On the Achievable Rates of Decentralized Equalization in Massive MU-MIMO Systems

Massive multi-user (MU) multiple-input multiple-output (MIMO) promises significant gains in spectral efficiency compared to traditional, small-scale MIMO technology. Linear equalization algorithms, such as zero forcing (ZF) or minimum mean-square error (MMSE)-based methods, typically rely on centralized processing at the base station (BS), which results in (i) excessively high interconnect and chip input/output data rates, and (ii) high computational complexity. In this paper, we investigate the achievable rates of decentralized equalization that mitigates both of these issues. We consider two distinct BS architectures that partition the antenna array into clusters, each associated with independent radio-frequency chains and signal processing hardware, and the results of each cluster are fused in a feedforward network. For both architectures, we consider ZF, MMSE, and a novel, non-linear equalization algorithm that builds upon approximate message passing (AMP), and we theoretically analyze the achievable rates of these methods. Our results demonstrate that decentralized equalization with our AMP-based methods incurs no or only a negligible loss in terms of achievable rates compared to that of centralized solutions.Comment: Will be presented at the 2017 IEEE International Symposium on Information Theor

Local-Partial Signal Combining Schemes for Cell-Free Large-Scale MU-MIMO Systems with Limited Fronthaul Capacity and Spatial Correlation Channels

Cell-free large-scale multi-user MIMO is a promising technology for the 5G-and-beyond mobile communication networks. Scalable signal processing is the key challenge in achieving the benefits of cell-free systems. This study examines a distributed approach for cell-free deployment with user-centric configuration and finite fronthaul capacity. Moreover, the impact of scaling the pilot length, the number of access points (APs), and the number of antennas per AP on the achievable average spectral efficiency are investigated. Using the dynamic cooperative clustering (DCC) technique and large-scale fading decoding process, we derive an approximation of the signal-tointerference-plus-noise ratio in the criteria of two local combining schemes: Local-Partial Regularized Zero Forcing (RZF) and Local Maximum Ratio (MR). The results indicate that distributed approaches in the cell-free system have the advantage of decreasing the fronthaul signaling and the computing complexity. The results also show that the Local-Partial RZF provides the highest average spectral efficiency among all the distributed combining schemes because the computational complexity of the Local-Partial RZF is independent of the UTs. Therefore, it does not grow as the number of user terminals (UTs) increases

Energy and spectral efficiency tradeoff in wireless communication

In the wireless communication world, a significant number of new user equipments is connecting to the network each and every day, and day after day this amount is increasing with no known bounds. Diverse quality of service (QoS) along with better system throughput are the crying needs at present. With the advancement in the field of massive multiple-input multiple-output (MMIMO) and Internet-of-things (IoT), the QoS is provided smoothly with the limited spectrum by the wireless operator. Hundreds of antenna elements in the digital arrays are set up at the base station in order to provide the smooth coverage and the best throughput within these spectra. However, implementing hundreds of antenna elements with associated a huge number of RF chains for digital beamforming consumes too much energy. Energy efficiency optimization has become a requirement at the present stage of wireless infrastructure. Due to the conflicting nature between the energy efficiency and the spectral efficiency, it is hard to make a balance. This thesis investigates how to achieve a good tradeoff between the energy and the spectral efficiency with maximum throughput outcomes from MMIMO, with the help of existing topologies and a futuristic perspective. Although the signal noise power is less in massive MIMO than the conventional cellular system, it still needs to be decreased and at the same time, the average channel gain per user equipment must be increased. Fixed power requirement for control signaling and load-independent power of backhaul infrastructure must be cut at least by a factor two as well as the power amplifier efficiency has to increase by 10% than LTE networks. The minimum mean square error (MMSE) estimator can be a possible solution in terms of the energy and the spectral efficiency despite having computational complexity which can be solved with the aid of Moore’s law and it is proposed by the non-profit research organization IMEC, which has developed an online web tool for observing and predicting contemporary as well as futuristic cellular base station’s power consumption. It supports various types of base stations with a wide range of operating conditions. The multicell minimum mean square error (M-MMSE) scheme can perform better than other existing schemes and showcase satisfactory tradeoff with frequency reuse factor higher than 2, where regularized zero-forcing (RZF) and maximum ratio (MR) combining fall down their capabilities for performing. With the precipitous rising of IoT, the Narrowband Internet-of-things (NB-IoT) may play an efficient supportive role if we can collaborate it with MMIMO. With its low power, wide area topologies combining with MMIMO technologies can show better tradeoffs. Due to its narrow bandwidth, the signal noise power would be less compared to the existent wideband topologies, and the average channel gain of active user equipment would be higher too. Hence it will give a great impact in terms of the tradeoff between energy and the spectral efficiency which is addressed in this thesis

