  The evolution of fifth generation (5G) wireless communication networks has
led to an increased need for wireless resource management solutions that
provide higher data rates, wide coverage, low latency, and power efficiency.
Yet, many of existing traditional approaches remain non-practical due to
computational limitations, and unrealistic presumptions of static network
conditions and algorithm initialization dependencies. This creates an important
gap between theoretical analysis and real-time processing of algorithms. To
bridge this gap, deep learning based techniques offer promising solutions with
their representational capabilities for universal function approximation. We
propose a novel unsupervised deep learning based joint power allocation and
beamforming design for multi-user multiple-input single-output (MU-MISO)
system. The objective is to enhance the spectral efficiency by maximizing the
sum-rate with the proposed joint design framework, NNBF-P while also offering
computationally efficient solution in contrast to conventional approaches. We
conduct experiments for diverse settings to compare the performance of NNBF-P
with zero-forcing beamforming (ZFBF), minimum mean square error (MMSE)
beamforming, and NNBF, which is also our deep learning based beamforming design
without joint power allocation scheme. Experiment results demonstrate the
superiority of NNBF-P compared to ZFBF, and MMSE while NNBF can have lower
performances than MMSE and ZFBF in some experiment settings. It can also
demonstrate the effectiveness of joint design framework with respect to NNBF.
