Recently, massive multiple-input multiple-output (MIMO) systems have gained significant attention as a new network architecture to not only achieving unprecedented spectral and energy efficiencies, but also to alleviating propagation losses and inter-user/inter-cell interference. Therefore, massive MIMO has been identified as one of the key candidate technologies for the 5th generation wireless standard. This dissertation thus focuses on (1) developing a performance analysis framework for cognitive massive MIMO systems by investigating the uplink transmissions of multi-cell multi-user massive MIMO secondary systems, which are underlaid in multi-cell multi-user primary massive MIMO systems, with taking into consideration the detrimental effects of practical transmission impairments, (2) proposing a new wireless-powered underlay cognitive massive MIMO system model, as the secondary user nodes is empowered by the ability to efficiently harvest energy from the primary user transmissions, and then access and utilize the primary network spectrum for information transmission, and (3) developing a secure communication strategy for cognitive multi-user massive MIMO systems, where physical layer secure transmissions are provisioned for both primary and secondary systems by exploiting linear precoders and artificial noise (AN) generation in order to degrade the signal decodability at eavesdropper. The key design feature of the proposed cognitive systems is to leverage the spatial multiplexing strategies to serve a large number of spatially distributed user nodes by using very large numbers of antennas at the base-stations. Moreover, the fundamental performance metrics, the secondary transmit power constraints, which constitute the underlay secondary transmissions subject to a predefined primary interference temperature, and the achievable sum rates of the primary and secondary systems, are characterized under different antenna array configurations. Additionally, the detrimental impact of practical wireless transmission impairments on the performance of the aforementioned systems are quantified. The important insights obtained throughout these analyses can be used as benchmarks for designing practical cognitive spectrum sharing networks.

This thesis first presents a new cooperative wireless communication network strategy that incorporates energy cooperation and data cooperation. The model establishment, design goal formulations, and algorithms for throughput maximization of the proposed protocol are presented and illustrated using a three-node network with two energy harvesting (EH) user nodes and a destination node. Transmission models are established from the performance analysis for a total of four scenarios. Based on the models, we seek to find optimal energy management strategies by jointly optimizing time allocation for each user, power allocations over these time intervals, and data throughputs at user nodes so as to maximize the sum-throughput or, alternatively, the minimum throughput of the two users in all scenarios. An accelerated Newton barrier algorithm and an alternative algorithm based on local quadratic approximation of the transmission models are developed to solve the aforementioned optimization problems. Then the thesis extends the cooperative strategy to multi-source wireless communication network, where N source users communicate with the destination via one relay that harvests energy from the RF signals transmitted by the sources through time-division multiple access (TDMA). We characterize the Energy-Throughput (E-T) tradeoff regions between the maximum achievable average throughput of the sources and the total amount of saved energy in three circumstances. For the case N=1, all harvested energy will be used to forward the message. For the case N>1, we compare two transmission strategies: one is common PS ratio strategy that the relay adopts the same PS ratio for all sources; the other is individual PS ratio strategy that each source uses an individual PS ratio. Numerical experiments under practical settings provide supportive evidences to our performance analysis. / Graduate