Wireless Communications

A new achievable rate region for the general interference channel which extends previous results is presented and evaluated. The technique used is a generalization of superposition coding to the multivariable case. A detailed computation for the Gaussian channel case clarifies to what extent the new region improves previous ones. The capacity of a class of Gaussian interference channels is also established.

A new achievable rate region for the interference channel

An interference channel is a communication medium shared by M sender-receiver pairs. Transmission of information from each sender to its corresponding receiver interferes with the communications between the other senders and their receivers. This corresponds to a frequent situation in communications, and defines an M -dimensional capacity region. In this paper, we obtain general bounds on the capacity region for discrete memoryless interference channels and for linear-superposition interference channels with additive white Gaussian noise. The capacity region is determined in special cases.

Interference channels

It is shown that, under certain conditions, two strongly interfering communication links with additive white Gaussian noise can achieve rates as high as would be achievable without this interference.

A case where interference does not reduce capacity (Corresp.)

The emerging massive/large-scale multiple-input multiple-output (LS-MIMO) systems that rely on very large antenna arrays have become a hot topic of wireless communications. Compared to multi-antenna aided systems being built at the time of this writing, such as the long-term evolution (LTE) based fourth generation (4G) mobile communication system which allows for up to eight antenna elements at the base station (BS), the LS-MIMO system entails an unprecedented number of antennas, say 100 or more, at the BS. The huge leap in the number of BS antennas opens the door to a new research field in communication theory, propagation and electronics, where random matrix theory begins to play a dominant role. Interestingly, LS-MIMOs also constitute a perfect example of one of the key philosophical principles of the Hegelian Dialectics, namely, that “quantitative change leads to qualitative change.” In this treatise, we provide a recital on the historic heritages and novel challenges facing LS-MIMOs from a detection perspective. First, we highlight the fundamentals of MIMO detection, including the nature of co-channel interference (CCI), the generality of the MIMO detection problem, the received signal models of both linear memoryless MIMO channels and dispersive MIMO channels exhibiting memory, as well as the complex-valued versus real-valued MIMO system models. Then, an extensive review of the representative MIMO detection methods conceived during the past 50 years (1965–2015) is presented, and relevant insights as well as lessons are inferred for the sake of designing complexity-scalable MIMO detection algorithms that are potentially applicable to LS-MIMO systems. Furthermore, we divide the LS-MIMO systems into two types, and elaborate on the distinct detection strategies suitable for each of them. The type-I LS-MIMO corresponds to the case where the number of active users is much smaller than the number of BS antennas, which is currently the mainstream definition of LS-MIMO. The type-II LS-MIMO corresponds to the case where the number of active users is comparable to the number of BS antennas. Finally, we discuss the applicability of existing MIMO detection algorithms in LS-MIMO systems, and review some of the recent advances in LS-MIMO detection.

/pdf/fifty-years-of-mimo-detection-the-road-to-large-scale-mimos-44ejto9lpw.pdf

Fifty Years of MIMO Detection: The Road to Large-Scale MIMOs

This paper shows that several transmitters operating in an additive white Gaussian noise environment can send at rates strictly dominating time-multiplex and frequency-multiplex rates by use of a superposition scheme that pools the time, bandwidth, and power allocations of the transmitters. This pooling can be achieved without cooperative action, except for agreement on the actual rate of transmission each transmitter will allow itself. The superposition scheme involves subtraction from the received signal of the estimated signals sent by the other transmitters, followed by decoding of the intended signal. This scheme has been shown to be optimal. We conclude that present methods of allocating different frequency bands to different transmitters are necessarily suboptimal.

/pdf/cooperative-broadcasting-43icw2wmz4.pdf

P. Bergmans

Papers

Cooperative broadcasting