A Practical Cooperative Multicell MIMO-OFDMA Network Based on Rank Coordination

An important challenge of wireless networks is to boost the cell edge performance and enable multi-stream transmissions to cell edge users. Interference mitigation techniques relying on multiple antennas and coordination among cells are nowadays heavily studied in the literature. Typical strategies in OFDMA networks include coordinated scheduling, beamforming and power control. In this paper, we propose a novel and practical type of coordination for OFDMA downlink networks relying on multiple antennas at the transmitter and the receiver. The transmission ranks, i.e.\ the number of transmitted streams, and the user scheduling in all cells are jointly optimized in order to maximize a network utility function accounting for fairness among users. A distributed coordinated scheduler motivated by an interference pricing mechanism and relying on a master-slave architecture is introduced. The proposed scheme is operated based on the user report of a recommended rank for the interfering cells accounting for the receiver interference suppression capability. It incurs a very low feedback and backhaul overhead and enables efficient link adaptation. It is moreover robust to channel measurement errors and applicable to both open-loop and closed-loop MIMO operations. A 20% cell edge performance gain over uncoordinated LTE-A system is shown through system level simulations.Comment: IEEE Transactions or Wireless Communications, Accepted for Publicatio

A New Efficient Group-Wise Spatial Multiplexing Design for Closed-Loop MIMO Systems

Abstract — This paper introduces a new efficient design scheme for spatial multiplexing (SM) systems over closed loop multiple-input multiple-output (MIMO) wireless channels. Extending the orthogonalized spatial multiplexing (OSM) scheme which was developed recently for transmitting two data streams, we propose a new SM scheme where a larger number of data streams can be supported. To achieve this goal, we partition the data streams into several subblocks and execute the block-diagonalization process at the receiver. The proposed scheme still guarantees single-symbol maximum likelihood (ML) detection with small feedback information. Simulation results verify that the proposed scheme achieves a huge performance gain at a bit error rate (BER) of 10−4 over conventional closed-loop schemes based on minimum mean-square error (MSE) or bit error rate (BER) criterion. We also show that an additional 2.5dB gain can be obtained by optimizing the group selection with extra feedback information. I

Fueling the Next Quantum Leap in Cellular Networks: Embracing AI in 5G Evolution towards 6G

Cellular networks, such as 5G systems, are becoming increasingly complex for supporting various deployment scenarios and applications. Embracing artificial intelligence (AI) in 5G evolution is critical to managing the complexity and fueling the next quantum leap in 6G cellular networks. In this article, we share our experience and best practices in applying AI in cellular networks. We first present a primer on the state of the art of AI in cellular networks, including basic concepts and recent key advances. Then we discuss 3GPP standardization aspects and share various design rationales influencing standardization. We also present case studies with real network data to showcase how AI can improve network performance and enable network automation