726 research outputs found
An Information Theoretic Charachterization of Channel Shortening Receivers
Optimal data detection of data transmitted over a linear channel can always
be implemented through the Viterbi algorithm (VA). However, in many cases of
interest the memory of the channel prohibits application of the VA. A popular
and conceptually simple method in this case, studied since the early 70s, is to
first filter the received signal in order to shorten the memory of the channel,
and then to apply a VA that operates with the shorter memory. We shall refer to
this as a channel shortening (CS) receiver. Although studied for almost four
decades, an information theoretic understanding of what such a simple receiver
solution is actually doing is not available.
In this paper we will show that an optimized CS receiver is implementing the
chain rule of mutual information, but only up to the shortened memory that the
receiver is operating with. Further, we will show that the tools for analyzing
the ensuing achievable rates from an optimized CS receiver are precisely the
same as those used for analyzing the achievable rates of a minimum mean square
error (MMSE) receiver
Beyond Massive-MIMO: The Potential of Positioning with Large Intelligent Surfaces
We consider the potential for positioning with a system where antenna arrays
are deployed as a large intelligent surface (LIS), which is a newly proposed
concept beyond massive-MIMO where future man-made structures are electronically
active with integrated electronics and wireless communication making the entire
environment \lq\lq{}intelligent\rq\rq{}. In a first step, we derive
Fisher-information and Cram\'{e}r-Rao lower bounds (CRLBs) in closed-form for
positioning a terminal located perpendicular to the center of the LIS, whose
location we refer to as being on the central perpendicular line (CPL) of the
LIS. For a terminal that is not on the CPL, closed-form expressions of the
Fisher-information and CRLB seem out of reach, and we alternatively find
approximations of them which are shown to be accurate. Under mild conditions,
we show that the CRLB for all three Cartesian dimensions (, and )
decreases quadratically in the surface-area of the LIS, except for a terminal
exactly on the CPL where the CRLB for the -dimension (distance from the LIS)
decreases linearly in the same. In a second step, we analyze the CRLB for
positioning when there is an unknown phase presented in the analog
circuits of the LIS. We then show that the CRLBs are dramatically increased for
all three dimensions but decrease in the third-order of the surface-area.
Moreover, with an infinitely large LIS the CRLB for the -dimension with an
unknown is 6 dB higher than the case without phase uncertainty, and
the CRLB for estimating converges to a constant that is independent
of the wavelength . At last, we extensively discuss the impact of
centralized and distributed deployments of LIS, and show that a distributed
deployment of LIS can enlarge the coverage for terminal-positioning and improve
the overall positioning performance.Comment: Submitted to IEEE Trans. on Signal Processing on Apr. 2017; 30 pages;
13 figure
Beyond Massive-MIMO: The Potential of Data-Transmission with Large Intelligent Surfaces
In this paper, we consider the potential of data-transmission in a system
with a massive number of radiating and sensing elements, thought of as a
contiguous surface of electromagnetically active material. We refer to this as
a large intelligent surface (LIS). The "LIS" is a newly proposed concept, which
conceptually goes beyond contemporary massive MIMO technology, that arises from
our vision of a future where man-made structures are electronically active with
integrated electronics and wireless communication making the entire environment
"intelligent".
We consider capacities of single-antenna autonomous terminals communicating
to the LIS where the entire surface is used as a receiving antenna array. Under
the condition that the surface-area is sufficiently large, the received signal
after a matched-filtering (MF) operation can be closely approximated by a
sinc-function-like intersymbol interference (ISI) channel. We analyze the
capacity per square meter (m^2) deployed surface, \hat{C}, that is achievable
for a fixed transmit power per volume-unit, \hat{P}. Moreover, we also show
that the number of independent signal dimensions per m deployed surface is
2/\lambda for one-dimensional terminal-deployment, and \pi/\lambda^2 per m^2
for two and three dimensional terminal-deployments. Lastly, we consider
implementations of the LIS in the form of a grid of conventional antenna
elements and show that, the sampling lattice that minimizes the surface-area of
the LIS and simultaneously obtains one signal space dimension for every spent
antenna is the hexagonal lattice. We extensively discuss the design of the
state-of-the-art low-complexity channel shortening (CS) demodulator for
data-transmission with the LIS.Comment: Submitted to IEEE Trans. on Signal Process., 30 pages, 12 figure
Massive MIMO performance evaluation based on measured propagation data
Massive MIMO, also known as very-large MIMO or large-scale antenna systems,
is a new technique that potentially can offer large network capacities in
multi-user scenarios. With a massive MIMO system, we consider the case where a
base station equipped with a large number of antenna elements simultaneously
serves multiple single-antenna users in the same time-frequency resource. So
far, investigations are mostly based on theoretical channels with independent
and identically distributed (i.i.d.) complex Gaussian coefficients, i.e.,
i.i.d. Rayleigh channels. Here, we investigate how massive MIMO performs in
channels measured in real propagation environments. Channel measurements were
performed at 2.6 GHz using a virtual uniform linear array (ULA) which has a
physically large aperture, and a practical uniform cylindrical array (UCA)
which is more compact in size, both having 128 antenna ports. Based on
measurement data, we illustrate channel behavior of massive MIMO in three
representative propagation conditions, and evaluate the corresponding
performance. The investigation shows that the measured channels, for both array
types, allow us to achieve performance close to that in i.i.d. Rayleigh
channels. It is concluded that in real propagation environments we have
characteristics that can allow for efficient use of massive MIMO, i.e., the
theoretical advantages of this new technology can also be harvested in real
channels.Comment: IEEE Transactions on Wireless Communications, 201
Massive MIMO Extensions to the COST 2100 Channel Model: Modeling and Validation
To enable realistic studies of massive multiple-input multiple-output
systems, the COST 2100 channel model is extended based on measurements. First,
the concept of a base station-side visibility region (BS-VR) is proposed to
model the appearance and disappearance of clusters when using a
physically-large array. We find that BS-VR lifetimes are exponentially
distributed, and that the number of BS-VRs is Poisson distributed with
intensity proportional to the sum of the array length and the mean lifetime.
