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

The cBioPortal for Cancer Genomics (http://cbioportal.org) provides a Web resource for exploring, visualizing, and analyzing multidimensional cancer genomics data. The portal reduces molecular profiling data from cancer tissues and cell lines into readily understandable genetic, epigenetic, gene expression, and proteomic events. The query interface combined with customized data storage enables researchers to interactively explore genetic alterations across samples, genes, and pathways and, when available in the underlying data, to link these to clinical outcomes. The portal provides graphical summaries of gene-level data from multiple platforms, network visualization and analysis, survival analysis, patient-centric queries, and software programmatic access. The intuitive Web interface of the portal makes complex cancer genomics profiles accessible to researchers and clinicians without requiring bioinformatics expertise, thus facilitating biological discoveries. Here, we provide a practical guide to the analysis and visualization features of the cBioPortal for Cancer Genomics.

Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal

What will 5G be? What it will not be is an incremental advance on 4G. The previous four generations of cellular technology have each been a major paradigm shift that has broken backward compatibility. Indeed, 5G will need to be a paradigm shift that includes very high carrier frequencies with massive bandwidths, extreme base station and device densities, and unprecedented numbers of antennas. However, unlike the previous four generations, it will also be highly integrative: tying any new 5G air interface and spectrum together with LTE and WiFi to provide universal high-rate coverage and a seamless user experience. To support this, the core network will also have to reach unprecedented levels of flexibility and intelligence, spectrum regulation will need to be rethought and improved, and energy and cost efficiencies will become even more critical considerations. This paper discusses all of these topics, identifying key challenges for future research and preliminary 5G standardization activities, while providing a comprehensive overview of the current literature, and in particular of the papers appearing in this special issue.

What Will 5G Be

The global bandwidth shortage facing wireless carriers has motivated the exploration of the underutilized millimeter wave (mm-wave) frequency spectrum for future broadband cellular communication networks. There is, however, little knowledge about cellular mm-wave propagation in densely populated indoor and outdoor environments. Obtaining this information is vital for the design and operation of future fifth generation cellular networks that use the mm-wave spectrum. In this paper, we present the motivation for new mm-wave cellular systems, methodology, and hardware for measurements and offer a variety of measurement results that show 28 and 38 GHz frequencies can be used when employing steerable directional antennas at base stations and mobile devices.

Millimeter Wave Mobile Communications for 5G Cellular: It Will Work!

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.

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Massive MIMO for next generation wireless systems

∗ p ∗ p T −K. Note that, with the above corrected choice of ¯ pu, showing that S(¯ τ, ¯ pp, ¯ pu) > S(τ ∗ ,p ∗ ,p ∗ ) is not straightforward. In the following, we will provide a detailed proof of the inequality S(¯ τ, ¯p, ¯ pu) > S(τ ∗ ,p ∗ ,p ∗ ).

Correction to “Massive MIMO With Optimal Power and Training Duration Allocation”

Linköping University Post Print Differential Signaling of Scheduling Information in Wireless Multiple Access Systems

Recent research on histologically healthy human tissues identified omnipresent mutational microclones, driven by somatic mutations known to be responsible for carcinogenesis (e.g., in TP53 or NOTCH1). These new insights are fundamentally changing current tumour evolution models, with broad oncological implications. Most studies are based on surgical remnant tissues, which are not available for many organs and rarely in a pan-organ setting (multiple organs from the same individual). Here, we describe an approach based on clinically annotated post-mortem tissues, derived from whole-body donors that are routinely used for educational purposes at human anatomy units. We validated this post-mortem approach using UV-exposed and unexposed epidermal skin tissues and confirm the presence of positively selected NOTCH1/2-, TP53- and FAT1-driven clones. No selection signals were detected in a set of immune genes or housekeeping genes. Additionally, we provide the first evidence for smoking-induced clonal changes in oral epithelia, likely underlying the origin of head and neck carcinogenesis. In conclusion, the whole-body donor-based approach provides a nearly unlimited healthy tissue resource to study mutational clonality and gain fundamental mutagenic insights in the presumed earliest stages of tumour evolution.

A clinically annotated post-mortem approach to study multi-organ somatic mutational clonality in histologically healthy tissues

Wireless communication technology has progressed dramatically over the past 25 years, in terms of societal adoption as well as technical sophistication. In 1998, mobile phones were still in the process of becoming compact and affordable devices that could be widely utilized in both developed and developing countries. There were"only"300 million mobile subscribers in the world [1]. Cellular networks were among the first privatized telecommunication markets, and competition turned the devices into fashion accessories with attractive designs that could be individualized. The service was circumscribed to telephony and text messaging, but it was groundbreaking in that, for the first time, telecommunication was between people rather than locations. Wireless networks have changed dramatically over the past few decades, enabling this revolution in service provisioning and making it possible to accommodate the ensuing dramatic growth in traffic. There are many contributing components, including new air interfaces for faster transmission, channel coding for enhanced reliability, improved source compression to remove redundancies, and leaner protocols to reduce overheads. Signal processing is at the core of these improvements, but nowhere has it played a bigger role than in the development of multiantenna communication. This article tells the story of how major signal processing advances have transformed the early multiantenna concepts into mainstream technology over the past 25 years. The story therefore begins somewhat arbitrarily in 1998. A broad account of the state-of-the-art signal processing techniques for wireless systems by 1998 can be found in [2], and its contrast with recent textbooks such as [3]-[5] reveals the dramatic leap forward that has taken place in the interim.

25 Years of Signal Processing Advances for Multiantenna Communications

The knowledge of channel covariance matrices is of paramount importance to the estimation of instantaneous channels and the design of beamforming vectors in multi-antenna systems. In practice, an abrupt change in channel covariance matrices may occur due to the change in the environment and the user location. Although several works have proposed efficient algorithms to estimate the channel covariance matrices after any change occurs, how to detect such a change accurately and quickly is still an open problem in the literature. In this paper, we focus on channel covariance change detection between a multi-antenna base station (BS) and a single-antenna user equipment (UE). To provide theoretical performance limit, we first propose a genie-aided change detector based on the log-likelihood ratio (LLR) test assuming the channel covariance matrix after change is known, and characterize the corresponding missed detection and false alarm probabilities. Then, this paper considers the practical case where the channel covariance matrix after change is unknown. The maximum likelihood (ML) estimation technique is used to predict the covariance matrix based on the received pilot signals over a certain number of coherence blocks, building upon which the LLR-based change detector is employed. Numerical results show that our proposed scheme can detect the change with low error probability even when the number of channel samples is small such that the estimation of the covariance matrix is not that accurate. This result verifies the possibility to detect the channel covariance change both accurately and quickly in practice.

Erik G. Larsson

Papers

Correction to “Massive MIMO With Optimal Power and Training Duration Allocation”

Linköping University Post Print Differential Signaling of Scheduling Information in Wireless Multiple Access Systems

A clinically annotated post-mortem approach to study multi-organ somatic mutational clonality in histologically healthy tissues

25 Years of Signal Processing Advances for Multiantenna Communications

Detection of Abrupt Change in Channel Covariance Matrix for Multi-Antenna Communication.