arXiv subjects
Publications and source records attributed to Hiroki Iimori.
We present a novel algorithm for the completion of low-rank matrices whose entries are limited to a finite discrete alphabet. The proposed method is based on the recently-emerged proximal gradient (PG) framework of optimization theory, which is applied here to solve a regularized formulation of the completion problem that includes a term enforcing the discrete-alphabet membership of the matrix entries.
We consider a bidirectional in-band full-duplex (FD) multiple-input multiple-output (MIMO) system subject to imperfect channel state information (CSI), hardware distortion, and limited analog cancellation capability as well as the self-interference (SI) power requirement at the receiver analog domain so as to avoid the saturation of low noise amplifier (LNA). A novel minimum mean square error (MMSE)-based joint design of digital precoder and combiner for SI cancellation is offered, which combines the well-known gradient projection method and non-monotonicity considered in recent machine-learning literature in order to tackle the non-convexity of the optimization problem formulated in this article. Simulation results illustrate the effectiveness of the proposed SI cancellation algorithm.
We present a new Beamforming-based (BB) Multiple-Input Single-Output (MISO)-Non-orthogonal Multiple Access (NOMA) scheme for Power Domain NOMA (PD-NOMA), in which the total transmit power consumption is minimized subjected to prescribed signal-to-interference-plus-noise ratio (SINR) requirements for each user, and under the assumption that only imperfect channel state information (CSI) is available at the transmitter. To this end, the fractional programming (FP)-based quadratic transform is employed to reformulate the non-convex SINR constraint of the original problem into a tractable quadratic form, which contains an estimate of the CSI error vector as a parameter. Taking advantage of the fact that the zero duality gap holds for the non-convex quadratic problems, a closed-form expression for an estimate of the CSI error vector is derived, completing the formulation. Finally, a novel iterative algorithm based on both the herein derived CSI error vector and the semidefinite relaxation (SDR) technique is contributed, which is shown to capable of efficiently solving the constrained min-power problem. Simulation results are given which illustrate the effectiveness of the proposed algorithm, which is found to sacrifice only small quantities of transmit power in return for a substantial increase in robustness against CSI imperfection.
We propose a new scheme for the robust estimation of the millimeter wave (mmWave) channel. Our approach is based on a sparse formulation of the channel estimation problem coupled with a frame theoretic representation of the sensing dictionary. To clarify, under this approach, the combined effect of transmit precoders and receive beamformers is modeled by a single frame, whose design is optimized to improve the accuracy of the sparse reconstruction problem to which the channel estimation problem is ultimately reduced. The optimized sensing dictionary frame is then decomposed via a Kronecker decomposition back into the precoding and beamforming vectors used by the transmitter and receiver. Simulation results illustrate the significant gain in estimation accuracy obtained over state of the art alternatives. As a bonus, the work offers new insights onto the sparse mmWave-multiple-input multiple-output (MIMO) channel estimation problem by casting the trade-off between correlation and variation range in terms of frame coherence and tightness
We consider a bi-directional Full-Duplex (FD) Multiple-Input Multiple-Output (MIMO) communication system in which nodes are capable of performing transitter (TX)- Receiver (RX) digital precoding/combining and multi-tap analog cancellation, and have individual Signal-to-Interference-plus-Noise Ratio (SINR) requirements. We present an iterative algorithm for the TX powers minimization that includes closed-form expressions for the TX/RX digital beamformers at each algorithmic iteration step. Our representative simulation results demonstrate that the proposed algorithm can reduce residual Self-Interference (SI) due to FD operation to below -110dB, which is the typical noise floor level for wireless communications. In addition, our design outperforms relevant recent solutions proposed for 2-user MIMO systems (the so called MIMO X channel) in terms of both power efficiency and computational complexity.