A realistic Recursive Spatial Multiplexing (RSM) scheme for the case of a wireless feedback channel with limited capacity is considered where the identifier for the critical subspaces are encoded using either Grassmannian or vector quantizers. Optimum and suboptimum encoding schemes are proposed and analyzed. The threshold for the eigenvalues of the channel matrix required in the receiver for determining the critical subspaces is chosen based on optimizing channel capacity and uncoded biterror rates within the inner transceiver. It is shown that the limited feedback channel capacity limits the RSM performance, critically determines the achievable bit-error rates and defines the range of signal-to-noise ratio values where RSM outperforms open-loop multiple-input multiple-output transmission schemes.

We consider a simple closed-loop spatial multiplexing architecture which can be incorporated into an outer system with off-the-shelf single-input single-output encoders/decoders. Instead of relying on transmitter-side channel state information for matching the layers of the multidimensional signal to the eigenmodes of the multiple-input multiple-output channel through a precoding, the eigenmodes with high attenuation are identified in the receiver and a retransmission of the affected signal parts requested through a feedback channel. The requested backup facilitates a linear array signal reconstruction subject to limited noise amplification. Since the backup is always embedded in a subsequent signal frame, the multiplexing becomes a recursive procedure. We propose appropriate transmit antenna selection policies for limiting the number of recursions and thereby the memory requirement and incurred delay. Optimal policies are obtained using the theory of Markov decision processes. Moreover, the use of a simple unsupervised learning method lets the system adapt to unknown channel conditions while always guaranteeing a target signal-to-noise ratio at the decoder input.

We consider the uplink of a wireless sensor network with single-antenna sensors, an access point (AP) equipped with multiple antennas, and a common radio channel. To reduce the number of outages resulting from the noise amplification by the zero-forcing (ZF) within the successive interference cancellation (SIC) procedure, the AP is given the possibility to request a selected number of sensors to retransmit their signals in a subsequent time slot. If necessary the AP suspends the ZF-SIC until the requested signals have been retransmitted, making the signal recovery a recursive procedure. The number of sensors required to retransmit depends on the order of the processed sensor signals within the ZF-SIC. We propose two algorithms for determining the decoding order: an algorithm which finds a decoding order necessitating a minimal number of retransmissions, and a suboptimal alternative algorithm with significantly lower complexity. By numerical means the performance and complexities of the considered algorithms are analyzed.

We propose a closed-loop multiple-input multiple-output (MIMO) architecture which incorporates unitary precoding and recursive spatial multiplexing. Both techniques take advantage of a feedback channel in a similar way for the transmission of data at high rates with limited signal processing complexity. Unitary precoding is effective in situations with accurate channel state information (CSI) at the transmitter end. In situations with outdated CSI due to time-variant channels, a linear signal reconstruction at the receiver end may result in severe noise amplification. Recursive multiplexing mitigates the noise enhancement by means of retransmissions of signal parts in critical subspaces. This significantly increases the ergodic capacity achieved by precoding based on outdated CSI, as shown for the cases of time-variant (4x4) and (8x8) MIMO channels.

We address the demodulation and decoding of array signals in the presence of fluctuating cochannel interference (CCI). The maximum-likelihood decoder is formulated for the case of N receive antennas and stationary, temporally white Gaussian CCI within blocks of K samples, where KgesN. The pair wise error probability (PEP) is utilized to quantify the degradation in performance resulting from the absence of side information about the CCI. For pairs of code words that differ over a large number of blocks, an asymptotic PEP performance degradation of 10log10(K/(K-N)) decibels is found. An iterative maximization of the log-likelihood function leads to an alternative suboptimal receiver with reduced complexity, including a block-wise adaptive beamforming via a sample matrix inversion (SMI) and a nearest-neighbor decoding. From the statistics of the spatially filtered signals we find the asymptotic PEP performance, and see that the presence of the desired signal, which is known to adversely affect the output of SMI-based beamformers with small sample support, is gradually mitigated in the iterative procedure

The problem of mitigating Gaussian cochannel interference with unknown spatial correlation within short array signal observation intervals is addressed in this paper. We show that the classic sample matrix inversion schemes can be represented as the projection of the ideally combined signal onto a subspace that is uniformly distributed on the Grassmann manifold. This brings us to a novel beamforming scheme with enhanced mean-square error performance

