86,126 research outputs found
Resource Allocation for Multiple-Input and Multiple-Output Interference Networks
To meet the exponentially increasing traffic data driven by the rapidly growing mobile subscriptions, both industry and academia are exploring the potential of a new genera- tion (5G) of wireless technologies. An important 5G goal is to achieve high data rate. Small cells with spectrum sharing and multiple-input multiple-output (MIMO) techniques are one of the most promising 5G technologies, since it enables to increase the aggregate data rate by improving the spectral efficiency, nodes density and transmission bandwidth, respectively. However, the increased interference in the densified networks will in return limit the achievable rate performance if not properly managed.
The considered setup can be modeled as MIMO interference networks, which can be classified into the K-user MIMO interference channel (IC) and the K-cell MIMO interfering broadcast channel/multiple access channel (MIMO-IBC/IMAC) according to the number of mobile stations (MSs) simultaneously served by each base station (BS). The thesis considers two physical layer (PHY) resource allocation problems that deal with the interference for both models: 1) Pareto boundary computation for the achiev- able rate region in a K-user single-stream MIMO IC and 2) grouping-based interference alignment (GIA) with optimized IA-Cell assignment in a MIMO-IMAC under limited feedback. In each problem, the thesis seeks to provide a deeper understanding of the system and novel mathematical results, along with supporting numerical examples. Some of the main contributions can be summarized as follows.
It is an open problem to compute the Pareto boundary of the achievable rate region for a K-user single-stream MIMO IC. The K-user single-stream MIMO IC models multiple transmitter-receiver pairs which operate over the same spectrum simultaneously. Each transmitter and each receiver is equipped with multiple antennas, and a single desired data stream is communicated in each transmitter-receiver link. The individual achievable rates of the K users form a K-dimensional achievable rate region. To find efficient operating points in the achievable rate region, the Pareto boundary computation problem, which can be formulated as a multi-objective optimization problem, needs to be solved. The thesis transforms the multi-objective optimization problem to two single-objective optimization problems–single constraint rate maximization problem and alternating rate profile optimization problem, based on the formulations of the ε-constraint optimization and the weighted Chebyshev optimization, respectively. The thesis proposes two alternating optimization algorithms to solve both single-objective optimization problems. The convergence of both algorithms is guaranteed. Also, a heuristic initialization scheme is provided for each algorithm to achieve a high-quality solution. By varying the weights in each single-objective optimization problem, numerical results show that both algorithms provide an inner bound very close to the Pareto boundary. Furthermore, the thesis also computes some key points exactly on the Pareto boundary in closed-form.
A framework for interference alignment (IA) under limited feedback is proposed for a MIMO-IMAC. The MIMO-IMAC well matches the uplink scenario in cellular system, where multiple cells share their spectrum and operate simultaneously. In each cell, a BS receives the desired signals from multiple MSs within its own cell and each BS and each MS is equipped with multi-antenna. By allowing the inter-cell coordination, the thesis develops a distributed IA framework under limited feedback from three aspects: the GIA, the IA-Cell assignment and dynamic feedback bit allocation (DBA), respec- tively. Firstly, the thesis provides a complete study along with some new improvements of the GIA, which enables to compute the exact IA precoders in closed-form, based on local channel state information at the receiver (CSIR). Secondly, the concept of IA-Cell assignment is introduced and its effect on the achievable rate and degrees of freedom (DoF) performance is analyzed. Two distributed matching approaches and one centralized assignment approach are proposed to find a good IA-Cell assignment in three scenrios with different backhaul overhead. Thirdly, under limited feedback, the thesis derives an upper bound of the residual interference to noise ratio (RINR), formulates and solves a corresponding DBA problem. Finally, numerical results show that the proposed GIA with optimized IA-Cell assignment and the DBA greatly outperforms the traditional GIA algorithm
Jointly Optimal Channel and Power Assignment for Dual-Hop Multi-channel Multi-user Relaying
We consider the problem of jointly optimizing channel pairing, channel-user
assignment, and power allocation, to maximize the weighted sum-rate, in a
single-relay cooperative system with multiple channels and multiple users.
