Wanming Hao

Power Allocation for Massive MIMO Cognitive Radio Networks with Pilot Sharing under SINR Requirements of Primary Users

In this paper, we investigate the power allocation problem in massive multiple-input-multiple-output cognitive radio networks. We propose an orthogonal pilot sharing scheme at pilot transmission phase, where secondary users are allowed to use pilots for channel estimation only when there are temporarily unused orthogonal pilots. Following this, we formulate the power allocation optimization problem of the secondary network (SN) to maximize the downlink sum rate of the SN subject to the total transmit power and primary users’ signal-interference-plus-noise-ratio constraints. Next, we transform the original (nonconvex) problem into a convex one by using convex approximation techniques and propose an iterative algorithm to obtain the solution. Furthermore, we prove that the proposed algorithm converges to Karush–Kuhn–Tucker points of the original problem. Meanwhile, the impact of the number of the secondary base station (SBS) antennas or the primary BS (PBS) antennas on the downlink rate of the SN and primary network is theoretically studied. Finally, the numerical results present the downlink sum rate of the SN under different parameters through our proposed algorithm.

Dynamic Small Cell Clustering and Non-Cooperative Game-Based Precoding Design for Two-Tier Heterogeneous Networks With Massive MIMO

In this paper, we investigate the dynamic small cell (SC) clustering strategy and their precoding design problem for interference coordination in two-tier heterogeneous networks (HetNets) with massive MIMO (mMIMO). To reduce interference among different SCs, an interference graph-based dynamic SC clustering scheme is proposed. Based on this, we formulate an optimization problem as design precoding weights at macro base station (MBS) and clustered SCs for maximizing the downlink sum rate of SC users (SUs) subject to the power constraint of each SC BS (SBS), while mitigating inter-cluster, eliminating inter-tier, intra-cluster and multi-macro user (MU) interference. To eliminate the inter-tier and multi-MU interference simultaneously, we propose a clustered SC block diagonalization precoding scheme for the MBS. Next, each SU’s precoding vector at clustered SCs is designed as the product of the following two parts. The first part is designed with singular value decomposition to remove the intra-cluster interference. The second part is designed to coordinate the inter-cluster interference for maximizing the downlink sum rate of SUs, which is a non-convex optimization problem and difficult to solve directly. A non-cooperative game-based distributed algorithm is proposed to obtain a suboptimal solution. Meanwhile, we prove the existence and uniqueness of Nash equilibrium for the formed game. Finally, simulation results verify the effectiveness of our proposed schemes.

Energy-Efficient Resource Allocation for Cooperative Cognitive Radio Networks with Imperfect Spectrum Sensing

Energy-efficient resource allocation is considered for cognitive cooperative radio networks (CCRNs) with imperfect spectrum sensing. 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 primary user (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. Finally, numerical results show that the EE design greatly improves EE compared with the conventional spectrum-efficient design.