Abstract For the ultra‐dense distributed network (UDN), the intensive deployment leads to the collision problem of the unlicensed spectrum access due to the random contention access mechanism of the unlicensed spectrum, which is difficult to improve the user quality of service (QoS). Here, the wireless access process of the licensed assisted access (LAA) network is modelled as a four‐state semi‐Markov model. Then, the effective capacity (EC) of the UDN based on the random access is derived, and the network capacity limit under the QoS requirements in the unlicensed spectrum is given. Based on the obtained EC, two power control algorithms are proposed to maximize the EC and effective energy efficiency (EEE) respectively under the user QoS requirements. Finally, numerical results verify the accuracy of the proposed theory, and the capacity‐delay domain of the UDN is given. The simulation results show that the proposed algorithms improve the EC and the EEE compared with existing classical schemes.

Abstract Sum‐rate optimization problem is a key issue in a time‐varying multi‐user multi‐input multi‐output (MU‐MIMO) distributed antenna system. Channel precoding matrix is the key technology in the achievable sum‐rate optimization problem. In this paper, two algorithms are proposed to solve the achievable sum‐rate optimization problem for a time‐varying MU‐MIMO distributed antenna system, namely one‐dimensional search algorithm (OSA) and cyclic MMSE (Minimum Mean Square Error) search algorithm (CMSA). In order to design the channel precoding matrix for the achievable sum‐rate optimization, a one‐dimensional search algorithm is proposed in the time‐varying MU‐MIMO distributed antenna system. For the problem with a large amount of computation in OSA, a cyclic MMSE search algorithm is proposed in the time‐varying MU‐MIMO distributed antenna system. Simulation results show that the proposed algorithms can effectively improve the sum‐rate compared to other algorithms. Simulation results also show that OSA is superior to CMSA in the sum‐rate, and the channel state information (CSI) coefficient has little effect on OSA and CMSA.

In this study, the authors consider the user rate optimisation for wireless powered communication networks (WPCNs). To improve the user rate, a novel minimum mean square error (MMSE)‐based transmission method is proposed for WPCNs. For maximising the user rate, a user rate optimisation problem with MMSE constraints in the uplink transmission is formulated, which is considered as a convex optimisation problem. Considering the uplink power allocation, downlink energy beamforming, and the time ratio between uplink and downlink durations, a novel Newton iterative algorithm is proposed to obtain the optimal solution of the optimisation problem. Furthermore, the computational complexity of the MMSE‐based method is illustrated by theoretical analysis. Simulation results show that the MMSE‐based method can effectively improve the user rate compared to the zero‐forcing suboptimal method and time division multiple access‐energy harvesting method. Moreover, simulation results also show that the experimental runtime tends to coincide with the theoretical runtime in the MMSE‐based method.

Resource allocation problem is a key issue in multi‐carrier non‐orthogonal multiple access (NOMA) networks. Maximum access problem and power allocation problem are two important problems in resource allocation problem. In this study, two algorithms are proposed to solve the two problems for multi‐carrier NOMA networks, namely mixed integer programming algorithm (MIPA) and dynamic power allocation algorithm (DPAA). In order to solve the maximum access problem, a MIPA is proposed in uplink multi‐carrier NOMA networks. For the power allocation problem, a DPAA is proposed in downlink multi‐carrier NOMA networks. Simulation results show that MIPA can increase the number of supported users compared to other schemes, DPAA can improve user data rate by increasing mean channel quality indicator and make power allocation more reasonable. According to DPAA, the gains of weighted sum‐rate utility per subcarrier can be improved with the increase in the number of subcarriers. Simulation results also show that the proposed scheme is superior to reservation channel technique in terms of the probability of call dropping and probability of call blocking.