Common relaying strategies are considered, and transmission power constraints
are imposed on both individual transmitters and the aggregate over all
transmitters. The joint optimization problem naturally leads to a mixed-integer
program. Despite the general expectation that such problems are intractable, we
construct an efficient algorithm to find an optimal solution, which incurs
computational complexity that is polynomial in the number of channels and the
number of users. We further demonstrate through numerical experiments that the
jointly optimal solution can significantly improve system performance over its
suboptimal alternatives.Comment: This is the full version of a paper to appear in the IEEE Journal on
Selected Areas in Communications, Special Issue on Cooperative Networking -
Challenges and Applications (Part II), October 201
Interference Alignment for Partially Connected MIMO Cellular Networks
In this paper, we propose an iterative interference alignment (IA) algorithm
for MIMO cellular networks with partial connectivity, which is induced by
heterogeneous path losses and spatial correlation. Such systems impose several
key technical challenges in the IA algorithm design, namely the overlapping
between the direct and interfering links due to the MIMO cellular topology as
well as how to exploit the partial connectivity. We shall address these
challenges and propose a three stage IA algorithm. As illustration, we analyze
the achievable degree of freedom (DoF) of the proposed algorithm for a
symmetric partially connected MIMO cellular network. We show that there is
significant DoF gain compared with conventional IA algorithms due to partial
connectivity. The derived DoF bound is also backward compatible with that
achieved on fully connected K-pair MIMO interference channels.Comment: Submitted to IEEE Transactions on Signal Processing, accepte
Flexible Backhaul Design and Degrees of Freedom for Linear Interference Networks
The considered problem is that of maximizing the degrees of freedom (DoF) in
cellular downlink, under a backhaul load constraint that limits the number of
messages that can be delivered from a centralized controller to the base
station transmitters. A linear interference channel model is considered, where
each transmitter is connected to the receiver having the same index as well as
one succeeding receiver. The backhaul load is defined as the sum of all the
messages available at all the transmitters normalized by the number of users.
When the backhaul load is constrained to an integer level B, the asymptotic per
user DoF is shown to equal (4B-1)/(4B), and it is shown that the optimal
assignment of messages to transmitters is asymmetric and satisfies a local
cooperation constraint and that the optimal coding scheme relies only on
zero-forcing transmit beamforming. Finally, an extension of the presented
coding scheme is shown to apply for more general locally connected and
two-dimensional networks.Comment: Submitted to IEEE International Symposium on Information Theory (ISIT
2014
Resource Allocation for Power Minimization in the Downlink of THP-based Spatial Multiplexing MIMO-OFDMA Systems
In this work, we deal with resource allocation in the downlink of spatial
multiplexing MIMO-OFDMA systems. In particular, we concentrate on the problem
of jointly optimizing the transmit and receive processing matrices, the channel
assignment and the power allocation with the objective of minimizing the total
power consumption while satisfying different quality-of-service requirements. A
layered architecture is used in which users are first partitioned in different
groups on the basis of their channel quality and then channel assignment and
transceiver design are sequentially addressed starting from the group of users
with most adverse channel conditions. The multi-user interference among users
belonging to different groups is removed at the base station using a
Tomlinson-Harashima pre-coder operating at user level. Numerical results are
used to highlight the effectiveness of the proposed solution and to make
comparisons with existing alternatives.Comment: 12 pages, 6 figures, IEEE Trans. Veh. Techno
Optimal Design of Multiple Description Lattice Vector Quantizers
In the design of multiple description lattice vector quantizers (MDLVQ),
index assignment plays a critical role. In addition, one also needs to choose
the Voronoi cell size of the central lattice v, the sublattice index N, and the
number of side descriptions K to minimize the expected MDLVQ distortion, given
the total entropy rate of all side descriptions Rt and description loss
probability p. In this paper we propose a linear-time MDLVQ index assignment
algorithm for any K >= 2 balanced descriptions in any dimensions, based on a
new construction of so-called K-fraction lattice. The algorithm is greedy in
nature but is proven to be asymptotically (N -> infinity) optimal for any K >=
2 balanced descriptions in any dimensions, given Rt and p. The result is
stronger when K = 2: the optimality holds for finite N as well, under some mild
conditions. For K > 2, a local adjustment algorithm is developed to augment the
greedy index assignment, and conjectured to be optimal for finite N.
Our algorithmic study also leads to better understanding of v, N and K in
optimal MDLVQ design. For K = 2 we derive, for the first time, a
non-asymptotical closed form expression of the expected distortion of optimal
MDLVQ in p, Rt, N. For K > 2, we tighten the current asymptotic formula of the
expected distortion, relating the optimal values of N and K to p and Rt more
precisely.Comment: Submitted to IEEE Trans. on Information Theory, Sep 2006 (30 pages, 7
figures
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