Sean A. Ramprashad

Rethinking network MIMO: Cost of CSIT, performance analysis, and architecture comparisons

We compare the downlink throughput of various cellular architectures with multi-antenna base stations and multiple single-antenna users per cell, by considering a number of inherent physical layer issues such as path-loss and time and frequency selective fading. In particular, we focus on Multiuser MIMO (MU-MIMO) downlink techniques that require channel state information at the transmitter (CSIT). Our analysis takes explicit account of the cost of CSIT estimation and illuminates the tradeoffs between CSIT, estimation error, and system resource dedicated to training. This tradeoff shows that the number of antennas that can be jointly coordinated (either on the same base station or across multiple base stations) is intrinsically limited not just by “external factors,” such as complexity and rate of the backbone wired network, but by the inherent time and frequency variability of the fading channels. Our analysis, in agreement with a number of recent simulation results, shows that conventional MU-MIMO cellular architectures may outperform schemes based on coordinated transmission from base stations (referred to as Network MIMO schemes, NW-MIMO), at the negligible cost of a few extra antennas per station. In light of these results, it appears that the inherent bottleneck of NW-MIMO systems is not the backbone network (which here is assumed ideal with infinite capacity) but the intrinsic dimensional limitation of estimating the channels.

Multiuser MIMO downlink with limited inter-cell cooperation: Approximate interference alignment in time, frequency and space

We consider a realistic albeit simplified scenario for wireless cellular systems of the next generation (4G and beyond), where MIMO-OFDM, opportunistic scheduling, channel state information at the transmitter and limited base-station cooperation are envisaged. We propose two strategies with limited base-station cooperation that can be easily implemented with todays technology and achieve an approximate form of inter-cell interference alignment. The first strategy consists of imposing a ldquopower maskrdquo in frequency such that adjacent cooperative clusters of base stations generate different interference levels in different frequency subchannels. The second strategy consists of switching between different cooperative clusters such that no user is in a permanently disadvantaged location.We compare single-user and multiuser MIMO systems in terms of average throughput as a function of the user location.

Technical advances in digital audio radio broadcasting

The move to digital is a natural progression taking place in all aspects of broadcast media applications from document processing in newspapers to video processing in television distribution. This is no less true for audio broadcasting which has taken a unique development path in the United States. This path has been heavily influenced by a combination of regulatory and migratory requirements specific to the U.S. market. In addition, competition between proposed terrestrial and satellite systems combined with increasing consumer expectations have set ambitious, and often changing, requirements for the systems. The result has been a unique set of evolving requirements on source coding, channel coding, and modulation technologies to make these systems a reality. This paper outlines the technical development of the terrestrial wireless and satellite audio broadcasting systems in the U.S., providing details on specific source and channel coding designs and adding perspective on why specific designs were selected in the final systems. These systems are also compared to other systems such as Eureka-147, DRM, and Worldspace, developed under different requirements.

Cellular vs. Network MIMO: A comparison including the channel state information overhead

Cooperative base-station (BS) signaling using MU-MIMO (Network MIMO) has received a great deal of attention given its ability to reduce inter-cell interference (ICI) and improve the system spectral efficiency. Indeed for a given number of antennas per BS, cooperative systems can have significant benefits over conventional cellular architectures. However cooperative signaling requires and uses more channel state information (CSI). This increases CSI signaling overhead, and can have a non-negligible effect on the system throughput. In fact once systems are compared while taking into account CSI overhead, the question of what system architecture is best becomes interesting and non-trivial. We provide one such alternative look at cooperative architectures. We show, despite prevailing views, that cellular architectures, using coordinated colocated antennas, can be quite attractive compared to Network MIMO.

Cellular and Network MIMO architectures: MU-MIMO spectral efficiency and costs of channel state information

Cooperative Multi-User MIMO signaling across cells, also known as Network MIMO, has recently received a great deal of attention as a potential physical layer technique for future high-throughput wireless systems. Indeed, cooperative signaling is able to reduce inter-cell interference within groups of coordinated cells thereby improving system throughput. Such benefits, however, are often assessed ignoring inherent overheads such as those required to obtain channel state information (CSI) to enable downlink signaling by MU-MIMO. In fact CSI estimation does take physical layer resources from data and thus should be accounted for in assessing the net benefits of any cooperative signalling strategy. The paper characterizes some elements of this overhead and its effect on CSI estimation error and MU-MIMO efficiency. The results show that, when CSI overheads are taken into account, conventional cellular architectures, with no coordination across cells, can be quite attractive relative to Network MIMO.

