2,233 research outputs found
How Much Cache is Needed to Achieve Linear Capacity Scaling in Backhaul-Limited Dense Wireless Networks?
Dense wireless networks are a promising solution to meet the huge capacity
demand in 5G wireless systems. However, there are two implementation issues,
namely the interference and backhaul issues. To resolve these issues, we
propose a novel network architecture called the backhaul-limited cached dense
wireless network (C-DWN), where a physical layer (PHY) caching scheme is
employed at the base stations (BSs) but only a fraction of the BSs have wired
payload backhauls. The PHY caching can replace the role of wired backhauls to
achieve both the cache-induced MIMO cooperation gain and cache-assisted
Multihopping gain. Two fundamental questions are addressed. Can we exploit the
PHY caching to achieve linear capacity scaling with limited payload backhauls?
If so, how much cache is needed? We show that the capacity of the
backhaul-limited C-DWN indeed scales linearly with the number of BSs if the BS
cache size is larger than a threshold that depends on the content popularity.
We also quantify the throughput gain due to cache-induced MIMO cooperation over
conventional caching schemes (which exploit purely the cached-assisted
multihopping). Interestingly, the minimum BS cache size needed to achieve a
significant cache-induced MIMO cooperation gain is the same as that needed to
achieve the linear capacity scaling.Comment: 14 pages, 8 figures, accepted by IEEE/ACM Transactions on Networkin
A Survey on MIMO Transmission with Discrete Input Signals: Technical Challenges, Advances, and Future Trends
Multiple antennas have been exploited for spatial multiplexing and diversity
transmission in a wide range of communication applications. However, most of
the advances in the design of high speed wireless multiple-input multiple
output (MIMO) systems are based on information-theoretic principles that
demonstrate how to efficiently transmit signals conforming to Gaussian
distribution. Although the Gaussian signal is capacity-achieving, signals
conforming to discrete constellations are transmitted in practical
communication systems. As a result, this paper is motivated to provide a
comprehensive overview on MIMO transmission design with discrete input signals.
We first summarize the existing fundamental results for MIMO systems with
discrete input signals. Then, focusing on the basic point-to-point MIMO
systems, we examine transmission schemes based on three most important criteria
for communication systems: the mutual information driven designs, the mean
square error driven designs, and the diversity driven designs. Particularly, a
unified framework which designs low complexity transmission schemes applicable
to massive MIMO systems in upcoming 5G wireless networks is provided in the
first time. Moreover, adaptive transmission designs which switch among these
criteria based on the channel conditions to formulate the best transmission
strategy are discussed. Then, we provide a survey of the transmission designs
with discrete input signals for multiuser MIMO scenarios, including MIMO uplink
transmission, MIMO downlink transmission, MIMO interference channel, and MIMO
wiretap channel. Additionally, we discuss the transmission designs with
discrete input signals for other systems using MIMO technology. Finally,
technical challenges which remain unresolved at the time of writing are
summarized and the future trends of transmission designs with discrete input
signals are addressed.Comment: 110 pages, 512 references, submit to Proceedings of the IEE
How much feedback is required in MIMO Broadcast Channels?
