Channel measurement plays an important role in the emerging 5G‐enabled Internet of Things (IoT) networks, which reflects the channel quality and link reliability. In this paper, we address the channel measurement for link reliability evaluation in filter‐bank multicarrier with offset quadrature amplitude modulation‐ (FBMC/OQAM‐) based IoT network, which is considered as a promising technique for future wireless communications. However, resulting from the imaginary interference and the noise correlation among subcarriers in FBMC/OQAM, the existing frequency correlation method cannot be directly applied in the FBMC/OQAM‐based IoT network. In this study, the concept of the block repetition is applied in FBMC/OQAM. It is demonstrated that the noises among subcarriers are independent by the block repetition and linear combination, instead of correlated. On this basis, the classical frequency correlation method can be applied to achieve the channel measurement. Then, we also propose an advanced frequency correlation method to improve the accuracy of the channel measurement, by assuming channel frequency responses to be quasi‐invariant for several successive subcarriers. Simulations are conducted to validate the proposed schemes.

The flexible deployment without new infrastructure makes unmanned aerial vehicles employing as base stations (UAV-BS) promising for many applications. Since the signals in millimeter-wave frequencies have very small wavelengths, large antenna arrays can be placed in the UAV-BS. Thus, the UAV-BS is capable of providing abundant spatial resources. Spatial modulation (SM) is an effective technology in exploiting additional capacity of the spatial domain by transmitting antenna indices as virtual bits information. However, a limitation of the classical SM is a single transmit antenna activated at each time slot. As a result, the multiplexing gain offered by the multiple transmit antennas has a significant loss. Generalised spatial modulation (GSM) allows several antennas to be activated to overcome the problem of SM. However, GSM has an improvement in throughput, while suffers from the performance loss resulting from the channel correlation, which is generated by multiple active antennas. Thus, the grouping SM (GrSM) is utilized to offer spatial capacity for the UAV-BS in mmWave frequency. Specially, the transmit antennas of the UAV-BS are partitioned into groups based on their channel characteristics. The SM is adopted by each group, and the multiplexing gain is achieved across groups. Moreover, the deployment of the UAV-BS has significant influence on the throughput of the system. In this paper, we formulate a problem to maximize the achievable sum rate of the ground user. The GrSM scheme is utilized to obtain extra throughput in spatial domain. Since the dimension of the UAV position is not very high, a grid based exhaustive search method is adopted to solve the optimization problem. Simulation results demonstrate the proposed solution has an improvement in terms of the sum rate performance.

In filter bank multicarrier with offset quadrature amplitude modulation (FBMC/OQAM) systems, a large pilot overhead is required due to the existence of the imaginary interference. In this paper, we present an approach to reduce the pilot overhead of channel estimation. A part of pilot overhead is used for transmitting data, and compensating symbols are required and designed to remove the imaginary interference. It is worthwhile to point out that the power of compensating symbols can be helpful for data recovery; hence, the proposed approach decreases the overhead of pilots significantly without the cost of additional pilot energy. In addition, the proposed scheme is extended into multiple input multiple output systems without the performance loss. Compared with the conventional preamble consisting of 3 columns symbols, the pilot overhead is equivalent to 2 column symbols in the proposed preamble. To verify the proposed preamble, numerical simulations are carried out with respects to bit error ratio.

Massive machine-type communications (mMTC) is a new key feature of 5G cellular and is expected to be further improved in future evolutions of the cellular standards. Data collection from machine-type communication devices (MTCDs), which can be achieved by various approaches, is important to operation of mMTC networks. This work studies data collection for mMTC networks enabled by unmanned aerial vehicle (UAV) stations moving in the air. Consider the limitation in battery lifetime at both the MTCDs and the UAV station, the UAV trajectory design problem is investigated from an energy efficiency perspective. In a generalized model where the target MTCDs are grouped into multiple clusters, the UAV station travels across the clusters and collect data from each cluster while hovering above the cluster. The corresponding MTCD clustering strategy, UAV hovering strategy and UAV flying strategy all have impacts on the energy consumption of the system, which results in a strongly coupled energy minimization problem that is difficult to solve. The sub-problems obtained through decomposition are decoupled in the proposed solution approach. Clustering of the MTCDs is done by a greedy learning clustering (GLC) algorithm. A novel modeling technique based on the idea of artificial energy map (AEM) is proposed to find the optimal hovering position within a cluster. The flying strategy that minimizes the energy consumption is equivalently transformed into a classic travelling salesman problem that is readily solved by the genetic algorithm (GA). Through alternating iterative optimization of the clustering and hovering strategies, the communication energy consumption and the UAV hovering energy consumption are monotonically decreasing until convergence.

