VLSI Designs for Joint Channel Estimation and Data Detection in Large SIMO Wireless Systems

Abstract

Abstract-Channel estimation errors have a critical impact on the reliability of wireless communication systems. While virtually all existing wireless receivers separate channel estimation from data detection, it is well known that joint channel estimation and data detection (JED) significantly outperforms conventional methods at the cost of high computational complexity. In this paper, we propose a novel JED algorithm and corresponding VLSI designs for large single-input multiple-output (SIMO) wireless systems that use constant-modulus constellations. The proposed algorithm is referred to as PRojection Onto conveX hull (PrOX) and relies on biconvex relaxation (BCR), which enables us to efficiently compute an approximate solution of the maximumlikelihood JED problem. Since BCR solves a biconvex problem via alternating optimization, we provide a theoretical convergence analysis for PrOX. We design a scalable, high-throughput VLSI architecture that uses a linear array of processing elements to minimize hardware complexity. We develop corresponding field-programmable gate array (FPGA) and application-specific integrated circuit (ASIC) designs, and we demonstrate that PrOX significantly outperforms the only other existing JED design in terms of throughput, hardware-efficiency, and energy-efficiency.Index Terms-FPGA and ASIC designs, joint channel estimation and data detection (JED), large single-input multiple-output (SIMO) wireless systems, biconvex relaxation (BCR).

Joint Channel Estimation and Data Detection in Cell-Free Massive MU-MIMO Systems

“…2) Joint Channel Estimation and Data Detection: JED has been studied in the small-scale MIMO literature [23]- [27]. While the complexity of such methods does not scale well to large systems, an efficient JED algorithm has been proposed in [28] for massive single-input multiple-output (SIMO) systems. For massive MU-MIMO systems, JED algorithms have been proposed only recently in [29]- [35].…”

“…We now provide means that enable us to approximately solve the MAP-JED problem in ( 8) with manageable complexity. We start by relaxing the discrete constellation set Q to its convex hull, which is defined as [28]…”

Minimizing Pilot Overhead in Cell-Free Massive MIMO Systems via Joint Estimation and Detection

“…Joint channel estimation and data detection has been studied in [9]- [16] for small-scale MIMO systems. For massive singleinput multiple-output (SIMO) wireless systems, an efficient JED algorithm and VLSI design has been proposed in [17]. For massive MU-MIMO systems, suitable algorithms have been proposed only recently [18]- [21].…”

“…While the problem (8) remains nonconvex, it is biconvex in H and S D as long as the parameter γ is sufficiently small (see, e.g., [17] for a precise justification of this property). We next show an FBS-based approach to solve the problem (8) approximately and at low complexity.…”

Soft-Output Joint Channel Estimation and Data Detection using Deep Unfolding

“…In the uplink of multiuser multiple-input multiple-output (MU-MIMO) systems, where user equipments (UEs) transmit pilots and data to a base station (BS), deploying optimal joint channel estimation and data detection (JED) is an elusive goal, and has been practicable only for small-scale MIMO systems due to the combinatorial nature and the high-dimensionality of the JED problem [1]- [3]. To overcome the high complexity of such methods, references [4], [5] proposed gradient-based algorithms that efficiently compute approximate solutions to the single-UE JED problem for large BS antenna arrays. The multi-UE JED case has been tackled recently in [6] for cellfree massive MU-MIMO systems, also using a gradient-based algorithm.…”