End-to-end (E2E) learning has recently been proposed to jointly design the modulator and symbol detector by using deep neural networks (DNNs). However, existing schemes lack sufficient capability to cancel multi-user interference (MUI) in uplink multi-user multiple-input multiple-output (MU-MIMO) systems. In this paper, we propose a graph neural network (GNN)-based E2E learning scheme that employs a GNN-based modulator to generate learned constellation points, and a GNN-based detector to cancel MUI. They are jointly optimized to minimize the symbol error rate (SER) performance loss. Simulation results demonstrate that the proposed E2E outperforms existing schemes with a predefined modulator. Specifically, it achieves an approximate 2 dB gain in a high MUI environment and surpasses even the maximum-likelihood (ML) detector in a low MUI condition.

Inter-carrier interference (ICI) caused by mobile reflectors significantly degrades the conventional orthogonal frequency division multiplexing (OFDM) performance in high-mobility environments. The orthogonal time frequency space (OTFS) modulation system effectively represents ICI in the delay-Doppler domain, thus significantly outperforming OFDM. Existing iterative and neural network (NN) based OTFS detectors suffer from high complex matrix operations and performance degradation in untrained environments, where the real wireless channel does not match the one used in the training, which often happens in real wireless networks. In this paper, we propose to embed the prior knowledge of interference extracted from the estimated channel state information (CSI) as a directed graph into a decoder untrained neural network (DUNN), namely graph-based DUNN (GDUNN). We then combine it with Bayesian parallel interference cancellation (BPIC) for OTFS symbol detection, resulting in GDUNN-BPIC. Simulation results show that the proposed GDUNN-BPIC outperforms state-of-the-art OTFS detectors under imperfect CSI.

The O-RAN architecture standardized by the O-RAN Alliance does not support the latency requirement of real-time fifth-generation (5G) edge intelligence, typically at a msec level. In this paper, we proposed a deep reinforcement learning (DRL) packet scheduler framework to manage users with different quality of service (QoS) requirements. The DRL framework uses an advantage actor-critic (A2C) algorithm, referred to as a QoS-A2C scheduler. The developed QoS-A2C scheduler is then proposed as an O-RAN real-time App at the edge network. Simulation results show that the latency of the App is at$\mu\sec$level. It also improves the QoS satisfaction level by more than 50% compared to other DRL-based scheduler schemes.

This paper studies the early hybrid automatic repeat request (E-HARQ) in the 5G new radio (NR). The earliest ARQ feedback to the transmitter within 0.2 msec, suitable for ultra-reliable-low-latency (URLLC) services, happens when the feedback indicates retransmission or a new data request is sent before the decoding process. In this case, the feedback is based on predicting the decoding outcome of the codeword bits sent as symbols with a specific modulation and coding scheme (MCS). Existing state-of-the-art neural network-based E-HARQ predictor exploits log-likelihood-ratio (LLR), calculated by the symbol detector, to predict ARQ feedback. They also did not include MCS as an input, so individual predictors are needed for different MCSs. This paper proposes a single NN-based E-HARQ predictor for different MCSs. The predictor has a single hidden layer, and it uses the channel estimates, the MCS information, the redundancy versions, and the approximate probability distribution function of LLRs at the receiver as inputs to predict decoding outcomes. Simulation results show that the proposed predictor reduces the latency of existing NN-based E-HARQ predictors and traditional HARQ by 46% and 60%, respectively. Its complexity is shown to be, on average, 99% lower than other predictors.

Restricted access window (RAW) in Wi-Fi 802.11ah networks manages contention and interference by grouping users and allocating periodic time slots for each group’s transmissions. We will find the optimal user grouping decisions in RAW to maximize the network’s worst-case user throughput. We review existing user grouping approaches and highlight their performance limitations in the above problem. We propose formulating user grouping as a graph construction problem where vertices represent users and edge weights indicate the contention and interference. This formulation leverages the graph’s max cut to group users and optimizes edge weights to construct the optimal graph whose max cut yields the optimal grouping decisions. To achieve this optimal graph construction, we design an actor-critic graph representation learning (AC-GRL) algorithm. Specifically, the actor neural network (NN) is trained to estimate the optimal graph’s edge weights using path losses between users and access points. A graph cut procedure uses semidefinite programming to solve the max cut efficiently and return the grouping decisions for the given weights. The critic NN approximates user throughput achieved by the above-returned decisions and is used to improve the actor. Additionally, we present an architecture that uses the online-measured throughput and path losses to fine-tune the decisions in response to changes in user populations and their locations. Simulations show that our methods achieve$30\%\sim80\%$higher worst-case user throughput than the existing approaches and that the proposed architecture can further improve the worst-case user throughput by$5\%\sim30\%$while ensuring timely updates of grouping decisions.

