Hampei Sasahara

Massive overloaded MIMO signal detection via convex optimization with proximal splitting

In this paper, we propose signal detection schemes for massive overloaded multiple-input multiple-output (MIMO) systems, where the number of receive antennas is less than that of transmitted streams. Using the idea of the sum-of-absolute-value (SOAV) optimization, we formulate the signal detection as a convex optimization problem, which can be solved via a fast algorithm based on Douglas-Rachford splitting. To improve the performance, we also propose an iterative approach to solve the optimization problem with weighting parameters update in a cost function. Simulation results show that the proposed scheme can achieve much better bit error rate (BER) performance than conventional schemes, especially in large-scale overloaded MIMO systems.

Multiuser detection by MAP estimation with sum-of-absolute-values relaxation

In this article, we consider multiuser detection that copes with multiple access interference caused in star-topology machine-to-machine (M2M) communications. We assume that the transmitted signals are discrete-valued (e.g. binary signals taking values of ±1), which is taken into account as prior information in detection. We formulate the detection problem as the maximum a posteriori (MAP) estimation, which is relaxed to a convex optimization called the sum-of-absolute-values (SOAV) optimization. The SOAV optimization can be efficiently solved by a proximal splitting algorithm, for which we give the proximity operator in a closed form. Numerical simulations are shown to illustrate the effectiveness of the proposed approach compared with the linear minimum mean-square-error (LMMSE) and the least absolute shrinkage and selection operator (LASSO) methods.

Symbol Detection for Faster-Than-Nyquist Signaling by Sum-of-Absolute-Values Optimization

In this letter, we propose a new symbol detection method in faster-than-Nyquist signaling for effective data transmission. Based on the frame theory, the symbol detection problem is described as underdetermined linear equations on a finite alphabet. While the problem is itself NP (nondeterministic polynomial-time) hard, we propose convex relaxation using the sum-of-absolute-values optimization, which can be efficiently solved by proximal splitting. Simulation results are shown to illustrate the effectiveness of the proposed method compared to a recent ℓ∞-based (ellinfinity-based) method.

Sampled-data H ∞ design of couplingwave cancelers in single-frequency full-duplex relay stations

In this article, we propose sampled-data H∞ design of digital filters that cancel the continuous-time effect of coupling waves in a single-frequency full-duplex relay station. In this study, we model a relay station as a continuous-time system while conventional researches treat it as a discrete-time system. For a continuous-time model, we propose digital feedforward and feedback cancelers based on the sampled-data control theory to cancel coupling waves taking intersample behavior into account. Simulation results are shown to illustrate the effectiveness of the proposed method.

Faster-than-nyquist signaling by sum-of-absolute-values optimization

In this article, we propose a new signal detection method for faster-than-Nyquist signaling (FTNS) systems for high-speed data communications. The symbol detection problem is formulated as under-determined linear equations on a finite set. We transform the problem to the sum-of-absolute-values (SOAV) optimization, for which we derive a fast algorithm based on proximal splitting. The effectiveness of the proposed method is shown by simulations.

Sampled-data H∞ optimization for self-interference suppression in baseband signal subspaces

In this article, we propose a design method of self-interference cancelers for wireless relay stations taking account of the baseband signal subspace. The problem is first formulated as a sampled-data H∞ control problem with a generalized sampler and a generalized hold, which can be reduced to a discrete-time ℓ2-induced norm minimization problem. Taking account of the implementation of the generalized sampler and hold, we adopt the filter-sampler structure for the generalized sampler, and the uspampler-filter-hold structure for the generalized hold. Under these implementation constraints, we reformulate the problem as a standard discrete-time H∞ control problem by using the discrete-time lifting technique. A simulation result is shown to illustrate the effectiveness of the proposed method.

Multiuser Detection Based on MAP Estimation With Sum-of-Absolute-Values Relaxation

In this paper, we consider multiuser detection that copes with multiple access interference caused in low-rate wireless communication systems with a large number of nodes, such as machine-to-machine communications. We take account of discreteness of transmitted signals (e.g., binary signals taking values of

Loop-back interference suppression for OFDM signals via sampled-data control

\pm 1

Digital Cancelation of Self-Interference for Single-Frequency Full-Duplex Relay Stations via Sampled-Data Control

), which we utilize as prior information in detection. We first formulate the detection problem as a maximum a posteriori estimation problem. Since this problem is highly nonconvex and discontinuous, we propose convex relaxation based on the sum-of-absolute-values (SOAV) optimization, which can be efficiently solved by proximal splitting. We also derive an upper bound of the relaxation error by the SOAV optimization in noisy cases based on the restricted isometry property. Numerical simulations are shown to illustrate the effectiveness of the proposed approach.

Glocal control for network systems via hierarchical state-space expansion

In this article, we consider the problem of loop-back interference suppression for orthogonal frequency division multiplexing (OFDM) signals in amplify-and-forward single-frequency full-duplex relay stations. The loop-back interference makes the system a closed-loop system, and hence it is important not only to suppress the interference but also to stabilize the system. For this purpose, we propose sampled-data H∞ design of digital filters that ensure the stability of the system and suppress the continuous-time effect of interference at the same time. Simulation results are shown to illustrate the effectiveness of the proposed method.