An aggregate medium access control (MAC) service data unit (A-MSDU) contains multiple subframes with a single sequence number. Hence, it has a major drawback in environments with high error rates because if any subframes are corrupted, then the entire A-MSDU will be lost. In addition, performance of the A-MSDU depends strongly on the choice of parameters, such as frame size, modulation level, coding rate, and spatial mode. In this paper, a novel link-adaptation mechanism, dubbed physical (PHY)supported frame aggregation (PSFA), is proposed over IEEE 802.11 networks, and its performance is analyzed. The proposed PSFA technique is based on a cross-layer interaction that enables joint optimization of various parameters between the PHY and MAC layers. This paper derives a new packet error rate (PER) expression for convolutionally coded multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems as an example. Then, this PER expression is used to efficiently estimate the link quality, given the channel conditions and system parameters, and most importantly, it is able to facilitate a parametric study of the cross-layer interaction. In the proposed PSFA algorithm, we present a rule for selecting the parameters so that MAC throughput is maximized. It is shown analytically and verified using Monte Carlo simulations that this choice of parameters can improve throughput performance significantly and also ensure quality of service (QoS) requirements compared to existing conventional algorithms.

Demands for high throughput and stable service quality are increasing. Frame aggregation mechanisms in IEEE 802.11n wireless local area networks (WLANs) can provide improved throughput, but the effect of A-MSDU decreases significantly in error-prone channels. Therefore, adaptive frame size estimation (FSE) depending on the channel condition is required to maintain the improved throughput. In this paper, we proposed frame error rate (FER) based FSE scheme in error-prone and time-varying channel such as a high-stressed network. A tight FER bound is derived to obtain instantaneous link condition, and extended Kalman filter (EKF) is used to estimate frame size optimally for next transmission with current channel information. Our simulation results show that the proposed FSE scheme improves the throughput two times higher than a nonadaptation approach in high-stressed network condition.

This paper describes the exploitation of physical layer confidence in bits (SoftPHY) in order to improve the rate selection in a bit-interleaved coded orthogonal frequency division multiplexing (BIC-OFDM) system. Instead of relying on a measured instantaneous bit error rate (I-BER) from received packets, we built a closed-form I-BER expression that gives accurate and simple functional mapping between the instantaneous channel state and the selected set of modulation level and coding rate. The accuracy of the rate selection and the enhancement of the proposed scheme are confirmed through Monte-Carlo simulations.

This paper introduces a new link adaptation (LA) approach called the adaptive modulation, coding, and spatial mode (AMCS) scheme for multiple-input multiple-output bit-interleaved coded orthogonal frequency division multiplexing (MIMO BIC-OFDM) systems. The AMCS technique can provide both minimal performance degradation and significant throughput gain by controlling the spatial streams. With this technique, we can derive a simple and accurate closed-form expression of an instantaneous bit error rate (I-BER). Based on the I-BER, the proposed AMCS scheme chooses an appropriate modulation, coding, and spatial mode (MCS) type that improves the system performance while satisfying the quality of service (QoS) requirement. A simplified MCS type search method is also proposed, which allows the receiver to effectively select a MCS type and then send it back to the transmitter by using only a small amount of information bits. The simulation results confirm the superiority of the proposed AMCS approach over conventional LA schemes in trading high-throughput for reduced complexity.

The instantaneous bit error rate (I-BER) is a direct representation of system performance given a particular channel realization. The I-BER knowledge enables wireless system to combat fading channel impairments by properly adapting transmission parameters. In this paper, we derive the I-BER for a bit-interleaved coded orthogonal frequency division multiplexing (BIC-OFDM) system. In high signal-to-noise ratio (SNR) regions, under the assumption that an erroneous symbol contains only one bit error, a codeword pairwise error probability (PEP) is approximated by a symbol PEP. The above assumption is used again to evaluate the symbol PEP for an arbitrary QAM with Gray labeling. Compared with a previously obtained result, the new I-BER expression provides a tighter upper bound for convolutional coded BIC-OFDM systems. We also discuss a practical use of the I-BER in a link adaptation process.

An exact form of symbol error rate (SER) is derived for zero-forcing decision feedback equalization (ZF-DFE) on fast fading channels. We present an error propagation framework in ZF-DFE, which is dependent on the length of multipath and the size of M-ary quadrature amplitude modulation (M-QAM). By applying the concepts of “error event” and “state” on this framework, we can evaluate the error probability considering the error propagation. Compared with the previously obtained results, our approach gives an accurate SER expression for Gray-mapped M-QAM on L-tap multipath fading channels. The simulation results are provided to verify the accuracy of the new error rate expression.

This paper introduces an efficient multiple-decision-feedback (MDF) detection for space time block code (STBC) over time-selective fading channels. The MDF detection performs several decision-feedback (DF) detections for the most probable points obtained by a log-likelihood ratio (LLR) based bit flipping scheme. Simulation results show that, with reasonable complexity, the MDF detection provides near maximum likelihood (ML) performance by achieving the considerable SNR gain, compared with the existing DF detections.

This paper proposes two efficient decision-feedback (DF) detection schemes for space time transmit diversity (STTD) over time-selective fading channels. The existing DF detection causes error propagation when the first symbol is not detected correctly. However, the proposed detection schemes provide two candidates for the first symbol and choose a better candidate according to a channel gain or an average log-likelihood ratio (LLR) based selection rule. Simulation results show that the proposed detection schemes reduce error propagation and yield significant signal-to-noise ratio (SNR) gain with slightly increased complexity, compared to existing DF detection scheme.