133 research outputs found
Joint Channel Estimation and Pilot Allocation in Underlay Cognitive MISO Networks
Cognitive radios have been proposed as agile technologies to boost the
spectrum utilization. This paper tackles the problem of channel estimation and
its impact on downlink transmissions in an underlay cognitive radio scenario.
We consider primary and cognitive base stations, each equipped with multiple
antennas and serving multiple users. Primary networks often suffer from the
cognitive interference, which can be mitigated by deploying beamforming at the
cognitive systems to spatially direct the transmissions away from the primary
receivers. The accuracy of the estimated channel state information (CSI) plays
an important role in designing accurate beamformers that can regulate the
amount of interference. However, channel estimate is affected by interference.
Therefore, we propose different channel estimation and pilot allocation
techniques to deal with the channel estimation at the cognitive systems, and to
reduce the impact of contamination at the primary and cognitive systems. In an
effort to tackle the contamination problem in primary and cognitive systems, we
exploit the information embedded in the covariance matrices to successfully
separate the channel estimate from other users' channels in correlated
cognitive single input multiple input (SIMO) channels. A minimum mean square
error (MMSE) framework is proposed by utilizing the second order statistics to
separate the overlapping spatial paths that create the interference. We
validate our algorithms by simulation and compare them to the state of the art
techniques.Comment: 6 pages, 2 figures, invited paper to IWCMC 201
Adaptive Precoding and Resource Allocation in Cognitive Radio Networks
In this thesis, we develop efficient resource allocation and adaptive precoding schemes for two scenarios: multiuser MIMO-OFDM and multiuser MIMO based CR networks. In the context of the multiuser MIMO-OFDM CR network, we have developed resource allocation and adaptive precoding schemes for both the downlink (DL) and uplink (UL). The proposed schemes are characterized by both computational and spectral efficiencies. The adaptive precoder operates based on generating degrees of freedom (DoF). The resource allocation has been formulated as a sum-rate maximization problem subject to the upper-limit of total power and interference at primary user constraints. The formulated optimization problem is a mixed integer programming having a combinatorial complexity which is hard to solve, and therefore we separated it into a two-phase procedure to elaborate computational efficiency: Adaptive precoding (DoF assignment) and subcarrier mapping. From the implementation perspective, the resource allocation of the DL is central based processing, but the UL is semi-distributed based. The DL and UL problems are sorted out using the Lagrange multiplier theory which is regarded as an efficient alternative methodology compared to the convex optimization theory. The solution is not only characterized by low-complexity, but also by optimality. Numerical simulations illustrate remarkable spectral and SNR gains provided by the proposed schemes.In dieser Dissertation werden effiziente Ressourcenallokation und adaptive Vorkodierungsverfahren für zwei Szenarios entwickelt: Mehrbenutzer-MIMO-OFDM und Mehrbenutzer-MIMO jeweils basierend auf CR-Netzwerken. Im Bereich der Mehrbenutzer-MIMO-OFDM CR-Netzwerke wurden Verfahren zur Ressourcenallokation und zur adaptiven Vorkodierung jeweils für den Downlink (DL) und den Uplink (UL) entwickelt. Die Ressourcenallokation wurde als Optimierungsproblem formuliert, bei dem die Summenrate maximiert wird, wobei die Gesamtsendeleistung und die Interferenz an den Primärnutzern begrenzt ist. Das formulierte Optimierungsproblem ist ein sogenanntes Mixed-Integer-Programm, dessen kombinatorische Komplexität nur extrem aufwendig lösbar ist. Auf Grund dessen wurde es zur Komplexitätsreduktion in zwei Phasen aufgeteilt: Adaptive Vorkodierung (DoF-Zuordnung) und Subkanalzuordnung. Während die Ressourcenallokation für den DL aus Implementierungssicht ein zentralistischer Prozess ist, kann sie für den UL als semiverteilt eingeordnet werden. Die Aufgabe der zentralen Ressourcenallokation wird gelöst, um die zentrale adaptive Vorkodierung und die Subkanalzuordnung für UL und DL zu verwalten. Die Subkanalzuordnung ist für den DL optimal und effizient gelöst, indem das Problem als konvexes Problem modelliert ist. Für den UL wiederum ist das Problem trotz der Konvexität quasi-optimal gelöst, da in der Problemformulierung eine Begrenzung der Ressourcen pro Benutzer existiert
