New adaptive compandor for LTE signal compression based on spline approximations

Abstract

With the constant increase in network traffic, wireless operators are finding it more challenging to keep network hardware costs to a minimum. At the same time, the energy cost associated with operating a network has increased proportionally. Therefore, the search for higher network capacity is simultaneously accompanied by the search for a cost-efficient network deployment. In this paper, we show that a saving in transmitted signal energy can be achieved at the signal design level by deploying very specific signal processing techniques. Using an orthogonal frequency-division multiplexing signal for Long-Term Evolution networks as an example, we utilize a novel non-uniform companding quantizer to save a transmitted signal energy. Our results show that by using non-uniform quantization it is possible to further optimize 4G wireless networks.

Forward Adaptive Dual-Mode Quantizer Based on the First-Degree Spline Approximation and Embedded G.711 Codec

“…The superiority of our solution can also be perceived with respect to other baselines based on the forward adaptive technique. As baseline approaches scheme, we use G.711 quantizer (FAG.711), N = 256-levels μ-law compandor (μ = 255) described in [12] (FAμC) and the forward adaptive N = 256-levels PLCSQ from [22], which is based on approximation of optimal compression function with the first-degree spline functions. We report the gains in average SQNR of 6 dB when compared to FAG.711, 5.77 dB in regard to FAμC and 8.5 dB in regard to the solution from [22], achieved at the cost of slightly increased bit rate of 0.1667 bit/sample (information about the selected quantizer, one-bit per subframe).…”

“…the cases where the restricted quantizer is designed with c PLC (x,σ ref ) composed of L = 2, L = 4 and L = 8 line segments. In a given figure, for comparison purposes, we also provide SQNR curve for N = 256 levels-DMSQ baseline reported in [12], which serves as the upper bound of performance due to the fact that PLCSQs always provide lower SQNR in comparison to scalar compandors based on nonlinear compression function [22], [23]. Note that the performance in terms of SQNR of the proposed DMSQ with the restricted quantizer designed by c PLC (x,σ ref ) using higher number of segments (L = 8) converges to the upper bound given in [12].…”

“…for the bit rate of 8 bit/sample. Recall that our fixed DMSQ has increased bit rate for 1/M bits when compared to the baseline (see (22)). Therefore, the estimated SQNR value at the bit rate of 8 bit/sample can be determined as [12]:…”

Dual-mode quasi-logarithmic quantizer with embedded G.711 codec

“…In Fig. 5 we present SQNR vs bit rate for the proposed dual-mode quasi-logarithmic quantizer and the different baselines including the optimal compandor [18], the piecewise optimal compandor [10] and the uniform quantizer [5]. Granular and overload distortion of an optimal compandor are defined as follows [18]:…”

“…Although companding is dominantly used in speech coding, it was also applied in other fields. For example, in [10] it was employed for the purpose of reducing the long-term evolution (LTE) networks traffic. Beside companding, another possibility to increase the signal quality and reduce the complexity of design and realization is to exploit the log-polar uniform quantization [11].…”