Realizing Cooperative Multiuser OFDMA Systems with Subcarrier Resource Allocation

OFDM systems are very sensitive in phase noise and frequency dispersion. Thus, time and frequency synchronization tasks are very crucial for the proper signal recovery in the receiver. The difficulties and challenges are multiplied if we consider a multiuser OFDM system (OFDMA) where the subcarriers are shared between different users. The receiver has the task to choose optimal sampling instants and to align the subcarrier frequencies of an OFDM symbol that contains signals from multiple users. In this study the use of filter banks is considered in order to reduce Intercarrier interference in such a system and simplify the synchronization procedures that must take place. The proposed schemes are applicable to adjacent subcarrier permutation OFDMA systems where users are separated in bands of adjacent subcarriers. In order to achieve low processing load, techniques of multirate signal processing are adopted. The DFT - polyphase filters are the basic elements of the processing chain. Moreover the filtering procedure is preferred to be divided in multiple stages. The computational workload for each structure is determined and some measures for the reduction of the computational burden are presented.

Reconfigurable intelligent surfaces (RISs) play an important role in extending the connectivity and improving the data rate of future wireless communication systems. However, the conventional (passive) RISs have their limitations and, thus, a hybrid-relay RIS (HR-RIS) architecture is proposed to reap the benefits of relaying systems with high power consumption but higher throughput, and passive RIS systems with cascaded fading effects but low complexity. In this paper, we investigate the performance of HR-RISs in a massive multiple-input multiple-output (M-MIMO) system with zero-forcing (ZF) processing, where channel state information (CSI) is unavailable. We first model the uplink/downlink channels and derive the linear minimum mean square error (LMMSE) estimate of the effective channels. We, then, derive a closed-form expression for the signal to interference and noise ratio (SINR) and spectral efficiency (SE). Finally, we provide some useful engineering insights with our asymptotic analysis and numerical results.

In this paper, we study the joint allocation of three types of resources, namely, power, subcarriers and relay nodes, in multi-relay assisted dual-hop cooperative OFDM systems. All the relays adopt the amplify-and-forward protocol and assist the transmission from the source to destination simultaneously but on orthogonal subcarriers. The objective is to maximize the system transmission rate subject to individual power constraints on each node or a total network power constraint. We formulate such a problem as a subcarrier-pair based resource allocation that seeks the joint optimization of subcarrier pairing, subcarrier-pair-to-relay assignment, and power allocation. Using a dual approach, we solve this problem efficiently in an asymptotically optimal manner. Specifically, for the optimization problem with individual power constraints, the computational complexity is polynomial in the number of subcarriers and relay nodes, whereas the complexity of the problem with a total power constraint is polynomial in the number of subcarriers.We further propose two suboptimal algorithms for the former to trade off performance for complexity. Simulation studies are conducted to evaluate the average transmission rate and outage probability of the proposed algorithms. The impact of relay location is also discussed.

This paper investigates relay selection and subcarrier allocation in cooperative OFDMA systems where users with best-effort (BE) service and rate-constrained (RC) service coexist. We formulate the problem as a binary integer linear programming to maximize the network data rate with quality-of-service (QoS) requirements. A greedy algorithm is proposed for the degradation problem of only BE users and a direct search algorithm for hybrid users is proposed to reduce the number of variables and the size of the constraint matrix. Simulation results demonstrate that the proposed algorithm significantly improves the computing speed and enhances the system total throughput.

Focusing on the low throughput of fair subcarrier allocation in cooperative OFDMA system, an improved algorithm with single subcarrier capacity threshold and cooperative power factor is proposed. In this algorithm, the users with bad channel can not occupy too many subcarrier, and the power for cooperating with other users of users with good channel is limited. Simulation results show that this algorithm can increase the total channel capacity with less loss of fairness.

This paper analyzes the use of Random Frequency Hopping to enable self-organizing Orthogonal Frequency Division Multiple Access systems (RFH-OFDMA). While LTE (Long Term Evolution) macrocell resource planning is typically based on centralized planning of orthogonal deterministic frequency hopping patterns, the integration of femtocells located within macrocells introduces either complex planning efforts or uncontrolled interference issues. The results presented in this paper show that random frequency hopping is a new effective way to reduce interference for the integration of femtocells without resource planning into macrocells. An analytical model for the SINR (Signal-to-Interference-and-Noise Ratio) and a simulation model for the BER (Bit Error Rate) are presented for interfering RFH-OFDMA systems. All OFDMA parameters can be freely selected in time and frequency in the model, enabling to dimension systems with minimal interference. Based on the analytical model, a performance evaluation is presented, which uses typical LTE parameters. Compared to the performance of systems without resource planning and random frequency hopping, a significant gain is achieved for self-organizing RFH-OFDMA systems with respect to the SINR and BER.

Deploying large scale antenna arrays is a key characteristic of current and future wireless communication systems. However, due to some non-ideal practical conditions, such as the unknown array geometry or possible hardware impairments, the accurate channel state information becomes hard to acquire. This impedes the design of beamforming/combining vectors that are crucial to fully exploit the potential of the large-scale MIMO systems or to combat the high path-loss in millimeter wave (mmWave) communications. In this paper, we propose a novel solution that leverages deep reinforcement learning (DRL) to learn the beam pattern that is optimized for a group of users without the explicit knowledge of the channels. Simulation results show that the developed solution is capable of finding the near optimal beam pattern with quantized phase shifters and with only requiring the beamforming gain feedback from the users.

