Burst-by-burst adaptive turbo-coded radial basis function-assisted decision feedback equalization

Abstract

Abstract-The performance of the proposed radial basis function (RBF) assisted turbo-coded adaptive modulation scheme is characterized in a wideband channel scenario. We commence by introducing the novel concept of the Jacobian RBF equalizer, which is a reduced-complexity version of the conventional RBF equalizer. Specifically, the Jacobian logarithmic RBF equalizer generates its output in the logarithmic domain and hence it can be used to provide soft outputs for the turbo-channel decoder. We propose using the average magnitude of the log-likelihood ratio (LLR) of the bits in the received transmission burst before channel decoding as the channel quality measure for controlling the mode-switching regime of our adaptive scheme. [7]. However, there is little information concerning its potential in either BbB AQAM or in forward error correction (FEC)-coded scenarios, despite the advantageous interactions of RBF-aided DFE BbB AQAM in conjunction with turbo FEC, which were demonstrated in our preliminary work [8]. Hence in this contribution, we set out to document these interactions. IndexThe symbol-spaced channel output can be defined by (1) where ; channel impulse response (CIR); channel's input sequence; noiseless channel output.Paper approved by C. Schlegel, the Editor for Coding Theory and Techniques of the IEEE Communications Society. Manuscript received April 5, 2000; revised March 20, 2001. This work was supported by the EPSRC, the U.K., and the Commission of the European Communities, Brussels, Belgium.The authors are with the Department of Electronics and Computer Science, University of Southampton, Highfield Southampton SO17 1BJ, U.K. (e-mail: lh@ecs.soton.ac.uk).Publisher Item Identifier S 0090-6778 (01) [17] promoted the employment of RBF based equalizers, which exhibit a structure identical to that of the optimal Bayesian symbol-decision based equalizer. Therefore, RBF equalizers can rely on optimal detection theory [18]. In this contribution, we use a decision feedback assisted RBF equalizer, which involves its previous decisions for either RBF subset center selection [17] or space translation [19], [20], in order to reduce its computational complexity. Both versions of the DFE realize the same optimal solution, but the space translation based version [19], [20] requires less storage for the RBF centers, since the centers corresponding to different decision feedback vectors are equivalent in the translated space [19], [20]. Therefore the latter version is preferred in hardware implementations.BbB AQAM schemes [21] employ a higher-order modulation scheme in a certain transmission burst, when the channel quality is favorable, in order to increase the throughput and conversely, a more robust, lower-order modulation scheme is utilized in those transmission bursts, where the instantaneous channel quality drops [1]. The performance benefits of the RBF DFE [17] have been documented in the context of AQAM over dispersive wideband mobile channels in [22], [23], demonstrating that a certain target bit error rate ...

Neural networks-based turbo equalization of a satellite communication channel

“…Based on the optimal decision theory, it showed good performance allowing a reduced complexity implementation. Later, it has been used for turbo equalization of linear channels and has shown good performance for different modulation techniques [9], [10]. In this paper, we show how RBF-Based equalization can be successfully applied to non linear channels equalization when Volterra-based non linear channel models are considered.…”

Turbo Decoding and Detection for Wireless Applications

“…In [57], a TEQ scheme was proposed, which employs a radial basis function (RBF)-based equalizer [52], [58], [59] instead of the classic trellis-based equalizer of Douillard et al [33]. It was shown with the aid of plausible graphical examples in [52] that at the output of a dispersive fading channel the phasor constellation points may become linearly nonseparable even in the absence of noise and in this scenario only nonlinear receivers, such as the RBF-based TEQ, are capable of operating without an error floor.…”

Combined block-turbo coding and RBF-based equalization with decision feedback

“…Furthermore despite the trellis equalizer, the RBF equalizer simply gives a soft output value, which is necessary for soft decision decoding or turbo decoding. As fi(vk) is proportional to Pr(Ik=s,lvk), the RBF soft output can be computed as: m+L,+L2-l (4) i=l Logarithmic RBF equalizer To avoid the multiplication and exponential operations in (2) for complexity reduction, we can use the logarithmic version of the RBF equalizer [7,8]. If we define the Jacobean function the logarithmic RBF functions are computed as (6), and maximization of h f i ( v k ) is equivalent to maximization of g i (v,) defined as (7).…”

“…Such a decision feedback RBFbased equalizer is introduced in [6]. The system of concatenated RBF-based equalizer and BTC is introduced in [7], while joint BTC and RBF-based equalizer is introduced in [8]. This paper introduces a different design for combination of RBF-based equalizer and BTC.…”

“…This paper introduces a different design for combination of RBF-based equalizer and BTC. The channel situation used in [8] is slowly fading channel so that it is possible to use M-QAM modulations. Here, we consider a faster fading channel and use 4,8-PSK modulations, and show the gain of the combined RBF-based equalizer and BTC versus a non-combined system and also the system of DFE equalizer and BTC.…”