In this paper, we investigate delay-Doppler (DD) sensing in a communication-centric integrated sensing and communication (ISAC) framework based on Zak transform-based orthogonal time frequency space (Zak-OTFS) modulation. Specifically, we consider target sensing with communication waveforms and propose a novel backpropagation (BP) algorithm for multi-target DD parameter estimation. We formulate the radar sensing task as a maximum likelihood parameter estimation problem, which is highly non-convex. By exploiting the structural analogy between parameter estimation and neural network training, the BP algorithm treats the DD parameters as tunable network weights and efficiently computes their gradients via the chain rule, enabling accurate and parallelized estimation. To facilitate the algorithm implementation, a successive interference cancellation method based on DD domain twisted convolution is developed to obtain coarse DD estimates. Furthermore, a constant false alarm rate based dynamic merging strategy is introduced to adaptively estimate the number of targets during the BP process. Comprehensive theoretical analyses are conducted, including the derivation of the Cramér–Rao bound (CRB) for Zak-OTFS systems and performance evaluation under various challenging sensing scenarios. Simulation results demonstrate that the proposed algorithm achieves high estimation accuracy and validates the theoretical analysis.

This paper investigates the joint optimization of satellite attitude and hybrid analog-digital beamforming (HBF) for low Earth orbit satellite multi-user MIMO communication systems. By introducing satellite attitude as an additional optimization variable, the directional characteristics of the antenna radiation pattern are exploited to enhance system performance. To achieve this, a sum-rate maximization problem is formulated and transformed into an equivalent weighted modified mean square error (MSE) minimization problem. A closed-form solution-assisted collaborative optimization (CSACO) framework is proposed to address the problem. In this framework, we leverage closed-form solutions for precoding and the modified MSE factor to reformulate the attitude optimization problem. By applying manifold optimization and sequentially updating the closed-form solutions, the algorithm converges efficiently to a stationary point, making it well-suited for resource-constrained onboard processing. Simulation results show that the proposed CSACO framework significantly outperforms baseline methods, adaptively optimizing both satellite attitude and HBF based on varying power budgets and user locations.

High-resolution range profile (HRRP) has been recognized as a crucial criterion for target classification because it reveals the distribution of the radar cross section and captures the structural and physical features. Traditional methods for estimating the HRRP rely on specific radar waveforms, such as linear frequency modulation signals. However, they are not suitable for integrated sensing and communication systems. To deal with this issue, in this paper, we investigate the HRRP estimation by exploring the orthogonal frequency-division multiplexing (OFDM) based communication signals. In particular, considering the high peak-to-average power ratio (PAPR) problem inherent in OFDM, we propose an HRRP estimation method based on the discrete Fourier transform spread OFDM (DFT-S-OFDM) waveform, which maintains reliable communication performance in multipath scenarios of high resolution radar detection, while exhibits low PAPR characteristic that minimizes the impact of various nonlinear effects. To estimate the HRRP, we first propose a preprocessing algorithm on the radar echoes of the transmitted DFT-S-OFDM signal, transforming them into a solvable form. Based upon the preprocessed signal, we further propose two HRRP estimation algorithms by applying the orthogonal matching pursuit algorithm and the multiple signal classification algorithm, respectively. Simulation results indicate that the proposed HRRP estimation method based on DFT-S-OFDM achieves a significant performance improvement compared to the equivalent OFDM-based method under the influence of nonlinear factors.

This paper investigates beamforming in a multiuser multiple-input multiple-output (MIMO) communication system assisted by a new type of circle track antenna (CTA) that can move along a circular trajectory. With this CTA structure, we formulate a sum rate maximization problem by jointly optimizing the beamforming and rotation angles of the CTAs, and transform it into an equivalent weighted minimum mean square error (WMMSE) minimization problem for efficient solution. To solve this highly non-convex problem, we use alternating optimization strategy to decompose the WMMSE problem into two sub-problems corresponding to the beamforming and the antenna rotation optimization, respectively. Specifically, in rotation angle optimization, we perform variable substitution to transform the optimization variables into ones with a constant modulus constraint and solve the problem using the manifold optimization (MO) method. Simulation results demonstrate that the proposed algorithm significantly outperforms the benchmark schemes.

This paper presents a quasi-neural network (Quasi-NN) approach for parametric channel estimation in multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. A unified Quasi-NN framework is proposed for both the single-user (SU) and the multi-user (MU) scenarios, enabling the joint estimation of the direction of arrival, direction of departure, time delay, complex gain, and the number of multipaths. The artificial neural network-like structure of the Quasi-NN allows the application of the backpropagation algorithm while requiring only real-time pilot signals for online network training. Furthermore, considering the issue of high pilot overhead in MU MIMO-OFDM systems, we develop a location assisted Quasi-NN (LA-QNN) that utilizes a location-parameter database to reduce the pilot overhead while maintaining accurate channel estimation. Simulation results show that the proposed Quasi-NN approaches the Cramér Rao bound, and the proposed LA-QNN scheme provides a better balance between channel estimation performance and pilot overhead in the MU scenario.

