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   Reconfigurable Intelligent Surface-Assisted Massive MIMO: Favorable propagation, channel hardening, and rank deficiencyTrinh, van Chien  ; ; Chatzinotas, Symeon et alin IEEE Signal Processing Magazine (2022), 39(3), Massive multiple-input multiple-output (MIMO) and reconfigurable intelligent surface (RIS) are two promising technologies for 5G-and-beyond wireless networks, capable of providing large array gain and ... [more ▼] Massive multiple-input multiple-output (MIMO) and reconfigurable intelligent surface (RIS) are two promising technologies for 5G-and-beyond wireless networks, capable of providing large array gain and multiuser spatial multiplexing. Without requiring additional frequency bands, those technologies offer significant improvements in both spectral and energy efficiency by simultaneously serving many users. The performance analysis of an RIS-assisted massive MIMO system as a function of channel statistics relies heavily on fundamental properties, including favorable propagation, channel hardening, and rank deficiency. The coexistence of both direct and indirect links results in aggregated channels, whose properties are the main concerns of this “Lecture Notes” article. For practical systems with a finite number of antennas and engineered scattering elements of the RIS, we evaluate the corresponding deterministic metrics, with Rayleigh fading channels as a typical example. [less ▲] Detailed reference viewed: 21 (0 UL) Toward Millimeter-Wave Joint Radar Communications: A Signal Processing Perspective; Shankar, Bhavani ; et alin IEEE Signal Processing Magazine (2019), 36(5), 100-114 Synergistic design of communications and radar systems with common spectral and hardware resources is heralding a new era of efficiently utilizing a limited radio-frequency (RF) spectrum. Such a joint ... [more ▼] Synergistic design of communications and radar systems with common spectral and hardware resources is heralding a new era of efficiently utilizing a limited radio-frequency (RF) spectrum. Such a joint radar communications (JRC) model has advantages of low cost, compact size, less power consumption, spectrum sharing, improved performance, and safety due to enhanced information sharing. Today, millimeter-wave (mmwave) communications have emerged as the preferred technology for short distance wireless links because they provide transmission bandwidth that is several gigahertz wide. This band is also promising for short-range radar applications, which benefit from the high-range resolution arising from large transmit signal bandwidths. Signal processing techniques are critical to the implementation of mm-wave JRC systems. Major challenges are joint waveform design and performance criteria that would optimally trade off between communications and radar functionalities. Novel multiple-input, multiple-output (MIMO) signal processing techniques are required because mm-wave JRC systems employ large antenna arrays. There are opportunities to exploit recent advances in cognition, compressed sensing, and machine learning to reduce required resources and dynamically allocate them with low overheads. This article provides a signal processing perspective of mm-wave JRC systems with an emphasis on waveform design. [less ▲] Detailed reference viewed: 153 (4 UL)Signal Processing for High-Throughput Satellites: Challenges in New Interference-Limited Scenarios; ; Shankar, Bhavani et alin IEEE Signal Processing Magazine (2019) The field of satellite communications (SatCom) is enjoying renewed attention in the global telecommunications market, and high-throughput satellites, with their multiple spot beams, are critical ... [more ▼] The field of satellite communications (SatCom) is enjoying renewed attention in the global telecommunications market, and high-throughput satellites, with their multiple spot beams, are critical components for delivering the rates that will be demanded in the future. In this article, we present the state of the art and the open research challenges in the area of high-throughput satellites, with a focus on signal processing approaches for efficient interference mitigation. [less ▲] Detailed reference viewed: 94 (8 UL)Optimal Multiuser Transmit Beamforming: A Difficult Problem with a Simple Solution Structure [Lecture Notes]; ; Ottersten, Björn in IEEE Signal Processing Magazine (2014), 31(4), 142-148 Transmit beamforming is a versatile technique for signal transmission from an array of antennas to one or multiple users [1]. In wireless communications, the goal is to increase the signal power at the ... [more ▼] Transmit beamforming is a versatile technique for signal transmission from an array of antennas to one or multiple users [1]. In wireless communications, the goal is to increase the signal power at the intended user and reduce interference to nonintended users. A high signal power is achieved by transmitting the same data signal from all antennas but with different amplitudes and phases, such that the signal components add coherently at the user. Low interference is accomplished by making the signal components add destructively at nonintended users. This corresponds mathematically to designing beamforming vectors (that describe the amplitudes and phases) to have large inner products with the vectors describing the intended channels and small inner products with nonintended user channels. [less ▲] Detailed reference viewed: 304 (18 UL)Multiobjective Signal Processing Optimization : The way to balance conflicting metrics in 5G systems; ; et al in IEEE Signal Processing Magazine (2014), 31(6), 14-23 The evolution of cellular networks is driven by the dream of ubiquitous wireless connectivity: any data service is instantly accessible everywhere. With each generation of cellular networks, we have moved ... [more ▼] The evolution of cellular networks is driven by the dream of ubiquitous wireless connectivity: any data service is instantly accessible everywhere. With each generation of cellular networks, we have moved closer to this wireless dream; first by delivering wireless access to voice communications, then by providing wireless data services, and recently by delivering a Wi-Fi-like experience with wide-area coverage and user mobility management. The support for high data rates has been the main objective in recent years [1], as seen from the academic focus on sum-rate optimization and the efforts from standardization bodies to meet the peak rate requirements specified in IMT-Advanced. In contrast, a variety of metrics/objectives are put forward in the technological preparations for fifth-generation (5G) networks: higher peak rates, improved coverage with uniform user experience, higher reliability and lower latency, better energy efficiency (EE), lower-cost user devices and services, better scalability with number of devices, etc. These multiple objectives are coupled, often in a conflicting manner such that improvements in one objective lead to degradation in the other objectives. Hence, the design of future networks calls for new optimization tools that properly handle the existence of multiple objectives and tradeoffs between them. [less ▲] Detailed reference viewed: 158 (6 UL)Convex Optimization-based Beamforming: From Receive to Transmit and Network Designs; ; et al in IEEE Signal Processing Magazine (2010), 27(3), 62-75 In this article, an overview of advanced convex optimization approaches to multisensor beamforming is presented, and connections are drawn between different types of optimization-based beamformers that ... [more ▼] In this article, an overview of advanced convex optimization approaches to multisensor beamforming is presented, and connections are drawn between different types of optimization-based beamformers that apply to a broad class of receive, transmit, and network beamformer design problems. It is demonstrated that convex optimization provides an indispensable set of tools for beamforming, enabling rigorous formulation and effective solution of both long-standing and emerging design problems. [less ▲] Detailed reference viewed: 248 (2 UL)When will smart antennas be ready for the market? Part II - results; ; et al in IEEE Signal Processing Magazine (2005), 22(6), 174176 Detailed reference viewed: 38 (0 UL)Antenna Arrays for CommunicationsOttersten, Björn ; in IEEE Signal Processing Magazine (1999), 16(2), 2527 Detailed reference viewed: 28 (0 UL) |
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