## 1 Introduction  
Physical layer multicasting with a base station (BS) transmitting a common information to multiple terminals simultaneously has attracted intensive attentions recently. This system can support various applications such as the delivery of headline news, service information, and live broadcast, _etc._[PERSON] _et al._[1] studied the transmit power minimization problem constrained by the signal-to-noise ratios (SNRs) of a group of receivers. In addition to proving the NP hardness of the problem, they optimized the beamforming vector using the semidefinite relaxation (SDR) and randomization techniques. The problem for multiple cochannel groups was investigated in [2]. Since the number of antennas (\(N\)) is usually more than the number of available radio frequency (RF) chains (\(K\)) at the BS, the joint antenna selection and beamforming for a single group and multiple cochannel groups was studied in [3, 4], respectively. Through jointly selecting \(K\) antennas from the total \(N\) antennas and optimizing the beamforming vector, the transmit power is minimized while meeting the quality of service (QoS) requirement of each receiver. Compared with the routine SDR approach, [PERSON] _et al._ have solved the transmit power minimization problem using a successive convex approximation (SCA) approach with a massively reduced computational complexity [5]. Because of the low computational complexity, the SCA approach is suitable for the physical layer multicasting system with large-scale antenna arrays. The multiple-input multiple-output (MIMO) system with large-scale antenna arrays was proposed in [6], and has been intensively studied for a higher spectral efficiency, reduced signal processing complexity, and improved energy efficiency compared with the traditional MIMO [7; 8]. It is shown that the required transmit power to support a target rate is inversely proportional to the number of antennas in the massive MIMO system [9]. The optimal configurations of the RF chains was studied in [10] to maximize the transmit rate under the total power consumption constraint with or without the channel state information (CSI). In the existing literature, the transmit power has been minimized, while the circuit power is neglected. However, the circuit power actually scales with the number of antennas and it should be considered for MIMO systems, especially the massive MIMO. In contrast to previous work, this paper aims to minimize the total power consumption under the SNR constraint of each receiver through jointly determining the antenna subset and the beamforming vector. Since the circuit power depends directly on the number of active RF chains in the antenna selection, we explicitly consider the circuit power in the total power consumption model. The main contributions of this paper are detailed as follows:  
* We formulate the total power consumption minimization problem by joint antenna selection and beamforming for physical layer multicasting with massive antennas. * Due to the higher complexity of the exhaustive search for the optimal solution, we propose three decremental antenna selection algorithms for the sub-optimal solution with low complexity. * On the basis of the SCA approach, we propose an algorithm to randomly select the antenna in a decremental manner, and modify the algorithm using the asymptotic orthogonality of the channels to improve the computational efficiency. * Instead of random selection, a more effective algorithm is further proposed based on the \(\ell_{0}\) norm minimization. * Simulation results are provided to compare the performance of our proposed algorithms in terms of the total power consumption and the average run time. The paper is organized as follows. Section 2 formulates the total power minimization problem. Three decremental antenna selction algorithms are proposed in Section 3. Simulation results are provided in Section 4 and conclusions are drawn in Section 5.