This paper considers opportunistic scheduler (OS) design using statistical channel state information (CSI). We apply max-weight schedulers (MWSs) to maximize a utility function of users’ average data rates. MWSs schedule the user with the highest weighted instantaneous data rate every time slot. Existing methods require hundreds of time slots to adjust the MWS’s weights according to the instantaneous CSI before finding the optimal weights that maximize the utility function. In contrast, our MWS design requires few slots for estimating the statistical CSI. Specifically, we formulate a weight optimization problem using the mean and variance of users’ signal-to-noise ratios (SNRs) to construct constraints bounding users’ feasible average rates. Here, the utility function is the formulated objective, and the MWS’s weights are optimization variables. We develop an iterative solver for the problem and prove that it finds the optimal weights. We also design an online architecture where the solver adaptively generates optimal weights for networks with varying mean and variance of the SNRs. Simulations show that our methods effectively require 4~10 times fewer slots to find the optimal weights and achieve$5\sim 15\%$better average rates than the existing methods.

Efficient massive/ultra-massive multiple-input multiple-output (MIMO) detection algorithms with satisfactory performance and low complexity are critical to meet the high throughput and ultra-low latency requirements in 5G and beyond communications, given the extremely large number of antennas. In this paper, we propose a low complexity graph neural network (GNN) enhanced approximate message passing (AMP) algorithm, AMP-GNN, for massive/ultra-massive MIMO detection. The structure of the neural network is customized by unfolding the AMP algorithm and introducing the GNN module for multiuser interference cancellation. Numerical results will show that the proposed AMP-GNN significantly improves the performance of the AMP detector and achieves comparable performance as the state-of-the-art deep learning-based MIMO detectors but with reduced computational complexity. Furthermore, it presents strong robustness to the change of the number of users.

In this paper, we design the joint decoding (JD) of non-orthogonal multiple access (NOMA) systems employing short block length codes. We first proposed a low-complexity soft-output ordered-statistics decoding (LC-SOSD) based on a decoding stopping condition, derived from approximations of the a-posterior probabilities of codeword estimates. Simulation results show that LC-SOSD has the similar mutual information transform property to the original SOSD with a significantly reduced complexity. Then, based on the analysis, an efficient JD receiver which combines the parallel interference cancellation (PIC) and the proposed LC-SOSD is developed for NOMA systems. Two novel techniques, namely decoding switch (DS) and decoding combiner (DC), are introduced to accelerate the convergence speed. Simulation results show that the proposed receiver can achieve a lower bit-error rate (BER) compared to the successive interference cancellation (SIC) decoding over the additive-white-Gaussian-noise (AWGN) and fading channel, with a lower complexity in terms of the number of decoding iterations.

Multiuser massive multiple-input multiple-output (MU-MIMO) systems can be used to meet high throughput requirements of 5G and beyond networks. In an uplink MU-MIMO system, a base station is serving a large number of users, leading to a strong multi-user interference (MUI). Designing a high performance detector in the presence of a strong MUI is a challenging problem. This work proposes a novel detector based on the concepts of expectation propagation (EP) and graph neural network, referred to as the GEPNet detector, addressing the limitation of the independent Gaussian approximation in EP. The simulation results show that the proposed GEPNet detector significantly outperforms the state-of-the-art MU-MIMO detectors in strong MUI scenarios with equal number of transmit and receive antennas.

IEEE 802.11ah targets long-range IoT applications where a large number of low power wireless sensor stations are connected to an access point (AP). Rate adaptation (RA) algorithm plays a prominent role in adaptively selecting an appropriate transmission rate in order to maximize throughput in that. Minstrel RA algorithm that uses a random sampling approach has been the defacto RA in industry due to its ability to adapt rate fast in a time-varying channel and non-dependency to the air-interface design used in 802.11ah as compared to other existing RA approaches. However, the throughput performance and convergence time of the Minstrel RA algorithm are still suboptimal due to its random sampling mechanism. In this paper, we propose to improve the Minstrel RA algorithm for the 802.11ah system by designing a shallow neural networks (SNNs) module that predicts a rate input for the random sampling mechanism in Minstrel. The SNNs module is implemented in the access point and consists of several SNNs. Our simulation results in the network simulator-3 (NS-3) show that our proposed algorithm is able to improve the conventional Minstrel algorithm’s convergence time and throughput by around 3.5 times and 40%.

Multi-user multiple-input multiple-output (MU-MIMO) systems can be used to meet high throughput requirements of 5G and beyond networks. A base station serves many users in an uplink MU-MIMO system, leading to a substantial multi-user interference (MUI). Designing a high-performance detector for dealing with a strong MUI is challenging. This paper analyses the performance degradation caused by the posterior distribution approximation used in the state-of-the-art message passing (MP) detectors in the presence of high MUI. We develop a graph neural network based framework to fine-tune the MP detectors’ cavity distributions and thus improve the posterior distribution approximation in the MP detectors. We then propose two novel neural network based detectors which rely on the expectation propagation (EP) and Bayesian parallel interference cancellation (BPIC), referred to as the GEPNet and GPICNet detectors, respectively. The GEPNet detector maximizes detection performance, while GPICNet detector balances the performance and complexity. We provide proof of the permutation equivariance property, allowing the detectors to be trained only once, even in the systems with dynamic changes of the number of users. The simulation results show that the proposed GEPNet detector performance approaches maximum likelihood performance in various configurations and GPICNet detector doubles the multiplexing gain of BPIC detector.

