Transmitter channel state information (CSIT) is indispensable for the spectral efficiency gains offered by massive multiple-input multiple-output (MIMO) systems. In a frequency-division-duplexing (FDD) massive MIMO system, CSIT is typically acquired through downlink channel estimation and user feedback, but as the number of antennas increases, the over-head for CSI training and feedback per user grows, leading to a decrease in spectral efficiency. In this paper, we show that, using uplink pilots in FDD, the downlink sum spectral efficiency gain with perfect downlink CSIT is achievable when the number of antennas at a base station is infinite by leveraging the partial channel reciprocity between uplink and downlink channels. Specifically, the key idea showing our result is the mean squared error-optimal downlink channel reconstruction method using uplink pilots, and the robust downlink precoding method harnessing the reconstructed channel with the error covariance matrix. Our simulation results show that our proposed precoding method can attain comparable sum spectral efficiency to zero-forcing precoding with perfect downlink CSIT, without CSI training and feedback.

The need for channel state information (CSIT) is crucial for the improved spectral efficiency of massive multiple-input multiple-output (MIMO) systems. In FDD massive MIMO systems, CSIT is obtained through downlink channel estimation and user feedback, but this process becomes challenging as the number of antennas increases, resulting in reduced spectral efficiency. In this paper, we show that even in FDD systems, the TDD massive MIMO gain is attainable without explicit CSIT training and feedback, by using UL pilots. We present a novel DL channel reconstruction method from uplink pilots and a robust downlink precoding technique, proving that the FDD massive MIMO gains are achievable without CSI training and feedback. Our results are verified through system-level simulations.

A cloud radio access network (C-RAN) is a promising cellular network, wherein densely deployed multi-antenna remote-radio-heads (RRHs) jointly serve many users using the same time-frequency resource. By extremely high signaling overheads for both channel state information (CSI) acquisition and data sharing at a baseband unit (BBU), finding a joint transmission strategy with a significantly reduced signaling overhead is indispensable to achieve the cooperation gain in practical C-RANs. In this paper, we present a novel sparse joint transmission (sparse-JT) method for C-RANs, where the number of transmit antennas per unit area is much larger than the active downlink user density. Considering the effects of noisy-and-incomplete CSI and the quantization errors in data sharing by a finite-rate fronthaul capacity, the key innovation of sparse-JT is to find a joint solution for cooperative RRH clusters, beamforming vectors, and power allocation to maximize a lower bound of the sum-spectral efficiency under the sparsity constraint of active RRHs. To find such a solution, we present a computationally efficient algorithm that guarantees to find a local-optimal solution for a relaxed sum-spectral efficiency maximization problem. By system-level simulations, we exhibit that sparse-JT provides significant gains in ergodic spectral efficiencies compared to existing joint transmissions.

Cell densification is a key driver to increase area spectral efficiencies in multi-antenna cellular systems. As increasing the densities of base stations (BSs) and users that share the same spectrum, however, both inter-user-interference (IUI) and inter-cell interference (ICI) problems give rise to a significant loss in spectral efficiencies in such systems. To resolve this problem under the constraint of local channel state information per BS, in this paper, we present a novel noncooperative multi-user multiple-input multiple-output (MIMO) precoding technique, called signal-to-interference-plus-leakage-plus-noise-ratio (SILNR) maximization precoding. The key innovation of our distributed precoding method is to maximize the product of SILNRs of users per cell using local channel state information at the transmitter (CSIT). We show that our precoding technique only using local CSIT can asymptotically achieve the multi-cell cooperative bound attained by cooperative precoding using global CSIT in some cases. We also present a precoding algorithm that is robust to CSIT errors in multi-cell scenarios. By multi-cell system-level simulations, we demonstrate that our distributed precoding technique outperforms all existing noncooperative precoding methods considerably and can also achieve the multi-cell bound very tightly even with not-so-many antennas at BSs.

Cell densification is a key driver to increase area spectral efficiencies in multi-antenna cellular systems. As increasing the density of base stations (BSs) and users that share the same spectrum, however, both inter-user-interference (IUI) and inter-cell interference (ICI) problems give rise to a significant loss in spectral efficiencies in such systems. To resolve this problem under the constraint of local channel state information per BS, in this paper, we present a novel noncooperative multi-user multiple-input multiple-output (MIMO) precoding technique, called signal-to-interference-pulse-leakage-pulse-noise-ratio (SILNR) maximization precoding. The key innovation of our distributed precoding method is to maximize the product of SILNRs of users per cell using local channel state information at the transmitter (CSIT). We show that our precoding technique only using local CSIT can asymptotically achieve the multi-cell cooperative bound attained by cooperative precoding using global CSIT in some cases. We also present a precoding algorithm that is robust to CSIT errors in multi-cell scenarios. By multi-cell system-level simulations, we demonstrate that our distributed precoding technique outperforms all existing noncooperative precoding methods considerably and can also achieve the multi-cell bound very tightly even with not-so-many antennas at BSs.

A cloud radio access network (cloud-RAN) is a promising cellular architecture to increase both network spectral efficiency and energy efficiency. In the downlink transmission of cloud-RAN, a fundamental trade-off exists between the sum-spectral efficiency and the network power consumption induced by the fronthual links. To optimize this trade-off, it is essential to jointly identify a set of active remote radio heads (RRHs) and the beamforming vectors used at the active RRHs. To resolve this problem, this paper presents a novel group-sparse beamforming algorithm inspired by sparse principal component analysis (sparse-PCA). The key idea of the proposed method is to reformulate the sum-spectral efficiency maximization problem under a group-sparsity constraint into a generalized sparse-PCA problem, which is a tractable non-convex optimization problem. Using this reformulated optimization problem, a computationally efficient algorithm is proposed, which finds the solution that guarantees the first-order necessary optimality condition of the non-convex optimization problem. Simulation results demonstrate significant advantage of the proposed group-sparse beamfroming method.