Orthogonal Frequency Division Multiplexing Uplink Research Articles

A Dual-Function Massive MIMO Uplink OFDM Communication and Radar Architecture

This paper proposes a joint uplink massive multiple-input-multiple-output (MIMO) communication and orthogonal frequency-division multiplexing (OFDM) radar sensing architecture. Specifically, uplink communication and short-range radar sensing are considered, where the user equipments (UEs) transmit data to the base-station (BS), which simultaneously receives radar returns from the targets over the same subcarriers. Hence, the signals received at each BS antenna include radar returns and communication signals to be processed for extracting the sensing and communication data. The separation and detection of such signals are achieved by utilizing the channel diversity between the UEs and the targets. To this end, the UEs’ signals are first detected and demodulated, and then subtracted from the received signal to acquire the radar returns. Symbol-based radar processing is then employed, as it provides substantial processing gains, and its detection performance is independent of the transmitted radar waveform. Furthermore, self-interference—due to the simultaneous operation of transmit and receive antennas—is taken into account. The communication rate and normalized error of the radar-target channel estimation are mathematically analyzed, and the trade-off between the achievable rate and radar detection performance is demonstrated in terms of the output power of the communication and radar sub-systems and the signal-to-noise ratio.

Information Freshness in Uplink OFDM Based Multiple Access Networks in Flat Nakagami-$m$ Fading Channels

In flat Nakagami-<inline-formula><tex-math notation="LaTeX">$m$</tex-math></inline-formula> fading channels, we study the total information freshness maximization problem for an uplink orthogonal frequency division multiplexing (OFDM) based multiple access network. Without channel state information, each user transmits the freshest version of the file to the base station for information updating using the OFDM scheme. Considering the actual transmission durations, we theoretically derive the analytical expression for average information freshness. Furthermore, we propose an alternating optimization algorithm to solve the total information freshness maximization problem. In the proposed algorithm, the bandwidth allocation problem is solved using the majorization-minimization based iterative algorithm and transmission rate optimization problem is solved by the bisection search. Numerical results illustrate that our proposed updating scheme is superior to the rate proportional and rate inverse proportional updating schemes.

Performances of OSIC Detector of an UpLink OFDM Massive-MIMO System in Rayleigh and Ricain Fading Channels

This paper presents a least squares channel estimation for Massive-Multiple-Input Multiple- Output (Massive-MIMO) systems in next generation of wireless communications denominated 5G, combined with Orthogonal Frequency Division Multiplexing (OFDM) and 64-QAM modulation. In this paper, after determining the estimated channel, we first consider one OFDM symbol and presented the flat fading and the frequency-selective fading in the case when channel taps equal to one and strictly superior to one respectively. In addition to that, we consider the uplink transmission where the transmitter antenna equal to $$N_t=50$$ and a Base Station (BS) equipped with several antenna superior to that of transmitter, in the same way the data detected using a linear detectors Zero-Forcing and Minimum Mean Square Error. Moreover we improve their performance by an ordered successive interference cancellation (OSIC) method. Using two channels fading namely the Rayleigh and the Ricain respectively and evaluate their performance with a system of Massive-MIMO antenna. Increasing the number of channel taps increase the inter-symbol interference allows to degrade the performance of the system both the Rayleigh and Rician channel, even we used a pilot sequence equipowered, equispaced and phase shift orthogonal. Hence increasing the number of antennas arrays at the receiver (i.e BS) decreases more the Bit Error Rate, consequently increasing the performance of the system. the OSIC methods provide a best performance when the number of antenna grow in a smaller range of Signal-to-Noise Ratio.

Signal Detection in Uplink Time-Varying OFDM Systems Using RNN With Bidirectional LSTM

In this letter, we propose a deep learning-assisted approach for signal detection in uplink orthogonal frequency-division multiplexing (OFDM) systems over time-varying channels. In particular, we utilize a recurrent neural network (RNN) with bidirectional long short-term memory (LSTM) architecture to achieve signal detection. In addition, with the help of convolutional neural network (CNN) and batch normalization (BN), a new network structure CNN-BN-RNN Network (CBR-Net) is proposed to obtain better performance. The sequence feature information of the OFDM received signal is extracted from big data to successfully train a RNN-based signal detection model, which simplifies the architecture of OFDM systems and can adapt to the change of channel paths. Simulation results also demonstrate that the trained RNN model has the ability to recall the characteristics of wireless time-varying channels and provide accurate and robust signal recovery performance.