Massive machine-type communications (mMTC) is a key enabling technology for Internet of Things (IoT) services in 5G and beyond. Efficient data collection from massive machine-type communication devices (MTCDs) performing sensing tasks is an important part of the service. In this paper, we consider an unmanned aerial vehicle (UAV) being deployed to facilitate data collection from MTCDs. Taking into account the limited energy of battery-powered MTCDs, the UAV trajectory is optimized to improve the energy efficiency of data collection. By fixing the starting and ending points of the UAV trajectory, a globally optimal (GO) trajectory can be obtained, based on the assumption that the UAV's serving radius and access capacity (number of served MTCDs) are unlimited. Interestingly, it is shown that the optimal trajectory always exists as long as the UAV flying height is greater than its service radius multiplied by a constant. However, the increase of the UAV flying height deteriorates the channel, leading to reduced efficiency of energy consumption. Alternatively, a greedy dynamic (GD) trajectory optimization scheme with limited UAV service radius and access capacity is then investigated, resulting in the optimal service location of the UAV being at a lower flying height, and the energy consumption for accomplishing the data collection task being reduced. Specifically, the UAV sorts the MTCDs within its serving radius based on the distance and selects its closest serving MTCD set. In a serving MTCD set, there is an optimal hovering location that maximizes the data collection efficiency. The UAV dynamically adjusts its service set and the optimal data collection location when MTCDs finish their data transmission and exit the service set. The process continues until all the MTCDs are served and the UAV arrives at the ending point of the trajectory. Simulation results show that both the GO and GD algorithm can improve the efficiency of overall energy consumption. In particular, the online dynamic trajectory optimization scheme is less restrictive and achieves higher efficiency.

Massive multiple‐input multiple‐output (MIMO) systems with hundreds of correlated antennas at base station are capable of offering abundant spatial resources. When spatial‐domain modulation is applied to these systems, the spatial modulation (SM) using only one radiofrequency chain benefits practical implementation, but suffers from significantly reduced multiplexing gain offered by the massive number of antennas. By contrast, the generalised SM (GSM) allows to simultaneously activate multiple transmit antennas, which improves spatial multiplexing gain, but degrades the achievable error performance, as its design pays no attention to the antenna correlation. The authors propose an antenna grouped SM (GrSM) scheme, which is capable of circumventing the shortcomings of both the SM and GSM schemes suffered in massive MIMO scenarios. In the proposed GrSM scheme, transmit antennas are partitioned into multiple groups, where the relatively strongly correlated antennas within individual groups are used to implement component SM schemes, while the relatively weakly correlated antennas in different groups are beneficial to obtain multiplexing gain. In order to further improve the spectral efficiency, adaptive modulation is integrated with GrSM to form the adaptive GrSM (AGrSM). The achievable error and spectral efficiency performance of GrSM and AGrSM systems are investigated based on both mathematical analysis and Monte‐Carlo simulations, which are also compared with that of the conventional SM and GSM schemes, when massive MIMO communication scenarios are considered. Their studies and performance results show that GrSM is a promising transmit scheme for massive MIMO, which can outperform both the SM and GSM schemes.

