We investigate the use of multiple transmitting and/or receiving antennas for single user communications over the additive Gaussian channel with and without fading. We derive formulas for the capacities and error exponents of such channels, and describe computational procedures to evaluate such formulas. We show that the potential gains of such multi-antenna systems over single-antenna systems is rather large under independenceassumptions for the fades and noises at different receiving antennas.

/pdf/capacity-of-multi-antenna-gaussian-channels-3axdfdd5ns.pdf

Capacity of Multi‐antenna Gaussian Channels

Cognitive radio is viewed as a novel approach for improving the utilization of a precious natural resource: the radio electromagnetic spectrum. The cognitive radio, built on a software-defined radio, is defined as an intelligent wireless communication system that is aware of its environment and uses the methodology of understanding-by-building to learn from the environment and adapt to statistical variations in the input stimuli, with two primary objectives in mind: /spl middot/ highly reliable communication whenever and wherever needed; /spl middot/ efficient utilization of the radio spectrum. Following the discussion of interference temperature as a new metric for the quantification and management of interference, the paper addresses three fundamental cognitive tasks. 1) Radio-scene analysis. 2) Channel-state estimation and predictive modeling. 3) Transmit-power control and dynamic spectrum management. This work also discusses the emergent behavior of cognitive radio.

/pdf/cognitive-radio-brain-empowered-wireless-communications-9m6gu0vz1z.pdf

Cognitive radio: brain-empowered wireless communications

Wireless Communications

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!

A security mechanism is provided for wireless power transfer applications including resonant source and device objects, wherein tuned resonance between source and device objects is necessary for efficient power transfer. Tuning parameters associated with the source object are periodically adjusted so as to require corresponding changes in tuning parameter(s) of the device object to maintain tuned resonance. The tuning parameters are communicated to authorized users such that only authorized users capable of matching the changes made by the transmitter would be capable of receiving power. Unauthorized users that are unaware of the transmit tuning parameters will be rendered unable to maintain tuned resonance and thus unable to receive power.

Security for wireless transfer of electrical power

Cell-Free Massive MIMO comprises a large number of distributed single-antenna access points (APs) serving a much smaller number of users. There is no partitioning into cells and each user is served by all APs. In this paper, the uplink performance of cell-free systems with minimum mean squared error (MMSE) and large scale fading decoding (LSFD) receivers is investigated. The main idea of LSFD receiver is to maximize achievable throughput using only large scale fading coefficients between APs and users. Capacity lower bounds for MMSE and LSFD receivers are derived. An asymptotic approximation for signal-to-interference-plus-noise ratio (SINR) of MMSE receiver is derived as a function of large scale fading coefficients only. The obtained approximation is accurate even for a small number of antennas. MMSE and LSFD receivers demonstrate five-fold and two-fold gains respectively over matched filter (MF) receiver in terms of 5%-outage rate.

Performance of cell-free massive MIMO systems with MMSE and LSFD receivers

Prior treatments of space-time communications in Rayleigh flat fading generally assume that channel coding covers either one fading interval-in which case there is a nonzero "outage capacity"-or multiple fading intervals-in which case there is a nonzero Shannon capacity. However, we establish conditions under which channel codes span only one fading interval and yet are arbitrarily reliable. In short, space-time signals are their own channel codes. We call this phenomenon space-time autocoding, and the accompanying capacity the space-time autocapacity. Let an M-transmitter antenna, N-receiver antenna Rayleigh flat fading channel be characterized by an M/spl times/N matrix of independent propagation coefficients, distributed as zero-mean, unit-variance complex Gaussian random variables. This propagation matrix is unknown to the transmitter, it remains constant during a T-symbol coherence interval, and there is a fixed total transmit power. Let the coherence interval and number of transmitter antennas be related as T=/spl beta/M for some constant /spl beta/. A T/spl times/M matrix-valued signal, associated with R/spl middot/T bits of information for some rate R is transmitted during the T-symbol coherence interval. Then there is a positive space-time autocapacity C/sub a/ such that for all R<C/sub a/, the block probability of error goes to zero as the pair (T, M)/spl rarr//spl infin/ such that T/M=/spl beta/. The autocoding effect occurs whether or not the propagation matrix is known to the receiver, and C/sub a/=Nlog(1+/spl rho/) in either case, independently of /spl beta/, where /spl rho/ is the expected signal-to-noise ratio (SNR) at each receiver antenna. Lower bounds on the cutoff rate derived from random unitary space-time signals suggest that the autocoding effect manifests itself for relatively small values of T and M. For example, within a single coherence interval of duration T=16, for M=7 transmitter antennas and N=4 receiver antennas, and an 18-dB expected SNR, a total of 80 bits (corresponding to rate R=5) can theoretically be transmitted with a block probability of error less than 10/sup -9/, all without any training or knowledge of the propagation matrix.

Space-time autocoding

Massive MIMO relies on the asymptotic orthogonality of channel vectors to different users. For $M$ service antennas, the expected correlation between a pair of channel vectors under line-of-sight (LoS) conditions decreases at least as fast as $\log (M)/M$ , while in independent and identically distributed (iid) Rayleigh fading, it decreases much slower at $1/\sqrt {M}$ , but the variance is higher under LoS. This signifies that typically channel vectors are more nearly orthogonal under LoS, but with a non-negligible probability, they can have an anomalously large correlation. A single-cell analysis discloses that Massive MIMO with max-min power control performs comparably under LoS and iid Rayleigh, when a simple algorithm is applied under LoS to drop a small number of high-correlation users from service.

Massive MIMO With Max-Min Power Control in Line-of-Sight Propagation Environment

A massive MIMO system entails a large number (tens or hundreds) of base station antennas serving a much smaller number of terminals. These systems demonstrate large gains in spectral and energy efficiency compared with the conventional MIMO technology. As the number of antennas grows, the performance of a massive MIMO system gets limited by the interference caused by pilot contamination. Ashikhmin and Marzetta proposed (under the name of Pilot Contamination Precoding) large scale fading precoding (LSFP) and large scale fading decoding (LSFD) based on limited cooperation between base stations. They showed that zero-forcing LSFP and LSFD eliminate pilot contamination entirely and lead to an infinite throughput as the number of antennas grows. In this paper, we focus on the uplink and show that even in the case of a finite number of base station antennas, LSFD yields a very large performance gain. In particular, one of our algorithms gives a more than 140 fold increase in the 5% outage data transmission rate! We show that the performance can be improved further by optimizing the transmission powers of the users. Finally, we present decentralized LSFD that requires limited cooperation only between neighboring cells.

Thomas L. Marzetta

Papers

Security for wireless transfer of electrical power

Performance of cell-free massive MIMO systems with MMSE and LSFD receivers

Space-time autocoding

Massive MIMO With Max-Min Power Control in Line-of-Sight Propagation Environment

Uplink Interference Reduction in Large-Scale Antenna Systems