In this paper, a downlink communication system, in which a Base Station (BS)
equipped with M antennas communicates with N users each equipped with K receive
antennas (), is considered. It is assumed that the receivers have
perfect Channel State Information (CSI), while the BS only knows the partial
CSI, provided by the receivers via feedback. The minimum amount of feedback
required at the BS, to achieve the maximum sum-rate capacity in the asymptotic
case of and different ranges of SNR is studied. In the fixed and
low SNR regimes, it is demonstrated that to achieve the maximum sum-rate, an
infinite amount of feedback is required. Moreover, in order to reduce the gap
to the optimum sum-rate to zero, in the fixed SNR regime, the minimum amount of
feedback scales as , which is achievable by the Random
Beam-Forming scheme proposed in [14]. In the high SNR regime, two cases are
considered; in the case of , it is proved that the minimum amount of
feedback bits to reduce the gap between the achievable sum-rate and the maximum
sum-rate to zero grows logaritmically with SNR, which is achievable by the
"Generalized Random Beam-Forming" scheme, proposed in [18]. In the case of , it is shown that by using the Random Beam-Forming scheme and the total
amount of feedback not growing with SNR, the maximum sum-rate capacity is
achieved.Comment: Submitted to IEEE Trans. on Inform. Theor
Cross-Layer Optimization of MIMO-Based Mesh Networks with Gaussian Vector Broadcast Channels
MIMO technology is one of the most significant advances in the past decade to
increase channel capacity and has a great potential to improve network capacity
for mesh networks. In a MIMO-based mesh network, the links outgoing from each
node sharing the common communication spectrum can be modeled as a Gaussian
vector broadcast channel. Recently, researchers showed that ``dirty paper
coding'' (DPC) is the optimal transmission strategy for Gaussian vector
broadcast channels. So far, there has been little study on how this fundamental
result will impact the cross-layer design for MIMO-based mesh networks. To fill
this gap, we consider the problem of jointly optimizing DPC power allocation in
the link layer at each node and multihop/multipath routing in a MIMO-based mesh
networks. It turns out that this optimization problem is a very challenging
non-convex problem. To address this difficulty, we transform the original
problem to an equivalent problem by exploiting the channel duality. For the
transformed problem, we develop an efficient solution procedure that integrates
Lagrangian dual decomposition method, conjugate gradient projection method
based on matrix differential calculus, cutting-plane method, and subgradient
method. In our numerical example, it is shown that we can achieve a network
performance gain of 34.4% by using DPC
Distributed User Scheduling for MIMO-Y Channel
In this paper, distributed user scheduling schemes are proposed for the
multi-user MIMO-Y channel, where three -antenna users ()
are selected from three clusters to exchange information via an -antenna
amplify-and-forward (AF) relay (), and represents the number
of data stream(s) of each unicast transmission within the MIMO-Y channel. The
proposed schemes effectively harvest multi-user diversity (MuD) without the
need of global channel state information (CSI) or centralized computations. In
particular, a novel reference signal space (RSS) is proposed to enable the
distributed scheduling for both cluster-wise (CS) and group-wise (GS) patterns.
The minimum user-antenna (Min-UA) transmission with is first
considered. Next, we consider an equal number of relay and user antenna (ER-UA)
transmission with , with the aim of reducing CSI overhead as
compared to Min-UA. For ER-UA transmission, the achievable MuD orders of the
proposed distributed scheduling schemes are analytically derived, which proves
the superiority and optimality of the proposed RSS-based distributed
scheduling. These results reveal some fundamental behaviors of MuD and the
performance-complexity tradeoff of user scheduling schemes in the MIMO-Y
channel
Large-System Analysis of Joint User Selection and Vector Precoding with Zero-Forcing Transmit Beamforming for MIMO Broadcast Channels
Multiple-input multiple-output (MIMO) broadcast channels (BCs) (MIMO-BCs)
with perfect channel state information (CSI) at the transmitter are considered.
As joint user selection (US) and vector precoding (VP) (US-VP) with
zero-forcing transmit beamforming (ZF-BF), US and continuous VP (CVP) (US-CVP)
and data-dependent US (DD-US) are investigated. The replica method, developed
in statistical physics, is used to analyze the energy penalties for the two
US-VP schemes in the large-system limit, where the number of users, the number
of selected users, and the number of transmit antennas tend to infinity with
their ratios kept constant. Four observations are obtained in the large-system
limit: First, the assumptions of replica symmetry (RS) and 1-step replica
symmetry breaking (1RSB) for DD-US can provide acceptable approximations for
low and moderate system loads, respectively. Secondly, DD-US outperforms CVP
with random US in terms of the energy penalty for low-to-moderate system loads.
