1,377 research outputs found
Ubiquitous Cell-Free Massive MIMO Communications
Since the first cellular networks were trialled in the 1970s, we have
witnessed an incredible wireless revolution. From 1G to 4G, the massive traffic
growth has been managed by a combination of wider bandwidths, refined radio
interfaces, and network densification, namely increasing the number of antennas
per site. Due its cost-efficiency, the latter has contributed the most. Massive
MIMO (multiple-input multiple-output) is a key 5G technology that uses massive
antenna arrays to provide a very high beamforming gain and spatially
multiplexing of users, and hence, increases the spectral and energy efficiency.
It constitutes a centralized solution to densify a network, and its performance
is limited by the inter-cell interference inherent in its cell-centric design.
Conversely, ubiquitous cell-free Massive MIMO refers to a distributed Massive
MIMO system implementing coherent user-centric transmission to overcome the
inter-cell interference limitation in cellular networks and provide additional
macro-diversity. These features, combined with the system scalability inherent
in the Massive MIMO design, distinguishes ubiquitous cell-free Massive MIMO
from prior coordinated distributed wireless systems. In this article, we
investigate the enormous potential of this promising technology while
addressing practical deployment issues to deal with the increased
back/front-hauling overhead deriving from the signal co-processing.Comment: Published in EURASIP Journal on Wireless Communications and
Networking on August 5, 201
A Coordinated Approach to Channel Estimation in Large-scale Multiple-antenna Systems
This paper addresses the problem of channel estimation in multi-cell
interference-limited cellular networks. We consider systems employing multiple
antennas and are interested in both the finite and large-scale antenna number
regimes (so-called "massive MIMO"). Such systems deal with the multi-cell
interference by way of per-cell beamforming applied at each base station.
Channel estimation in such networks, which is known to be hampered by the pilot
contamination effect, constitute a major bottleneck for overall performance. We
present a novel approach which tackles this problem by enabling a low-rate
coordination between cells during the channel estimation phase itself. The
coordination makes use of the additional second-order statistical information
about the user channels, which are shown to offer a powerful way of
discriminating across interfering users with even strongly correlated pilot
sequences. Importantly, we demonstrate analytically that in the
large-number-of-antennas regime, the pilot contamination effect is made to
vanish completely under certain conditions on the channel covariance. Gains
over the conventional channel estimation framework are confirmed by our
simulations for even small antenna array sizes.Comment: 10 pages, 6 figures, to appear in IEEE Journal on Selected Areas in
Communication
Cell-Free Massive MIMO versus Small Cells
A Cell-Free Massive MIMO (multiple-input multiple-output) system comprises a
very large number of distributed access points (APs)which simultaneously serve
a much smaller number of users over the same time/frequency resources based on
directly measured channel characteristics. The APs and users have only one
antenna each. The APs acquire channel state information through time-division
duplex operation and the reception of uplink pilot signals transmitted by the
users. The APs perform multiplexing/de-multiplexing through conjugate
beamforming on the downlink and matched filtering on the uplink. Closed-form
expressions for individual user uplink and downlink throughputs lead to max-min
power control algorithms. Max-min power control ensures uniformly good service
throughout the area of coverage. A pilot assignment algorithm helps to mitigate
the effects of pilot contamination, but power control is far more important in
that regard.
Cell-Free Massive MIMO has considerably improved performance with respect to
a conventional small-cell scheme, whereby each user is served by a dedicated
AP, in terms of both 95%-likely per-user throughput and immunity to shadow
fading spatial correlation. Under uncorrelated shadow fading conditions, the
cell-free scheme provides nearly 5-fold improvement in 95%-likely per-user
throughput over the small-cell scheme, and 10-fold improvement when shadow
fading is correlated.Comment: EEE Transactions on Wireless Communications, accepted for publicatio
INTERFERENCE MANAGEMENT IN LTE SYSTEM AND BEYOUND
The key challenges to high throughput in cellular wireless communication system are interference, mobility and bandwidth limitation. Mobility has never been a problem until recently, bandwidth has been constantly improved upon through the evolutions in cellular wireless communication system but interference has been a constant limitation to any improvement that may have resulted from such evolution. The fundamental challenge to a system designer or a researcher is how to achieve high data rate in motion (high speed) in a cellular system that is intrinsically interference-limited.
Multi-antenna is the solution to data on the move and the capacity of multi-antenna system has been demonstrated to increase proportionally with increase in the number of antennas at both transmitter and receiver for point-to-point communications and multi-user environment. However, the capacity gain in both uplink and downlink is limited in a multi-user environment like cellular system by interference, the number of antennas at the base station, complexity and space constraint particularly for a mobile terminal.
This challenge in the downlink provided the motivation to investigate successive interference cancellation (SIC) as an interference management tool LTE system and beyond. The Simulation revealed that ordered successive interference (OSIC) out performs non-ordered successive interference cancellation (NSIC) and the additional complexity is justified based on the associated gain in BER performance of OSIC. The major drawback of OSIC is that it is not efficient in network environment employing power control or power allocation. Additional interference management techniques will be required to fully manage the interference.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format
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
- …