This paper proposes a new approach to channel estimation and channel feedback, based on continuous time-domain parameter (CTDP) extraction. The improvement in channel feedback overhead and channel estimation accuracy are shown to range from an order of magnitude to more than two orders of magnitude depending on channel conditions. Due to its extremely low channel feedback overhead, CTDP extraction enables two key features in LTE-advanced: coordinated multipoint transmission (CoMP) and multi-user MIMO (MU-MIMO), both of which aim to increase the network capacity multi-fold. CTDP extraction is also computationally efficient.

This paper presents a novel time-domain parameter (TDP) extraction scheme for channel estimation and feedback in the LTE-advanced systems. The TDP extraction improves the channel estimation accuracy by an order of magnitude over the interpolation-based methods, has extremely low complexity compared to MMSE approaches, and is very close to or even outperforms the Bayesian MMSE lower bound. The mobile device feeds back the extracted TDPs instead of the complex amplitudes of the channel at reference signal (RS) subcarriers, reducing the feed-back overhead by an order of magnitude and making possible for implementation of CoMP, a key feature in the LTE-advanced.

A methodology is presented for the design and development of efficient trellis-based soft decision decoders for extended Hamming and BCH codes. A new metric for noncoherent discriminator detection is proposed that substantially improves the performance of trellis-based decoders over additive white Gaussian noise (AWGN) channels. Minimal edge trellises are then presented for the class of extended Hamming codes and the (32, 21) extended BCH code. The latter is in extensive use in narrow-band wireless data systems. An automatic request (ARQ) protocol is described that allows the soft decision decoders to outperform their hard decision counterparts in both reliability and throughput.

The Viterbi algorithm is a maximum likelihood means for decoding convolutional codes and has thus played an important role in applications ranging from satellite communications to cellular telephony. In the past, Viterbi decoders have usually been implemented using digital circuits. The speed of these digital decoders is directly related to the amount of parallelism in the design. As the constraint length of the code increases, parallelism becomes problematic due to the complexity of the decoder. In this paper an artificial neural network (ANN) Viterbi decoder is presented. The ANN decoder is significantly faster than comparable digital-only designs due to its fully parallel architecture. The fully parallel structure is obtained by implementing most of the Viterbi algorithm using analog neurons as opposed to digital circuits. Several modifications to the ANN decoder are considered, including an analog/digital hybrid design that results in an extremely fast and efficient decoder. The ANN decoder requires one-sixth the number of transistors required by the digital decoder. The connection weights of the ANN decoder are either +1 or -1, so weight considerations in the implementation are eliminated. This, together with the design's modularity and local connectivity, makes the ANN Viterbi decoder a natural fit for VLSI implementation. Simulation results are provided to show that the performance of the ANN decoder matches that of an ideal Viterbi decoder.

In order to fully utilize the SDD (soft-decision decoding) capacity of the outer codes in a concatenated system, reliability information on the inner decoder outputs (called soft outputs) needs to be provided to the outer decoder. This paper shows that a modified MAP algorithm can be effectively and accurately used to generate such information. In the course of the presentation, a metric based on the reliability information is proposed for the outer decoder. This metric has the Euclidean metric on AWGN channels as its special case, which leads to the concept of generalized SDD (GSDD). Several practical concerns regarding the proposed soft-output decoder are addressed through theoretical analysis and simulation: the effect of finite decoding depth, computational complexity, range overflow, and scaling. Comparisons to previous work on soft-output decoders are made.