Intelligent reflecting surfaces (IRSs) have been regarded as a promising enabler for future wireless communication systems due to their capability of customizing favorable propagation environments. In the literature, IRSs have been considered power-free or assumed to have constant power consumption. However, recent experimental results have shown that for positive-intrinsic-negative (PIN) diode-based IRSs, the power consumption dynamically changes with the phase shift configuration, which implies that the beamforming quality of the IRS depends on the available power. Therefore, this phase shift-dependent power consumption (PS-DPC) introduces a challenging power allocation problem between the base station (BS) and the IRS, requiring to balance the BS transmit power and the IRS beamforming quality during system design. To tackle this issue, in this paper, we investigate a rate maximization problem for IRS-assisted systems under a practical PS-DPC model. For the single-user case, we propose a generalized Benders decomposition-based beamforming method to maximize the achievable rate while satisfying a total system power consumption constraint. Moreover, we propose a low-complexity beamforming design, where the powers allocated to BS and IRS are optimized offline based on statistical channel state information. Furthermore, we extend the beamforming design to the multi-user case, where we solve an equivalent weighted mean square error minimization problem with two different joint power allocation and phase shift optimization methods. Simulation results indicate that compared to baseline schemes, our proposed methods can flexibly optimize the power allocation between BS and IRS, thus achieving better performance. The optimized power allocation strategy strongly depends on the system power budget. Specifically, when the available system power budget is high, the PS-DPC is not the dominant factor in the system power consumption, allowing the IRS to turn on as many PIN diodes as needed to achieve high beamforming quality. When the system power budget is limited, however, more power tends to be allocated to the BS to enhance the transmit power, which consequently reduces the beamforming quality at the IRS due to the limited PS-DPC budget.

In this paper, we investigate the optimization of a double-intelligent reflecting surface (DIRS)-aided system under a practical power consumption model of DIRS elements. Specifically, the power consumption of DIRS elements, driven by the on/off-state of positive-intrinsic-negative (PIN) diodes, varies significantly based on their phase shift configurations. This phase shift-dependent power consumption (PS-DPC) model introduces an intractable power allocation problem to balance the power consumption at the BS and DIRS. To address this challenge, we develop a low-complexity joint beamforming and power allocation method to minimize the total system power consumption. By effectively exploiting the channel statistical characteristics (CSC), our method can determine the optimal power allocation strategy offline, thereby avoiding frequent updates in response to specific instantaneous channel state information. Moreover, the beamforming at the base station (BS) and DIRS can also be obtained by low-complexity closed-form solutions. Simulations reveal that by considering the PS-DPC, the proposed CSC method can effectively balance the power consumption between the BS and DIRS, thus significantly reducing the total system power consumption.

Obtaining accurate channel state information is crucial to fully harness the considerable bandwidth advantages of millimeter wave (mmWave) communications. However, as the bandwidth and the size of antenna array increase, the spatial-wideband effect causes the array response vector to be frequency-dependent, also known as the beam squint effect, making channel estimation more challenging. To address this issue, in this paper, we introduce the Jacobi-Anger expansion to extract common angular information from the frequency-dependent array response vectors and formulate the channel estimation as an atomic norm minimization problem. To circumvent the prohibitively high computational complexity, we propose a decoupled-atomic norm minimization (D-ANM) algorithm by decoupling the original problem into three sub-problems for sequentially estimating the angles of arrival, angles of departure, and path delays. Simulation results show that our proposed D-ANM algorithm achieves significant performance improvement in the channel estimation accuracy over the benchmark algorithms for the mmWave systems with the beam squint effect.

In this paper, we investigate the uplink channel estimation problem in intelligent reflecting surface (IRS)-assisted broadband millimeter wave multi-input multi-output communication systems. Considering the channel sparsity in both the angle and delay domains, we decouple the channel estimation problem into several sub-problems, namely, sequential estimations of the angles at the user, the base station, and the IRS, alongside the propagation path delays. By exploiting the Vandermonde structure of both the array manifold and the phase difference among multiple subcarriers caused by delays, we propose a multi-stage atomic norm minimization-based (MS-ANM) channel estimation algorithm. In particular, we take full advantage of the sharing of angle information among all subcarriers in the broadband channel to enhance the estimation accuracy. To reduce the computational complexity, we further propose a multi-stage orthogonal matching pursuit-based (MS-OMP) algorithm. Simulation results show that the MS-ANM algorithm significantly outperforms the benchmark algorithms, and the MS-OMP algorithm strikes a good balance between performance and computational complexity. By fully exploring the channel’s sparsity in the angle and delay domains along with the Vandermonde structure, both proposed algorithms remarkably reduce the training overhead and thus greatly improve the spectral efficiency over the benchmark algorithms.