Frequency Synchronization With Beamforming Network Optimization for Uplink Massive MIMO Systems

In this correspondence, we develop a novel frequency synchronization scheme for multiuser orthogonal frequency division multiplexing (OFDM) uplink with massive uniform linear array (ULA) at base station (BS). With proper user scheduling strategy, the directions of arrival (DOAs) of all users are considered to be disjunct. Our proposed method performs multiuser interference (MUI) cancellation through beamforming network, after which the multiuser OFDM uplink model can be decomposed into a set of parallel single user models. Then, carrier frequency offset (CFO) estimation for each user can be performed individually. The beamforming network is further optimized to minimize the mean square error (MSE) of the CFO estimation. The provided numerical results validate the proposed studies.

Computationally Efficient Blind CFO Estimation for Massive MIMO Uplink

In this correspondence paper, we design a new computationally efficient blind carrier frequency offset (CFO) estimator for multiuser orthogonal frequency division multiplexing (OFDM) uplink. We consider that massive receive antennas are configured at the base station (BS), such that channel matrices of different users tend to be orthogonal to each other. The proposed method first performs multiuser interference (MUI) cancellation, after which the multiuser OFDM uplink model can be decomposed into a set of parallel single user signal models. The conventional single user CFO estimation can then be carried out individually. Moreover, considering the number of antennas at the BS is limited in practice, an iterative procedure is further developed to gradually improve MUI cancellation as well as CFO estimation. The provided numerical results validate the proposed studies. As compared to existing multiuser CFO estimators, the proposed method can achieve much better estimation performance with a lower order of computational burden.

Large random matrix‐based channel estimation for massive MIMO‐OFDM uplink

This study investigates the benefits offered by random matrix theory (RMT) towards the design of reliable channel estimation algorithms for a multi-user massive multiple-input multiple-out (MIMO)-orthogonal frequency-division multiplexing uplink. Assuming no a priori knowledge of channel statistics (KCS) at the massive base station, the authors propose RMT-aided minimum mean square estimation (MMSE) and RMT-aided sparse Bayesian learning (SBL) approaches for massive channel estimation. These approaches render efficient channel estimates, as illustrated through mean square error (MSE) performance, extracted via Monte-Carlo simulations. The results also show that with increasing antennas at the base station, MSE from the RMT-aided MMSE approach decreases, suggesting its aptness to massive MIMO systems. To further enhance the MSE performance, the MMSE and SBL estimated channel impulse responses are pruned using threshold computed from RMT analysis. The authors characterise MSE degradation due to the randomness in the threshold, with the help of the Marcenko-Pastur law-based non-asymptotic framework and concentration inequalities. Analysis results show that, for channels with approximate sparse common support, this MSE degradation is quite insignificant. Altogether, the study demonstrates that RMT analysis is competent in improving channel estimation at a massive MIMO system, when a priori KCS is completely unavailable.

Frequency Synchronization for Uplink Massive MIMO Systems

In this paper, we propose a frequency synchronization scheme for multiuser orthogonal frequency division multiplexing uplink with a large-scale uniform linear array at base station (BS) by exploiting the angle information of users. Considering that the incident signal at BS from each user can be restricted within a certain angular spread, the proposed scheme could perform carrier frequency offset (CFO) estimation for each user individually through a joint spatial-frequency alignment procedure and can be completed efficiently with the aid of fast Fourier transform. A multi-branch receive beamforming is further designed to yield an equivalent single user transmission model for which the conventional single-user channel estimation and data detection can be carried out. To make the study complete, theoretical performance analysis of the CFO estimation is also conducted. We further develop a user grouping scheme to deal with the unexpected scenarios that some users may not be separated well from the spatial domain. Finally, various numerical results are provided to verify the proposed studies.

Scattered Pilots-Based Frequency Synchronization for Multiuser OFDM Systems With Large Number of Receive Antennas

Large-scale multi-input multi-output (MIMO) technique has drawn considerable research interest recently. However, multiuser transmissions in large-scale MIMO systems would face challenging estimation and compensation for multiple carrier frequency offsets (CFOs) that co-exist at the receiver. In this paper, we design a new frequency synchronization scheme in multiuser orthogonal frequency division multiplexing uplink with the aid of scattered pilot symbols. We specifically consider a base station with a large number of antennas and propose to assign a number of scattered pilot subcarriers to each user. With sufficient spatial dimensions offered by the large number of antennas, the designed scheme could estimate CFO for each user individually and eliminate the necessity of the multidimensional search. Moreover, after the CFO estimation, the receive beamforming matrix is further designed for inter-user interference cancelation, which yields an equivalent single user transmission model. The conventional single user channel estimation and data detection can then be performed. Finally, the numerical results are provided to verify the proposed studies.

