The widespread application of wireless mobile services and requirements of ubiquitous access have resulted in drastic growth of the mobile traffic and huge energy consumption in ultradense networks (UDNs). Therefore, energy-efficient design is very important and is becoming an inevitable trend. To improve the energy efficiency (EE) of UDNs, we present a joint optimization method considering user association and small-cell base station (SBS) on/off strategies in UDNs. The problem is formulated as a nonconvex nonlinear programming problem and is then decomposed into two subproblems: user association and SBS on/off strategies. In the user association strategy, users associate with base stations (BSs) according to their movement speeds and utility function values, under the constraints of the signal-to-interference ratio (SINR) and load balancing. In particular, taking care of user mobility, users are associated if their speed exceeds a certain threshold. The macrocell base station (MBS) considers user mobility, which prevents frequent switching between users and SBSs. In the SBS on/off strategy, SBSs are turned off according to their loads and the amount of time required for mobile users to arrive at a given SBS to further improve network energy efficiency. By turning off SBSs, negative impacts on user associations can be reduced. The simulation results show that relative to conventional algorithms, the proposed scheme achieves energy efficiency performance enhancements.

Device-to-device (D2D) communication with direct terminal connection is a promising candidate for 5G communication, which increases the capacity of cellular networks and spectral efficiency. Introducing D2D communication to cellular users (CUs) will increase system capacity, and CUs will provide reusable channel resources for D2D users (DUs). However, the sharing of channel resources between CUs and DUs will lead to cofrequency interference and affect the communication quality of user terminals. As a means of improving spectrum utilization and solving cofrequency interference problems, a one-to-many D2D communication system model is established in cellular networks. Through model analysis, the interference between CUs and DUs is correlated with their distance from one another. Considering the different interference of CUs to DUs at different distances, an algorithm for resource allocation based on distance grouping is proposed. With this algorithm, DUs will reuse channel resources of CUs within a reasonable distance in the group, and interference between DUs and CUs will be minimized. The improved particle swarm optimization algorithm is used to solve the optimal power, to achieve the maximum transmission rate of the system. Simulated results show that the algorithm will significantly improve system throughput and performance while also lowering the computational complexity of the algorithm, enabling the whole system to have better communication quality.

With increasing complexity of electronic warfare environments, smart jammers are beginning to play an important role. This study investigates a method of power minimization-based jamming waveform design in the presence of multiple targets, in which the performance of a radar system can be degraded according to the jammers’ different tasks. By establishing an optimization model, the power consumption of the designed jamming spectrum is minimized. The jamming spectrum with power control is constrained by a specified signal-to-interference-plus-noise ratio (SINR) or mutual information (MI) requirement. Considering that precise characterizations of the radar-transmitted spectrum are rare in practice, a single-robust jamming waveform design method is proposed. Furthermore, recognizing that the ground jammer is not integrated with the target, a double-robust jamming waveform design method is studied. Simulation results show that power minimization-based single-robust jamming spectra can maximize the power-saving performance of smart jammers in the local worst-case scenario. Moreover, double-robust jamming spectra can minimize the power consumption in the global worst-case scenario and provide useful guidance for the waveform design of ground jammers.

Compared with traditional cellular networks, cloud wireless access network (C-RAN) can save more energy consumption because it centrally processes the signals in the baseband unit (BBU) pool served by the cloud platform. Aiming at the limited fronthaul capacity of the fronthaul link caused by massive data and the huge energy consumption caused by the explosive growth of mobile traffic, an energy-saving resource management scheme that is powered by a mixture of on-grid and green energy is proposed in this paper. Specifically, a new network energy efficiency (EE) model is constructed. The RRH with hybrid energy to supply power of the model in response to green communication and the model can also realize two functions: information decoding and wireless charging at the same time. The total power consumption including remote radio head (RRH), fronthaul link, BBU pool, and wireless charging equipment of user equipment is considered. Based on network energy efficiency model, in addition to considering the user’s quality of service (QoS), data rate requirements, transmission power of RRHs, and maximum battery capacity constraints, the constraints of fronthaul link capacity constraints also are considered in this paper. In order to solve the proposed optimization problem, a joint strategy of power allocation, resource block (RB) allocation, and power allocation ratio adjustment is proposed in this paper. However, the objective function of energy efficiency optimization problem is nonconvex and cannot be solved directly. Therefore, an equivalent convex feasibility problem is reconstructed, and the optimal solution is obtained by Lagrange dual decomposition method. Simulation results show that the proposed resource allocation scheme can provide higher energy efficiency for the network.

High peak-to-average power ratio (PAPR) is one of the major drawbacks in orthogonal frequency division multiplexing (OFDM) systems. Partial transmit sequence (PTS) is highly successful in PAPR reduction, but the considerable computational complexity for exhaustive search over all combinations of allowed phase factors is potential problem for a practical implementation. In this paper, the harmony research algorithm is applied to search the optimal combination of phase factors, which can significantly reduce the computational complexity and offers lower PAPR at the same time. Simulation results show that the HS-PTS algorithm can achieve the good tradeoff between PAPR reduction performance and computational complexity.

PTS is highly efficient in peak-to-average power ratio (PAPR) reduction of OFDM systems, but the considerable computational complexity for the required search through a high-dimensional vector space is potential problem for a practical implementation. In this paper, we formulate the search problem of PTS as a combinatorial optimization (CO) problem, then propose an algorithm based on improved discrete particle swarm optimization (I-DPSO), which reduces the number of multiplications required for PTS. Numerical results show that, the proposed algorithm can achieve performance-complexity tradeoff for moderate PAPR reduction.