Energy-efficient resource allocation is considered in sensing-based spectrum sharing for cooperative cognitive radio networks (CCRNs). The secondary user first listens to the spectrum allocated to the primary user (PU) to detect the PU state and then initiates data transmission with two power levels based on the sensing decision (e.g., idle or busy). Under this model, the optimization problem of maximizing energy efficiency (EE) is formulated over the transmission power and sensing time subject to some practical limitations, such as the individual power constraint for secondary source and relay, the quality of service (QoS) for the secondary system, and effective protection for the PU. Given the complexity of this problem, two simplified versions (i.e., perfect and imperfect sensing cases) are studied in this paper. We transform the considered problem in fractional form into an equivalent optimization problem in subtractive form. Then, for perfect sensing, the Lagrange dual decomposition and iterative algorithm are applied to acquire the optimal power allocation policy; for imperfect sensing, an exhaustive search and iterative algorithm are proposed to obtain the optimal sensing time and corresponding power allocation strategy. Finally, numerical results show that the energy-efficient design greatly improves EE compared with the conventional spectrum-efficient design.

Massive MIMO (mMIMO) is a promising technology for smart multimedia and wireless communication fields. In this paper, we investigate pilot allocation problem in two-tier time division duplex (TDD) heterogeneous network (HetNet) with mMIMO. First, we propose a new pilot allocation scheme for maximizing ergodic downlink sum rate of macro users (MUs) and small cell users (SUs), where the uplink pilot overhead and cross-tier interference are jointly considered. Then, we theoretically analyze the formulated problem and propose a low complexity one-dimensional search algorithm to obtain the optimum pilot allocation. In addition, we propose two suboptimal pilot allocation algorithms to simplify the computational process and improve SUs' fairness, respectively. Finally, simulation results show that the performance of the proposed scheme outperforms that of the traditional schemes.

Pilot contamination due to pilot reuse in adjacent cells is a very serious problem in massive multi-input multiple-output (MIMO) systems. Therefore, proper pilot allocation is essential for improving system performance. In this paper, we formulate the pilot allocation optimization problem so as to maximize uplink sum rate of the system. To reduce the required complexity inherent in finding the optimum pilot allocation, we propose a low-complexity pilot allocation algorithm, where the formulated problem is decoupled into multiple subproblems; in each subproblem, the pilot allocation at a given cell is optimized while the pilot allocation in other cells id held fixed. This process is continued until the achievable sum rate converges. Through multiple iterations, the optimum pilot allocation is found. In addition, to improve users' fairness, we formulate fairness-aware pilot allocation as maximization problem of sum of user's logarithmic rate and solve the formulated problem using a similar algorithm. Simulation results show that the proposed algorithms match the good performance of the exhaustive search algorithm, meanwhile the users' fairness is improved.