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In the past two decades convex optimization gained increasing popularity in signal processing and communications, as many fundamental problems in this area can be modelled, analyzed and solved using convex optimization theory and algorithms. In emerging large scale applications such as compressed sensing, massive MIMO and machine learning, the underlying optimization problems often exhibit convexity, however, the classic interior point methods do not scale well with the problem dimensions.

There exists a large variety of applications, for instance in estimation and detection as well as network optimization, that involve both integer (discrete) decision making andthe optimization of continuous parameters. The integer decision making requirementsusually stem from the nature of the problem. In many applications, the physical quantitiesto be optimized are naturally undividable. Think for example of a cellular networkin which a subset of users needs to be selected for transmission. For a given user aconnection is either established or not.

This paper considers the one-bit precoding problem for the multiuser downlink massive multiple-input multiple-output (MIMO) system with phase shift keying (PSK) modulation and focuses on the celebrated constructive interference (CI)-based problem formulation. The existence of the discrete one-bit constraint makes the problem generally hard to solve. In this paper, we propose an efficient negative ℓ 1 penalty approach for finding a high-quality solution of the considered problem.

This paper focuses on channel estimation for mmWave MIMO systems with 1-bit spatial sigma-delta analog-to-digital converters (ADCs) and digital-to-analog converters (DACs). The channel estimation performance with 1-bit spatial sigma-delta modulators (i.e., ADCs or DACs) depends on the quantization noise modeling. Therefore, we present a new method for modeling the quantization noise by leveraging the deterministic input-output relation of the 1-bit spatial sigma-delta modulator.

This paper proposes a computationally efficient algorithm to solve the joint data and activity detection problem for massive random access with massive multiple-input multiple-output (MIMO). The BS acquires the active devices and their data by detecting the transmitted preassigned nonorthogonal signature sequences. This paper employs a covariance based approach that formulates the detection problem as a maximum likelihood estimation (MLE) problem.