Differential spatial modulation (DSM) is a novel multiple-input-multiple-output (MIMO) transmission technology that uses the transmit antenna index matrices to carry part of the information in non-coherent communication scenarios. However, when high-order modulation should be used for spectral efficiency considerations, the conventional use of constant modulus Phase Shift Keying (PSK) constellations in DSM would result in significant performance loss, whereas using non-constant modulus constellations such as Quadrature Amplitude Modulation (QAM) in DSM is very challenging because the peak signal power may grow without bound through the differential iterative processing. In this work, a generalized transmission scheme that uses non-constant modulus constellations for DSM signaling is investigated. By normalizing the power of all symbols in the previous transmit matrix when performing differential transmission, the Precoding Normalized DSM (PN-DSM) scheme which can adopt non-constant modulus constellations is proposed. In addition to the legacy QAM, another two non-constant modulus constellations in the literature, Amplitude Phase Shift Keying (APSK) and star-QAM, are considered for PN-DSM transmissions. It is revealed by numerical studies that, compared with the legacy QAM, APSK and star-QAM are more robust to the error propagation issue of the proposed PN-DSM scheme. In addition, because of the increased minimum distance in the constellation, the star-QAM slightly outperforms the APSK in terms of the average error rate performance.

In this work, we study the buffer-aided relaying mechanism in a UAV-enabled mobile relaying system assisting the terrestrial communications. Optimal UAV trajectory design against a randomly located eavesdropper is investigated from the physical layer (PHY) security perspective considering the wireless channel dynamics as the UAV relay moves in the air. Specifically, we maximize the sum secrecy rate by optimizing the discrete trajectory anchor points based on the information causality and UAV mobility constraints. To make the non- convex problem tractable, the increments of the trajectory anchor points are optimized instead through an iterative updating procedure, and successive convex approximation technique is applied for progressive optimization. The convergence of the proposed iterative optimization technique is proved by introducing additional rate bound constraints and employing the squeeze principle. Simulation results show that the proposed optimal trajectory finding algorithm is effective and fast converging. Simulation results also reveal that the distribution of the eavesdropper location has a significant impact on the PHY security performance.

In this paper, we consider a single-input multiple-output (SIMO) system, in which a single-antenna transmitter communicates with a receiver having multiple antennas. It is assumed that the channel coefficients keep constant during two consecutive time slots, after which they become new independent values. For such a system, we focus on the concept of uniquely factorable constellations (UFCs) in the design of noncoherent coding schemes under two communication scenarios. The first scenario is the massive SIMO system, in which only the first and second order channel statistical information is available. The second is the traditional Rayleigh fading SIMO system. First, parameterized massive UFC (MUFC) and unitary UFC (UUFC) are designed for each scenario. It is proved that these designs ensure the unique identification of the transmitted signals when there is no additive Gaussian white noise. Second, using the Riemannian distance (RD) metric for the RD-based detector and coding gain criterion for generalized likelihood ratio test receiver, the optimal MUFCs and UUFCs of various sizes are constructed, respectively. Finally, we exploit the structures of our proposed designs to reduce the decoding complexities compared to the exhaustive search methods. Comprehensive computer simulations show that our proposed schemes outperform the current designs in literature.

Spatial modulation orthogonal frequency division multiplexing with subcarrier index modulation (ISM-OFDM) is a novel OFDM technique that conveys additional information bits by active antenna indices and grouped subcarrier indices. However, it takes a huge amount of resources to acquire the channel state information (CSI) at the receiver. In this paper, we propose two differential spatial frequency modulation with orthogonal frequency division multiplexing (DSFM-OFDM) systems, termed DSFM-OFDM-single (DSFM-OFDM-S) and DSFM-OFDM- multiple (DSFM-OFDM-M) over space-time-frequency, which completely avoid the need for CSI at the transmitter and receiver. In DSFM-OFDM, the transmitted symbols are modulated through ordinary signal modulation. And then part of some additional information bits are transmitted by selecting a permutation of the active antennas, whereas another part of it are employed to determine the subcarrier index matrices. Simulation results show that at low spectral efficiency, the performance of the proposed two schemes are better than differential spatial modulation (DSM) with the high SNR. Besides, the performance degradation of DSFM-OFDM-M and DSFM- OFDM-S over ISM-OFDM can be less than 6dB. And that the loss decreases as the number of receive antennas decreases.