Bilayer intelligent omni-surface (BIOS) has recently attracted increasing attention due to its capability of independent beamforming on both reflection and refraction sides. However, its specific bilayer structure makes the channel estimation problem more challenging than the conventional intelligent reflecting surface (IRS) or intelligent omni-surface (IOS). In this paper, we investigate the channel estimation problem in the BIOS-assisted multi-user multiple-input multiple-output system. We find that in contrast to the IRS or IOS, where the forms of the cascaded channels of all user equipments (UEs) are the same, in the BIOS, those of the UEs on the reflection side are different from those on the refraction side, which is referred to as the heterogeneous channel property. By exploiting it along with the two-timescale and sparsity properties of channels and applying the manifold optimization method, we propose an efficient channel estimation scheme to reduce the training overhead in the BIOS-assisted system. Moreover, we investigate the joint optimization of base station digital beamforming and BIOS passive analog beamforming. Simulation results show that the proposed estimation scheme can significantly reduce the training overhead with competitive estimation quality, and thus keeps the performance advantage of BIOS over IRS and IOS with imperfect channel state information.

Due to the passive nature and the unique capability of customizing propagation environments, intelligent reflecting surface (IRS) has been considered as a promising green communication technology to deal with the dramatically increasing power consumption of future wireless communication systems. However, most existing studies on IRS-aided green communication only considered the constant power consumption of IRSs, while ignoring the dynamic power consumption with respect to the states of PIN diodes. As the phase shifts of scattering elements are controlled by the PIN diodes in IRSs, this dynamic power consumption is strongly related to the phase shifts of IRS elements, and thus needs to be considered during the beamforming design. Therefore, in this paper, we investigate the green beamforming design for IRS-aided multi-input single-output systems under this phase shift-related (PSR) power consumption model. By taking the power consumption of the PIN diodes into consideration, we propose a generalized Benders decomposition-based green beamforming (GBD-GBF) scheme to minimize the total power consumption of the proposed system. Our simulation results reveal that the proposed GBD-GBF scheme can achieve a good balance between the transmit power consumption at the base station and the PSR power consumption of the IRS, and thus significantly reduce the total power consumption of the IRS-aided communication system.

Intelligent reflecting surface (IRS) consisting of a large number of low-cost and passive reflecting elements, has been proposed as a promising technology for future wireless communications due to its ability of customizing favorable propagation environment. In spite of its advantages, channel state information acquisition is an important and challenging task due to the passive nature of IRS. In this paper, we investigate the channel estimation problem for broadband IRS-aided millimeter wave (mm-wave) multiple-input multiple-output (MIMO) systems. From the practical implementation point of view, we consider the broadband scenario, realize the phase-amplitude-frequency relationship of the reflected signals, and adopt a practical model of reflection coefficients. Then, by utilizing the sparsity of the mm-wave channels, an efficient manifold optimization (MO) based algorithm is proposed to obtain a local optimal solution. Moreover, we propose a design approach for the optimization of the IRS reflection matrix to further improve the estimation performance. Simulation results show that the proposed MO based algorithm significantly outperforms the benchmark algorithm and the performance gain is especially significant for high-level sparse mm-wave MIMO channels.

Intelligent reflecting surface (IRS) has been regarded as a promising technology because of its ability in intelligently adjusting the propagation environment of wireless communication systems. However, channel estimation becomes a tough question due to the passive elements of IRS. In this paper, we investigate the channel estimation problem in broadband millimeter wave multi-input multi-output (MIMO) communication systems. Considering the channel sparsity in both the angular domain and the delay domain, we first perform a sparse representation of the cascaded MIMO channel. Then, we decompose the sparse recovery operation into several subproblems to reduce the computational complexity, and propose two algorithms in the frequency domain and the time domain, respectively, based on the compressed sensing technique. Simulation results verify the effectiveness of the two proposed algorithms.

Due to their ability to create favorable line-of-sight (LoS) propagation environments, intelligent reflecting surfaces (IRSs) are regarded as promising enablers for future millimeter-wave (mm-wave) wireless communication. In this paper, we investigate channel estimation for IRS-assisted mm-wave multiple-input multiple-output (MIMO) wireless systems. By leveraging the sparsity of mm-wave channels in the angular domain, we formulate the channel estimation problem as an$\ell _{1}$-norm regularized optimization problem with fixed-rank constraints. To tackle the non-convexity of the formulated problem, an efficient algorithm is proposed by capitalizing on alternating minimization and manifold optimization (MO), which yields a locally optimal solution. To further reduce the computational complexity of the estimation algorithm, we propose a compressive sensing- (CS-) based channel estimation approach. In particular, a three-stage estimation protocol is put forward where the subproblem in each stage can be solved via low-complexity CS methods. Furthermore, based on the acquired channel state information (CSI) of the cascaded channel, we design a passive beamforming algorithm for maximization of the spectral efficiency. Simulation results reveal that the proposed MO-based estimation (MO-EST) and beamforming algorithms significantly outperform two benchmark schemes while the CS-based estimation (CS-EST) algorithm strikes a balance between performance and complexity.

