In this article, we use deep neural networks (DNNs) to develop a wireless end-to-end communication system, in which DNNs are employed for all signal-related functionalities, such as encoding, decoding, modulation, and equalization. However, accurate instantaneous channel transfer function, i.e., the channel state information (CSI), is necessary to compute the gradient of the DNN representing. In many communication systems, the channel transfer function is hard to obtain in advance and varies with time and location. In this article, this constraint is released by developing a channel agnostic end-to-end system that does not rely on any prior information about the channel. We use a conditional generative adversarial net (GAN) to represent the channel effects, where the encoded signal of the transmitter will serve as the conditioning information. In addition, in order to deal with the time-varying channel, the received signal corresponding to the pilot data can also be added as a part of the conditioning information. From the simulation results, the proposed method is effective on additive white Gaussian noise (AWGN) and Rayleigh fading channels, which opens a new door for building data-driven communication systems.

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Dual Connectivity in LTE network can significantly improve per-user throughput and mobility robustness by allowing users to be connected simultaneously to master cell group (MCG) and secondary cell group (SCG) via MeNB (master eNB) and SeNB (secondary eNB), respectively. The increase in per-user throughput is achieved by aggregating radio resources from at least two eNBs. Moreover, dual connectivity also helps in load balancing between MCG and SCG. However, it imposes several technical challenges. The main ones are buffer status report calculation and reporting, power headroom calculation and reporting, logical channel prioritization, user power saving operations such as discontinuous reception (DRX), and increased device complexity to support bearer split. The coordination between eNBs over X2 interface may be effective to resolve some of these issues. The higher delay due to non-ideal backhaul between MeNB and SeNB, however, limits an efficient coordination between these eNBs. In this paper, we explain and explore these technical challenges and investigate potential solution directions. We also provide a quantitative analysis of potential gains in terms of peruser throughput and load balancing that can be achieved by data bearer split in uplink at the cost of complex UE behaviors using a system level simulation study.