In this letter, we consider a two-way relay system in which there are two source nodes with each having a single antenna and one relay node equipped with a large number of antennas. It is assumed that each node has no channel information. For such a relay system, we systematically design a pair of absolutely additively uniquely decomposable phase-shift keying (PSK) constellations such that when any linear combination of two PSK signals with positive weight coefficients is received, each individual PSK signal can be uniquely decoded. With this, a novel distributed space-time modulation scheme is proposed for the system. We prove that when the number of antennas at the relay node grows to infinite, two user PSK symbols and large scale coefficients can be uniquely determined in a noise-free case. In a noisy case, a minimum Riemannian distance detector is developed for efficiently and effectively estimating both the channel coefficients and the transmitted signals.

This paper considers a flat fading noncoherent wireless communication system with a single transmitter antenna and massive multiple receiver antennas, in which the channel coefficients change every two time slots. For such a system, we first parameterize all the high rate constellations which enable each transmitted symbol and the channel coefficients to be uniquely identified in a noise-free case when the number of the receiver antennas goes to infinity. Then, for a noisy channel with the limited number of the receiver antennas, we propose the design of an optimal constellation that maximizes the minimum distance between any two distinct constellation points using the first kind of a Riemannian distance (RD) measure subject to constraints on an average power and total transmission bits. A closed-form optimal solution is attained by first characterizing the optimal structure for any fixed bits on each parameter space and then, finding an optimal bit assignment that further maximizes the achieved minimum distance. One of the significant advantages of such optimal design is that it enables us to develop a fast closedform RD detector. Finally, computer simulations demonstrate that our proposed scheme outperforms the methods in literature for the same system.

We consider a multi-user massive MIMO uplink system in which there are multiple users with each having a single antenna and one base station (BS) with a large number of antennas. It is assumed that each user and BS have neither small scale nor large scale channel state information. For such a noncoherent system, we systematically design a series of absolutely additively uniquely decomposable constellation pairs (AAUDCPs) using the phase-shift keying (PSK) constellations such that when any linear combination of two PSK constellation points with positive weighting coefficients is received, each individual PSK signal can be uniquely decoded. With this, a novel noncoherent massive space-time block code with PSK modulation is developed for the system. In a noise-free case, we prove that when the number of antenna at BS grows to infinite, the transmitted symbols and uplink channel can be uniquely determined if the channel coherence time is at least equal to the number of users. In a noisy case, a robust Euclidean distance receiver is developed for efficiently and effectively estimating both the channel coefficients and the transmitted signals.

A non-cooperative game approach is employed to optimize the energy efficiency (EE) for multi-user cognitive radio over multi-input-multi-output (MIMO) interference channels (ICs). Both the per-secondary-user (SU) power constraints and the total interference threshold are taken into consideration in the problem formulation. Although optimizing EE for the formulated multi-constraint fractional problem is non-convex and multi-objective, we show that it can be reformulated as an equivalent multi-objective unconstrained non-fractional problem. A distributed iterative EE optimization algorithm (DIEEOA) for multi-user cognitive radio over MIMO ICs is proposed to achieve the Nash Equilibrium of the non-cooperative game. Effectiveness of the algorithm is validated through computer simulation, and system parameters' impact on the EE is discussed.

This paper considers the problem of pilot contamination (PC) in large-scale multi-cell multiple-input multiple-output-aided orthogonal frequency division multiplexing systems. We propose an efficient scheme relying on an optimal pilot design conceived for time-domain channel estimation, which can either completely eliminate PC or significantly reduce it, depending on the channel's coherence time. This is achieved by designing an optimal pilot set allowing us to beneficially group the users in all the cells and to assign a time-shifted pilot transmission to the different groups. Unlike the existing PC elimination schemes, which require an excessively long channel coherence time, our proposed scheme is capable of completely eliminating PC under a much shorter coherence time. Moreover, the existing PC elimination schemes can no longer be used if the channel coherent time is insufficiently large. By contrast, even for extremely short channel coherent time, our scheme can still be implemented to significantly reduce PC. This is particularly beneficial for high velocity scenarios. Our simulation results demonstrate the efficiency of the proposed scheme.