Direction-of-arrival (DOA) estimation is one of the most demanding tasks for the millimeter wave (mmWave) communication of massive multiple-input multiple-output (MIMO) systems with the hybrid beamforming (HBF) architecture. In this letter, we focus on the optimization of the HBF matrix for receiving pilots to enhance the DOA estimation performance. Motivated by the fact that many existing DOA estimation algorithms can achieve the Cramér-Rao bound (CRB), we formulate the HBF optimization problem aiming at minimizing the CRB with the prior knowledge of the rough DOA range. Then, to tackle the problem with intractable non-convex constraints introduced by the analog beamformers, we propose an efficient manifold optimization (MO) based algorithm. Simulation results demonstrate the significant improvement of the proposed CRB-MO algorithm over the conventional HBF algorithms and provide insights for the HBF design in the beam training stage for practical applications.

Hybrid beamforming (HBF) is an attractive technology for practical massive multiple-input and multiple-output (MIMO) millimeter wave (mmWave) systems. Compared with the fully-connected HBF architecture, the partially-connected one can further reduce the hardware cost and power consumption. However, the special block diagonal structure of its analog beamforming matrix brings additional design challenges. In this paper, we develop effective HBF algorithms for spectral efficiency maximization (SEM) in wideband mmWave massive MIMO systems with the partially-connected architecture. One main contribution is that we prove the equivalence of the SEM problem and a weighted mean square error minimization (WMMSE) problem, which leads to a convenient algorithmic approach to directly tackle the SEM problem. Specifically, we decompose the equivalent WMMSE problem into the hybrid precoding and hybrid combining subproblems, for which both the optimal digital precoder and combiner have closed-form solutions. For the more challenging analog precoder and combiner, we propose an element iteration based algorithm and a manifold optimization based algorithm. Finally, the hybrid precoder and combiner are alternatively updated. The overall HBF algorithms are proved to monotonously increase the spectral efficiency and converge. Furthermore, we also propose modified algorithms with reduced computational complexity and finite-resolution phase shifters. Simulation results demonstrate that the proposed HBF algorithms achieve significant performance gains over conventional algorithms.

Intelligent reflecting surfaces (IRSs) are regarded as promising enablers for future millimeter wave (mmWave) wireless communication, due to their ability to create favorable line-of-sight (LoS) propagation environments. In this paper, we investigate channel estimation in downlink IRS-assisted mmWave multiple-input multiple-output (MIMO) systems. By leveraging the sparsity of mmWave channels, we formulate the channel estimation problem as a fixed-rank constrained non-convex optimization problem. To tackle the non-convexity, an efficient algorithm is proposed by capitalizing on alternating minimization and manifold optimization (MO), which yields a locally optimal solution. Simulation results show that the proposed MObased estimation (MO-EST) algorithm significantly outperforms two benchmark schemes and demonstrate the robustness of the MO-EST algorithm with respect to imperfect knowledge of the sparsity level of the channels in practical implementations.

Hybrid analog and digital beamforming (HBF) has recently emerged as an attractive technique for millimeter-wave (mmWave) communication systems. It well balances the demand for sufficient beamforming gains to overcome the propagation loss and the desire to reduce the hardware cost and power consumption. In this paper, the mean square error (MSE) is chosen as the performance metric to characterize the transmission reliability. Using the minimum sum-MSE criterion, we investigate the HBF design for broadband mmWave transmissions. To overcome the difficulty of solving the multi-variable design problem, the alternating minimization method is adopted to optimize the hybrid transmit and receive beamformers alternatively. Specifically, a manifold optimization-based HBF algorithm is first proposed, which directly handles the constant modulus constraint of the analog component. Its convergence is then proved. To reduce the computational complexity, we then propose a low-complexity general eigenvalue decomposition-based HBF algorithm in the narrowband scenario and three algorithms via the eigenvalue decomposition and orthogonal matching pursuit methods in the broadband scenario. A particular innovation in our proposed alternating minimization algorithms is a carefully designed initialization method, which leads to a faster convergence. Furthermore, we extend the sum-MSE-based design to that with weighted sum-MSE, which is then connected to the spectral efficiency-based design. Simulation results show that the proposed HBF algorithms achieve a significant performance improvement over existing ones and perform close to full-digital beamforming.