Energy and spectral efficiency tradeoff in wireless communication

In the wireless communication world, a significant number of new user equipments is connecting to the network each and every day, and day after day this amount is increasing with no known bounds. Diverse quality of service (QoS) along with better system throughput are the crying needs at present. With the advancement in the field of massive multiple-input multiple-output (MMIMO) and Internet-of-things (IoT), the QoS is provided smoothly with the limited spectrum by the wireless operator. Hundreds of antenna elements in the digital arrays are set up at the base station in order to provide the smooth coverage and the best throughput within these spectra. However, implementing hundreds of antenna elements with associated a huge number of RF chains for digital beamforming consumes too much energy. Energy efficiency optimization has become a requirement at the present stage of wireless infrastructure. Due to the conflicting nature between the energy efficiency and the spectral efficiency, it is hard to make a balance. This thesis investigates how to achieve a good tradeoff between the energy and the spectral efficiency with maximum throughput outcomes from MMIMO, with the help of existing topologies and a futuristic perspective. Although the signal noise power is less in massive MIMO than the conventional cellular system, it still needs to be decreased and at the same time, the average channel gain per user equipment must be increased. Fixed power requirement for control signaling and load-independent power of backhaul infrastructure must be cut at least by a factor two as well as the power amplifier efficiency has to increase by 10% than LTE networks. The minimum mean square error (MMSE) estimator can be a possible solution in terms of the energy and the spectral efficiency despite having computational complexity which can be solved with the aid of Moore’s law and it is proposed by the non-profit research organization IMEC, which has developed an online web tool for observing and predicting contemporary as well as futuristic cellular base station’s power consumption. It supports various types of base stations with a wide range of operating conditions. The multicell minimum mean square error (M-MMSE) scheme can perform better than other existing schemes and showcase satisfactory tradeoff with frequency reuse factor higher than 2, where regularized zero-forcing (RZF) and maximum ratio (MR) combining fall down their capabilities for performing. With the precipitous rising of IoT, the Narrowband Internet-of-things (NB-IoT) may play an efficient supportive role if we can collaborate it with MMIMO. With its low power, wide area topologies combining with MMIMO technologies can show better tradeoffs. Due to its narrow bandwidth, the signal noise power would be less compared to the existent wideband topologies, and the average channel gain of active user equipment would be higher too. Hence it will give a great impact in terms of the tradeoff between energy and the spectral efficiency which is addressed in this thesis

Toward Massive MIMO 2.0: Understanding Spatial Correlation, Interference Suppression, and Pilot Contamination

Since the seminal paper by Marzetta from 2010, Massive MIMO has changed from being a theoretical concept with an infinite number of antennas to a practical technology. The key concepts are adopted into the 5G New Radio Standard and base stations (BSs) with M = 64 fully digital transceivers have been commercially deployed in sub-6GHz bands. The fast progress was enabled by many solid research contributions of which the vast majority assume spatially uncorrelated channels and signal processing schemes developed for single-cell operation. These assumptions make the performance analysis and optimization of Massive MIMO tractable but have three major caveats: 1) practical channels are spatially correlated; 2) large performance gains can be obtained by multicell processing, without BS cooperation; 3) the interference caused by pilot contamination creates a finite capacity limit, as M → ∞. There is a thin line of papers that avoided these caveats, but the results are easily missed. Hence, this tutorial article explains the importance of considering spatial channel correlation and using signal processing schemes designed for multicell networks. We present recent results on the fundamental limits of Massive MIMO, which are not determined by pilot contamination but the ability to acquire channel statistics. These results will guide the journey towards the next level of Massive MIMO, which we call "Massive MIMO 2.0"

Two-Stage Subspace Constrained Precoding in Massive MIMO Cellular Systems

We propose a subspace constrained precoding scheme that exploits the spatial channel correlation structure in massive MIMO cellular systems to fully unleash the tremendous gain provided by massive antenna array with reduced channel state information (CSI) signaling overhead. The MIMO precoder at each base station (BS) is partitioned into an inner precoder and a Transmit (Tx) subspace control matrix. The inner precoder is adaptive to the local CSI at each BS for spatial multiplexing gain. The Tx subspace control is adaptive to the channel statistics for inter-cell interference mitigation and Quality of Service (QoS) optimization. Specifically, the Tx subspace control is formulated as a QoS optimization problem which involves an SINR chance constraint where the probability of each user's SINR not satisfying a service requirement must not exceed a given outage probability. Such chance constraint cannot be handled by the existing methods due to the two stage precoding structure. To tackle this, we propose a bi-convex approximation approach, which consists of three key ingredients: random matrix theory, chance constrained optimization and semidefinite relaxation. Then we propose an efficient algorithm to find the optimal solution of the resulting bi-convex approximation problem. Simulations show that the proposed design has significant gain over various baselines.Comment: 13 pages, accepted by IEEE Transactions on Wireless Communication