1,317 research outputs found
Adaptive and Iterative Multi-Branch MMSE Decision Feedback Detection Algorithms for MIMO Systems
In this work, decision feedback (DF) detection algorithms based on multiple
processing branches for multi-input multi-output (MIMO) spatial multiplexing
systems are proposed. The proposed detector employs multiple cancellation
branches with receive filters that are obtained from a common matrix inverse
and achieves a performance close to the maximum likelihood detector (MLD).
Constrained minimum mean-squared error (MMSE) receive filters designed with
constraints on the shape and magnitude of the feedback filters for the
multi-branch MMSE DF (MB-MMSE-DF) receivers are presented. An adaptive
implementation of the proposed MB-MMSE-DF detector is developed along with a
recursive least squares-type algorithm for estimating the parameters of the
receive filters when the channel is time-varying. A soft-output version of the
MB-MMSE-DF detector is also proposed as a component of an iterative detection
and decoding receiver structure. A computational complexity analysis shows that
the MB-MMSE-DF detector does not require a significant additional complexity
over the conventional MMSE-DF detector, whereas a diversity analysis discusses
the diversity order achieved by the MB-MMSE-DF detector. Simulation results
show that the MB-MMSE-DF detector achieves a performance superior to existing
suboptimal detectors and close to the MLD, while requiring significantly lower
complexity.Comment: 10 figures, 3 tables; IEEE Transactions on Wireless Communications,
201
Advanced Radio Resource Management for Multi Antenna Packet Radio Systems
In this paper, we propose fairness-oriented packet scheduling (PS) schemes
with power-efficient control mechanism for future packet radio systems. In
general, the radio resource management functionality plays an important role in
new OFDMA based networks. The control of the network resource division among
the users is performed by packet scheduling functionality based on maximizing
cell coverage and capacity satisfying, and certain quality of service
requirements. Moreover, multiantenna transmit-receive schemes provide
additional flexibility to packet scheduler functionality. In order to mitigate
inter-cell and co-channel interference problems in OFDMA cellular networks soft
frequency reuse with different power masks patterns is used. Stemming from the
earlier enhanced proportional fair scheduler studies for single-input
multiple-output (SIMO) and multiple-input multipleoutput (MIMO) systems, we
extend the development of efficient packet scheduling algorithms by adding
transmit power considerations in the overall priority metrics calculations and
scheduling decisions. Furthermore, we evaluate the proposed scheduling schemes
by simulating practical orthogonal frequency division multiple access (OFDMA)
based packet radio system in terms of throughput, coverage and fairness
distribution among users. As a concrete example, under reduced overall transmit
power constraint and unequal power distribution for different sub-bands, we
demonstrate that by using the proposed power-aware multi-user scheduling
schemes, significant coverage and fairness improvements in the order of 70% and
20%, respectively, can be obtained, at the expense of average throughput loss
of only 15%.Comment: 14 Pages, IJWM
Multiuser MIMO-OFDM for Next-Generation Wireless Systems
This overview portrays the 40-year evolution of orthogonal frequency division multiplexing (OFDM) research. The amelioration of powerful multicarrier OFDM arrangements with multiple-input multiple-output (MIMO) systems has numerous benefits, which are detailed in this treatise. We continue by highlighting the limitations of conventional detection and channel estimation techniques designed for multiuser MIMO OFDM systems in the so-called rank-deficient scenarios, where the number of users supported or the number of transmit antennas employed exceeds the number of receiver antennas. This is often encountered in practice, unless we limit the number of users granted access in the base station’s or radio port’s coverage area. Following a historical perspective on the associated design problems and their state-of-the-art solutions, the second half of this treatise details a range of classic multiuser detectors (MUDs) designed for MIMO-OFDM systems and characterizes their achievable performance. A further section aims for identifying novel cutting-edge genetic algorithm (GA)-aided detector solutions, which have found numerous applications in wireless communications in recent years. In an effort to stimulate the cross pollination of ideas across the machine learning, optimization, signal processing, and wireless communications research communities, we will review the broadly applicable principles of various GA-assisted optimization techniques, which were recently proposed also for employment inmultiuser MIMO OFDM. In order to stimulate new research, we demonstrate that the family of GA-aided MUDs is capable of achieving a near-optimum performance at the cost of a significantly lower computational complexity than that imposed by their optimum maximum-likelihood (ML) MUD aided counterparts. The paper is concluded by outlining a range of future research options that may find their way into next-generation wireless systems
Efficient DSP and Circuit Architectures for Massive MIMO: State-of-the-Art and Future Directions
Massive MIMO is a compelling wireless access concept that relies on the use
of an excess number of base-station antennas, relative to the number of active
terminals. This technology is a main component of 5G New Radio (NR) and
addresses all important requirements of future wireless standards: a great
capacity increase, the support of many simultaneous users, and improvement in
energy efficiency. Massive MIMO requires the simultaneous processing of signals
from many antenna chains, and computational operations on large matrices. The
complexity of the digital processing has been viewed as a fundamental obstacle
to the feasibility of Massive MIMO in the past. Recent advances on
system-algorithm-hardware co-design have led to extremely energy-efficient
implementations. These exploit opportunities in deeply-scaled silicon
technologies and perform partly distributed processing to cope with the
bottlenecks encountered in the interconnection of many signals. For example,
prototype ASIC implementations have demonstrated zero-forcing precoding in real
time at a 55 mW power consumption (20 MHz bandwidth, 128 antennas, multiplexing
of 8 terminals). Coarse and even error-prone digital processing in the antenna
paths permits a reduction of consumption with a factor of 2 to 5. This article
summarizes the fundamental technical contributions to efficient digital signal
processing for Massive MIMO. The opportunities and constraints on operating on
low-complexity RF and analog hardware chains are clarified. It illustrates how
terminals can benefit from improved energy efficiency. The status of technology
and real-life prototypes discussed. Open challenges and directions for future
research are suggested.Comment: submitted to IEEE transactions on signal processin
Generalized space-time shift keying designed for flexible diversity-, multiplexing- and complexity-tradeoffs
In this paper, motivated by the recent concept of Spatial Modulation (SM), we propose a novel Generalized Space-Time Shift Keying (G-STSK) architecture, which acts as a unified Multiple-Input Multiple-Output (MIMO) framework. More specifically, our G-STSK scheme is based on the rationale that P out of Q dispersion matrices are selected and linearly combined in conjunction with the classic PSK/QAM modulation, where activating P out of Q dispersion matrices provides an implicit means of conveying information bits in addition to the classic modem. Due to its substantial flexibility, our G-STSK framework includes diverse MIMO arrangements, such as SM, Space-Shift Keying (SSK), Linear Dispersion Codes (LDCs), Space-Time Block Codes (STBCs) and Bell Lab’s Layered Space-Time (BLAST) scheme. Hence it has the potential of subsuming all of them, when flexibly adapting a set of system parameters. Moreover, we also derive the Discrete-input Continuous-output Memoryless Channel (DCMC) capacity for our G-STSK scheme, which serves as the unified capacity limit, hence quantifying the capacity of the class of MIMO arrangements. Furthermore, EXtrinsic Information Transfer (EXIT) chart analysis is used for designing our G-STSK scheme and for characterizing its iterative decoding convergence
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