This paper introduces an experimental inter-slice RAN controller to allow resource isolation between slices in 4G/5G networks. The inter-slice RAN controller differs from others in the literature in that it allows radio resources in each transmission time interval (TTI) to be used by different slices, dynamically adjusted according to slice feedback to the controller. The inter-slice controller also allows different scheduling strategies to be used for different slices. The proof-of-concept is then implemented by using Software Defined Radio (SDR) and srsLTE software suite. The experimental results show the effectiveness of the RAN inter-slice controller in effectively managing radio resources for different slices.

Recently, the orthogonal time frequency space (OTFS) modulation is proposed for 6G wireless system to deal with high Doppler spread. The high Doppler spread happens when the transmitted signal is reflected towards the receiver by fast moving objects (e.g. high speed cars), which causes inter-carrier interference (ICI). Recent state-of-the-art OTFS detectors fail to achieve an acceptable bit-error-rate (BER) performance as the number of mobile reflectors increases which in turn, results in high inter-carrier-interference (ICI). In this paper, we propose a novel detector for OTFS systems, referred to as the Bayesian based parallel interference and decision statistics combining (B-PIC-DSC) OTFS detector that can achieve a high BER performance, under high ICI environments. The B-PIC-DSC OTFS detector employs the PIC and DSC schemes to iteratively cancel the interference, and the Bayesian concept to take the probability measure into the consideration when refining the transmitted symbols. Our simulation results show that in contrast to the state-of-the-art OTFS detectors, the proposed detector is able to achieve a BER of less than 10−5, when SNR is over 14 dB, under high ICI environments.

Cell-free (CF) massive multiple-input multiple-output (M-MIMO) technology plays a prominent role in the beyond fifth-generation (5G) networks. However, designing a high performance CF M-MIMO detector is a challenging task due to the presence of pilot contamination which appears when the number of pilot sequences is smaller than the number of users. This work proposes a CF M-MIMO detector referred to as CF expectation propagation (CF-EP) that incorporates the pilot contamination when calculating the posterior belief. The simulation results show that the proposed detector achieves significant improvements in terms of the bit-error rate and sum spectral efficiency performances as compared to the ones of the state-of-the-art CF detectors.

The Industrial Internet-of-Thing (IIoT) has been considered as one of the most challenging application scenarios in future wireless networks. In this paper, we investigate how to improve the overall reliability of massive IIoT networks by optimizing user association. Specifically, the decoding error probability in the physical layer and the collision probability with grant-free random access are taken into account. We first propose a centralized optimization algorithm to achieve a good balance between decoding errors and collisions. To reduce computational complexity and communication overheads of the centralized optimization algorithm, a deep neural network (DNN) is trained offline in the central server and executed by each user in a distributed manner. Our results show that the communication overheads of the distributed DNN do not increase with the number of users, and the reliability achieved by the distributed DNN is close to the centralized optimization algorithm. In addition, the distributed DNN can reduce the packet loss probability by 40% when compared with an existing policy, where each user is connected to the base station with the highest signal-to-noise ratio.

In this paper, we develop a knowledge-assisted deep reinforcement learning (DRL) algorithm to design wireless schedulers in the fifth-generation (5G) cellular networks with time-sensitive traffic. Since the scheduling policy is a deterministic mapping from channel and queue states to scheduling actions, it can be optimized by using deep deterministic policy gradient (DDPG). We show that a straightforward implementation of DDPG converges slowly, has a poor quality-of-service (QoS) performance, and cannot be implemented in real-world 5G systems, which are non-stationary in general. To address these issues, we propose a theoretical DRL framework, where theoretical models from wireless communications are used to formulate a Markov decision process in DRL. To reduce the convergence time and improve the QoS of each user, we design a knowledge-assisted DDPG (K-DDPG) that exploits expert knowledge of the scheduler design problem, such as the knowledge of the QoS, the target scheduling policy, and the importance of each training sample, determined by the approximation error of the value function and the number of packet losses. Furthermore, we develop an architecture for online training and inference, where K-DDPG initializes the scheduler off-line and then fine-tunes the scheduler online to handle the mismatch between off-line simulations and non-stationary real-world systems. Simulation results show that our approach reduces the convergence time of DDPG significantly and achieves better QoS than existing schedulers (reducing 30% ~ 50% packet losses). Experimental results show that with off-line initialization, our approach achieves better initial QoS than random initialization and the online fine-tuning converges in few minutes.

