172 research outputs found
Sparse Signal Processing Concepts for Efficient 5G System Design
As it becomes increasingly apparent that 4G will not be able to meet the
emerging demands of future mobile communication systems, the question what
could make up a 5G system, what are the crucial challenges and what are the key
drivers is part of intensive, ongoing discussions. Partly due to the advent of
compressive sensing, methods that can optimally exploit sparsity in signals
have received tremendous attention in recent years. In this paper we will
describe a variety of scenarios in which signal sparsity arises naturally in 5G
wireless systems. Signal sparsity and the associated rich collection of tools
and algorithms will thus be a viable source for innovation in 5G wireless
system design. We will discribe applications of this sparse signal processing
paradigm in MIMO random access, cloud radio access networks, compressive
channel-source network coding, and embedded security. We will also emphasize
important open problem that may arise in 5G system design, for which sparsity
will potentially play a key role in their solution.Comment: 18 pages, 5 figures, accepted for publication in IEEE Acces
Low-Density Code-Domain NOMA: Better Be Regular
A closed-form analytical expression is derived for the limiting empirical
squared singular value density of a spreading (signature) matrix corresponding
to sparse low-density code-domain (LDCD) non-orthogonal multiple-access (NOMA)
with regular random user-resource allocation. The derivation relies on
associating the spreading matrix with the adjacency matrix of a large
semiregular bipartite graph. For a simple repetition-based sparse spreading
scheme, the result directly follows from a rigorous analysis of spectral
measures of infinite graphs. Turning to random (sparse) binary spreading, we
harness the cavity method from statistical physics, and show that the limiting
spectral density coincides in both cases. Next, we use this density to compute
the normalized input-output mutual information of the underlying vector channel
in the large-system limit. The latter may be interpreted as the achievable
total throughput per dimension with optimum processing in a corresponding
multiple-access channel setting or, alternatively, in a fully-symmetric
broadcast channel setting with full decoding capabilities at each receiver.
Surprisingly, the total throughput of regular LDCD-NOMA is found to be not only
superior to that achieved with irregular user-resource allocation, but also to
the total throughput of dense randomly-spread NOMA, for which optimum
processing is computationally intractable. In contrast, the superior
performance of regular LDCD-NOMA can be potentially achieved with a feasible
message-passing algorithm. This observation may advocate employing regular,
rather than irregular, LDCD-NOMA in 5G cellular physical layer design.Comment: Accepted for publication in the IEEE International Symposium on
Information Theory (ISIT), June 201
MAC Aspects of Millimeter-Wave Cellular Networks
The current demands for extremely high data rate wireless services and the spectrum scarcity at the sub-6 GHz bands are forcefully motivating the use of the millimeter-wave (mmWave) frequencies. MmWave communications are characterized by severe attenuation, sparse-scattering environment, large bandwidth, high penetration loss, beamforming with massive antenna arrays, and possible noise-limited operation. These characteristics imply a major difference with respect to legacy communication technologies, primarily designed for the sub-6 GHz bands, and are posing major design challenges on medium access control (MAC) layer. This book chapter discusses key MAC layer issues at the initial access and mobility management (e.g., synchronization, random access, and handover) as well as resource allocation (interference management, scheduling, and association). The chapter provides an integrated view on MAC layer issues for cellular networks and reviews the main challenges and trade-offs and the state-of-the-art proposals to address them
Radio resource management for OFDMA systems under practical considerations.
