D2D transmission radiates in all directions, generating undesirable interference to primary cellular users and other D2D users sharing the same radio resources resulting in severe performance degradation. Containment of the interference among D2D nodes and cellular users is one of the major problems. However, integration of D2D communication in cellular systems at the same time introduces new technical challenges that need to be addressed. It results in several transmission gains, such as improved throughput, energy gain, hop gain, reuse gain, etc. D2D provides the opportunity for users located in close proximity of each other to communicate directly, without traversing data traffic through the eNB. To respond to this challenge, Device–to–Device (D2D) communication in cellular networks is viewed as a promising solution, which is expected to operate, either within the coverage area of the existing eNB and under the same cellular spectrum (in-band) or separate spectrum (outband). It is driving the telecommunication industry as well as the research community to come up with new paradigms that will support such high data rate requirements within the existing wireless access network, in an efficient and effective manner. The mobile data traffic has risen exponentially in recent days due to the emergence of data intensive applications such as online gaming, video sharing etc. Simulation results show that the proposed algorithm can effectively improve the system performance. Finally, we use the Kuhn–Munkres (KM) algorithm to achieve the optimal matching between D2D clusters and cellular channel to maximize the total capacity of D2D users. Then, D2D users in the same cluster are modeled as a non-cooperative game, the expressions of D2D users’ transmission power and energy harvesting time are derived by using the Karush–Kuhn–Tucker (KKT) condition, and the optimal transmission power and energy harvesting time are allocated to D2D users by the joint iteration optimization method. This algorithm firstly uses a heuristic dynamic clustering method to cluster D2D users and those in the same cluster can share the same channel. Experimental results justify the validity of our design decisions through improved distortion and FID scores.This paper assumes that multiple device-to-device (D2D) users can reuse the same uplink channel and base station (BS) supplies power to D2D transmitters by means of wireless energy transmission the optimization problem aims at maximizing the total capacity of D2D users, and proposes a power control and channel allocation algorithm for the energy harvesting D2D communications underlaying the cellular network. Finally, sampling from the joint model is improved by introducing auxiliary latent variables in the decoder. Second, we propose a design to model missing information with a prior, and incorporate this into the channel model. To facilitate learning, we introduce a differentiable and computationally efficient version of the bandwidth-limited channel.

Framing the problem as a variational learning problem, we conclude that joint systems outperform their separate counterparts when coding is performed by flexible learnable function approximators such as neural networks. First, instead of separating the sub-tasks of compression (source coding) and error correction (channel coding), we propose to model both jointly. Specifically,we find three modelling choices that are relevant under expected information loss. We build upon this work by studying neural communication systems with the BWLC. Recently, neural communication models such as variational autoencoders have been studied for the task of source compression. In this study we consider learning coding with the bandwidth-limited channel (BWLC).

The bandwidth-limited channel models this phenomenon. via wifi, is that it may happen at varying levels of information transfer.

One of the most important aspects of real world communication, e.g. Reliably transmitting messages despite information loss due to a noisy channel is a core problem of information theory.

Bandwidth in communication