We derive a power loading procedure optimizing the bit-error rates of multiple-input single-output (MISO) bit-interleaved coded modulation (BICM) orthogonal frequency-division multiplexing (OFDM) systems performing hard-decisions at the receiver and ideal interleaving. The adaptive subcarrier power allocation is based on either perfect or outdated channel state information at the transmitter. The scheme has the same complexity as the one for the single transmit antenna case. For a standard BICM-OFDM system in Rayleigh fading, Monte Carlo simulations show that the relative signal-to-noise ratio gain by the adaptation is up to 4 dB at an average bit-error rate level of 10-66. The relative gain in MISO systems decreases to 1.05 dB for increasing the number of transmit antennas. The achievable gain decreases for decreasing cross-correlation of the outdated and the actual channel state

Despite the efforts to leverage the network performance through the use of Directional Communications, most protocols still rely on the basic mechanisms employed by the IEEE802.11 standards. While this might be desirable to facilitate compatibility, nodes empowered with directional antennas should be able to explore the benefits of directional communications whenever possible. With that in mind, we propose two mechanisms, termed Adaptive Carrier Sensing and Adaptive Packet Sending, aiming to allow nodes equipped with directional antennas to exploit the advantages of such systems to improve network performance.

We first consider the problem of decoding a signal from a memoryless SIMO (single-input/multiple-output) channel subject to block-wise stationary cochannel interference (CCI). We employ an iterative expectation-maximization method, arriving at an algorithm which alternately performs a beamforming via a sample matrix inversion and a maximum-likelihood decoding. An analytical analysis of the sample errors after the beamforming provides insight into the procedure. A numerical study shows that with the number of iteration cycles increasing, the sample error squares decrease towards an analytical lower bound, and the error rates at the decoder output improve. Second, the iterative algorithm is employed for the reception of coded OFDM (orthogonal frequency-division multiplexing) signals subject to non-stationary CCI in both the time and frequency dimensions, as typically encountered in decentralized broad-band wireless networks with uncoordinated channel access. Results from computer simulations indicate that the proposed algorithm successfully mitigates the CCI, achieving gains of several decibels over conventional receiver techniques.

We focus on the channel estimation and the decoding of orthogonal frequency-division multiplexing (OFDM) signals in networking scenarios with multipath propagation and strong cochannel interference (CCI). The receiver views the CCI contained in the subchannel signal observations as additional independent Gaussian noise terms with varying variance from subchannel to subchannel as a consequence of the frequency-selectivity of the radio channels. We find that the capacity of OFDM channels with both the gain and the noise variance subject to broad-band fading may significantly exceed the capacity of a comparable OFDM channel with uniform noise power. Linear minimum mean-square error criterion based subchannel gain and noise variance estimators are presented which can exploit this margin, i.e., improve the spectral efficiency. The performance of the estimators is investigated for bit-interleaved coded OFDM systems and Rayleigh fading channels by computer simulations.

We derive power adaptation strategies optimizing the error rates of broadband bit-interleaved coded OFDM systems performing hard-decisions after the demodulation and ideal interleaving. The adaptive subchannel power allocation is based on either perfect or outdated channel state information in a time-division duplex transmission. We find that for an average bit error rate level of 10/sup -6/ a gain of up to 4 dB can be achieved by the proposed adaptation in Rayleigh fading channels.

We consider multicarrier transmission schemes in which the elementary signal pulses relate to the elements of a Weyl-Heisenberg system, i.e., resulting from a prototype function shifted in time and frequency. The overlapping of the information-bearing signal parts at the output of doubly dispersive channels and the resulting interference are confined by utilizing a prototype function whose energy is concentrated in both time and frequency. We derive a symbol detector which first calculates a sufficient statistic for the unknown data symbols from the linearly combined output signals of a filter bank, and second, performs an iterative maximization of the likelihood function. The presented receiver takes full advantage of the confined pulse overlapping to limit the computational effort. An analysis of the computational complexity and bit-error rate performance of the iterative detection scheme is provided for wide-sense stationary uncorrelated scattering channels.