Deterministic Dataflow Research Articles

Massive MIMO Detection Techniques: A Survey

Massive multiple-input multiple-output (MIMO) is a key technology to meet the user demands in performance and quality of services (QoS) for next generation communication systems. Due to a large number of antennas and radio frequency (RF) chains, complexity of the symbol detectors increased rapidly in a massive MIMO uplink receiver. Thus, the research to find the perfect massive MIMO detection algorithm with optimal performance and low complexity has gained a lot of attention during the past decade. A plethora of massive MIMO detection algorithms has been proposed in the literature. The aim of this paper is to provide insights on such algorithms to a generalist of wireless communications. We garner the massive MIMO detection algorithms and classify them so that a reader can find a distinction between different algorithms from a wider range of solutions. We present optimal and near-optimal detection principles specifically designed for the massive MIMO system such as detectors based on a local search, belief propagation and box detection. In addition, we cover detectors based on approximate inversion, which has gained popularity among the VLSI signal processing community due to their deterministic dataflow and low complexity. We also briefly explore several nonlinear small-scale MIMO (2-4 antenna receivers) detectors and their applicability in the massive MIMO context. In addition, we present recent advances of detection algorithms which are mostly based on machine learning or sparsity based algorithms. In each section, we also mention the related implementations of the detectors. A discussion of the pros and cons of each detector is provided.

An extensional fixed-point semantics for nondeterministic data flow

A fixed point semantics for nondeterministic data flow is introduced which refines and extends work of Park (1983). It can be seen also as an extension to the general case of Kahn's (1974) successful fixed point semantics for deterministic data flow. An associativity result for network construction is proved which shows that anomalies such as those of Brock and Ackerman do not arise in this semantics. The semantics is shown to be extensional, in the natural sense that nondeterministic processes which induce identical input-output relations in all contexts are equal.

A fixpoint semantics for nondeterministic data flow

Criteria for adequacy of a data flow semantics are discussed and Kahn's successful semantics for functional (deterministic) data flow is reviewed. Problems arising from nondeterminism are introduced and the paper's approach to overcoming them is introduced. The approach is based on generalizing the notion of input-output relation, essentially to a partially ordered multiset of input-output histories. The Brock-Ackerman anomalies concerning the input-output relation model of nondeterministic data flow are reviewed, and it is indicated how the proposed approach avoids them. A new anomaly is introduced to motivate the use of multisets. A formal theory of asynchronous processes is then developed. The main result is that the operation of forming a process from a network of component processes is associative. This result shows that the approach is not subject to anomalies such as that of Brock and Ackerman.