On the capacity of OFDM-based spatial multiplexing systems

Abstract

Abstract-This paper deals with the capacity behavior of wireless orthogonal frequency-division multiplexing (OFDM)-based spatial multiplexing systems in broad-band fading environments for the case where the channel is unknown at the transmitter and perfectly known at the receiver. Introducing a physically motivated multiple-input multiple-output (MIMO) broad-band fading channel model, we study the influence of physical parameters such as the amount of delay spread, cluster angle spread, and total angle spread, and system parameters such as the number of antennas and antenna spacing on ergodic capacity and outage capacity. We find that, in the MIMO case, unlike the single-input single-output (SISO) case, delay spread channels may provide advantages over flat fading channels not only in terms of outage capacity but also in terms of ergodic capacity. Therefore, MIMO delay spread channels will in general provide both higher diversity gain and higher multiplexing gain than MIMO flat-fading channels.Index Terms-Broad-band fading channels, diversity gain, ergodic capacity, MIMO, multiplexing gain, OFDM, outage capacity.

Optimization of MIMO Systems Capacity Using Large Random Matrix Methods

“…where the various matrices (C l ,C l , X l ) l=1,...,L satisfy the same conditions as in the context of model (27).…”

“…As shown in [7,12], model (27) is useful in the context of multipath channels with independent paths. Matrices T andT are given by:…”

“…The asymptotic approach presented in this paper allows to obtain insights on matrix Q * , and thus on an approximate capacity achieving covariance matrix. In order to present the corresponding result, we recall that in the context of models (27,36), I(Q) is given by (58). We denote by δ * andδ * the vectors δ(Q * ) andδ(Q * ), and by G * the matrix G(δ * ,δ * ).…”

“…Models (27,36) are more complicated, and the structure of Q * is unknown. The asymptotic approach presented in this paper allows to obtain insights on matrix Q * , and thus on an approximate capacity achieving covariance matrix.…”

Channel estimation for multiuser MIMO-OFDM systems based on Kalman algorithms

“…OFDM converts a broadband frequency selective channel to a series of narrowband channels by transmitting data in parallel over many subcarriers. Combining OFDM with MIMO, producing so called MIMO-OFDM, significantly reduces receiver complexity in wireless multiuser broadband systems [7], thus making it a competitive choice for future broadband wireless communication systems. Since OFDM uses multiple subcarriers, optimal linear precoding for MIMO-OFDM can be implemented by deriving linear precoders for each subcarrier independently.However, due to the generally large number of subcarriers, the computational load is excessive, and this approach is probably impossible to implement in practice.…”