Single-carrier frequency-domain equalization (SC-FDE) with multi-input multi-output (MIMO) has been recognized as an alternative technology to orthogonal frequency-division multiplexing with MIMO for broadband wireless communication systems because of its single carrier transmission advantages. Conventional SC-FDE MIMO schemes are designed under the assumption of perfect channel estimation. In this paper, we investigate the robust SC-FDE MIMO design for systems with imperfect channel estimation. Based on a statistical model for channel estimation, the optimal equalization coefficients for the robust SC-FDE MIMO schemes with both parallel interference cancellation and successive interference cancellation (SIC) are derived with the objective of minimizing the sum of the multiple data streams' mean square errors (sum-MSE). We propose an optimal ordering algorithm in the sense of minimum sum-MSE for the SC-FDE MIMO scheme with SIC, and further propose a low complexity sub-optimal ordering algorithm. The bit-error-rate (BER) performance in the uncoded case is analyzed and a tight BER approximation is derived. Numerical results show that the proposed robust SC-FDE MIMO schemes achieve 1.5dB~4dB gains in Eb/Noover the conventional non-robust schemes.

Millimeter wave (mmWave) communication has been considered as a key technology for future 5G or beyond wireless communication systems due to its huge spectrum bandwidth and great potential when combined with the large scale antenna technology. Considering the new challenge of high hardware cost and huge power consumption with the increase of bandwidth and number of antennas, hybrid digital and analog signal processing has been receiving a lot of interest recently. This paper investigates the hybrid precoding for multi-user communication in a downlink mmWave system, aiming at minimizing the sum of the users' mean square errors (sum-MSE) with the constraints of maximum transmit power and constant modulus for the phase shifters in the radio frequency precoding block. A two-stage optimization scheme is proposed to obtain a near-optimal solution. Simulation results show that the proposed hybrid precoding scheme performs closely to the fully digital precoding scheme.

Multiview video is essential for various mobile three-dimensional (3D) and immersive applications that can capture scenes from multiple angles for better user experience. However, robust transmission of multiview video is very challenging in wireless networks due to high bandwidth requirement and time-varying channel quality. Though the up-to-date 802.11 system enables spatial multiplexing MIMO to enhance transmission capacity, it is still agnostic to 3D source coding structure in the transmission. In this paper, we study the optimal resource allocation problem in MIMO systems that deliver 3D content with multiview video coding. The basic idea is to exploit the channel diversity of multiple antennas and the source coding characteristics so as to achieve unequal error protection against channel errors. To achieve this goal, we develop a nonlinear mixed integer programming framework to perform antenna selection and power allocation, and propose low-complexity algorithms to assign these resources. We implement a proof-of-concept system, namely MuVi, on the software-defined-radio platform, WARP, to evaluate the proposed algorithms. MuVi is the practical system to tackle 3D multiview streaming in the latest Wi-Fi networks such as IEEE 802.11ac under realistic channel conditions. Extensive experimental results demonstrate that the peak signal-to-noise-ratio of MuVi significantly outperforms that of the conventional power allocation scheme in a variety of indoor environments.

Analog beamforming has been considered an attractive technology for future single carrier millimeter-wave multiple-input multiple-output (MIMO) systems because of the high cost and huge power consumption of mixed-signal devices. Most conventional studies have focused on joint base station and user equipment (BS-TIE) analog beamforming with the objective of improving the average signal to noise ratio performance before the baseband equalization. In contrast, this paper aims to optimize the BS-TIE analog beamforming vectors in the sense of minimizing the mean square error of the baseband equalized signal. Considering practical implementation requirement, we combine the gradient descent (GD) method and the iterative antenna training (IAT) technique, and propose an iterative local GD (ILGD) algorithm. We analyze the convergence property, bit error rate (BER) performance, training overhead, and computational complexity of the ILGD algorithm. Simulation results show that the proposed ILGD algorithm can achieve a gain of more than 2 dB at a BER of 10-4over the conventional IAT algorithm with the same training overhead.

Single-carrier frequency-domain equalization (SC-FDE) with multi-input multi-output (MIMO) has been recognized as an alternative technology to orthogonal frequency-division multiplexing (OFDM) MIMO for broadband wireless communication systems, especially for the uplink transmission. Conventional studies focused on the FDE design with the assumption of perfect channel estimation. In this paper, we investigate the robust SC-FDE design for MIMO systems with imperfect channel estimation. Based on a statistical model for channel estimation, we derive the optimal equalization coefficients for the robust FDE with parallel interference cancellation and successive interference cancellation (SIC). We also propose a sub- optimal ordering algorithm for the SC-FDE scheme with SIC. Numerical results show that the proposed robust FDE MIMO schemes achieve 1.5dB~3dB gains in signal-to- noise ratio over the conventional non-robust FDE MIMO schemes.