Blind Frequency Synchronization for Multiuser OFDM Uplink With Large Number of Receive Antennas

In this paper, we propose a new blind frequency synchronization scheme for multiuser orthogonal-frequency-division multiplexing (OFDM) uplink transmissions. The spatial multiplexing is supported in the considered model that allows the subcarriers to be simultaneously occupied by multiple users, while the orthogonal frequency division multiple access (OFDMA) can serve as a special case of our discussion. We propose to assign different null subcarriers to different users and design algorithm that can perform blind carrier frequency offset (CFO) estimation for each individual user with the aid of large number of receive antennas, which then removes the necessity of multidimensional searching. After estimating CFO for one user, the orthogonal complement space of the other users can be obtained which could be further utilized for multiuser interference cancelation. Hence, the multiuser OFDM uplink model reduces to an equivalent single user signal model, where the blind channel estimation with scalar ambiguity can be designed and the conventional single-user data detection can be peformed. To make the proposed study complete, the closed-form performance analysis for mean square error (MSE) of the CFO estimation is derived, based on which the discussion about optimal null subcarrier assignment scheme is further provided. Finally, the Cramer–Rao bound (CRB) is derived, and the numerical results are provided to corroborate the proposed studies.

Performance of the Massive MIMO Uplink With OFDM and Phase Noise

The performance of multi-user massive multiple-input multiple-output orthogonal frequency division multiplexing uplink systems in the presence of base station (BS) phase noise impairments is investigated. Closed-form achievable rate expressions are rigorously derived under two different operations, namely, the case of a common oscillator (synchronous operation) at the BS and the case of independent oscillators at each BS antenna (non-synchronous operation). It is observed that the non-synchronous operation exhibits superior performance due to the averaging of a portion of the intercarrier interference. Furthermore, radiated power scaling laws are derived, which are identical to the phase-noise-free case.

Enabling In-Band Bidirectional OFDM-Uplink and OOK-Downlink Transmission in Long-Reach RSOA-Based WDM-PON Systems

We present a simple, straightforward technique to enable bidirectional transmission of analog and digital signals in single-fiber wavelength division multiplexing passive optical network systems employing reflective semiconductor optical amplifiers (RSOA). An On-Off-Keying (OOK-DL) downlink modulation is effectively erased in a saturated RSOA, reflecting a carrier for retransmitting on the same fiber an in-band analog WiFi orthogonal frequency division multiplexing uplink signal (OFDM-UL). We investigate the operating conditions and the optimization procedure to attain required signal quality for both the uplink and downlink. We demonstrate experimentally the maximum distance and the best attainable performances for different lengths of standard single mode fiber (transmission). Bit error rate below the forward error correction threshold is obtained for WiFi OFDM-UL signal over 40 km and up to 80 km of SSMF using 64QAM and QPSK modulation modes, respectively. In all cases, we achieve error-free operation for the OOK-DL signal at 1 Gb/s.

Robust Energy Efficiency Power Allocation for Uplink OFDM-Based Cognitive Radio Networks

This paper studies the energy efficiency power allocation for cognitive radio networks based on uplink orthogonal frequency-division multiplexing. The power allocation problem is intended to minimize the maximum energy efficiency measured by Joule per bit metric, under total power constraint and robust aggregate mutual interference power constraint. However, the above problem is non-convex. To make it solvable, an equivalent convex optimization problem is derived that can be solved by general fractional programming. Then, a robust energy efficiency power allocation scheme is presented. Simulation results corroborate the effectiveness of the proposed methods.

A Multidimensional Resource-Allocation Optimization Algorithm for the Network-Coding-Based Multiple-Access Relay Channels in OFDM Systems

Joint scheduling and resource allocation in uplink orthogonal frequency-division multiplexing (OFDM) systems is complicated and even becomes intractable with a large subcarrier and a large user number. This paper investigates the resource allocation for OFDM-based multiuser multiple-access relay channels (MARCs) with network coding. We formulate a joint optimization problem considering source-node pairing, subcarrier assignment, subcarrier pairing, and power allocation to maximize the sum rate under per-user power constraints. The problem is solved in polynomial time by optimizing three separate subproblems. To further reduce the complexity, three low-complexity suboptimal algorithms are then proposed when fixing partial resource. The simulation results show the performance gains of the proposed algorithms versus per-node transmit power and source-node number, and the impact of relay location is also evaluated.

