Noncooperative and Cooperative Transmission Schemes with Precoding and Beamforming

The next generation mobile networks are expected to provide multimedia applications with a high quality of service. On the other hand, interference among multiple base stations (BS) that co-exist in the same location limits the capacity of wireless networks. In conventional wireless networks, the base stations do not cooperate with each other. The BSs transmit individually to their respective mobile stations (MS) and treat the transmission from other BSs as interference. An alternative to this structure is a network cooperation structure. Here, BSs cooperate with other BSs to simultaneously transmit to their respective MSs using the same frequency band at a given time slot. By doing this, we significantly increase the capacity of the networks. This thesis presents novel research results on a noncooperative transmission scheme and a cooperative transmission scheme for multi-user multiple-input-multiple-output orthogonal frequency division multiplexing (MIMO-OFDM). We first consider the performance limit of a noncooperative transmission scheme. Here, we propose a method to reduce the interference and increase the throughput of orthogonal frequency division multiplexing (OFDM) systems in co-working wireless local area networks (WLANs) by using joint adaptive multiple antennas(AMA) and adaptive modulation (AM) with acknowledgement (ACK) Eigen-steering. The calculation of AMA and AM are performed at the receiver. The AMA is used to suppress interference and to maximize the signal-to-interference-plus-noise ratio (SINR). The AM scheme is used to allocate OFDM sub-carriers, power, and modulation mode subject to the constraints of power, discrete modulation, and the bit error rate (BER). The transmit weights, the allocation of power, and the allocation of sub-carriers are obtained at the transmitter using ACK Eigen-steering. The derivations of AMA, AM, and ACK Eigen-steering are shown. The performance of joint AMA and AM for various AMA configurations is evaluated through the simulations of BER and spectral efficiency (SE) against SIR. To improve the performance of the system further, we propose a practical cooperative transmission scheme to mitigate against the interference in co-working WLANs. Here, we consider a network coordination among BSs. We employ Tomlinson Harashima precoding (THP), joint transmit-receive beamforming based on SINR (signal-to-interference-plus-noise-ratio) maximization, and an adaptive precoding order to eliminate co-working interference and achieve bit error rate (BER) fairness among different users. We also consider the design of the system when partial channel state information (CSI) (where each user only knows its own CSI) and full CSI (where each user knows CSI of all users) are available at the receiver respectively. We prove analytically and by simulation that the performance of our proposed scheme will not be degraded under partial CSI. The simulation results show that the proposed scheme considerably outperforms both the existing noncooperative and cooperative transmission schemes. A method to design a spectrally efficient cooperative downlink transmission scheme employing precoding and beamforming is also proposed. The algorithm eliminates the interference and achieves symbol error rate (SER) fairness among different users. To eliminate the interference, Tomlinson Harashima precoding (THP) is used to cancel part of the interference while the transmit-receive antenna weights cancel the remaining one. A new novel iterative method is applied to generate the transmit-receive antenna weights. To achieve SER fairness among different users and further improve the performance of MIMO systems, we develop algorithms that provide equal SINR across all users and order the users so that the minimum SINR for each user is maximized. The simulation results show that the proposed scheme considerably outperforms existing cooperative transmission schemes in terms of the SER performance and complexity and approaches an interference free performance under the same configuration. We could improve the performance of the proposed interference cancellation further. This is because the proposed interference cancellation does not consider receiver noise when calculating the transmit-receive weight antennas. In addition, the proposed scheme mentioned above is designed specifically for a single-stream multi-user transmission. Here, we employ THP precoding and an iterative method based on the uplink-downlink duality principle to generate the transmit-receive antenna weights. The algorithm provides an equal SINR across all users. A simpler method is then proposed by trading off the complexity with a slight performance degradation. The proposed methods are extended to also work when the receiver does not have complete Channel State Informations (CSIs). A new method of setting the user precoding order, which has a much lower complexity than the VBLAST type ordering scheme but with almost the same performance, is also proposed. The simulation results show that the proposed schemes considerably outperform existing cooperative transmission schemes in terms of SER performance and approach an interference free performance. In all the cooperative transmission schemes proposed above, we use THP to cancel part of the interference. In this thesis, we also consider an alternative approach that bypasses the use of THP. The task of cancelling the interference from other users now lies solely within the transmit-receive antenna weights. We consider multiuser Gaussian broadcast channels with multiple antennas at both transmitter and receivers. An iterative multiple beamforming (IMB) algorithm is proposed, which is flexible in the antenna configuration and performs well in low to moderate data rates. Its capacity and bit error rate performance are compared with the ones achieved by the traditional zero-forcing method

Interference-Limited Ultra-Reliable and Low-Latency Communications: Graph Neural Networks or Stochastic Geometry?

