Matched Filtering in Massive MU-MIMO Systems

<p>This thesis considers the analysis of matched filtering (MF) processing in massive multi-user multiple-input-multiple-output (MU-MIMO) wireless communication systems. The main focus is the analysis of system performance for combinations of two linear processers, analog maximum ratio combining (MRC) and digital MRC. We consider implementations of these processing techniques both at a single base-station (BS) and in distributed BS layouts. We further consider extremely low complexity distributed variants of MRC for such systems. Since MRC relies on the massive MIMO properties of favourable propagation (FP) and channel hardening, we also present a detailed analysis of FP and channel hardening. This analysis employs modern ray-based models rather than classical channel models as the models are more reliable for the large arrays and higher frequencies envisaged for future systems. The importance of MRC processing is being driven by the emergence of massive MIMO and millimetre wave as strong candidates for next generation wireless communication systems. Massive MIMO explores the spatial dimension by providing significant increases in data rate, link reliability and energy efficiency. However, with a large number of antennas co-located in a fixed physical space, correlation between the elements of antennas may have a negative impact. Distributed systems, where the total number of antennas are divided into different locations, make this problem less serious. Also, linear processing techniques, analog MRC and digital MRC, due to their simplicity and efficiency, are more practical in massive MU-MIMO systems. For these reasons we consider MRC processing in both co-located and distributed scenarios. Although distributed systems reduce the adverse impact of correlation caused by closely-spaced large antenna arrays by dividing the antennas into multiple antenna clusters, the correlation within the cluster still exists. Thus, we extend MRC analysis for massive MIMO to correlated channels. Approximations of expected per-user spectrum efficiency (SE) with correlation effects for massive MIMO systems with analog MRC and digital MRC are derived. Useful insights are given for future system deployments. A convergence analysis of the interference behaviour under different correlation models is presented. Furthermore, a distributed fully cooperative system, where all the received signals are sent to the central processor, offers attractive performance gains but at the cost of high computational complexity at the central node. Thus, we propose four low-complexity, two-stage processors, where only processed signals after local processing (first-stage) are transmitted to the global processing node (second-stage). We present analytical expressions for the expected per user SINR in an uplink distributed MU-MIMO system with two-stage beam-forming. This leads to an approximation of expected per-user SE. The analysis of both millimetre wave and massive MIMO systems requires a strong link to the physical environment and ray-based models are more practical and suitable for such systems. However, it is unclear how the key properties in conventional MIMO systems, such as FP and channel hardening, will behave in a ray-based channel model. In this thesis, remarkably simple and general results are obtained demonstrating that: a) channel hardening may or may nor occur depending on the nature of the channel models; b) FP is guaranteed for all models as long as the ray angles are continuous random variables; c) we also propose a novel system metric, denoted large system potential (LSP) as the ratio of the mean desired signal power to the total mean interference power, where both the numbers of antennas and end-users are growing to infinity at a fixed ratio. We derive simple approximations to LSP and demonstrate that LSP will not normally hold as the mean interference power usually grows logarithmically relative to the mean signal power.</p>

Matched Filtering in Massive MU-MIMO Systems

<p>This thesis considers the analysis of matched filtering (MF) processing in massive multi-user multiple-input-multiple-output (MU-MIMO) wireless communication systems. The main focus is the analysis of system performance for combinations of two linear processers, analog maximum ratio combining (MRC) and digital MRC. We consider implementations of these processing techniques both at a single base-station (BS) and in distributed BS layouts. We further consider extremely low complexity distributed variants of MRC for such systems. Since MRC relies on the massive MIMO properties of favourable propagation (FP) and channel hardening, we also present a detailed analysis of FP and channel hardening. This analysis employs modern ray-based models rather than classical channel models as the models are more reliable for the large arrays and higher frequencies envisaged for future systems. The importance of MRC processing is being driven by the emergence of massive MIMO and millimetre wave as strong candidates for next generation wireless communication systems. Massive MIMO explores the spatial dimension by providing significant increases in data rate, link reliability and energy efficiency. However, with a large number of antennas co-located in a fixed physical space, correlation between the elements of antennas may have a negative impact. Distributed systems, where the total number of antennas are divided into different locations, make this problem less serious. Also, linear processing techniques, analog MRC and digital MRC, due to their simplicity and efficiency, are more practical in massive MU-MIMO systems. For these reasons we consider MRC processing in both co-located and distributed scenarios. Although distributed systems reduce the adverse impact of correlation caused by closely-spaced large antenna arrays by dividing the antennas into multiple antenna clusters, the correlation within the cluster still exists. Thus, we extend MRC analysis for massive MIMO to correlated channels. Approximations of expected per-user spectrum efficiency (SE) with correlation effects for massive MIMO systems with analog MRC and digital MRC are derived. Useful insights are given for future system deployments. A convergence analysis of the interference behaviour under different correlation models is presented. Furthermore, a distributed fully cooperative system, where all the received signals are sent to the central processor, offers attractive performance gains but at the cost of high computational complexity at the central node. Thus, we propose four low-complexity, two-stage processors, where only processed signals after local processing (first-stage) are transmitted to the global processing node (second-stage). We present analytical expressions for the expected per user SINR in an uplink distributed MU-MIMO system with two-stage beam-forming. This leads to an approximation of expected per-user SE. The analysis of both millimetre wave and massive MIMO systems requires a strong link to the physical environment and ray-based models are more practical and suitable for such systems. However, it is unclear how the key properties in conventional MIMO systems, such as FP and channel hardening, will behave in a ray-based channel model. In this thesis, remarkably simple and general results are obtained demonstrating that: a) channel hardening may or may nor occur depending on the nature of the channel models; b) FP is guaranteed for all models as long as the ray angles are continuous random variables; c) we also propose a novel system metric, denoted large system potential (LSP) as the ratio of the mean desired signal power to the total mean interference power, where both the numbers of antennas and end-users are growing to infinity at a fixed ratio. We derive simple approximations to LSP and demonstrate that LSP will not normally hold as the mean interference power usually grows logarithmically relative to the mean signal power.</p>

