Index Terms-Energy efficiency (EE), non-orthogonal multiple access (NOMA), simultaneous wireless information and power transfer (SWIPT), time switching (TS).

Spectral efficiency (SE) and energy efficiency (EE) are the main metrics for designing wireless networks. Rather than focusing on either SE or EE separately, recent works have focused on the relationship between EE and SE and provided good insights into the joint EE-SE tradeoff. However, such works have assumed that bandwidth are fully occupied regardless of the transmission requirements and therefore are only valid for this scenario. In this paper, we propose a new paradigm for EE-SE tradeoff, namely the resource efficiency (RE) for orthogonal frequency division multiple access (OFDMA) cellular network in which we take into consideration different transmission-bandwidth requirements. We analyse the properties of the proposed RE and prove that it is capable of exploiting the tradeoff between EE and SE by balancing consumption power and occupied bandwidth; hence simultaneously optimizing both EE and SE. We then formulate the generalized RE optimization problem with guaranteed quality of service (QoS) and provide a gradient based optimal power adaptation scheme to solve it. We also provide an upper bound near optimal method to jointly solve the optimization problem. Furthermore, a low-complexity suboptimal algorithm based on uniform power allocation scheme is proposed to reduce the complexity. Numerical results confirm the analytical findings and demonstrate the effectiveness of the proposed resource allocation schemes for efficient resource usage.

Heterogeneous network (HetNet) deployment is considered a de facto solution for meeting the ever increasing mobile traffic demand. However, excessive power usage in such networks is a critical issue, particularly for the mobile operators. Characterizing the fundamental energy efficiency (EE) performance of HetNets is therefore important for the design of green wireless systems. In this paper, we address the EE optimization problem for downlink two-tier HetNets comprised of a single macrocell and multiple pico-cells. Considering a heterogeneous realtime and non-real-time traffic, transmit beamforming design and power allocation policies are jointly considered in order to optimize the system energy efficiency. The EE resource allocation problem under consideration is a mixed combinatorial and nonconvex optimization problem, which is extremely difficult to solve. In order to reduce the computational complexity, we decompose the original problem with multiple inequality constraints into multiple optimization problems with single inequality constraint. For the latter problem, a two-layer resource allocation algorithm is proposed based on the quasiconcavity property of EE. Simulation results confirm the theoretical findings and demonstrate that the proposed resource allocation algorithm can efficiently approach the optimal EE. Index Terms-Green radio (GR), energy efficiency (EE), heterogeneous network (HetNet), resource allocation.

Simultaneous wireless information and power transfer (SWIPT) and multi-carrier non-orthogonal multiple access (MC-NOMA) are promising technologies for future fifth generation and beyond wireless networks due to their potential capabilities in energy-efficient and spectrum-efficient system designs, respectively. In this paper, the joint downlink resource allocation problem for a SWIPT-enabled MC-NOMA system with time switching-based receivers is investigated, where pattern division multiple access (PDMA) technique is employed. We focus on minimizing the total transmit power of the system while satisfying the quality-of-service requirements of each user in terms of data rate and harvested power. The corresponding optimization problem is a non-convex and a mixed integer programming problem which is difficult to solve. Different from the conventional iterative searching-based algorithms, we propose an efficient deep learning-based approach to determine an approximated optimal solution. Specifically, we employ a typical class of deep learning model, namely, deep belief network (DBN), where the detailed procedure of the developed approach consists of three parts, i.e., data preparation, training, and running. The simulation results demonstrate that the proposed DBN-based approach can achieve similar performance of power consumption to the exhaustive search method. Furthermore, the results also confirm that MC-NOMA with PDMA outperforms MC-NOMA with sparse code multiple access, single-carrier non-orthogonal multiple access, and orthogonal frequency division multiple access in terms of power consumption in SWIPT-enabled systems. INDEX TERMS Non-orthogonal multiple access (NOMA), simultaneous wireless information and power transfer (SWIPT), machine learning.

Abstract-Non-orthogonal multiple access (NOMA) has attracted a lot of attention recently due to its superior spectral efficiency and could play a vital role in improving the capacity of future networks. In this paper, a resource allocation scheme is developed for a downlink multi-user NOMA system. An optimization problem is formulated to maximize the sum rate under the total power and proportional rate constraints. Due to the complexity of computing the optimal solution, we develop a low complexity sub-optimal solution for two-user scenario and then extend it to the multi-user case by proposing a user-pairing approach as well as a number of power allocation techniques that facilitate dealing with a large number of users in NOMA system. Simulation results support the effectiveness of the proposed approaches and show the close performance to the optimal one. In addition, we propose a new hybrid multiple access technique that combines the properties of NOMA and the orthogonal frequency division multiple access (OFDMA). Simulation results show that the proposed hybrid method provides better performance than NOMA in terms of the overall achievable sum rate and the coverage probability.