An efficient and accurate point matching algorithm named advanced weight graph transformation matching (AWGTM) is proposed in this study. Instead of relying only on the elimination of dubious matches, the method iteratively reserve correspondences which have a small angular distance between two nearest‐neighbour graphs. The proposed algorithm is compared against weight graph transformation matching (WGTM) and graph transformation matching (GTM). Experimental results demonstrate the superior performance in eliminating outliers and reserving inliers of AWGTM algorithm under various conditions for images, such as duplication of patterns and non‐rigid deformation of objects. An execution time comparison is also presented, where AWGTM shows the best results for high outlier rates.

Joint channel estimation (CE) and turbo multiuser detection (MUD)/decoding for space-division multiple-access based orthogonal frequency-division multiplexing communication has to consider both the decision-directed CE optimisation on a continuous search space and the MUD optimisation on a discrete search space, and it iteratively exchanges the estimated channel information and the detected data between the channel estimator and the turbo MUD/decoder to gradually improve the accuracy of both the CE and the MUD. We evaluate the capabilities of a group of evolutionary algorithms (EAs) to achieve optimal or near optimal solutions with affordable complexity in this challenging application. Our study confirms that the EA assisted joint CE and turbo MUD/decoder is capable of approaching both the Cramér-Rao lower bound of the optimal channel estimation and the bit error ratio performance of the idealised optimal turbo maximum likelihood (ML) MUD/decoder associated with the perfect channel state information, respectively, despite only imposing a fraction of the complexity of the idealised turbo ML-MUD/decoder.

MIMO-OFDM technique combines the advantages of MIMO and OFDM and is widely used in high data rate system. Conventional linear channel estimators are considered optimal under the assumption of rich multipath, while the practical physical multipath channels tend to exhibit sparse structure. By exploiting the coherent sparsity of the multipath channels, channel estimation based on compressive sensing (CS) method can greatly decrease the pilot overhead burden. In this paper, we present CS-based channel estimation method by exploiting the time-domain sparsity and one of the CS algorithms-Subspace Pursuit (SP) in order to estimate the channel impulse response in MIMO-OFDM system. Simulation results demonstrate a significant reduction of the number of pilots compared with least squares (LS) channel estimation.

SUMMARY Because of the presence of incumbents in cognitive radio networks, the unused spectrum in the TV bands, popularly referred to as ‘white spaces’, are fragmented with the size of each fragment varying from one TV channel to several TV channels. What is more, because the secondary transmissions adjust their spectrum usage over time, white spaces become increasingly partitioned into a collection of discrete fragments, which decreases the spectral utilization. To improve throughputs, most of the prior researches focused on selecting the best transmission channel in the context of spectrum fragmentation but have rarely involved aggregating the fragmentation to a contiguous channel. In this paper, we present two adaptive spectrum access strategies, both of which not only select the best transmission channel but also efficiently solve the fragmentation problem. The first strategy involves one‐agile radios that build a transmission using single fragment of frequency, which partially remedy the fragmentation problem using higher‐layer solutions. The second strategy suppresses the impact of spectrum fragmentation successfully at the physical layer by combining k spectrum fragments to form a single transmission. The simulation results show that both of the strategies bring larger throughputs compared with the prior solutions. Copyright © 2012 John Wiley & Sons, Ltd.