In this paper, we aim to improve the Quality-of-Service (QoS) of Ultra-Reliability and Low-Latency Communications (URLLC) in interference-limited wireless networks. To obtain time diversity within the channel coherence time, we first put forward a random repetition scheme that randomizes the interference power. Then, we optimize the number of reserved slots and the number of repetitions for each packet to minimize the QoS violation probability, defined as the percentage of users that cannot achieve URLLC. We build a cascaded Random Edge Graph Neural Network (REGNN) to represent the repetition scheme and develop a model-free unsupervised learning method to train it. We analyze the QoS violation probability using stochastic geometry in a symmetric scenario and apply a model-based Exhaustive Search (ES) method to find the optimal solution. Simulation results show that in the symmetric scenario, the QoS violation probabilities achieved by the model-free learning method and the model-based ES method are nearly the same. In more general scenarios, the cascaded REGNN generalizes very well in wireless networks with different scales, network topologies, cell densities, and frequency reuse factors. It outperforms the model-based ES method in the presence of the model mismatch.Comment: Submitted to IEEE journal for possible publicatio

Spectrum Sharing in Multi-Tenant 5G Cellular Networks: Modelling and Planning

A Multi-tenant cellular network is a paradigm where the physical infrastructure of the network is leased by various big industries, e.g., power utilities and transportation. Hence, a major challenge in a multi-tenant cellular network is the efficient allocation of the physical spectrum to various tenants with broadly distinct Quality-of-Service (QoS) requirements and communications traffic characteristics. In this paper, we approach this issue by presenting a versatile spectrum sharing scheme, which may be deployed to model any spectrum sharing strategy between various tenants in a multi-tenant cellular network. The proposed spectrum sharing scheme is based upon a queuing system that considers the various communications traffic characteristics of the tenants. In addition, by using the developed queuing system, mathematical expressions for the blocking probability and spectrum utilisation are derived.We then propose an optimal spectrum planning scheme, referred to as Reservation-Based Sharing (RBS) policy, that maximises the spectrum utilisation by allocating the spectrum resources to various tenants according to their traffic loads. The computational complexity of the optimal RBS policy is reduced by developing a learning automata technique, referred to as pursuit learning based RBS policy. By using real traffic parameters for various tenants, the results show that the simulation and analytical results match well, ensuring the accuracy of the proposed analytical model. Moreover, the results indicate that the proposed pursuit learning based RBS policy firmly matches the optimal solution and delivers a higher spectrum utilisation that increases linearly with the number of tenants

Pursuit Learning-Based Joint Pilot Allocation and Multi-Base Station Association in a Distributed Massive MIMO Network

2013 IEEE. Pilot contamination (PC) interference causes inaccurate user equipment (UE) channel estimations and significant signal-to-interference ratio (SINR) degradations. Pilot allocation and multi-base-station (BS) association have been used to combat the PC effect and to maximize the network spectral efficiency. However, current approaches solve the pilot allocation and multi-BS association separately. This leads to a sub-optimal solution. In this paper, we propose a parallel pursuit-learning-based joint pilot allocation and multi-BS association. We first formulate the pilot allocation and multi-BS association problem as a joint optimization function. To solve the optimization function, we use a parallel optimization solver, based on a pursuit learning algorithm, that decomposes the optimization function into multiple subfunctions. Each subfunction collaborates with the other ones to obtain an optimal solution by learning from rewards obtained from probabilistically testing random solution samples. A mathematical proof to guarantee the solution convergence is provided. Simulation results show that our scheme outperforms the existing schemes by an average of 18% in terms of the network spectral efficiency

Application of compressive sensing to channel estimation of high mobility OFDM systems

In this paper, we propose a new compressive sensing (CS) based channel estimation method for high mobility orthogonal frequency division multiplexing (OFDM) systems. The proposed scheme offers the benefits of orthogonal matching pursuit (OMP) and subspac