There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Wireless Communications

Mobile users' data rate and quality of service are limited by the fact that, within the duration of any given call, they experience severe variations in signal attenuation, thereby necessitating the use of some type of diversity. In this two-part paper, we propose a new form of spatial diversity, in which diversity gains are achieved via the cooperation of mobile users. Part I describes the user cooperation strategy, while Part II (see ibid., p.1939-48) focuses on implementation issues and performance analysis. Results show that, even though the interuser channel is noisy, cooperation leads not only to an increase in capacity for both users but also to a more robust system, where users' achievable rates are less susceptible to channel variations.

http://dns2.asia.edu.tw/~ysho/YSHO-English/2000%20Engineering/PDF/IEE%20Tra%20Com51,%201927.pdf

User cooperation diversity. Part I. System description

Multi-user MIMO offers big advantages over conventional point-to-point MIMO: it works with cheap single-antenna terminals, a rich scattering environment is not required, and resource allocation is simplified because every active terminal utilizes all of the time-frequency bins. However, multi-user MIMO, as originally envisioned, with roughly equal numbers of service antennas and terminals and frequency-division duplex operation, is not a scalable technology. Massive MIMO (also known as large-scale antenna systems, very large MIMO, hyper MIMO, full-dimension MIMO, and ARGOS) makes a clean break with current practice through the use of a large excess of service antennas over active terminals and time-division duplex operation. Extra antennas help by focusing energy into ever smaller regions of space to bring huge improvements in throughput and radiated energy efficiency. Other benefits of massive MIMO include extensive use of inexpensive low-power components, reduced latency, simplification of the MAC layer, and robustness against intentional jamming. The anticipated throughput depends on the propagation environment providing asymptotically orthogonal channels to the terminals, but so far experiments have not disclosed any limitations in this regard. While massive MIMO renders many traditional research problems irrelevant, it uncovers entirely new problems that urgently need attention: the challenge of making many low-cost low-precision components that work effectively together, acquisition and synchronization for newly joined terminals, the exploitation of extra degrees of freedom provided by the excess of service antennas, reducing internal power consumption to achieve total energy efficiency reductions, and finding new deployment scenarios. This article presents an overview of the massive MIMO concept and contemporary research on the topic.

/pdf/massive-mimo-for-next-generation-wireless-systems-1eydagg3js.pdf

Massive MIMO for next generation wireless systems

Multiuser diversity is a form of diversity inherent in a wireless network, provided by independent time-varying channels across the different users. The diversity benefit is exploited by tracking the channel fluctuations of the users and scheduling transmissions to users when their instantaneous channel quality is near the peak. The diversity gain increases with the dynamic range of the fluctuations and is thus limited in environments with little scattering and/or slow fading. In such environments, we propose the use of multiple transmit antennas to induce large and fast channel fluctuations so that multiuser diversity can still be exploited. The scheme can be interpreted as opportunistic beamforming and we show that true beamforming gains can be achieved when there are sufficient users, even though very limited channel feedback is needed. Furthermore, in a cellular system, the scheme plays an additional role of opportunistic nulling of the interference created on users of adjacent cells. We discuss the design implications of implementing. this scheme in a complete wireless system.

https://web.stanford.edu/~dntse/papers/oppbf_it.pdf

Opportunistic beamforming using dumb antennas

In this paper, we assess the random coding error exponents (EEs) corresponding to decode-and-forward (DF), compress-and-forward (CF) and quantize-and-forward (QF) relaying strategies for a parallel relay network (PRN), consisting of a single source and two relays. Moreover, through numerical analysis we show that the EEs achieved by using QF relaying along with non-Gaussian signaling (coded modulation, M-QAM) at the source and symbol-by-symbol uniform scalar quantizers (uSQs) at the relays is better than that achieved by DF and CF relaying strategies when the system is in the low signal-to-noise ratio (SNR) regime and the backhaul capacity is sufficient. This behavior is due to the structure of coded modulation, as opposed to Gaussian signaling, which leads to better EEs for simple relaying strategies compared to its more complex counterparts.