A Differential Evolution (DE) algorithm assisted Minimum Symbol Error Ratio (MSER) Multi-User Detection (MUD) scheme is proposed for multi-user Multiple-Input Multiple-Output (MIMO) aided Orthogonal Frequency-Division Multiplexing / Space Division Multiple Access (OFDM/SDMA) systems. Quadrature Amplitude Modulation (QAM) is employed in most wireless standards by virtue of providing a high throughput. The MSER Cost Function (CF) may be deemed to be the most relevant one for QAM, but finding its minimum is challenging. Hence we propose a sophisticated DE assisted MSER-MUD scheme, which directly minimizes the SER CF of multi-user OFDM/SDMA systems employing QAM. Furthermore, the effects of the DE assisted MSER-MUD's algorithmic parameters, namely those of the population size Ps, of the scaling factor λ and of the crossover probability Cron the number of DE generations required for attaining convergence were investigated in our simulations. This allowed us to directly quantify their complexity. The simulation results also demonstrate that the proposed DE assisted MSER-MUD scheme significantly outperforms the conventional MMSE-MUD in term of the system's overall BER and it is capable of narrowing its BER performance discrepancy with respect to the optimal Maximum Likelihood (ML) MUD to about 4dB, while requiring about 200 times less CF evaluations compared to the optimal ML-MUD scheme.

Stochastic optimization assisted joint Channel Estimation (CE) and Multi-User Detection (MUD) were conceived and compared in the context of multi-user Multiple-Input Multiple-Output (MIMO) aided Orthogonal Frequency-Division Multiplexing/Space Division Multiple Access (OFDM/SDMA) systems. The development of stochastic optimization algorithms, such as Genetic Algorithms (GA), Repeated Weighted Boosting Search (RWBS), Particle Swarm Optimization (PSO) and Differential Evolution (DE) has stimulated wide interests in the signal processing and communication research community. However, the quantitative performance versus complexity comparison of GA, RWBS, PSO and DE techniques applied to joint CE and MUD is a challenging open issue at the time of writing, which has to consider both the continuous-valued CE optimization problem and the discrete-valued MUD optimization problem. In this study we fill this gap in the open literature. Our simulation results demonstrated that stochastic optimization assisted joint CE and MUD is capable of approaching both the Cramer-Rao Lower Bound (CRLB) and the Bit Error Ratio (BER) performance of the optimal ML-MUD, respectively, despite the fact that its computational complexity is only a fraction of the optimal ML complexity.

We considers the beamforming assisted multiple-antenna receiver for space-division multiple-access based multiuser systems that employ high-throughput quadrature amplitude modulation (QAM) signalling. The bit error ratio (BER) expression as the function of the beamformer's weight vector is derived, and the minimum BER (MBER) beamforming receiver is then obtained as the solution of the resulting optimisation problem that minimises the MBER criterion. A simplified conjugate gradient algorithm, which has previously demonstrated its effectiveness in solving the minimum symbol error ratio (MSER) optimisation problem, is employed to solve this MBER optimisation. For high-order QAM, although the bit decision is an inherently more complicated procedure than making a symbol decision, it turns out that the computational complexity of computing the MBER solution is similar to that of computing the MSER solution. As expected, our simulation results show that both the MBER and MSER systems achieve the same BER performance, and they significantly outperform the standard minimum mean squares error based solution.

A differential evolution (DE) algorithm aided iterative channel estimation and turbo multi-user detection (MUD) scheme is proposed for multi-user multi-input multiple-output aided orthogonal frequency-division multiplexing / space-division multiple-access (OFDM/SDMA) systems. The proposed scheme iteratively exchanges the estimated channel information and the detected data between the channel estimator and MUD employing a turbo technique, which gradually improves the accuracy of the channel estimation and the MUD, especially for the first iteration. Quadrature amplitude modulation (QAM) is employed in most wireless standards by virtue of providing a high throughput. However, the optimal maximum likelihood (ML)-MUD becomes extremely complex for employment in QAM-aided multi-user systems. Hence, two different DE aided MUD schemes, the DE aided minimum symbol error rate (MSER)-MUD as well as the discrete DE aided ML-MUD, were developed, and their achievable performance versus complexity was characterized. The simulation results demonstrate that the proposed DE aided channel estimator is capable of approaching the Cramer-Rao lower bound with just two or three iterations. The ultimate bit error rate lower-bound of the single-user additive white Gaussian noise scenario has been approached in the range of Eb/ N0≥ 10 dB and Eb/ N0≥ 6 dB for the DE aided MSER-MUD and the discrete DE aided ML-MUD, respectively.