Cognitive Orthogonal Precoder for Two-tiered Networks Deployment
In this work, the problem of cross-tier interference in a two-tiered
(macro-cell and cognitive small-cells) network, under the complete spectrum
sharing paradigm, is studied. A new orthogonal precoder transmit scheme for the
small base stations, called multi-user Vandermonde-subspace frequency division
multiplexing (MU-VFDM), is proposed. MU-VFDM allows several cognitive small
base stations to coexist with legacy macro-cell receivers, by nulling the
small- to macro-cell cross-tier interference, without any cooperation between
the two tiers. This cleverly designed cascaded precoder structure, not only
cancels the cross-tier interference, but avoids the co-tier interference for
the small-cell network. The achievable sum-rate of the small-cell network,
satisfying the interference cancelation requirements, is evaluated for perfect
and imperfect channel state information at the transmitter. Simulation results
for the cascaded MU-VFDM precoder show a comparable performance to that of
state-of-the-art dirty paper coding technique, for the case of a dense cellular
layout. Finally, a comparison between MU-VFDM and a standard complete spectrum
separation strategy is proposed. Promising gains in terms of achievable
sum-rate are shown for the two-tiered network w.r.t. the traditional bandwidth
management approach.Comment: 11 pages, 9 figures, accepted and to appear in IEEE Journal on
Selected Areas in Communications: Cognitive Radio Series, 2013. Copyright
transferred to IEE
On the Design of Cognitive-Radio-Inspired Asymmetric Network Coding Transmissions in MIMO Systems
In this paper, a cognitive-radio-inspired asymmetric network coding (CR-AsNC) scheme is proposed for multiple-input-multiple-output (MIMO) cellular transmissions, where information exchange among users and base-station (BS) broadcasting can be accomplished simultaneously. The key idea is to apply the concept of cognitive radio (CR) in network coding transmissions, where the BS tries sending new information while helping users' transmissions as a relay. In particular, we design an asymmetric network coding method for information exchange between the BS and the users, although many existing works consider the design of network coding in symmetric scenarios. To approach the optimal performance, an iterative precoding design for CR-AsNC is first developed. Then, a channel-diagonalization-based precoding design with low complexity is proposed, to which power allocation can be optimized with a closed-form solution. The simulation results show that the proposed CR-AsNC scheme with precoding optimization can significantly improve system transmission performance
Decomposition by Successive Convex Approximation: A Unifying Approach for Linear Transceiver Design in Heterogeneous Networks
We study the downlink linear precoder design problem in a multi-cell dense
heterogeneous network (HetNet). The problem is formulated as a general
sum-utility maximization (SUM) problem, which includes as special cases many
practical precoder design problems such as multi-cell coordinated linear
precoding, full and partial per-cell coordinated multi-point transmission,
zero-forcing precoding and joint BS clustering and beamforming/precoding. The
SUM problem is difficult due to its non-convexity and the tight coupling of the
users' precoders. In this paper we propose a novel convex approximation
technique to approximate the original problem by a series of convex
subproblems, each of which decomposes across all the cells. The convexity of
the subproblems allows for efficient computation, while their decomposability
leads to distributed implementation. {Our approach hinges upon the
identification of certain key convexity properties of the sum-utility
objective, which allows us to transform the problem into a form that can be
solved using a popular algorithmic framework called BSUM (Block Successive
Upper-Bound Minimization).} Simulation experiments show that the proposed
framework is effective for solving interference management problems in large
HetNet.Comment: Accepted by IEEE Transactions on Wireless Communicatio
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