Single carrier frequency domain equalization (SC-FDE) is an alternative technology to orthogonal frequency division multiplexing to deal with the frequency selective channel fading effect in broadband wireless communication systems. In this paper, we consider a robust SC-FDE design with imperfect channel knowledge at a receiver due to the channel estimation error. Based on a statistical model for channel estimation, the optimal equalization coefficients are derived under the criterion of minimizing the mean square error conditioned on a given channel estimate. The bit error rate is further analyzed and a tight performance approximation is proposed. We also propose two robust FDE schemes in coded systems, where feedback from the channel decoder is utilized to improve the equalization and/or channel estimation performance. Simulation results show that the proposed robust FDE schemes achieve significant performance improvement over the conventional FDE schemes.

We consider the transmit beamforming design for a multi-user downlink with multiple transmit antennas at the base station. Different from the conventional sum-power constraint across the transmit antennas, we assume individual power constraints per antenna. Assuming that perfect channel state information (CSI) is available at the base station, we develop an efficient algorithm to find the optimal beamforming scheme for the classic max-min signal-to-interference-plus-noise ratio (SINR) problem based on solving a sequence of “dual” per-antenna power balancing problems as second-order cone programs. It is proven that the proposed algorithm can find the max-min SINR beamforming solution with guaranteed global optimality and fast convergence speed. Relying on robust optimization techniques, the approach is also generalized to obtain the robust beamforming design that maximizes the worst-case user SINR when the channel uncertainty is bounded by a spherical region. Numerical results are provided to demonstrate the merits of the proposed transmit beamforming schemes.

Due to its lower peak-to-average power ratio (PAPR) compared with orthogonal frequency division multiple access (OFDMA), single carrier frequency division multiple access (SC-FDMA) has been recently accepted as the uplink multiple access scheme in the Long Term Evolution (LTE) of cellular systems by the Third Generation Partnership Project (3GPP). However, similar to OFDMA, carrier frequency offset (CFO) can destroy the orthogonality among subcarriers and degrade the performance of SC-FDMA. To mitigate the effect of CFOs, we propose a combined minimum mean square error frequency-domain equalization (MMSE-FDE) and interference cancellation scheme. In this scheme, joint FDE with CFO compensation (JFC) is utilized to obtain the initial estimation for each user. In contrast to previous schemes, where the FDE and CFO compensation are done separately, in JFC, the MMSE FDE is designed to suppress the MUI after CFO compensation. To further eliminate the MUI, we combine JFC with parallel interference cancellation (PIC). In particular, we iteratively design the MMSE FDE equalizer to suppress the remaining MUI at each stage and obtain better estimation. Simulation results show that the proposed scheme can significantly improve the system performance.

Single carrier interleaved frequency division multiple access (SC-IFDMA) has been recently receiving much attention for uplink multiuser access in the next generation mobile systems because of its lower peak-to-average transmit power ratio (PAPR). In this paper, we investigate the effect of carrier frequency offset (CFO) on SC-IFDMA and propose a new low-complexity time domain linear CFO compensation (TD-LCC) scheme. The TD-LCC scheme can be combined with successive interference cancellation (SIC) to further improve the system performance. The combined method will be referred to as TD-CC-SIC. We shall study the use of user equipment (UE) ordering algorithms in our TD-CC-SIC scheme and propose both optimal and suboptimal ordering algorithms in the MMSE sense. We also analyze both the output SINR and the BER performance of the proposed TD-LCC and TD-CC-SIC schemes. Simulation results along with theoretical SINR and BER results will show that the proposed TD-LCC and TD-CC-SIC schemes greatly reduce the CFO effect on SC-IFDMA. We also propose a new blind CFO estimation scheme for SC-IFDMA systems when the numbers of subcarrier sets allocated to different UEs are not the same due to their traffic requirements. Compared to the conventional blind CFO estimation schemes, it is shown that by using a virtual UE concept, the proposed scheme does not have the CFO ambiguity problem, and in some cases can improve the throughput efficiency since it does not need to increase the length of cyclic prefix (CP).

Single carrier interleaved frequency division multiple access (SC-IFDMA) has been recently receiving much attention for uplink multiuser access in the next generation mobile systems because of its lower peak-to-average transmit power ratio. In this paper, we investigate the effect of carrier frequency offsets (CFO) on SC-IFDMA and propose a new low complexity time domain CFO compensation (TD-CC) scheme. The TD-CC scheme can be combined with successive interference cancellation technique to further improve the system performance. Simulation results will show that the proposed TD-CC and TD-CC-SIC schemes greatly reduce the CFO effect on SCIFDMA. Furthermore, we propose a new blind CFO estimation scheme for SC-IFDMA systems, which is based on the ESPRIT algorithm. Compared to the conventional blind CFO estimation scheme using ESPRIT, it is shown that in some cases the proposed scheme does not need to increase the length of cyclic prefix, and thus improve the data throughput efficiency.