This paper presents a planar microstrip-fed tab monopole antenna for ultra wideband wireless communications applications. The impedance bandwidth of the antenna is improved by adding slit in one side of the monopole,introducing a tapered transition between the monopole and the feed line,and adding two-step staircase notch in the ground plane. Numerical analysis for the antenna dimensional parameters using Ansoft HFSS is performed and presented. The proposed antenna has a small size of 16 × 19 mm,and provides an ultra wide bandwidth from 2.8 to 28 GHz with low VSWR level and good radiation characteristics to satisfy the requirements of the current and future wireless communications systems.

Accurate modulation classification under impaired wireless channels via shallow convolutional neural networks

Automatic modulation classification receives significant interest in the context of current and future wireless communication systems. Deep learning emerged as a powerful tool for modulation classification, as it allows for joint discriminative features learning and signal classification. However, the optimization of deep neural network architectures for modulation classification is a manual and time-consuming process that requires profound domain knowledge and much effort. Most state-of-the-art solutions focus mainly on classification accuracy, while optimization of network complexity is neglected. This paper presents a novel bi-objective memetic algorithm, BO-NSMA, to search optimal deep neural network architectures for modulation classification to maximize classification accuracy and minimize network complexity. The experiments show that BO-NSMA, with an initial population of six individuals and only ten generations, finds a deep neural network architecture that outperforms all human-crafted architectures. Furthermore, BO-NSMA discovered the first low-complexity Convolutional neural network architecture, which achieves slightly better performance than costly Recurrent neural network architectures, allowing a 2.9-fold reduction in network complexity with 1.43% performance improvement. Compared to counterparts from network architecture search, BO-NSMA finds the best architecture, which achieves up to 18.73% accuracy gain and up to an 82-fold reduction in network complexity. The results are validated using the Wilcoxon signed-rank test.

We consider a cellular network MIMO OFDM system where cooperating base stations apply joint signal processing to the receive signals of several users. In this situation, due to geometry, the problem of unavoidable differences in time of arrival between the users' signals occurs. As a result, symbol timing offsets can be larger than the cyclic prefix, which leads to OFDM inter-symbol interference. In addition to the multiuser interference, a coupling between adjacent subcarriers and consecutive OFDM symbols is induced in such scenarios. In this paper, we analyze the multi-user joint detection performance in systems with distance dependent asynchronous interference and pathloss environments for different channel delays as well as fixed cyclic prefix length. To this end we use an exact expression for the post equalization SINR that is used for the evaluation in numerical simulations. The system level cell setup considers two user positioning models based on a hexagonal cell geometry with varying cell radii.

In this work, we present a multilinear algebra operator referred to as generalized tensor contraction. This operator defines an inner product between two higher-order tensors over a set of modes of compatible dimensions. We show that this tensor operator is useful to model Multiple-Input Multiple-Output - Orthogonal Frequency Division Multiplexing (MIMO-OFDM) communication systems. In our application, the transmit signal tensor contains Khatri-Rao coded symbols that can be modeled via a Canonical Polyadic (CP) decomposition. This new tensor based model facilitates the design of a receiver based on the least squares Khatri-Rao factorization (LS-KRF) that jointly estimates the channel and the data symbols. We reduce the number of required pilot symbols compared to other receivers based on the LS-KRF by exploiting the correlation of the channel between adjacent subcarriers. Due to the tensor gain, the proposed receiver significantly outperforms the traditional Zero Forcing - Fast Fourier Transform (ZF-FFT) receiver while using the same amount of pilot symbols.

Carrier frequency offset (CFO) destroys the orthogonality of the subcarriers and causes intercarrier interference (ICI). ICI self-cancellation scheme proposed by Zhao et al. is the simplest scheme to mitigate the effect of ICI. It repeats the same data symbol on two adjacent subcarriers with phase inversion and therefore does not provide any frequency diversity. To achieve frequency diversity Tang et al. proposed a symmetric data conjugate transmission scheme to mitigate the effect of ICI but its BER performance degrades at higher values of CFO. In order to improve its BER performance, a phase rotated symmetric data conjugate transmission scheme to mitigate the effect of ICI has been proposed in this paper. Like symmetric data conjugate scheme, proposed scheme also repeats the same data symbol with phase rotation on two symmetrically located subcarriers to achieve frequency diversity. The analytical expression for optimal phase rotation factor to optimize the carrier-to-interference ratio (CIR) performance of the proposed scheme is also derived. It has been shown that proposed scheme provides better performance than that of the conventional symmetric data conjugate scheme especially at high CFO.

A new blind channel estimation algorithm is proposed based on the finite alphabet property of the transmitted symbols and the correlation between the channel attenuations at the adjacent subcarriers. The new method preserves the classical OFDM transmitter structure with cyclic prefix (CP) used in all standardized multicarrier systems (such as DAB, IEEE802.Ha, Hiperlan/2, etc.) as well as the newly proposed OFDM with zero padding (ZP). The new method proves via simulations to provide an unbiased channel estimates and its performance is independent of the channel zero locations.

Space-time codes and OFDM represent a key technology for future broadband wireless communication systems. In this paper a class of space-time block codes according to Marc Kuhn (2002) in combination with OFDM, is used which can lead to intersymbol interference (ISI) due to an optimized diversity performance. Therefore ISI compensation is an important task of the decoding process of these codes. Several known ISI compensation methods can he applied for example MMSE equalization or parallel or serial ISI cancellation. In this publication a new scalable decoder that works according to the parallel ISI cancellation method is presented. This decoder bases on a cascade structure with two branches for each cascade stage. The sub decoders in each branch were calculated coupledly according to the MMSE criterion. The inputs of the cascade are the receiver symbols and an estimation of the transmitter symbols realized with a MMSK estimator. The prioritization of the branches is made through the a priori symbol error probability. This probability bases on the transmitter symbols estimation. By varying the number of the cascade stages or the block length the performance of the decoder can be scaled.