A joint channel estimation and Multi-User Detection (MUD) scheme is proposed for multi-user Multiple-Input Multiple-Output (MIMO) Space Division Multiple Access / Orthogonal Frequency-Division Multiplexing (SDMA/OFDM) systems. We design a Dual Repeated Weighted Boosting Search (DRWBS) scheme for joint channel estimation and MUD, which is capable of providing 'soft' outputs, directly fed to the Forward Error Correction (FEC) decoder. The proposed scheme reduces the complexity of the receiver, since it integrates the channel estimation and MUD into a single module and it forwards the Log-Likelihood Ratios (LLRs) to the channel decoder. It also provides an effective solution to the multi-user MIMO channel estimation and MUD problem in ``rank-deficient'' scenarios, when the number of users is higher than the number of receiver antennas. The simulation results demonstrate that the proposed scheme is capable of attaining a BER performace close to the ideal scenario of the Maximum Likelihood (ML) MUD associated with perfect channel knowledge.

In this paper, a Forward Error Coded (FEC) Decision-Directed (FEC-DD) channel estimation scheme is proposed for Multiple-Input Multiple-Output (MIMO) Orthogonal Frequency Division Multiplexing/Space Division Multiple Access (OFDM/SDMA) systems which is based on the Expectation-Conditional Maximization Either (ECME) algorithm. The proposed DD technique is combined with the Optimised Hierarchy Reduced Search Algorithm (OHRSA) based Multi-User Detector (MUD) and directly calculates the Maximization-Step (M-Step) by conditionally maximizing the logarithmic likelihood function of the "incomplete" data. We avoid the employment of matrix inversion, since only a single subcarrier's Frequency-Domain CHannel Transfer Function (FD-CHTF) is calculated at a time, since we assume that the other subcarriers' FD-CHTFs are the most recent estimates from the previous iteration of the proposed scheme. Our simulation results have demonstrated that the proposed scheme is capable of reducing the received power requirement by 4dB upon exploiting the error correction capability of a FEC decoder within the ECME loop.

In this paper, a joint channel, carrier-frequency-offset (CFO) and noise-variance estimation scheme is proposed for OFDM systems which is based on Expectation and Maximization (EM) algorithm. The channel parameters are estimated using training sequences incorporated at the beginning of each transmission frame. Based on the assumption that the amplitude and CFO of different paths are independent, the received multipath components may be decomposed into $L$ independent data sets of the $L$ resolvable propagation paths. Hence the associated multi-dimensional minimization problem may be decomposed into separate single-dimensional minimization processes, the maximum likelihood and yet, remains capable of approaching performance at a signifucantly reduced complexity.

A joint channel impulse response (CIR) and noise-variance estimation scheme is proposed for multi-user Multiple-Input-Multiple-Output (MIMO) Orthogonal Frequency-Division Multiplexing/Space Division Multiple Access (OFDM/SDMA) systems, which is based on the Expectation Maximization (EM) algorithm. Multiple users communicating over time-invariant and/or time-variant channels are considered in this paper. Channel estimation becomes quite challenging in this scenario, since an increased number of independent transmitter-receiver links having different statistical characteristics have to be simultaneously estimated for each subcarrier. The proposed EM-based joint CIR and noise-variance estimator designed for multi-user MIMO OFDM/SDMA system is shown to simultaneously estimate the time-invariant and time-variant CIRs as well as noise-variance.

A decision-directed (DD) channel estimation based on iterative linear minimum mean square error (LMMSE) is proposed for orthogonal frequency division multiplexing systems. Existing DD channel estimation is well known to have the problem of error propagation because of symbol-by-symbol detection. The proposed algorithm can estimate the correction term of current channel state information (CSI) according to the error vector of previous CSI by applying the orthogonality principle, and corrects the current CSI with this correction term. Analysis and simulation results have shown that this method has no error propagation problem. The performance of the proposed algorithm is much better than the conventional DD channel estimation, and close to the optimal LMMSE estimator, but with much less computational complexity compared with the optimal LMMSE estimator.