The stringent requirements on reliability and processing delay in the fifth-generation (5G) cellular networks introduce considerable challenges in the design of massive multiple-input-multiple-output (M-MIMO) receivers. The two main components of an M-MIMO receiver are a detector and a decoder. To improve the trade-off between reliability and complexity, a Bayesian concept has been considered as a promising approach that enhances classical detectors, e.g. minimum-mean-square-error detector. This work proposes an iterative M-MIMO detector based on a Bayesian framework, a parallel interference cancellation scheme, and a decision statistics combining concept. We then develop a high performance M-MIMO receiver, integrating the proposed detector with a low complexity sequential decoding for polar codes. Simulation results of the proposed detector show a significant performance gain compared to other low complexity detectors. Furthermore, the proposed M-MIMO receiver with sequential decoding ensures one order magnitude lower complexity compared to a receiver with stack successive cancellation decoding for polar codes from the 5G New Radio standard.

To accommodate diverse Quality-of-Service (QoS) requirements in 5th generation cellular networks, base stations need real-time optimization of radio resources in time-varying network conditions. This brings high computing overheads and long processing delays. In this work, we develop a deep learning framework to approximate the optimal resource allocation policy that minimizes the total power consumption of a base station by optimizing bandwidth and transmit power allocation. We find that a fully-connected neural network (NN) cannot fully guarantee the QoS requirements due to the approximation errors and quantization errors of the numbers of subcarriers. To tackle this problem, we propose a cascaded structure of NNs, where the first NN approximates the optimal bandwidth allocation, and the second NN outputs the transmit power required to satisfy the QoS requirement with given bandwidth allocation. Considering that the distribution of wireless channels and the types of services in the wireless networks are non-stationary, we apply deep transfer learning to update NNs in non-stationary wireless networks. Simulation results validate that the cascaded NNs outperform the fully connected NN in terms of QoS guarantee. In addition, deep transfer learning can reduce the number of training samples required to train the NNs remarkably.

Much stringent reliability and processing latency requirements in ultra-reliable-low-latency-communication (URLLC) traffic make the design of linear massive multiple-input-multiple-output (M-MIMO) receivers becomes very challenging. Recently, Bayesian concept has been used to increase the detection reliability in minimum-mean-square-error (MMSE) linear receivers. However, the latency processing time is a major concern due to the exponential complexity of matrix inversion operations in MMSE schemes. This paper proposes an iterative M-MIMO receiver that is developed by using a Bayesian concept and a parallel interference cancellation (PIC) scheme, referred to as a linear Bayesian learning (LBL) receiver. PIC has a linear complexity as it uses a combination of maximum ratio combining (MRC) and decision statistic combining (DSC) schemes to avoid matrix inversion operations. Simulation results show that the bit-error-rate (BER) and latency processing performances of the proposed receiver outperform the ones of MMSE and best Bayesian-based receivers by minimum 2 dB and 19 times for various M-MIMO system configurations.

Pilot contamination (PC) interference causes an inaccurate user equipment's (UE) channel estimations and significant signal-to-interference ratio (SINR) degradations. To combat the PC effect and to maximize network spectral efficiency, pilot allocation can be combined with multi-Base Station (BS) association and then solved by using learning algorithm efficiently. However, current methods separate the pilot allocation and multi-BS association in the network. This results in suboptimal network spectral efficiency performance and can cause an outage where some UEs are not allocated pilots due to the limited availability of pilots at each BS. In this paper, we propose a multi-BS association and pilot allocation optimization via pursuit learning. Here, we design a parallel pursuit learning algorithm that decomposes the optimization function into smaller entities called learning automata. Each learning automaton computes the joint pilot allocation and BS association solution in parallel, by using the reward from the environment. Simulation results show that our scheme outperforms the existing schemes and does not cause an outage.

The current Downlink Shared Channel (DLSCH) resource scheduler for cellular networks has the following features: 1) it is integrated with an evolved NodeB (eNB) and 2) uses proprietary interfaces. The first causes a temporary outage whenever the scheduler logic is reprogrammed to accommodate traffic profiles that have different requirements, while the latter prevents multi-vendor interoperability. In this paper, we propose a real-time vendor-neutral programmable DLSCH scheduler architecture. The scheduler and eNB are separated into two binary files that communicate via an agent. The agent uses standard interfaces to interpret information from/to different eNB vendors in real time. The proposed architecture is implemented on two open source 3rd Generation Partnership Project standard-compliant eNB stacks from the OAI and SRS. Experimental results show that the proposed architecture addresses the real time and proprietary challenges mentioned above.

