4.1 One-Tap


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