We consider downlink of a multiuser massive multiple-input multiple-output (MIMO) system and focus on reducing the hardware costs by using a single common power amplifier and separate phase shifters (PSs) for antenna frontends. In the previous literature, the use of analog PSs in this setup has been considered. Here, we study the use of practical digital PSs, which only support a limited set of discrete phases. Considering the sum of interference powers as a metric, we formulate the corresponding nonlinear discrete optimization problem and solve for the phases to be used during transmission. We devise a low-complexity algorithm, which employs a trellis structure providing suboptimal, but efficient and effective solutions. We demonstrate via examples that the proposed solutions have comparable performance to conventional analog PS-based algorithms. Furthermore, we prove that by utilizing discretephase constant envelope precoding, the interference can be made arbitrarily small by increasing the number of antennas. Therefore, the asymptotic gains promised by massive MIMO systems are preserved. We also obtain closed-form expressions for the rate loss due to errors in the phase and amplitude of the PSs, for both low and high SNR regimes.

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In this study, the problem of resource allocation in multiple-input multiple-output-orthogonal frequency division multiplexing-based cooperative cognitive radio networks is considered. The cooperation strategies between the secondary users is the decode-and-forward (DF) strategies. In order to obtain an optimal subcarrier pairing, relay assignment and power allocation in the system, the dual decomposition technique is recruited. The optimal resource allocation is realised under the individual power constraints in source and relays, so that the sum rate is maximised, whereas the interference induced to the primary system is kept below a pre-specified interference temperature limit. Moreover, because of the high computational complexity of the optimal approach, a suboptimal algorithm is further proposed. The joint allocation of the resources in suboptimal algorithm is carried out taking into account the channel qualities, the DF cooperation strategy, the interference induced to the primary system and the individual power budgets. The performance of the different approaches and the impact of the constraint values and deploying multiple antennas at users are discussed through the numerical simulation results.

This paper considers a cognitive radio network. which has a two secondary users (SU) equipped with multiple antennas, one acting as transmitter (SU TX) and the other receives the signals transmitted by SU TX (SU RX) and coexists with a primary network users, each with only one antennas. Our objective is to maximize the achievable rates or symbol error rates of the SU subject to the interference constraints on the primary users exploiting Transmit Beamforming at SU transmitter (SU TX) while reducing the costs associated with RF chains at the radio front end. At first, the problem under consideration is formulated and then a new iterative method is proposed to fmd the best weight vector for beamformer in SU TX and best subset of antennas at SU RX based on convex optimization methods. Two different scenarios are considered and their advantages and disadvantages are investigated using appropriate simulations.

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