Proportional-fairness resource allocation for uplink OFDM-based cognitive radio systems

This paper has proposed a proportional-fairness resource allocation algorithm, including both subcarrier assignment algorithm and power allocation algorithm, for uplink orthogonal frequency division multiplexing (OFDM)-based cognitive radio (CR) systems. First, to get a better performance in the proposed system model, the influence factor (a,b,c) was introduced to realize the assignment of the subcarriers. Second, the transmit power of the secondary users (SUs) was allocated to the corresponding subcarriers in order to maximize the uplink capacity of the SUs subject to both power and interference constraints. With the appropriate influence factor in the subcarrier assignment, the loss of transmitted data rate arising from the fairness was minimized. Simulation results showed that the proposed algorithm can achieve a perfect fairness among the SUs while maximizing the system capacity simultaneously, and is of a low computation complexity.

Ordinal Optimization Approach for Throughput Maximization Problems in MOFDM Uplink System

This work presents a two-stage ordinal optimization theory-based approach for solving the throughput maximization problems with power constraints of sub-carrier assignment and power allocation in multi-user orthogonal frequency division multiplexing uplink systems. In the first stage, a crude but efficient model is employed to evaluate the performance of a sub-carrier assignment pattern and the genetic algorithm is used to search through the huge solution space. In the second stage, an exact model is employed to evaluate s best sub-carrier assignment patterns obtained in stage 1 and form the select subset. Finally, the best one of the select subset is the good enough solution that we seek. Via numerous tests, this work demonstrates the efficiency of the proposed algorithm and compares it with those of other heuristic methods.

Joint Channel Estimation, Equalization, and Data Detection for OFDM Systems in the Presence of Very High Mobility

This paper is concerned with the challenging and timely problem of joint channel estimation, equalization, and data detection for uplink orthogonal frequency division multiplexing (OFDM) systems in the presence of frequency selective and very rapidly time varying channels. The resulting algorithm is based on the space alternating generalized expectation maximization (SAGE) technique which is particularly well suited to multicarrier signal formats leading to a receiver structure that also incorporates interchannel interference (ICI) cancelation. In order to reduce the computational complexity of the algorithm, band-limited, discrete cosine orthogonal basis functions are employed to represent the rapidly time-varying fading channel by the discrete cosine serial expansion coefficients. It is shown that, depending on the normalized Doppler frequency, only a small number of expansion coefficients is sufficient to approximate the channel perfectly and there is no need to know the correlation function of the input signal. In this way, the resulting reduced dimensional channel coefficients are estimated and the data symbols detected iteratively with tractable complexity. The proposed SAGE joint detection algorithm updates the data sequences serially and the channel parameters are updated in parallel, leading to a receiver structure that also incorporates ICI cancelation. Computer simulations show that the cosine transformation represents the time-varying channel very effectively and the proposed algorithm has excellent symbol error rate and channel estimation performance even with a very small number of channel expansion coefficients employed in the algorithm, resulting in substantial reduction of the computational complexity.

Joint scheduling and resource allocation in uplink OFDM systems for broadband wireless access networks

Orthogonal frequency division multiplexing (OFDM) with dynamic scheduling and resource allocation is a key component of most emerging broadband wireless access networks such as WiMAX and LTE (long term evolution) for 3GPP. However, scheduling and resource allocation in an OFDM system is complicated, especially in the uplink due to two reasons: (i) the discrete nature of subchannel assignments, and (ii) the heterogeneity of the users' subchannel conditions, individual resource constraints and application requirements. We approach this problem using a gradient-based scheduling framework. Physical layer resources (bandwidth and power) are allocated to maximize the projection onto the gradient of a total system utility function which models application-layer Quality of Service (QoS). This is formulated as a convex optimization problem and solved using a dual decomposition approach. This optimal solution has prohibitively high computational complexity but reveals guiding principles that we use to generate lower complexity sub-optimal algorithms. We analyze the complexity and compare the performance of these algorithms via extensive simulations.