## 1 Introduction  
Several relaying strategies are applied in cooperative networks such as the amplify-and-forward (AF) [1], the decode-and-forward (DF) [2], and the coded cooperation (CC) [3] strategies. The relay node amplifies and forwards the received signal when deploying the AF protocol. This technique suffers from noise amplification. In contrast, the DF protocol allows one to decode source messages first and then forwards them to the destination. However, this approach assumes error-free decoding at the relay, which is not warranted in real networks, and error-propagation will affect the performance of the cooperative communication. In CC, cooperative signaling is integrated with channel coding. The idea is that nodes transmit incremental redundancy for their partner to mitigate error-propagation instead of direct relaying or repetition of messages. Coded cooperative schemes with low-density parity-check (LDPC) were presented in [4]. In [5], a joint source channel decoding for coded cooperative communication with error-corrupted relay observations was presented. The authors in [6] investigated the performance of a generalized distributed turbo codes-based coded cooperation scheme in a multiple relay network. One effective forwarding technique for handling with erroneous decoding at the relay is the soft information relaying (SIR). In [7], SIR was proposed to attenuate the error-propagation from the relay node, and suitable distributed coding schemes were presented for soft re-encoding. It is worth noting that the performance of the cooperative scheme using SIR strategy depends crucially on the deployed model applied to the soft information message that is passed to the destination node. Most of the work in the literature model the soft estimated symbols as output of additive Gaussian channels. For instance, in the soft noise (SN) model presented in [8], the log likelihood ratios (LLRs) at the output of the relay soft encoder were mapped to soft bits with soft noise modeled as non-zero-mean Gaussian noise. The authors in [9] proposed the Gaussian LLR (GL)-based model where the LLRs at the output of the relay soft encoder were modeled as output LLRs of an additive Gaussian channel. Nevertheless, the authors in [10] used the soft scalar (SS) model, where the soft bits were modeled through a soft error and a soft scalar equivalent to a fading coefficient. It is worth noting that the estimation of the models' parameters in [8; 10] need the knowledge of the transmitted symbols. The statistical parameters are either estimated by means of training sequences or computed offline to proceed to decoding at the destination. However, it is of great interest to work with a model where the parameters are online determined and without additional cost. In a multiple relay network, relay selection schemes are usually applied to enhance the diversity performance of cooperative communications [11]. Most relay selection criteria rely on the transmission reliability at the source-relay and relay-destination paths to minimize the sum bit error rate [12]. The reliability of paths and relay selection protocols depend on the deployed powers at each segment. Relay selection methods, such as partial [13; 14] and opportunistic [11; 15] selections, consider channel gains for the relay selection decision in DF and AF relaying techniques. In [16], the authors proposed relay selection schemes in two-way relay channels with SIR and network coding. Moreover, energy-harvesting (EH) and power-splitting based relaying protocol have gained a lot of attention over the last few years. EH is recognized as an efficient technique in which wireless devices collect radio-frequency signals from their surrounding environment to harvest energy [17]. In [18], energy constrained relays in an AF cooperative network were considered and power-splitting based relaying protocol were proposed to enable EH and information processing at the relay.