333 research outputs found
Precoded FIR and Redundant V-BLAST Systems for Frequency-Selective MIMO Channels
The vertical Bell labs layered space-time (V-BLAST) system is a multi-input multioutput (MIMO) system designed to achieve good multiplexing gain. In recent literature, a precoder, which exploits channel information, has been added in the V-BLAST transmitter. This precoder forces each symbol stream to have an identical mean square error (MSE). It can be viewed as an alternative to the bit-loading method. In this paper, this precoded V-BLAST system is extended to the case of frequency-selective MIMO channels. Both the FIR and redundant types of transceivers, which use cyclic-prefixing and zero-padding, are considered. A fast algorithm for computing a cyclic-prefixing-based precoded V-BLAST transceiver is developed. Experiments show that the proposed methods with redundancy have better performance than the SVD-based system with optimal powerloading and bit loading for frequency-selective MIMO channels. The gain comes from the fact that the MSE-equalizing precoder has better bit-error rate performance than the optimal bitloading method
A Practical Cooperative Multicell MIMO-OFDMA Network Based on Rank Coordination
An important challenge of wireless networks is to boost the cell edge
performance and enable multi-stream transmissions to cell edge users.
Interference mitigation techniques relying on multiple antennas and
coordination among cells are nowadays heavily studied in the literature.
Typical strategies in OFDMA networks include coordinated scheduling,
beamforming and power control. In this paper, we propose a novel and practical
type of coordination for OFDMA downlink networks relying on multiple antennas
at the transmitter and the receiver. The transmission ranks, i.e.\ the number
of transmitted streams, and the user scheduling in all cells are jointly
optimized in order to maximize a network utility function accounting for
fairness among users. A distributed coordinated scheduler motivated by an
interference pricing mechanism and relying on a master-slave architecture is
introduced. The proposed scheme is operated based on the user report of a
recommended rank for the interfering cells accounting for the receiver
interference suppression capability. It incurs a very low feedback and backhaul
overhead and enables efficient link adaptation. It is moreover robust to
channel measurement errors and applicable to both open-loop and closed-loop
MIMO operations. A 20% cell edge performance gain over uncoordinated LTE-A
system is shown through system level simulations.Comment: IEEE Transactions or Wireless Communications, Accepted for
Publicatio
Queue-Aware Distributive Resource Control for Delay-Sensitive Two-Hop MIMO Cooperative Systems
In this paper, we consider a queue-aware distributive resource control
algorithm for two-hop MIMO cooperative systems. We shall illustrate that relay
buffering is an effective way to reduce the intrinsic half-duplex penalty in
cooperative systems. The complex interactions of the queues at the source node
and the relays are modeled as an average-cost infinite horizon Markov Decision
Process (MDP). The traditional approach solving this MDP problem involves
centralized control with huge complexity. To obtain a distributive and low
complexity solution, we introduce a linear structure which approximates the
value function of the associated Bellman equation by the sum of per-node value
functions. We derive a distributive two-stage two-winner auction-based control
policy which is a function of the local CSI and local QSI only. Furthermore, to
estimate the best fit approximation parameter, we propose a distributive online
stochastic learning algorithm using stochastic approximation theory. Finally,
we establish technical conditions for almost-sure convergence and show that
under heavy traffic, the proposed low complexity distributive control is global
optimal.Comment: 30 pages, 7 figure
MIMO signal processing in offset-QAM based filter bank multicarrier systems
Next-generation communication systems have to comply with very strict requirements for increased flexibility in heterogeneous environments, high spectral efficiency, and agility of carrier aggregation. This fact motivates research in advanced multicarrier modulation (MCM) schemes, such as filter bank-based multicarrier (FBMC) modulation. This paper focuses on the offset quadrature amplitude modulation (OQAM)-based FBMC variant, known as FBMC/OQAM, which presents outstanding spectral efficiency and confinement in a number of channels and applications. Its special nature, however, generates a number of new signal processing challenges that are not present in other MCM schemes, notably, in orthogonal-frequency-division multiplexing (OFDM). In multiple-input multiple-output (MIMO) architectures, which are expected to play a primary role in future communication systems, these challenges are intensified, creating new interesting research problems and calling for new ideas and methods that are adapted to the particularities of the MIMO-FBMC/OQAM system. The goal of this paper is to focus on these signal processing problems and provide a concise yet comprehensive overview of the recent advances in this area. Open problems and associated directions for future research are also discussed.Peer ReviewedPostprint (author's final draft
Deep Reinforcement Learning for Multi-user Massive MIMO with Channel Aging
The design of beamforming for downlink multi-user massive multi-input
multi-output (MIMO) relies on accurate downlink channel state information (CSI)
at the transmitter (CSIT). In fact, it is difficult for the base station (BS)
to obtain perfect CSIT due to user mobility, latency/feedback delay (between
downlink data transmission and CSI acquisition). Hence, robust beamforming
under imperfect CSIT is needed. In this paper, considering multiple antennas at
all nodes (base station and user terminals), we develop a multi-agent deep
reinforcement learning (DRL) framework for massive MIMO under imperfect CSIT,
where the transmit and receive beamforming are jointly designed to maximize the
average information rate of all users. Leveraging this DRL-based framework,
interference management is explored and three DRL-based schemes, namely the
distributed-learning-distributed-processing scheme,
partial-distributed-learning-distributed-processing, and
central-learning-distributed-processing scheme, are proposed and analyzed. This
paper \textrm{1)} highlights the fact that the DRL-based strategies outperform
the random action-chosen strategy and the delay-sensitive strategy named as
sample-and-hold (SAH) approach, and achieved over 90 of the information
rate of two selected benchmarks with lower complexity: the zero-forcing
channel-inversion (ZF-CI) with perfect CSIT and the Greedy Beam Selection
strategy, \textrm{2)} demonstrates the inherent robustness of the proposed
designs in the presence of user mobility.Comment: submitted for publicatio
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