Waveform Design for Energy Efficient OFDM Transmission

In this chapter, a green radio transmission using the binary phase-shift keying (BPSK) modulated orthogonal frequency-division multiplexing (OFDM) signal is addressed. First, the OFDM transmission signal is clearly stated. For a specified performance of the system, the least transmit power occurs by the optimal OFDM shape, which is designed to minimize the average inter-carrier interference power taking into account the characteristic of the transmit antenna and the detection process at the receiver. The optimal waveform is obtained by applying the calculus of variations, which leads to a set of differential equations (known as Euler equations) with constraint and boundary conditions. Results show the transmission effectiveness of the proposed technique in the shaping of the signal, as well as its potential to be further applied to smart context-aware green wireless communications.

Enhancing the Reliability of Communication between Vehicle and Everything (V2X) Based on Deep Learning for Providing Efficient Road Traffic Information

Developing efficient communication between vehicles and everything (V2X) is a challenging task, mainly due to the characteristics of vehicular networks, which include rapid topology changes, large-scale sizes, and frequent link disconnections. This article proposes a deep learning model to enhance V2X communication. Various channel conditions such as interference, channel noise, and path loss affect the communication between a vehicle (V) and everything (X). Thus, the proposed model aims to determine the required optimum interference power to enhance connectivity, comply with the quality of service (QoS) constraints, and improve the communication link reliability. The proposed model fulfills the best QoS in terms of four metrics, namely, achievable data rate (Rb), packet delivery ratio (PDR), packet loss rate (PLR), and average end-to-end delay (E2E). The factors to be considered are the distribution and density of vehicles, average length, and minimum safety distance between vehicles. A mathematical formulation of the optimum required interference power is presented to achieve the given objectives as a constrained optimization problem, and accordingly, the proposed deep learning model is trained. The obtained results show the ability of the proposed model to enhance the connectivity between V2X for improving road traffic information efficiency and increasing road traffic safety.

A Deep Learning Based Automatic Interference Avoidance Resource Allocation Scheme for SCMA Systems

Sparse code multiple access (SCMA) is able to provide high spectral efficiency and massive connectivity, hence it is considered as a promising scheme for the fifth generation (5G) systems. This paper proposed a radio resource allocation scheme based on deep learning for SCMA systems, with the aim to automatically avoid the inter-cell interference. A long short term memory (LSTM) network is adopted to learn the past interference characteristics and predict the interference power in the current subframe. Radio resource blocks with less predicted interference power are then selected for users to transmit signals. Simulation results show that the proposed scheme outperforms the moving average prediction method and has significant gains over the random radio resource block allocation in terms of achievable bit error rate in SCMA systems.

Spatial Domain Suppression of Co-Channel Interference: A Reflection Amplifiers Surface (RAS) Approach

This paper adopts a novel reflection amplifiers surface (RAS) to suppress the co-channel interference in the spatial domain. The RAS can reflect and amplify the electromagnetic wave with phase shifts by designing the reflection coefficients, which enables it more flexibly reconfigure the wireless propagation environment, and even suppress interference channel gain. In this paper, a transmitter and an interferer send the desired signal and interference to the receiver, respectively, and a RAS is placed to suppress the unknown interference. First, we design the reflection coefficients for optimizing the interference suppression ratio, and prove that when the number of reflection amplifiers is greater than the number of antennas at the interferer, the interference can be perfectly suppressed. Next, a capacity maximization problem is formulated to design the optimal reflection coefficients, and an iterative algorithm based on fractional programming and the convex-concave procedure is proposed to obtain the solution for this problem. Moreover, the closed-form expression of the maximal capacity is obtained in the strong interference power case. In addition, this paper shows the upper and lower boundaries of the maximal capacity and discusses what kind of the channel conditions achieve the upper and lower boundaries. Lastly, the above results are generalized to the multiple interferer scenario.

Spatial Domain Suppression of Co-Channel Interference: A Reflection Amplifiers Surface (RAS) Approach

This paper adopts a novel reflection amplifiers surface (RAS) to suppress the co-channel interference in the spatial domain. The RAS can reflect and amplify the electromagnetic wave with phase shifts by designing the reflection coefficients, which enables it more flexibly reconfigure the wireless propagation environment, and even suppress interference channel gain. In this paper, a transmitter and an interferer send the desired signal and interference to the receiver, respectively, and a RAS is placed to suppress the unknown interference. First, we design the reflection coefficients for optimizing the interference suppression ratio, and prove that when the number of reflection amplifiers is greater than the number of antennas at the interferer, the interference can be perfectly suppressed. Next, a capacity maximization problem is formulated to design the optimal reflection coefficients, and an iterative algorithm based on fractional programming and the convex-concave procedure is proposed to obtain the solution for this problem. Moreover, the closed-form expression of the maximal capacity is obtained in the strong interference power case. In addition, this paper shows the upper and lower boundaries of the maximal capacity and discusses what kind of the channel conditions achieve the upper and lower boundaries. Lastly, the above results are generalized to the multiple interferer scenario.