Joint Interference and Phase Alignment among Data Streams in Multicell MIMO Broadcasting

Abstract

For the problem of channel state information (CSI) delay and error, this paper proposes a joint interference and phase alignment algorithm based on Bayesian estimation and power allocation among data streams for multicell, multiple-input multiple-output broadcast channels (MIMO-BC). Firstly, the sender obtains the best estimate of the current CSI through Bayesian estimation. Secondly, the interference suppression matrix is designed by maximizing the ratio of the desired signal power to the intercell interference plus noise ratio (SINR) in the forward link, and in the reverse communication, by maximizing the SINR design precoding. Further, the water-filling algorithm is combined to optimize power allocation among data streams. Finally, the phase alignment is used to rotate the interference between data streams into the signal space of the target receive data stream, thereby enhancing the received power of the target data stream. Simulation results show that the proposed algorithm has certain performance advantages over other algorithms, whether it is ideal CSI or delay and error CSI.

A Low Rank Channel Estimation Scheme in Massive Multiple-Input Multiple-Output

“…As can be seen from Figure 3, when SNR = 0dB, although the noise interference is severe at this time, the maximum value obtained according to Equation (26) is small. However, it still appears at the position of true sparsity S = 10, because according to Equation (23), the correlation between the submatrix corresponding to the non-zero sub-block and the received signal y G should be greater than the correlation between the sub-matrix corresponding to the zero sub-block and y G . Therefore, after performing the difference operation according to Equation (25), the maximum position found according to Equation (26) is a boundary position between the non-zero sub-block and the zero sub-block in a large probability, and the value is the sparsity.…”

“…Massive MIMO is a novel wireless communication technology that uses a large number of antennas at the BS to serve a relatively small number of users in the same frequency band, thereby greatly improving the spectral efficiency of the system [20][21][22][23][24]. In a massive MIMO system, the BS needs to use the downlink channel information for multiuser precoding, so it is critical to obtain accurate downlink channel information at the BS.…”

Interference Alignment Inspired Opportunistic Communications in Multi-Cluster MIMO Networks with Wireless Power Transfer

“…In [21], the authors investigate a K-user M × N MIMO interference channel with delayed channel state information at the transmitters, and propose a novel method that applies the IA procedure at the receiver side. More recently, a robust joint interference and phase alignment scheme based on Bayesian estimation and power allocation is proposed in [22] to overcome time delay error CSI in the MIMO broadcasting communication system.…”

Interference Alignment in Multi-Hop Cognitive Radio Networks under Interference Leakage

“…Even though cognitive multi-hop transmission offers numerous advantages to SUs, the primary-secondary interference is one of the most challenging issues to be solved in CR networks. To this end, IA, which can design coordinated signals to eliminate the interference in PU-SU, has become preferable [29]. Motivated by the advantages of multi-hop relaying and IA, herein, we investigate the interplay of the number of hops, relays, interference alignment, and interference leakage.…”