The order-of-magnitude increase in the dimension of antenna arrays, which forms extra-large-scale massive multiple-input-multiple-output (MIMO) systems, enables substantial improvement in spectral efficiency, energy efficiency, and spatial resolution. However, practical challenges, such as excessive computational complexity and excess of baseband data to be transferred and processed, prohibit the use of centralized processing. A promising solution is to distribute baseband data from disjoint subsets of antennas into parallel processing procedures coordinated by a central processing unit. This solution is called subarray-based architecture. In this work, we extend the application of expectation propagation (EP) principle, which effectively balances performance and practical feasibility in conventional centralized MIMO detector design, to fit the subarray-based architecture. Analytical results confirm the convergence of the proposed iterative procedure and that the proposed detector asymptotically approximates Bayesian optimal performance under certain conditions. The proposed subarray-based EP detector is reduced to centralized EP detector when only one subarray exists. In addition, we propose additional strategies for further reducing the complexity and overhead of the information exchange between parallel subarrays and the central processing unit to facilitate the practical implementation of the proposed detector. Simulation results demonstrate that the proposed detector achieves numerical stability within few iterations and outperforms its counterparts.

Multi-tenant multi-antenna base stations (MBS) allow multiple vertical industries or operators, acting as tenants, to run their networks on a single BS. The current implementation assumes spectrum orthogonality to ensure tenant network isolation. We propose an MBS without spectrum orthogonality requirement. A new duality concept is developed to allocate precoding weights and transmit power for MBS so that the minimum isolation level from interference between tenants is maximised. An MBS prototype is built and the over-the-air experiments show it provides a higher minimum isolation level and network capacity than other known algorithms.

The upcoming fifth-generation (5G) cellular networks and beyond are expected to support various new application scenarios with diverse quality-of-service requirements. In this work, we study how to allocate the transmit power and the number of subcarriers in a 5G New Radio system with delay-tolerant, delay-sensitive, and ultra- reliable and low-latency services. We first formulate an optimization framework with different kinds of services, and then propose a low- complexity algorithm to maximize the number of users that can be supported in this system. In addition, we study the optimality conditions of the proposed algorithm. The theoretical analysis shows that the conditions hold for delay-tolerant and delay-sensitive services. For ultra-reliable and low-latency services, we prove that the conditions hold when the number of antennas at the base station is large. Numerical results validate that the conditions hold even when the number of antennas is small. Simulation results show that compared with an existing method, our algorithm can support more users with a lower computing complexity.

In this paper, we study the cooperative beamforming schemes for multi-cell multi-user full-dimensional (FD) massive multiple-input multiple-out (MIMO) networks. In the considered network, base stations (BSs) work together to direct their beams to user equipments (UEs) such that inter-cell interference is minimized and each UE is assigned a beam from one BS by user association. We propose to maximize the network capacity by jointly optimizing the beamforming vectors and the user association factors (UAFs), which is further transformed into an optimization on UAFs only by maximizing a lower bound of the signal-to-leakage-and-noise ratio (SLNR). To solve the optimization on UAFs, we propose a belief propagation (BP) based algorithm to obtain the UAFs at the UE level in a parallel manner. Simulation results show that the proposed cooperative beamforming method significantly outperforms the benchmarks in the literature.

In this paper, we consider a mobile edge computing system with both ultra-reliable and low-latency communications services and delay tolerant services. We aim to minimize the normalized energy consumption, defined as the energy consumption per bit, by optimizing user association, resource allocation, and offloading probabilities subject to the quality-of-service requirements. The user association is managed by the mobility management entity (MME), while resource allocation and offloading probabilities are determined by each access point (AP). We propose a deep learning (DL) architecture, where a digital twin of the real network environment is used to train the DL algorithm off-line at a central server. From the pre-trained deep neural network (DNN), the MME can obtain user association scheme in a real-time manner. Considering that the real networks are not static, the digital twin monitors the variation of real networks and updates the DNN accordingly. For a given user association scheme, we propose an optimization algorithm to find the optimal resource allocation and offloading probabilities at each AP. The simulation results show that our method can achieve lower normalized energy consumption with less computation complexity compared with an existing method and approach to the performance of the global optimal solution.

Multi-tenant cellular networks for Internet-of-Things (IoT) services is an architecture in which a single wireless cellular network is tenanted by multiple large-scale IoT sectors such as energy and transportation. Thus, a challenging issue is how to efficiently allocate the spectrum resources to serve numerous IoT tenants with vastly different traffic load distributions. In this paper, we address this problem by proposing a spectrum sharing model, based on a queuing system, that can be used to model any spectrum sharing policy between various tenants and derive analytical expressions for the blocking probability and spectrum utilisation. The simulation and analytical results, generated by using a real-time traffic, match very well.

Multi-tenant BS (MBS) is a new architecture to improve network capacity in which a single BS is tenanted to multiple operators. MBS allows multiple tenants to transmit from a single BS by using their own spectrum resources, resulting in a closer proximity to UEs. In this paper, we propose a UEs to MBSs association scheme, executed in the centralised controller of wireless networks. Each UE can be associated with more than one MBS. We first formulate an optimisation problem that maximises the spectral efficiency of a MBS network with UEs to MBSs associations as its optimisation variables. To solve it, we develop an optimisation solver based on a pursuit learning technique where we model each optimisation variable as a learning automaton. The automata system is executed at the centralised controller where each automaton computes the UE to MBS association in parallel. This results in an extremely low computational complexity and high scalability as compared to other existing schemes. Furthermore, simulation results show that the MBS network has significantly higher spectral efficiency when compared to a single tenant BS network.

