Coherent detection is commonly facilitated by means of pilot-aided channel estimation. While this approach offers a high estimation accuracy, the spectral and power efficiencies are reduced due to the pilot overhead. In the case that redundancy due to channel coding is not used for the purpose of channel estimation, the maximum pilot spacing for which a time-varying channel can be reconstructed without distortion is given by the Nyquist-Shannon sampling theorem. In this paper, it is shown that if channel coding is taken into account for channel estimation in an iterative fashion, the maximum spacing can be significantly extended, which results in an increased spectral efficiency. Towards this goal, a so-called coded sampling bound is derived. This semi-analytical bound is compared with the Nyquist-Shannon sampling theorem. The presented results indicate that the maximum pilot spacing can be arbitrarily large given a suitable channel code and code rate.

In most modern wireless systems, adaptive modulation and channel coding is applied to achieve a high spectral efficiency. Hence, suitable receivers need to support a large variety of modulation and channel coding schemes (MCS) while maintaining a low complexity. The performance and complexity of a multi-dimensional factor-graph based (MD-GSIR) framework for iterative joint channel estimation and data detection is investigated for the application of adaptive modulation and coding in an LTE environment. Three detection algorithms within the MD-GSIR framework are investigated and a novel tree-based detection is proposed. The versatile structure of the MD-GSIR concept offers a flexible trade-off between computational complexity and performance. Furthermore, the message exchange is adapted to support the irregular training structure which may occur in adaptive environments.

The principles and benefits of soft decisions are well known and widely applied. The advantage of knowing that a decision is reliable or not is obvious. Belief propagation within a factor graph enables a unified use of soft information for both channel estimation and data detection. However, if soft information does not reflect the true reliability of a decision, the achievable performance may degrade. In this paper, the calculation of reliability information is refined to consider the event of unreliable soft decisions. The proposed solution is based on the mean bit error probability calculated after each iteration and integrates nicely within the existing factor graph. Simulation results are provided to illustrate the performance gains.

A graph-based receiver is presented that iteratively performs soft channel estimation and soft data detection. Reliability information of data symbols is utilized to improve channel estimation, and in turn, soft channel estimates refine data symbol estimates. The proposed multi-dimensional factor graph introduces transfer nodes that exploit correlation of adjacent channel coefficients in an arbitrary number of dimensions (e.g. time, frequency, and spatial domain). This establishes a simple and flexible receiver structure that facilitate soft channel estimation and data detection in multi-dimensional dispersive channels, and supports arbitrary modulation and channel coding schemes. Simulation results demonstrate that the proposed multi-dimensional graph-based receiver outperforms iterative and non-iterative state-of-the-art receivers.

Iterative receiver structures that jointly perform channel estimation and decoding promise substantial performance gains. However, these gains only materialize with sufficiently accurate initial channel estimates. In this paper, initialization by multi-objective particle swarm optimization (MOPSO) is investigated. MOPSO supports low-complexity initial channel estimation with superimposed training symbols. Furthermore, it is shown that MOPSO works well in rank-deficient scenarios with arbitrary training sequences. Numerical results validate the performance enhancement of MOPSO initialization integrated within a graph-based iterative receiver.

The substantial gains promised by multi-antenna systems require accurate channel knowledge, at least at the receiver side. Furthermore, the complexity of most receivers becomes unfeasible when the number of transmit antennas gets large (known as "large-MIMO" systems). In this paper, channel estimation by means of cooperative particle swarm optimization (CPSO) and factor-graph based data detection with belief propagation is presented. The overall complexity of the receiver grows linearly with respect to the number of transmit and receive antennas. Numerical results validate the performance of the CPSO channel estimation integrated within a graph-based iterative receiver, including a 24×16 MIMO system.

We address joint channel estimation and data detection based on factor graphs. The considered graph-based approach utilizes reliability information of channel estimates to facilitate soft-output data detection, and in-turn reliability information about the data symbols is taken into account for channel estimation. In this paper graph-based soft channel estimation and detection is extended to an OFDM based air interface, where the channel response varies in two dimensions; time and frequency. Initial channel estimates obtained by training symbols are conveyed by a two dimensional (2D) factor-graph in time and frequency with only a linear increase in complexity. The required training overhead for the proposed 2D graph-based soft channel estimation scheme may be substantially reduced by taking the redundancy introduced by the channel coding into account.