Orthogonal frequency division multiple access (OFDMA) is used on the downlink of broadband wireless access (BWA) networks such as Worldwide Interoperability for Microwave Access (WiMAX) and Long Term Evolution (LTE) as it is able to offer substantial advantages such as combating channel impairments and supporting higher data rates. Also, by dynamically allocating subcarriers to users, frequency domain diversity as well as multiuser diversity can be effectively exploited so that performance can be greatly improved. The main focus of this thesis is on the development of practical resource allocation schemes for the OFDMA downlink. Imperfect Channel State Information (CSI), the limited capacity of the dedicated link used for CSI feedback, and the presence of a Connection Admission Control (CAC) unit are issues that are considered in this thesis to develop practical schemes. The design of efficient resource allocation schemes heavily depends on the CSI reported from the users to the transmitter. When the CSI is imperfect, a performance degradation is realized. It is therefore necessary to account for the imperfectness of the CSI when assigning radio resources to users. The first part of this thesis considers resource allocation strategies for OFDMA systems, where the transmitter only knows the statistical knowledge of the CSI (SCSI). The approach used shows that resources can be optimally allocated to achieve a performance that is comparable to that achieved when instantaneous CSI (ICSI) is available. The results presented show that the performance difference between the SCSI and ICSI based resource allocation schemes depends on the number of active users present in the cell, the Quality of Service (QoS) constraint, and the signal-to- noise ratio (SNR) per subcarrier. In practical systems only SCSI or CSI that is correlated to a certain extent with the true channel state can be used to perform resource allocation. An approach to quantifying the performance degradation for both cases is presented for the case where only a discrete number of modulation and coding levels are available for adaptive modulation and coding (AMC). Using the CSI estimates and the channel statistics, the approach can be used to perform resource allocation for both cases. It is shown that when a CAC unit is considered, CSI that is correlated with its present state leads to significantly higher values of the system throughput even under high user mobility. Motivated by the comparison between the correlated and statistical based resource allocation schemes, a strategy is then proposed which leads to a good tradeoff between overhead consumption and fairness as well as throughput when the presence of a CAC unit is considered. In OFDMA networks, the design of efficient CAC schemes also relies on the user CSI. The presence of a CAC unit needs to be considered when designing practical resource allocation schemes for BWA networks that support multiple service classes as it can guarantee fairness amongst them. In this thesis, a novel mechanism for CAC is developed which is based on the user channel gains and the cost of each service. This scheme divides the available bandwidth in accordance with a complete partitioning structure which allocates each service class an amount of non-overlapping bandwidth resource. In summary, the research results presented in this thesis contribute to the development of practical radio resource management schemes for BWA networks
Efficient Radio Resource Allocation Schemes and Code Optimizations for High Speed Downlink Packet Access Transmission
An important enhancement on the Wideband Code Division Multiple Access
(WCDMA) air interface of the 3G mobile communications, High Speed Downlink
Packet Access (HSDPA) standard has been launched to realize higher spectral
utilization efficiency. It introduces the features of multicode CDMA transmission
and Adaptive Modulation and Coding (AMC) technique, which makes radio resource
allocation feasible and essential. This thesis studies channel-aware resource
allocation schemes, coupled with fast power adjustment and spreading code optimization
techniques, for the HSDPA standard operating over frequency selective
channel.
A two-group resource allocation scheme is developed in order to achieve a
promising balance between performance enhancement and time efficiency. It only
requires calculating two parameters to specify the allocations of discrete bit rates
and transmitted symbol energies in all channels. The thesis develops the calculation
methods of the two parameters for interference-free and interference-present
channels, respectively. For the interference-present channels, the performance of
two-group allocation can be further enhanced by applying a clustering-based channel
removal scheme.
In order to make the two-group approach more time-efficient, reduction in
matrix inversions in optimum energy calculation is then discussed. When the
Minimum Mean Square Error (MMSE) equalizer is applied, optimum energy allocation
can be calculated by iterating a set of eigenvalues and eigenvectors. By
using the MMSE Successive Interference Cancellation (SIC) receiver, the optimum
energies are calculated recursively combined with an optimum channel ordering
scheme for enhancement in both system performance and time efficiency.
This thesis then studies the signature optimization methods with multipath
channel and examines their system performances when combined with different
resource allocation methods. Two multipath-aware signature optimization methods
are developed by applying iterative optimization techniques, for the system
using MMSE equalizer and MMSE precoder respectively. A PAM system using
complex signature sequences is also examined for improving resource utilization
efficiency, where two receiving schemes are proposed to fully take advantage of
PAM features. In addition by applying a short chip sampling window, a Singular
Value Decomposition (SVD) based interference-free signature design method is
presented
Iterative Detection for Overloaded Multiuser MIMO OFDM Systems
Inspired by multiuser detection (MUD) and the ‘Turbo principle’, this thesis deals with iterative interference cancellation (IIC) in overloaded multiuser multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems.
