Massive multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) is a promising technology for next-generation wireless communications. However, when channel state information (CSI) at the transmitter is obtained using channel feedback, the benefits of this system are severely limited by the tradeoff between downlink capacity and feedback overhead. To solve this problem, we propose a time-domain differential feedback scheme for massive multiple-input single-output (MISO) OFDM systems. The proposed scheme exploits channel correlations in time, frequency, and space domains simultaneously by considering differential channel impulse response (CIR). To simplify the codebook design, and to reduce codeword-search complexity, we partition and then quantize the differential CIR using a number of subcodebooks. Because the total feedback bits are shared by the subcodebooks, we further optimize the bit allocation for them to minimize the total quantization error. For this, we analyze the quantization error of the proposed scheme and then use the analysis results for the bit-allocation optimization. In simulations, the proposed scheme efficiently exploits all correlations and achieves significant spectral-efficiency gain compared to conventional feedback schemes.

In this paper, we study low-complexity detection schemes for cooperative multiple-input multiple-output (MIMO) systems with decode-and-forward (DF) relays. In fixed DF protocols, the error propagation effect that limits the diversity gain can be overcome by using joint maximum-likelihood (JML) detection, but its complexity increases exponentially with the number of streams and modulation orders. To develop a low-complexity detection scheme, we first extend the cooperative maximum-ratio combining (C-MRC) scheme to MIMO relay networks, and then investigate the diversity order of the system. We show that C-MRC achieves full diversity gain for orthogonal space-time code, but its diversity gain is limited when the deployed space-time code is non-orthogonal. To address this problem, we propose an effective detection scheme that is applicable to arbitrary space-time codes. On the basis of pairwise error probability approximation, max-log approximation, and the Chernoff bound, we construct an equivalent point-to-point MIMO signal model, and then apply tree-search detection techniques. Compared to JML, the proposed detection scheme significantly reduces the detection complexity while preserving diversity gain. Moreover, it can be readily extended to complex-field network-coding systems whose performance is superior to that of Galois-field network-coding systems. Analysis and simulation results confirm the efficiency of the proposed scheme.

This paper proposes an improved spectrum-sharing protocol for multiuser cooperation in cognitive radio (CR) networks. In CR networks, a secondary user (SU) can access the licensed bands of a primary user (PU) as compensation for cooperative transmission. During cooperative transmission, the SU concurrently transmits its own signal and network-coded signal from the PUs. However, detection errors at the SU cause error propagation which degrades the performance of the PU and SU. To address this problem, we develop a cooperative maximal-ratio combining scheme that mitigates the error propagation and achieves diversity gain. To evaluate the combining scheme, we derive a diversity order and closed-form bit error rate (BER) expression for arbitrary M-QAM at high SNR. The analysis results show that the diversity order and BER depend on the fraction of the transmit power at the SU. Based on the dependency of the fraction factor, we propose an optimization problem to minimize the BER of the SU while guaranteeing the PU's BER. Further, we apply the BER-constrained optimization problem to the adaptive modulation system. Simulation results show that the proposed cooperation provides full diversity gain to the PU and thus improves its spectral efficiency by using the optimized fraction factor.

This paper proposes feedback optimization schemes for multiple-input single-output orthogonal frequency-division multiple access (MISO-OFDMA) systems with heterogeneous users. The proposed scheme adjusts the unit and resolution of the feedback to efficiently exploit the long-term channel statistics, including the path loss, delay spread, and mobility. To develop an analytical optimization framework, we first derive the sum-rate of MISO-OFDMA systems when the channel-state information (CSI) at the transmitter is imperfect because of the quantization error, channel variation, and feedback delay. Based on the derived results, we present effective solutions to the sum-rate maximization problem for distributed and centralized scenarios. When the channel statistics of the users are not available at the base station, each user can improve the sum-rate of the system by minimizing the distortion on CSI feedback under a fixed per-user feedback rate. With the channel statistics of the users, the base station can further control the user set and the feedback rate in addition to the feedback granularity. We evaluate the performance of both distributed and centralized schemes over practical wireless environments, where each user has different long-term channel statistics. Simulation results show that both methods significantly improve the performance of MISO-OFDMA systems.

In orthogonal frequency-division multiple access (OFDMA) downlink systems, user equipments over large delay spread channels reduce the cell spectral efficiency due to intersymbol interference (ISI). Channel shortening is an attractive filtering technique to mitigate ISI over such large delay spread scenarios, but the filter should be designed carefully to prevent subsequent spectrum null phenomenon. In this paper, we propose subband interference suppression (SIS) in channel shortening for OFDMA downlink systems. In our framework, we first develop the minimum subband interference (MSI) filter which reduces the interference and noise enhancement within the subcarriers of interest. To reduce its complexity, we present the delay preselection method, which predetermines significant delay candidates on the basis of estimation results. Simulation results show that the proposed SIS framework is effective over cellular scenarios, where conventional schemes do not work properly.

