In relay systems, the probability of errors occurring in the source-relay (S-R) link can be viewed as representing correlation between the source and the relay. This letter proposes a very simple iterative decoding technique, accumulator-assisted distributed turbo code (ACC-DTC) using 2-state (memory-1) convolutional codes (CC), where the correlation knowledge between the source and the relay is estimated and exploited at the destination.

Lossy decode-and-forward (DF) relaying, also referred to as lossy forwarding (LF), can significantly enhance the transmission reliability and expand the communication coverage at the cost of a small increase in computational effort compared to its DF counterpart. Furthermore, it can further simplify the operations at the relay nodes by removing the error-detecting operation, e.g., cyclic redundancy check, which is used in the conventional DF systems. Due to these advantages, LF has been intensively investigated with the aim of its applications to various cooperative communication networks with different topologies. This paper offers a comprehensive literature review on the LF relaying strategy and makes comparisons between LF and DF. Five basic exemplifying scenarios are taken into consideration. These are the three-node network, the single-source multi-relay network with direct source-to-destination link, the multiple access relay channel, the two-way relay network, and the general multi-source multi-relay network. The paper includes not only theoretical performance limit analyses, but also performance evaluation by employing low-complexity accumulator aided turbo codes at the sources and relays as well as joint decoding at the destination. As expected, the performance enhancement in terms of outage probability, frame error rate, and -outage achievable rate by LF over DF is significant, which is demonstrated in all the exemplifying scenarios in the literatures. Hence LF has a great potential to be applied to future 5G wireless communication networks, e.g., device-to-device, vehicle-to-vehicle, and machine-type communications, which are composed of the aforementioned exemplifying scenarios.

This letter proposes a novel relaying framework, semantic-forward (SF), for cooperative communications towards the sixth-generation (6G) wireless networks. The SF relay extracts and transmits the semantic features, which reduces forwarding payload, and also improves the network robustness against intra-link errors. Based on the theoretical basis for cooperative communications with side information and the turbo principle, we design a joint source-channel coding algorithm to iteratively exchange the extrinsic information for enhancing the decoding gains at the destination. Surprisingly, simulation results indicate that even in bad channel conditions, SF relaying can still effectively improve the recovered information quality.

This letter proposes a novel anti-interference technique, semantic interference cancellation (SemantIC), for enhancing information quality towards the sixth-generation (6G) wireless networks. SemantIC only requires the receiver to concatenate the channel decoder with a semantic auto-encoder. This constructs a turbo loop which iteratively and alternately eliminates noise in the signal domain and the semantic domain. From the viewpoint of network information theory, the neural network of the semantic auto-encoder stores side information by training, and provides side information in iterative decoding, as an implementation of the Wyner-Ziv theorem. Simulation results verify the performance improvement by SemantIC without extra channel resource cost.

This article analyzes the robustness and timeliness for cooperative lossy communications in unmanned aerial vehicle (UAV) networks. The analytical framework consists of two steps: 1) calculating the outage probability, and 2) characterizing the Age-of-Information (AoI) for a given outage probability. Initially, we determine the outage probability based on the Shannon's lossy source-channel separation theorem. Numerical results indicate that joint decoding reduces the outage probability, and the system can achieve higher diversity order for less stringent distortion requirement. Then, we derive a closed-form expression of the lower bound on the average AoI for the communication system where outage events are constrained to an acceptable level. Moreover, we conduct a series of simulations for verifying the lower bound on the average AoI and evaluating the impact of UAV locations on the average AoI. It is demonstrated that the lower bound is tight when the server utilization ratio is either relatively busy or idle. For the case with a relatively large outage probability, we propose an intermittent transmission scheme to refine the AoI performance without extra energy consumption. The effectiveness of the intermittent transmission scheme for reducing the average AoI is verified by both the simulations and the lower bound analysis.

This article establishes an analytical framework for the upper bound on the average Age-of-Information (AoI) in first-come-first-served (FCFS) wireless communications where a certain level of outage probability is unavoidable. To begin with, we analyze the average AoI and derive a general upper bound for G/G/1 systems with a certain outage probability. Subsequently, for an M/M/1 system with the FCFS scheme, we obtain a concise closed-form expression of the upper bound, and further refine the upper bound after analyzing the relative error. Interestingly, it is found by the analysis that the relative error is independent of the service rate, and the upper bound becomes tighter as the outage probability increases. Based on the refined upper bound, we minimize the average AoI for the communications suffering from block Rayleigh fading. We derive a closed-form expression of the outage probability over a fading channel, and then prove that the refined upper bound is a convex function with respect to the average update generating rate. Consequently, we optimize the AoI performance by solving a convex optimization problem formulated utilizing the refined upper bound expression. The numerical results indicate that the minimum average AoI can be reduced by either increasing the service rate or the transmission power.