Simulations suggest that under certain conditions longer lifetimes can help
decorrelating closely-located users. Second, the concept of a multipath
component visibility region (MPC-VR) is proposed to model birth-death processes
of individual MPCs at the mobile station side. We find that both MPC lifetimes
and MPC-VR radii are lognormally distributed. Simulations suggest that unless
MPC-VRs are applied the channel condition number is overestimated. Key
statistical properties of the proposed extensions, e.g., autocorrelation
functions, maximum likelihood estimators, and Cramer-Rao bounds, are derived
and analyzed.Comment: Submitted to IEEE Transactions of Wireless Communication
User Assignment with Distributed Large Intelligent Surface (LIS) Systems
In this paper, we consider a wireless communication system where a large
intelligent surface (LIS) is deployed comprising a number of small and
distributed LIS-Units. Each LIS-Unit has a separate signal process unit (SPU)
and is connected to a central process unit (CPU) that coordinates the behaviors
of all the LIS-Units. With such a LIS system, we consider the user assignments
both for sum-rate and minimal user-rate maximizations. That is, assuming
LIS-Units deployed in the LIS system, the objective is to select
() best LIS-Units to serve autonomous users simultaneously.
Based on the nice property of effective inter-user interference suppression of
the LIS-Units, the optimal user assignments can be effectively found through
classical linear assignment problems (LAPs) defined on a bipartite graph. To be
specific, the optimal user assignment for sum-rate and user-rate maximizations
can be solved by linear sum assignment problem (LSAP) and linear bottleneck
assignment problem (LBAP), respectively. The elements of the cost matrix are
constructed based on the received signal strength (RSS) measured at each of the
LIS-Units for all the users. Numerical results show that, the proposed
user assignments are close to optimal user assignments both under line-of-sight
(LoS) and scattering environments.Comment: submitted to IEEE conference; 6 pages;10 figure
Massive MIMO for Next Generation Wireless Systems
Multi-user Multiple-Input Multiple-Output (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" & "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 the extensive use of inexpensive
low-power components, reduced latency, simplification of the media access
control (MAC) layer, and robustness to intentional jamming. The anticipated
throughput depend 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 paper presents an overview of the massive MIMO concept and
contemporary research.Comment: Final manuscript, to appear in IEEE Communications Magazin
Decentralized Massive MIMO Processing Exploring Daisy-chain Architecture and Recursive Algorithms
Algorithms for Massive MIMO uplink detection and downlink precoding typically
rely on a centralized approach, by which baseband data from all antenna modules
are routed to a central node in order to be processed. In the case of Massive
MIMO, where hundreds or thousands of antennas are expected in the base-station,
said routing becomes a bottleneck since interconnection throughput is limited.
This paper presents a fully decentralized architecture and an algorithm for
Massive MIMO uplink detection and downlink precoding based on the Stochastic
Gradient Descent (SGD) method, which does not require a central node for these
tasks. Through a recursive approach and very low complexity operations, the
proposed algorithm provides a good trade-off between performance,
interconnection throughput and latency. Further, our proposed solution achieves
significantly lower interconnection data-rate than other architectures,
enabling future scalability.Comment: Manuscript accepted for publication in IEEE Transactions on Signal
Processin
Massive MIMO Performance - TDD Versus FDD: What Do Measurements Say?
Downlink beamforming in Massive MIMO either relies on uplink pilot
measurements - exploiting reciprocity and TDD operation, or on the use of a
predetermined grid of beams with user equipments reporting their preferred
beams, mostly in FDD operation. Massive MIMO in its originally conceived form
uses the first strategy, with uplink pilots, whereas there is currently
significant commercial interest in the second, grid-of-beams. It has been
analytically shown that in isotropic scattering (independent Rayleigh fading)
the first approach outperforms the second. Nevertheless there remains
controversy regarding their relative performance in practice. In this
contribution, the performances of these two strategies are compared using
measured channel data at 2.6 GHz.Comment: Submitted to IEEE Transactions on Wireless Communications,
31/Mar/201
- …