Phase locked loops (PLL) for RF carrier synthesis often employ oscillators that insert a considerable amount of time varying phase noise into the received signal. That noise must then be removed in digital basebandreceiver. This phase noise is an indivisible superposition of noise components from receiver and transmitter. Regarding to systems with multiple transmit and receive antennas (MIMO) and if multiple PLLs for carrier synthesis are used each of the superposed phase noise processes per transmit and receive antenna pair can be measured at the receiver. This paper provides a new scheme for high SNR scenarios that exploits spatial correlation between these overlaying phase noise processes at the receiver in order to improve estimation and compensation of the phase noise. Therefore the Wiener filter approach is applied.
In the current literature considering multi-cell multi-user massive multiple-input multiple-output (MU-Massive-MIMO) systems, equal uplink power allocation among users is typically assumed, which does not exploit the potential of peruser power control. By contrast, in this paper we apply multi-cell uplink power control, assuming the minimum mean-square-error receiver based on the pilot contaminated channel estimation and a very large but finite number of antennas at the base station. We derive the lower bound on the average post-processing uplink signal to interference-plus-noise ratio (SINR) with individual power assignment between pilot and data transmissions for each user, which facilitates a joint iterative uplink pilot and data power control strategy that minimizes the sum transmit power of all users subject to the per-user SINR and per-user power constraints. The convergence of the proposed algorithm to a unique fixed point optimal solution is discussed for both single-and multi-user scenarios. Numerical results indicate the significance of uplink power control which further improves the energy efficiency in MU-Massive-MIMO systems.
Abstract-Multiple-input multiple-output (MIMO) wireless transmission imposes huge challenges on the design of efficient hardware architectures for iterative receivers. A major challenge is soft-input soft-output (SISO) MIMO demapping, often approached by sphere decoding (SD). In this paper, we introduce the-to our best knowledge-first VLSI architecture for SISO SD applying a single tree-search approach. Compared with a soft-output-only base architecture similar to the one proposed by Studer et al. in IEEE J-SAC 2008, the architectural modifications for soft input still allow a one-node-per-cycle execution. For a 4×4 16-QAM system, the area increases by 57 % and the operating frequency degrades by 34 % only.Index Terms-VLSI architecture, Schnorr-Euchner (SE) enumeration, iterative multiple-input multiple-output (MIMO) decoding, soft-input soft-output (SISO) sphere decoding (SD)
Based on the Expectation Propagation framework, we derive a MIMO detector that consists of an interference cancellation (IC) step, followed by linear minimum mean square error (LMMSE) filtering. The distinguishing feature of our algorithm, compared to conventional IC-LMMSE detectors for iterative MIMO receivers, is the way how we compute the symbol statistics that are used in the IC and LMMSE steps. In an earlier publication, it has been shown empirically that full posterior feedback from the decoder to the IC-LMMSE detector can outperform the usual choice of extrinsic feedback, which seems to contradict the Turbo principle. The systematic derivation in the present paper provides an explanation of this phenomenon, and results in a further improved algorithm that outperforms the conventional IC-LMMSE detector with either extrinsic or posterior feedback.While we consider only multi-antenna systems, we note that the same principle can be applied to similar problems in signal processing, such as Turbo equalization for frequency-selective channels, or multi-user detection in CDMA systems. I. INTRODUCTIONIn this paper we examine algorithms for soft-input softoutput data detection in multi-antenna (MIMO) systems. The exact detector suffers from an exponential complexity in the number of bits per channel use, and is therefore infeasible for high-rate systems. This motivates the search for suboptimal algorithms with low computational complexity.A well-known strategy is to separate the spatially superimposed data streams with a linear MMSE filter, followed by simple stream-wise data detectors. In iterative receivers, where a non-uniform prior distribution of the data symbols is available due to the feedback from the channel decoder, the MMSE filter is affine instead of linear, or equivalently, can be seen as the concatenation of an interference cancellation (IC) step followed by LMMSE filtering. For these two steps, the mean and variance of the data symbols are needed, which are conventionally computed based on extrinsic feedback from the decoder (see [1] for a discussion on extrinsic vs. posterior feedback in the context of multi-user detection in CDMA systems). However, it has been observed experimentally that posterior feedback actually outperforms extrinsic feedback in certain scenarios [2], which seems to be a contradiction to the famous Turbo principle [3] of not counting the same information twice. To the best of our knowledge, a theoretical explanation of this phenomenon is still missing.
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