Thirdly, the asymptotic energy penalty of DD-US is indistinguishable from that
of US-CVP for low system loads. Finally, a greedy algorithm of DD-US proposed
in authors' previous work can achieve nearly optimal performance for
low-to-moderate system loads.Comment: submitted to ISITA201
MIMO Relaying Broadcast Channels with Linear Precoding and Quantized Channel State Information Feedback
Multi-antenna relaying has emerged as a promising technology to enhance the
system performance in cellular networks. However, when precoding techniques are
utilized to obtain multi-antenna gains, the system generally requires channel
state information (CSI) at the transmitters. We consider a linear precoding
scheme in a MIMO relaying broadcast channel with quantized CSI feedback from
both two-hop links. With this scheme, each remote user feeds back its quantized
CSI to the relay, and the relay sends back the quantized precoding information
to the base station (BS). An upper bound on the rate loss due to quantized
channel knowledge is first characterized. Then, in order to maintain the rate
loss within a predetermined gap for growing SNRs, a strategy of scaling
quantization quality of both two-hop links is proposed. It is revealed that the
numbers of feedback bits of both links should scale linearly with the transmit
power at the relay, while only the bit number of feedback from the relay to the
BS needs to grow with the increasing transmit power at the BS. Numerical
results are provided to verify the proposed strategy for feedback quality
control.Comment: 13pages appeared in IEEE Transactions on Signal Processin
User Selection in MIMO Interfering Broadcast Channels
Interference alignment aims to achieve maximum degrees of freedom in an
interference system. For achieving Interference alignment in interfering
broadcast systems a closed-form solution is proposed in [1] which is an
extension of the grouping scheme in [2]. In a downlink scenario where there are
a large number of users, the base station is required to select a subset of
users such that the sum rate is maximized. To search for the optimal user
subset using brute-force approach is computationally exhaustive because of the
large number of possible user subset combinations. We propose a user selection
algorithm achieving sum rate close to that of optimal solution. The algorithm
employs coordinate ascent approach and exploits orthogonality between the
desired signal space and the interference channel space in the reciprocal
system to select the user at each step. For the sake of completeness, we have
also extended the sum rate approach based algorithm to Interfering broadcast
channel. The complexity of both these algorithms is shown to be linear with
respect to the total number of users as compared to exponential in brute-force
search.Comment: 9 pages, 5 figure
Joint Source and Relay Precoding Designs for MIMO Two-Way Relaying Based on MSE Criterion
Properly designed precoders can significantly improve the spectral efficiency
of multiple-input multiple-output (MIMO) relay systems. In this paper, we
investigate joint source and relay precoding design based on the
mean-square-error (MSE) criterion in MIMO two-way relay systems, where two
multi-antenna source nodes exchange information via a multi-antenna
amplify-and-forward relay node. This problem is non-convex and its optimal
solution remains unsolved. Aiming to find an efficient way to solve the
problem, we first decouple the primal problem into three tractable
sub-problems, and then propose an iterative precoding design algorithm based on
alternating optimization. The solution to each sub-problem is optimal and
unique, thus the convergence of the iterative algorithm is guaranteed.
Secondly, we propose a structured precoding design to lower the computational
complexity. The proposed precoding structure is able to parallelize the
channels in the multiple access (MAC) phase and broadcast (BC) phase. It thus
reduces the precoding design to a simple power allocation problem. Lastly, for
the special case where only a single data stream is transmitted from each
source node, we present a source-antenna-selection (SAS) based precoding design
algorithm. This algorithm selects only one antenna for transmission from each
source and thus requires lower signalling overhead. Comprehensive simulation is
conducted to evaluate the effectiveness of all the proposed precoding designs.Comment: 32 pages, 10 figure
User-Antenna Selection for Physical-Layer Network Coding based on Euclidean Distance
In this paper, we present the error performance analysis of a multiple-input
multiple-output (MIMO) physical-layer network coding (PNC) system with two
different user-antenna selection (AS) schemes in asymmetric channel conditions.
For the first antenna selection scheme (AS1), where the user-antenna is
selected in order to maximize the overall channel gain between the user and the
relay, we give an explicit analytical proof that for binary modulations, the
system achieves full diversity order of in the
multiple-access (MA) phase, where , and denote the number of
antennas at user , user and relay respectively. We present a
detailed investigation of the diversity order for the MIMO-PNC system with AS1
in the MA phase for any modulation order. A tight closed-form upper bound on
the average SER is also derived for the special case when , which is
valid for any modulation order. We show that in this case the system fails to
achieve transmit diversity in the MA phase, as the system diversity order drops
to irrespective of the number of transmit antennas at the user nodes.
Additionally, we propose a Euclidean distance (ED) based user-antenna selection
scheme (AS2) which outperforms the first scheme in terms of error performance.
Moreover, by deriving upper and lower bounds on the diversity order for the
MIMO-PNC system with AS2, we show that this system enjoys both transmit and
receive diversity, achieving full diversity order of in the MA phase for any modulation order. Monte Carlo simulations are
provided which confirm the correctness of the derived analytical results.Comment: IEEE Transactions on Communications. arXiv admin note: text overlap
with arXiv:1709.0445
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