Recent research has shown that frequency domain pre-equalization (FDPE) can provide broadcast transmissions over multi-input multi-output (MIMO) frequency selective channels, where the multiple receivers need limited processing. In this paper, we consider the combination of FDPE with parallel and successive Tomlinson-Harashima Precoding (THP) and propose two novel FDPE MIMO schemes, which are referred to as FDPE-P-THP and FDPE-S-THP, respectively, based on the minimum mean square error (MMSE) criterion. The ordering algorithm in the FDPE-S-THP scheme is considered and it is shown that the system with even a randomly selected order can perform almost as well as that with the optimal one. This paper further develops an accurate theoretical performance analysis methodology for the proposed FDPE-THP schemes. Numerical results along with analytical results demonstrate the significant performance improvement of our proposed schemes compared to the conventional FDPE MIMO schemes. The channel estimation errors and channel variation effects on the proposed system are also investigated. It is shown that the performance degradation due to channel variation can be efficiently reduced by applying channel prediction.

Network coding has been receiving much attention recently for its ability to improve network throughput and enhance network robustness. In this paper, we investigate the design of network coding in wireless networks and propose a combined low complexity network coding and channel decoding scheme. We analyze the capacity of the proposed scheme for both the binary symmetric channel (BSC) and AWGN channel and show that it can achieve almost the same channel capacity as traditional network coding with a small degradation in the system bit error rate (BER) performance while achieving almost 50% complexity reduction. It is also shown that the proposed network coding design can be applied in wireless cooperative networks.

Single carrier frequency domain equalization (SC-FDE) has been recently receiving much attention as an alternative to orthogonal frequency-division multiplexing (OFDM) to deal with the detrimental effects of multipath fading. In this paper, we consider transmit frequency domain pre-equalization (FD-PRE) in the downlink transmission of space-division multiple access (SDMA) systems, where the FD-PRE is combined with the Tomlinson-Harashima precoding (THP) technique at the base station so that the mobile station requires limited processing. We propose two FD-PRE MIMO schemes, with parallel and successive precoding, based on the minimum mean square error (MMSE) criterion. In contrast to the previous FD-PRE with THP design, where the THP is only for co-channel interference (CCI) pre-cancellation, the proposed parallel and successive precoders are designed by taking both inter-symbol interference (ISI) and CCI into account. Numerical results will demonstrate the significant performance improvement of our proposed schemes compared to the conventional FD-PRE MIMO schemes. We will also investigate the channel estimation errors and channel variation effects on the proposed systems. It will be shown that by applying channel prediction such detrimental effects can be greatly reduced.

Single-carrier frequency-domain equalization (SC-FDE) has recently been receiving much attention as an attractive method for broadband wireless communications for its advantages such as lower peak-to-average ratio and reduced sensitivity to carrier frequency offsets, when compared with orthogonal frequency-division multiplexing (OFDM) systems. In this paper, we investigate its combination with multi-input multi-output (MIMO) technology and propose two novel FDE-MIMO structures, which we refer to as FDE with noise prediction (FDE-NP) and FDE-NP with successive interference cancellation (FDE-NP-SIC). It is shown that the proposed schemes have lower complexity and achieve better performance and complexity tradeoff than the conventional FDE scheme with decision feedback processing. To evaluate the system performance, we will propose an accurate theoretical analysis based on the modified Chernoff bound (MCB) and show that it is not only applicable to the proposed FDE-NP and FDE-NP-SIC schemes, but also to general MIMO systems with equalization. By using the developed MCB along with simulation results, we will show that the proposed FDE-NP and FDE-NP-SIC schemes can achieve significant performance improvement over the conventional FDE MIMO schemes

In this paper, we combine the transmit Tomlinson-Harashima precoding (THP) technique with single carrier frequency domain equalization (SC-FDE) and propose two new FDE multi-input multi-output (MIMO) schemes, which we refer to as parallel THP-FDE and successive THP-FDE, based on the minimum mean square error (MMSE) criterion. For the successive THP-FDE scheme, where all transmit streams are subsequently preceded, we propose an optimal MMSE ordering algorithm. Since the feedback processing is performed at the transmitter, no error propagation problem exists in the proposed THP-FDE MEMO schemes. Numerical results will demonstrate the significant performance improvement of our proposed schemes compared to the conventional FDE MIMO schemes. We will also analyze the error probability, and investigate the channel estimation errors and channel variation effects on the proposed THP-FDE MIMO systems. It will be shown that by applying channel prediction and THP compensation techniques such detrimental effects can be greatly reduced.