/pdf/error-exponents-for-backhaul-constrained-parallel-relay-we5b6zej77.pdf

Error exponents for backhaul-constrained parallel relay networks

We present in this paper a software implementation of a cognitive radio (CR) communication based on long term evolution-time division duplex (LTE-TDD) channel reciprocity. We study the problem of calibration and beamforming design for a CR system in which a multiple-input single-output (MISO) secondary link, wants to opportunistically communicate without harming the primary SISO system. Specifically, we evaluate the CR communication through the EURECOM's experimental OpenAirInterface (OAI) software platform. We will show that it is feasible to restore the reciprocity after calibration in a non reciprocal channel, and provide an overview of challenges in the channel estimation for real conditions.

Software implementation of spatial interweave cognitive radio communication using OpenAirInterface platform

5G will be much more than just a new radio interface — 5G will fundamentally change the way networks are operated. Traditional telecoms equipment will be replaced successively with general purpose computing platforms. This change will affect both the core network and the radio network, where it is also called cloud-RAN (C-RAN). The C-RAN architecture allows for flexible splits between different processing elements in the radio network, for an optimal trade-off between processing in the cloud or in the remote radio unit (RRU). OpenAirInterface (OAI) is an open source initiative that today provides Rel-8/Rel-10 3GPP compliant reference implementation of eNodeB, UE, RRH and EPC that runs on general purpose computing platforms. Already today OAI offers several functional splits for its 4G radio stack, for example between the Radio Cloud Center (RCC) and a Remote Radio Unit (RRU). Moreover, Eurecom is currently deploying a C-RAN network at its premises in Sophia-Antipolis, France, using a low-cost solution for RRU based on off-the-shelf equipment. In this paper we are going to describe the OAI C-RAN architecture with a special emphasis on the possibilities to do multi-cell distributed MIMO processing. In particular we will show how to we can apply our recently developed TDD reciprocity calibration scheme to this distributed setting and integrate it seamlessly into the normal LTE operation.

From massive MIMO to C-RAN: The OpenAirInterface 5G testbed

Future wireless communication systems being characterized by tight frequency reuse, adaptive modulation and coding schemes and diversified data services will be interference limited by interfering signals of diverse rates and strengths. Keeping in view such a scenario, we propose in this paper the application of a recently proposed low complexity match filter (MF) based detector for spatial interference cancellation in the presence of one strong interferer. We derive an analytical upper bound for the coded pair wise error probability (PEP) for the proposed MF based detector using the moment generating function (MGF) based method and prove that this detector not only recuperates the diversity order lost by MMSE but also exhibits a coding gain as the interference gets stronger. We also study in this paper the effect of non Gaussian interference on coded PEP of MMSE linear detection and demonstrate the deficit of one order of diversity and a coding loss as the interference gets stronger. Our analysis provides insights to explain the relative performance of MMSE and proposed MF based detectors as a function of the strength of interference. Finally we demonstrate the strength of our new analytical PEP upper bounds by simulations.

Spatial Interference Cancellation and Pairwise Error Probability Analysis

This paper presents an overview of software radio architectures for testing quality-of-service (QoS) aware IP data services over a typical third-generation radio interface. The testbed is implemented using a hard real-time micro-kernel known as RTLinux, running beneath the Linux operating system, for providing real-time end-to-end functionality. The testbed runs on a variety of Intel Pentium-based computing platforms including laptops and high-end servers. Layers 1 and 2 are compliant with the 3GPP specifications forTdd operation and layer 3 provides a direct interconnection with an IPv6 core network. The intent is to study the impact of an ip core network and QoS constraints on the physical and link layers as well as the co-design of physical layer configurations and IP layer networking.

/pdf/an-all-ip-software-radio-architecture-under-rtlinux-4b0crkmz5v.pdf

Raymond Knopp

Papers

Error exponents for backhaul-constrained parallel relay networks

Software implementation of spatial interweave cognitive radio communication using OpenAirInterface platform

From massive MIMO to C-RAN: The OpenAirInterface 5G testbed

Spatial Interference Cancellation and Pairwise Error Probability Analysis

An all IP software radio architecture under RTLinux