Design and operation of Blind Interference Alignment in cellular and cluster-based systems

We consider the use of Blind Interference Alignment (BIA) in a cellular environment as a means for supporting downlink Multi-User MIMO (MU-MIMO) transmission without the need for channel state information at the transmitter (CSIT). This “CSIT-blind” characteristic of BIA is of particular interest for Frequency Division Duplex (FDD) systems, since it allows one to eliminate overheads and impairments due to downlink pilot training and CSIT quantization and feedback. As we demonstrate, when properly applied over cellular, BIA can enable control of both intra-cell and inter-cell interference. In this context, we explore the aspects of power-allocation, frequency reuse, and alignment-code reuse and their effect on cellular performance. BIA can also be jointly applied over clusters of cells. Cluster operation leads to lower inter-cell interference levels and potentially improved BIA system performance. BIA however, has some interesting operational aspects when used with clusters. These include some benefits over CSIT-based MU-MIMO since individual cells do not need to share channel knowledge and can transmit independent information streams, some challenges in more complex receiver hardware, and relationships to cellular with code-reuse. We discuss such aspects and investigate the application of BIA in both cellular and cluster deployments.

Cooperative cellular networks using multi-user MIMO: trade-offs, overheads, and interference control across architectures

Multi-user multiple-input multiple-output can be viewed as an interference control technique relying on cooperative transmission and/or reception over multiple antennas. A cooperating set of antennas can be collocated at a common cell site or distributed across multiple sites. As such, MUMIMO can be applied to a wide variety of architectures ranging from traditional cellular to more elaborate intertwined cooperative designs. We consider examples of such MU-MIMO architectures, and study the impact the scheduling criterion, cell density, and coordination can have on the average and cell edge user rates across different designs. Importantly, MU-MIMO has inherent physical layer overheads, which, as the examples illustrate, depend on the degree of coordination and the architecture. Such overhead considerations are very important in assessing net rates and, depending on the scenario, can motivate designs using less cooperation.

Multi-user MIMO with outdated CSI: Training, feedback and scheduling

Conventional MU-MIMO techniques, e.g. Linear Zero-Forced Beamforming (LZFB), require sufficiently accurate channel state information at the transmitter (CSIT) in order to realize spectral efficient transmission (degree of freedom gains). In practical settings, however, CSIT accuracy can be limited by a number of issues including CSI estimation, CSI feedback delay between user terminals to base stations, and the time/frequency coherence of the channel. The latter aspects of CSIT-feedback delay and channel-dynamics can lead to significant challenges in the deployment of efficient MU-MIMO systems. Recently it has been shown by Maddah-Ali and Tse (MAT) that degree of freedom gains can be realized by MU-MIMO even when the knowledge of CSIT is completely outdated. Specifically, outdated CSIT, albeit perfect CSIT, is known for transmissions only after they have taken place. This aspect of insensitivity to CSIT-feedback delay is of particular interest since it allows one to reconsider MU-MIMO design in dynamic channel conditions. Indeed, as we show, with appropriate scheduling, and even in the context of CSI estimation and feedback errors, the proposed MAT scheme can have performance advantages over conventional MU-MIMO in such scenarios.

Multi-Cell User-Scheduling and Random Beamforming Strategies for Downlink Wireless Communications

We consider random beamforming and user scheduling strategies in a multi-cell environment for down- link wireless communications. Our focus is on using combined scheduling and beamforming to mitigate performance losses due to inter-cell interference. Of interest are strategies requiring min- imal intra-cell and inter-cell system information exchange. Indeed the acquisition and inter-cell exchange of such information can be significant overheads in a practical deployment. Random beamforming requires substantially less channel state information than linear zero forcing strategies to schedule trans- missions. It is therefore of practical interest despite some limita- tions in controlling intra-cell interference. We consider how one can use a minimal exchange of information, in a multi-cell multi- step pattern of exchanges, to improve the overall performance of random beamforming in a multi-cell environment.

Optimizing CSI Feedback for MU-MIMO: Tradeoffs in Channel Correlation, User Diversity and MU-MIMO Efficiency

Multi-user MIMO (MU-MIMO) is seen as one of the key technologies for 4G communications systems. A critical factor affecting the throughput gains provided by MU-MIMO is the feedback mechanism that allows the transmitting basestation to periodically receive channel state information (CSI) from mobiles. Sufficient feedback is necessary for efficient MU-MIMO operation. At the same time feedback itself is an overhead using resources that could otherwise be used for data transmission. It is therefore important to carefully consider how feedback is designed and used to support MU-MIMO. This work studies key design parameters in such a feedback process: how often in time and in frequency should mobiles perform feedback (given coherence in the channel), the number of bits of feedback (accuracy of CSI quantization), and the number of mobiles to provide feedback. Within a bound on feedback resources these parameters can determine how best to use such resources to optimize MU-MIMO spectral efficiency. The results show that spectral efficiency is often maximized if mobiles quantize their channels very accurately but perform feedback at moderate intervals in time, such that CSI inaccuracy is primarily determined by the variation of the channel in time and frequency rather than being dominated by quantization errors.