Linear detection schemes, such as zero forcing (ZF) and minimum mean square error (MMSE) cannot be used for the overloaded system because of the rank deficiency of channel matrix, while the optimal approach, the maximum likelihood (ML) detection has high computational complexity. In this thesis, an iterative interference cancellation (IIC) multiuser detection scheme with matched filter and convolutional codes is considered. The main idea of this combination is a low complexity receiver. Parallel interference cancellation (PIC) is employed to improve the multiuser receiver performance for overloaded systems. A log-likelihood ratio (LLR) converter is proposed to further improve the reliability of the soft value converted from the output of the matched filter. Simulation results show that the bit error rate (BER) performance of this method is close to the optimal approach for a two user system. However, for the four user or more user system, it has an error floor of the BER performance. For this case, a channel selection scheme is proposed to distinguish whether the channel is good or bad by using the mutual information based on the extrinsic information transfer (EXIT) chart. The mutual information can be predicted in a look-up table which greatly reduces the complexity. For those ‘bad’ channels identified by the channel selection, we introduce two adaptive transmission methods to deal with such channels: one uses a lower code rate, and the other is multiple transmissions. The use of an IIC receiver with the interleave-division multiple access (IDMA) to further improve the BER performance without any channel selection is also investigated. It has been shown that this approach can remove the error floor.
Finally, the influence of channel accuracy on the IIC is investigated. Pilot-based Wiener filter channel estimation is used to test and verify how much the IIC is influenced by the channel accuracy
Signal Processing for Compressed Sensing Multiuser Detection
The era of human based communication was longly believed to be the main driver for the development of communication systems. Already nowadays we observe that other types of communication impact the discussions of how future communication system will look like. One emerging technology in this direction is machine to machine (M2M) communication. M2M addresses the communication between autonomous entities without human interaction in mind. A very challenging aspect is the fact that M2M strongly differ from what communication system were designed for. Compared to human based communication, M2M is often characterized by small and sporadic uplink transmissions with limited data-rate constraints. While current communication systems can cope with several 100 transmissions, M2M envisions a massive number of devices that simultaneously communicate to a central base-station. Therefore, future communication systems need to be equipped with novel technologies facilitating the aggregation of massive M2M. The key design challenge lies in the efficient design of medium access technologies that allows for efficient communication with small data packets. Further, novel physical layer aspects have to be considered in order to reliable detect the massive uplink communication. Within this thesis physical layer concepts are introduced for a novel medium access technology tailored to the demands of sporadic M2M. This concept combines advances from the field of sporadic signal processing and communications. The main idea is to exploit the sporadic structure of the M2M traffic to design physical layer algorithms utilizing this side information. This concept considers that the base-station has to jointly detect the activity and the data of the M2M nodes. The whole framework of joint activity and data detection in sporadic M2M is known as Compressed Sensing Multiuser Detection (CS-MUD). This thesis introduces new physical layer concepts for CS-MUD. One important aspect is the question of how the activity detection impacts the data detection. It is shown that activity errors have a fundamentally different impact on the underlying communication system than data errors have. To address this impact, this thesis introduces new algorithms that aim at controlling or even avoiding the activity errors in a system. It is shown that a separate activity and data detection is a possible approach to control activity errors in M2M. This becomes possible by considering the activity detection task in a Bayesian framework based on soft activity information. This concept allows maintaining a constant and predictable activity error rate in a system. Beyond separate activity and data detection, the joint activity and data detection problem is addressed. Here a novel detector based on message passing is introduced. The main driver for this concept is the extrinsic information exchange between different entities being part of a graphical representation of the whole estimation problem. It can be shown that this detector is superior to state-of-the-art concepts for CS-MUD. Besides analyzing the concepts introduced simulatively, this thesis also shows an implementation of CS-MUD on a hardware demonstrator platform using the algorithms developed within this thesis. This implementation validates that the advantages of CS-MUD via over-the-air transmissions and measurements under practical constraints
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