As an alternative to orthogonal frequency-division multiplexing (OFDM) to deal with the detrimental effects of multipath fading, single carrier frequency domain equalization (SC-FDE) has been recently receiving much attention. Most of the existing FDE structures focused on the signal processing at the receiver. In this paper, a new FDE structure, which consists of a Tomlinson-Harashima Precoder (THP) at the transmitter and an FDE at the receiver, is proposed based on the minimum mean square error (MMSE) criterion. Since the feedback processing is performed at the transmitter, no error propagation problem exists in the proposed THP-FDE scheme. Numerical results will demonstrate the improved performance of our proposed scheme compared to the conventional FDE schemes. We will analyze the error probability, and investigate the channel estimation errors and channel variation effects on the proposed THP-FDE system as well. It will be shown that by applying channel prediction and THP compensation techniques such detrimental effects of wireless links can be greatly reduced.

Single carrier frequency domain equalization (SC-FDE) has recently been receiving much attention as an attractive technology for broadband wireless communications for its advantages such as low peak-to-average ratio and reduced sensitivity to carrier frequency offsets, when compared to orthogonal frequency-division multiplexing (OFDM) systems. In this paper, we investigate its combination with multi-input multi-output (MIMO) technology and propose a novel hybrid time-frequency domain equalizer, FDE-NP, which consists of a feedforward frequency domain equalizer and an array of time domain noise predictors (NPs). It is shown that the proposed scheme has lower complexity and achieves better performance and complexity trade-off than the conventional FDE designs. To evaluate the system performance, we will propose an accurate theoretical analysis based on the modified Chernoff bound and show that it is not only applicable to the proposed FDE-NP scheme, but also to general MIMO systems with equalization. By using the developed modified Chernoff bound and along with the simulation results, we will show that the proposed FDE-NP scheme can achieve significant performance improvement over the conventional FDE MIMO schemes.

Recent research has shown that single carrier frequency domain equalization (SC-FDE) is an attractive technology for broadband wireless communications for its advantages such as lower peak-to-average power ratio and reduced sensitivity to carrier frequency offset, when compared to orthogonal frequency-division multiplexing (OFDM) systems. In this paper, we propose a novel structure, FDE-NP, which consists of a linear frequency domain equalizer (FDE) and a time domain noise predictor (NP). It is shown that the proposed scheme has lower complexity and achieves better performance and complexity trade-off than the conventional FDE designs. In particular, by using a simple block interleaver/deinterleaver pair to rearrange the order of the received signals prior to decoding, delayed reliable decisions can be fed back to predict and cancel their post-cursor inter-symbol interference (ISI) effects to the following signals. Simulation results show that the proposed FDE-NP scheme achieves 1.5-2 dB performance improvement over the conventional FD-LE and FD-DFE schemes. Furthermore, we extend the design of unbiased and biased time domain equalizers (TDEs) into the frequency domain and propose the unbiased FDE. It is shown that although the unbiased FDE has a larger MSE than the biased one, it reduces the required SNR by about 0.3 dB in some cases

In this letter, we propose to introduce frequency-domain equalization with decision-feedback processing (FD-DFE) for time-reversal space-time block-coded (TR-STBC) systems with multiple transmit antennas to effectively achieve both spatial diversity and multipath diversity over frequency-selective channels. This letter will also present a theoretical performance analysis result that relates the minimum mean-squared error (MMSE) to a new tight upper bound for the probability of error. It is shown that the new bound is not only valid for the TR-STBC systems, but also valid for single-input single-output, single-input multiple-output, and multiple-input multiple-output systems with MMSE equalization.

Recent research has shown that single carrier frequency domain equalization (SC-FDE) is an attractive technology for broadband wireless communications because it does not have the problems of OFDM systems such as large peak-to-average ratio and the sensitivity to carrier frequency offsets. The FD-DFE structure, which combines a frequency domain feedforward equalizer and a time domain feedback filter, has been proposed to bring better performance than the linear ME scheme for severely distorted channels. However, this structure cannot be applied directly to coded systems because of the decision delays associated with these systems. In this paper, we propose a novel structure, which consists of a linear frequency domain equalizer and a time domain noise predictor. By using a simple block interleaver/deinterleaver pair to rearrange the order of the received signals prior to decoding, delayed reliable decisions can be used for feedback. By doing so, significant performance improvement over the conventional FD-LE and the FD-DFE schemes can be achieved in the proposed FDE-NP scheme. It is also demonstrated that the performance complexity tradeoff of the proposed scheme can be obtained by only changing the order of the noise predictor. This is in contrast to the conventional FD-DFE scheme where both the ME and the time domain feedback filter must be modified simultaneously.