This paper considers a user association (UA) design for the base station (BS) and subcarrier (SC) allocation where a BS allocates different number of SCs to different users associated to it in a downlink heterogeneous cellular network. In jointly optimising the UA and SC allocation, we propose to use the Sharpe Ratio as the utility function for the optimisation objective. The Sharpe ratio is defined as the ratio between the mean of user achievable rates to its standard deviation. With this objective, the achieved user rates will be closer to each other, leading to a fair network access. To reduce the computational complexity of the solution, a simplified method based on binary Belief Propagation (BP) algorithm is proposed. Simulation results show that the achievable user rates are doubled in comparison with other schemes. The low computational complexity of the proposed method is achieved through BP solver by reducing the edges of the factor graph.

Cloud-based small cell networks (C-SCNs) have recently been proposed as new wireless cellular architecture. In cloud-based networks, optimization of radio resources at the base station (BS) is moved to a cloud data center for centralized optimization. In the center, multiple processors referred to as the cloud computational unit (CCU) are used for the optimization. As the cell size and networks become, respectively, smaller and denser, the number of BSs to be optimized grows exponentially, resulting in high computational complexity and latency at CCUs. In this paper, we propose belief propagation-based power allocation schemes for C-SCNs that can be used for any network optimization objectives, such as energy consumption minimization at the data center and BSs, and spectral efficiency. The computation for the schemes is distributed across multiple processors and done in parallel, leading to very low latency and computational complexity with increasing number of BSs. We prove mathematically that the messages of the proposed algorithms converge to a fixed point and show via simulation that their performances in terms of spectral and energy efficiencies are close to an exhaustive search solution in finding the best configuration.

Wireless Network Virtualisation (WNV) is a promising approach to address the explosive traffic growth in future mobile networks. In the WNV, the physical spectrum, which is owned by the Network Operator (NO), is shared among multiple Virtual Operators (VOs) according to a predefined sharing policy between the NO and the VOs. In this paper, we develop a unified analytical framework for WNV that is based on a queuing system. By using this framework, we derive analytical expressions for various performance metrics, e.g., the blocking probability, and use them to evaluate the performance of the WNV under different sharing policies. The analytical and simulations results agree very well, confirming that the framework is accurate and showing its suitability to serve as a tool to design an efficient policy for sharing the physical spectrum in the WNV.

A small cell networks (SCN) concept has been widely accepted as the most efficient method to increase cellular network capacity. As the cell size and networks become smaller and denser, respectively, inter-cell interference (ICI) at a user terminal equipment (UE), coming from the adjacent base station (BS) transmissions, grows considerably and becomes more complex to manage. In this paper, we develop a distributed cooperative downlink power allocation algorithm for SCN that maximises the network sum capacity, subject to the minimum received SINR requirements at the UEs. We first formulate the power optimisation problem, with BSs transmit powers as the variables to be optimised. A factor graph representation for ICI and Belief Propagation (BP) method for the power allocation optimisation are then developed. This optimisation representation allows each BS to cooperate by exchanging messages about the estimates of the sum capacity that can satisfy minimum SINR requirements at UEs. Each BS uses this information to optimise its transmit power allocation. To reduce the overhead information that needs to be exchanged by the BSs, we allow only a subset of BSs in the network, chosen randomly, to exchange messages. The simulation results show that the network sum capacity obtained by the proposed algorithm, with only 70% randomly chosen active BSs, is close to the one obtained by using a global optimal exhaustive search method and it outperforms the best existing scheme.

The most challenging issue in Machine-to-Machine (M2M) last mile wireless access networks is the management of a large number of M2M devices that generate a vast amount of M2M traffic. The communications traffic for various M2M applications or services is classified as Fixed-Scheduling (FS) or Event-Driven (ED). The FS traffic is an operational traffic, which occurs on a periodic basis, such as reports on the measured data, sent by M2M sensor devices. The ED traffic, which is assumed to have a higher priority, is triggered by occurrence of specific events, such as the traffic generated by M2M devices, due to failure in the monitored systems. To date, we have not seen any traffic model for M2M last mile wireless access networks, which incorporates the different characteristics of ED and FS traffic. Thus, in this paper, we develop an analytical traffic model for M2M last mile wireless access networks based on a priority queuing system, which considers the combination of both periodic FS and random ED traffic. By using the proposed model, we derive expressions for the mean queuing delay and blocking probability of each traffic class. The derived analytical expressions are validated by simulations of a wireless network model and are shown to agree very well.