In this paper, we propose an efficient soft-detector for spatial multiplexing multiple-input multiple-output systems. Maximum a posteriori probability detection employing QR decomposition with M algorithm (QRM-MAP) achieves improved performance by adopting unbiased minimum-mean-square error (UMMSE) preprocessing. The efficiency of QRM-MAP can be further improved by using statistical information obtained from MMSE detection. Based on this information, the proposed detector orders tree branches and excludes insignificant branches in advance. To achieve low-and-fixed complexity, the node enumeration pattern that considers the greedy nature of the M algorithm is predetermined according to the node order. In addition, the soft information from MMSE can be used to compensate for the inherent performance loss caused by an insufficient number of candidates. Simulation results show that the proposed scheme provides an enhanced performance-complexity tradeoff as compared to the conventional schemes.

In this paper, we propose a feedback granularity control scheme for multiuser multiple-input multiple-output orthogonal frequency-division multiple access (MIMO-OFDMA) systems. The proposed scheme resizes the unit and the resolution of the feedback to efficiently exploit channel correlations in both the time and the frequency domains. Two types (centralized and distributed) of granularity search methods are proposed, and their performance characteristics over practical wireless channels are investigated. Simulation results show that the proposed schemes (particularly the distributed method) effectively calculate the optimal granularity, and thus significantly improve the sum-rate of multiuser MIMO-OFDMA systems.

We propose an opportunistic two-stage feedback and scheduling algorithm that is based upon zero-forcing beamforming with semi-orthogonal user selection (ZFBF-SUS) to reduce its feedback load. In an SUS algorithm, a base station schedules semi-orthogonal users using the feedback of all users' channel information. However, such feedback overhead significantly increases with the number of users. To reduce the feedback load of ZFBF-SUS systems, the proposed two-stage feedback scheme opportunistically and separately exploits the signal-to-interference-plus-noise ratio and orthogonality in each stage such that only a fraction of users feed back their channel information. Based on this opportunistic feedback scheme, we decouple the entire process of the SUS into two sub-processes. The proposed two-stage feedback and scheduling scheme effectively exclude the inappropriate users for ZFBF transmission in both feedback and scheduling periods, and thus save the scarce resources consumed for the feedback process. We derive an analytical expression for the average number of feedback bits of the proposed system and optimize it in an averaged sense. Further, the sum-rate of the proposed system is theoretically investigated. Both analytical and simulation results show that the proposed algorithm achieves the performance of conventional ZFBF-SUS systems with a significantly reduced number of feedback bits.

QR-decomposition with M algorithm is a promising technique for multi-input multi-output (MIMO) systems. When the number of candidates is insufficient, its performance is, however, degraded due to an inaccurate log likelihood ratio (LLR). To address this problem, an enhanced soft LLR calculation scheme is proposed for soft-output MIMO detection. Based on QR-decomposition, the proposed algorithm can efficiently obtain a soft information of minimum mean-squared error (MMSE) equalization. With this information, an optimal weighted combining method is derived in an MSE sense. In addition, we compute a more reliable clipping value from the soft information of MMSE equalization. Simulation results show that the proposed algorithm provides a considerable performance gain over conventional algorithms and its performance is close to optimal performance with insufficient candidates.

Single-carrier frequency division multiple access (SC-FDMA) is an attractive air interface scheme for broadband wireless communications. For the coherent demodulation of SC-FDMA systems, the receiver performs channel estimation to obtain the channel frequency response by using the reference signals multiplexed in the transmitted signal. In this paper, we propose an iterative frequency domain channel estimation scheme for SC-FDMA systems. To prevent severe noise enhancements due to the Gaussianity of the frequency domain data signal, we consider two low complexity algorithms, namely, hard and soft frequency replacements. We first analytically derive the mean square error (MSE) to characterize the effects of frequency replacement and post iterative filtering. By analyzing MSE performance, we present a design framework to develop the optimal frequency replacement. Based on the design framework, hard and soft frequency replacement algorithms are optimized in the MSE sense. Then, their low complexity adaptation methods are proposed for practical SC-FDMA systems, where the proposed frequency replacement is performed according to the reliability of the data estimates. Simulation results show that the proposed iterative channel estimation techniques effectively compensate for noise enhancements, and thus give good MSE and frame error rate performances.

The relay employs network coding to transmit the packets from the source nodes simultaneously, for increasing spectral efficiency in wireless environments. The cooperative transmission based on network coding usually works on decode-and-forward (DF) protocols. However, detection errors at the relay cause error propagation, which degrades the performance of cooperative communications. To overcome this problem, we model the error propagation effect of the DF-based system at the destination as the addition of virtual noise, and then design a low complexity detection method. We derive the achievable diversity gain to evaluate the proposed model and corresponding detection scheme. To extend the proposed model to network-coded systems, we first express the channel conditions between the sources and relay as a single equivalent channel gain. Then, we develop low complexity detection schemes for the network-coded systems. From the error propagation model, we propose a dual mode network coding technique, which exploits different network coding schemes adaptively according to channel qualities. Simulation results show that the proposed model and detection scheme effectively reduce the error propagation effects. Also, the proposed dual mode network coding has gains for all channel conditions and thus gives better BER performance than conventional methods.