112 research outputs found
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
A Random Access Protocol for Pilot Allocation in Crowded Massive MIMO Systems
The Massive MIMO (multiple-input multiple-output) technology has great
potential to manage the rapid growth of wireless data traffic. Massive MIMO
achieves tremendous spectral efficiency by spatial multiplexing of many tens of
user equipments (UEs). These gains are only achieved in practice if many more
UEs can connect efficiently to the network than today. As the number of UEs
increases, while each UE intermittently accesses the network, the random access
functionality becomes essential to share the limited number of pilots among the
UEs. In this paper, we revisit the random access problem in the Massive MIMO
context and develop a reengineered protocol, termed strongest-user collision
resolution (SUCRe). An accessing UE asks for a dedicated pilot by sending an
uncoordinated random access pilot, with a risk that other UEs send the same
pilot. The favorable propagation of Massive MIMO channels is utilized to enable
distributed collision detection at each UE, thereby determining the strength of
the contenders' signals and deciding to repeat the pilot if the UE judges that
its signal at the receiver is the strongest. The SUCRe protocol resolves the
vast majority of all pilot collisions in crowded urban scenarios and continues
to admit UEs efficiently in overloaded networks.Comment: To appear in IEEE Transactions on Wireless Communications, 16 pages,
10 figures. This is reproducible research with simulation code available at
https://github.com/emilbjornson/sucre-protoco
Gaussian Message Passing for Overloaded Massive MIMO-NOMA
This paper considers a low-complexity Gaussian Message Passing (GMP) scheme
for a coded massive Multiple-Input Multiple-Output (MIMO) systems with
Non-Orthogonal Multiple Access (massive MIMO-NOMA), in which a base station
with antennas serves sources simultaneously in the same frequency.
Both and are large numbers, and we consider the overloaded cases
with . The GMP for MIMO-NOMA is a message passing algorithm operating
on a fully-connected loopy factor graph, which is well understood to fail to
converge due to the correlation problem. In this paper, we utilize the
large-scale property of the system to simplify the convergence analysis of the
GMP under the overloaded condition. First, we prove that the \emph{variances}
of the GMP definitely converge to the mean square error (MSE) of Linear Minimum
Mean Square Error (LMMSE) multi-user detection. Secondly, the \emph{means} of
the traditional GMP will fail to converge when . Therefore, we propose and derive a new
convergent GMP called scale-and-add GMP (SA-GMP), which always converges to the
LMMSE multi-user detection performance for any , and show that it
has a faster convergence speed than the traditional GMP with the same
complexity. Finally, numerical results are provided to verify the validity and
accuracy of the theoretical results presented.Comment: Accepted by IEEE TWC, 16 pages, 11 figure
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
Towards Massive Connectivity Support for Scalable mMTC Communications in 5G networks
The fifth generation of cellular communication systems is foreseen to enable
a multitude of new applications and use cases with very different requirements.
A new 5G multiservice air interface needs to enhance broadband performance as
well as provide new levels of reliability, latency and supported number of
users. In this paper we focus on the massive Machine Type Communications (mMTC)
service within a multi-service air interface. Specifically, we present an
overview of different physical and medium access techniques to address the
problem of a massive number of access attempts in mMTC and discuss the protocol
performance of these solutions in a common evaluation framework
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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