In this paper, we propose an uplink channel- and buffer-aware scheduling and resource allocation algorithm for a multi-cell LTE-A network that exploits the number of bits waiting for transmission in each user's buffer, referred to as buffer length, in addition to the wireless channel state information. The algorithm also takes into account the constraints imposed by the 3GPP standards on how the radio Resource Blocks (RBs) are allocated to the users in an LTE-A uplink deploying the Single Carrier Frequency Division Multiple Access (SCFDMA) scheme. These constraints state that a RB can only be assigned to one user and that all RBs assigned to the same user should be adjacent and have the same Modulation and Coding Scheme (MCS). Simulation results show that the proposed algorithm outperforms the existing schemes by at least 35% in terms of the system throughput.

The most challenging issue in Smart Grid (SG) communications networks is the management of a vast amount of SG traffic in the access network, which connects power substations to a large number of SG monitoring devices. In this paper, we develop an analytical traffic model for SG access networks based on a priority queuing system. The SG traffic in the access network is classified as Fixed-Scheduling (FS) or Event-Driven (ED). The FS traffic is an operational traffic, which occurs on a periodic basis, such as smart meter readings. The ED traffic, which is assumed to have a higher priority, occurs as a response to electricity supply conditions, such as demand response. To date, we have not seen any traffic model for SG access networks, which incorporates the different characteristics of ED and FS traffic. By using the proposed model, we derive expressions for the mean buffer length and queuing delay of each traffic. The derived analytical expressions are validated by a wireless network model with real-world traffic profiles from the Ausgrid Smart Grid Smart City project and shown to agree well with the simulations.

Small cell networks (SCN) concept has been widely accepted as the most efficient method to increase cellular network capacity. As the cell size and networks become smaller and denser, respectively, inter-cell interference (ICI) at a user terminal equipment (UE), coming from the adjacent base station (BS) transmissions to their respective UEs, grows considerably and becomes more complex to manage. In this paper, we developed a distributed cooperative downlink power allocation algorithm for SCN that maximises the number of BSs transmissions to UEs such that the received signal-to-interference-plus-noise ratio (SINR) at the UEs is greater than a minimum SINR threshold for wireless transmissions. We first formulate the BS power allocation problem with BS transmit power as binary variables to indicate whether the BS is on or off. A factor graph representation and Belief Propagation (BP) method based on a sum-product approach for power allocation optimisation are developed. This optimisation representation allows each BS to cooperate by exchanging messages about the probability distribution of the number of BS transmissions. BS then uses this information to optimise its own transmit power allocation. To reduce the overhead information that needs to be exchanged by the BSs, we allow only a subset of randomly chosen BSs in the network to exchange messages. The simulation results show that the number of BSs transmissions obtained by the proposed algorithm is on average 5% less than the one obtained by using a global optimal exhaustive search method.

A dynamic resource allocation algorithm is investigated for the orthogonal frequency division multiplexing access (OFMDA) system under the network architecture of High-Speed Railway (HSR). The mobile base station (MBS) on the top of the train forwards the signal received from the base station (BS) on the ground to the user equipments (UE) in the train. Due to the high mobility in the BS-MBS link, the inter-carrier interference (ICI) caused by the Doppler shift may degrade the system performance. In this paper, we aim to combat the influence of the ICI to improve the system capacity by means of the resource allocation, which includes subcarrier allocation, subcarrier pairing and power allocation. Simulation results demonstrate that the proposed resource allocation algorithm can improve the system performance dramatically.

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 subspace pursuit (SP) estimation methods combined with an inter-carrier interference (ICI) cancellation process. The proposed CS based channel estimation scheme, referred to as the hybrid pursuit (HP) based channel estimation method, operates in an iterative, decision-directed fashion. Here, in each iteration, once the channel is estimated, data symbols are detected and used to calculate the estimate of ICI, caused by the Doppler spread. After that, the ICI term is subtracted from the received signals. The whole process is then repeated, iteratively. The simulation results assess the performance gains achieved by the proposed scheme over the best known channel estimation methods.

In this paper, we propose an iterative channel estimation and inter-carrier interference (ICI) cancellation method for highly mobile users in Long-Term-Evolution (LTE) systems. The proposed scheme estimates the wireless channel by using pilot symbols, estimates of the data symbols and Doppler spread information at the receiver. The wireless channel is expressed by a weighted time-domain channel interpolation, where the interpolation weights are designed based on the Doppler spread. The channel estimates are obtained by employing a least square (LS) method. A simplified parallel interference cancellation (PIC) scheme coupled with decision statistical combining (DSC) is used to cancel the ICI and to improve the data symbols detection. These data symbols are then utilized to refine the channel estimation further, iteratively. Simulation results are used to verify the effectiveness of the proposed method.

