Non-orthogonal multiple access (NOMA) is one of the promising technologies to meet the huge access demand and high data rate requirements of the next generation networks. In this paper, we investigate the joint subchannel assignment and power allocation problem in an uplink multi-user NOMA system to maximize the energy efficiency (EE). Different from conventional model-based resource allocation methods, we propose three deep reinforcement learning (DRL) based frameworks to solve this non-convex optimization problem, referred to as discrete DRL based resource allocation (DDRA) framework, continuous DRL based resource allocation (CDRA) framework and joint DRL and optimization resource allocation (DORA) framework. Specifically, for the DDRA framework, a multi-DQN based network is designed to dynamically allocate resources discretely, which can reduce the output dimension and improve the learning efficiency. To overcome the loss of power discretization in DDRA, a joint DQN and deep deterministic policy gradient (DDPG) based network (CDRA framework) is designed to generate the resource allocation policy. The DORA framework is then proposed as a performance boundary. Finally, an event triggered learning method is combined with all three frameworks to further reduce the computational consumption. Numerical results show that the proposed frameworks can improve the EE performance of the uplink NOMA system and reduce the computation time.

In this paper, a resource allocation algorithm for maximizing the energy efficiency (EE) is studied in multicell orthogonal frequency-division multiple-access (OFDMA) wireless networks. The resource allocation is designed based on imperfect channel state information (CSI). Universal frequency reuse is considered, and cochannel interference is dealt with via the cooperation of multiple base stations (BSs) sharing CSI but not user data. We formulate the energy-efficient resource allocation problem as a probabilistic mixed nonconvex optimization problem. The user scheduling, data rate adaptation, and power allocation are jointly designed to maximize the system EE, under the maximum transmitted power constraint and the outage probability constraint. An iterative algorithm is proposed in which the EE keeps improving until algorithm convergence. In each iteration, the energy-efficient power allocation optimization problem is solved by a lower bound problem and a parameterized transformation. Numerical results illustrate the convergence and the effectiveness of the proposed algorithm. In a multicell environment with cochannel interference, the proposed algorithm has large EE gains relative to the spectral efficiency (SE) loss. Compared with noncoordinated methods, the proposed method can achieve higher EE when backhaul power consumption is low.

In this paper, we investigate the power allocation for maximizing weighted sum-rate (WSR) in downlink multiple carrier non-orthogonal multiple access (MC-NOMA) systems with imperfect successive interference cancellation (SIC). We formulate the power allocation problem as a non-convex optimization problem with the total power constraint of all sub-channels, while considering often-neglected issues of SIC error and power order constraints at users. Firstly, we discuss the optimization problem assuming receivers can perform perfect SIC, and we provide a concavity condition of the WSR maximization problem for the MC-NOMA system. When the concavity condition is not satisfied, a fractional quadratic transformation is used to overcome the difficulty of problem non-convexity. Based on the transformation, we propose an iterative power allocation algorithm. Then, we consider the SIC error and the power order constraints in the optimization problem, and present a power allocation method with imperfect SIC. Moreover, for both perfect and imperfect SIC, we derive some propositions of the optimal power allocation solution to the WSR maximization problem, and propose a low-complexity power allocation algorithm based on these propositions. Finally, we provide a joint user scheduling and power allocation algorithm for maximizing the WSR. Simulation results illustrate that the proposed resource allocation methods have better performance than the existing schemes.

Flexible titanium carbide (Ti₃C₂) antenna offers a breakthrough in the penetration of information communications for the spread of Internet of Things (IoT) applications. Current configurations are constrained to multi-layer complicated designs due to the limited conformal integration of the dielectric substrate and additive-free Ti₃C₂ inks. Here, we report the flexible ultrawideband Ti₃C₂ monopole antenna by combining strategies of interfacial modification and advanced extrusion printing technology. The polydopamine, as molecular glue nano-binder, contributes the tight adhesion interactions between Ti₃C₂ film and commercial circuit boards for high spatial uniformity and mechanical flexibility. The bandwidth and center frequency of Ti₃C₂ antenna can be well maintained and the gain differences fluctuate within ±0.2 dBi at the low frequency range after the bent antenna returns to the flat state, which conquers the traditional inelastic Cu antenna. It also achieves the demo instance for the fluent and stable real-time wireless transmission in bending states.

In this paper, we propose two deep learning (DL) based receiver schemes in uplink multiple-input multiple-output (MIMO) systems. In the first scheme, we design a pilot-assisted MIMO receiver using a data-driven full connected neural network. This data-driven receiver can recover transmitted signal directly in an end-to-end manner without explicitly estimating channel. In the second scheme, we adopt a model-driven network which combines communication knowledge with DL. The model-driven scheme divides the MIMO receiver into channel estimation subnet and signal detection subnet, and each subnet is composed of a traditional solution as initialization and a DL network to further improve the accurate. The simulation results show that both of the two schemes achieve better bit error ratio (BER) performance than traditional methods. In particular, the data-driven scheme can achieve optimal BER performance in low-dimensional MIMO systems, while the model-driven scheme can be trained with fewer trainable parameters and outperforms the data-driven scheme in high-dimension MIMO systems.

In this letter, we propose a pilot-assisted receiver scheme based on learnable successive interference cancellation (PA-LSIC) for uplink single-input multiple-output (SIMO) non-orthogonal multiple access (NOMA) systems. The PA-LSIC combines the successive interference cancellation (SIC) structure with the model-driven deep learning network. Considering the noise impact of channel estimation and the incomplete detection and cancellation in SIC process, we introduce some new parameters, such as noise cancellation factor and interference cancellation factor, which are optimized by using the back-propagation algorithm and random gradient descent algorithm. Numerical results show that the PA-LSIC has superior bit error rate (BER) performance and lower complexity during training and implementation.