This paper proposes a distributed multiple-access transmission scheme, designed to support the transmission of a small number of active nodes to a central base station when the total number of nodes is large. To achieve this, we propose the integration of multiple antenna technologies with compressed sensing algorithms. This integration is made possible by new precoding weight designs which utilize the multiple transmit antennas to provide a power-gain. We demonstrate that the use of these multiple transmit antennas increases the received signal power, thus enhancing the performance of the compressed sensing algorithm. We also propose the use of multiple receive antennas, which we show reduces the delay. Finally, we compare our distributed scheme with a carrier sense multiple-access scheme, and show that our scheme achieves a lower average delay for even a small number of active nodes.

The new wireless standard, Long-Term-Evolution (LTE), needs to support high data rate orthogonal frequency division multiplexing (OFDM) transmission for highly mobile users. Due to users' mobility, the wireless channel becomes time-variant and frequency-selective. The symbol transmission is thus impaired by Doppler spread. As a consequence, the known channel estimation methods do not give satisfactory performances. In this paper, we propose an iterative channel estimation and intercarrier interference (ICI) cancellation method that estimates the wireless channel by utilizing pilot symbols, estimates of the data symbols and Doppler spread information at the receiver. The wireless channel is expressed by a weighted time-domain channel interpolation, where the interpolation weights are designed based on the Doppler spread and time-domain channel correlations. The channel estimates are obtained by employing a least square (LS) method. Once the channel is estimated, the ICI is cancelled by performing a zero-forcing technique. Data symbols are then estimated by a detector. The estimates of data symbols are used to refine the estimation of channel coefficients, iteratively. The simulation results show that the performance degradation of the proposed scheme, when users move at the speed of up to 324 Km/h, compared to a system when users are static and perfect channel state information (CSI) is available at the receiver, is minimal.

In 3GPP Long Term Evolution (LTE) networks, the frequency reuse schemes such as fractional frequency reuse (FFR) and soft frequency reuse (SFR) are used to improve system capacity. The allocation of transmit power and subcarriers to each cell in these schemes are fixed prior to network deployment. This limits the potential performance of these frequency reuse schemes. In this paper, we propose to improve the capacity of SFR scheme by jointly optimizing subcarrier and power allocation in multi-cell LTE networks. An iterative algorithm that can adaptively vary the number of major subcarriers and adjust the transmit power for each cell according to wireless traffic loads is proposed. Simulation results show that the proposed algorithm outperforms the existing Reuse 1, FFR and static SFR schemes in both system throughput and cell edge user performance.

In this paper, we investigate a cooperative transmission method employing precoding, beamforming and power allocation for a multi-user multiple-input multiple-output (MU-MIMO) relay network. In the proposed scheme, the interference is canceled by using the combination of beamforming weights and Tomlinson-Harashima precoding (THP). To achieve symbol error rate (SER) fairness among different users and further improve the performance of MU-MIMO relay networks, we propose a low-complexity power allocation (LC-PA) that allocates power to each user so that the signal-to-interference-and-noise-ratio (SINR) for all users are equal. The simulation results show that the SER performance of the proposed scheme considerably outperforms other existing schemes under the same configuration.

Inter-cell interference coming from multiple base stations (BS) in adjacent cells limits the capacity of wireless cellular networks. In this paper, we exploit the uplink-downlink duality principle and BS cooperation to design linear and nonlinear downlink inter-cell interference management techniques for multi-user multiple-input-multiple-output (MIMO) systems. The proposed linear algorithm can effectively effectively eliminate the inter-cell interference and achieves bit-error-rate (BER) fairness among different users. The simulation results show that the BER performance of the proposed schemes outperforms existing cooperative transmission schemes and approaches an interference free performance under the same configuration.

For a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system operating in high mobility scenarios, channel estimation becomes a challenging issue, due to fast channel variation and severe inter-carrier interference (ICI). In this paper, we propose a novel pilot-aided iterative receiver, based on pilot symbols and iterative soft-estimate of data symbols. The channel is estimated by time-domain interpolation and least-square (LS) methods. Soft-estimate for data symbols are obtained by a maximum-a-posteriori (MAP) decoder and improved subsequently. The simulation results show that the performance of the proposed iterative receiver outperforms the existing schemes. The performance degradation of the proposed receiver structure when users move at speed of up to 324Km/h compared to the performance of a perfect CSI system with a zero Doppler shift is shown to be very marginal.

In this paper, we investigate transmission methods for a multi-user multiple-input multiple-output (MIMO) network that utilizes base stations (BS) cooperation. To eliminate the interference between users, iterative zero forcing- (ZF) and iterative Tomlinson Harashima precoding-based (THP) schemes are proposed. In the iterative ZF-based scheme, all interference is cancelled by using the transmit-receive weights. To reduce the complexity of iterative ZF scheme, in the iterative THP-based scheme, the interference is cancelled by using transmit-receive weights and the THP. To achieve symbol error rate (SER) fairness among different users and further improve the performance of multi-user MIMO relay systems, we develop optimal and sub-optimal power allocation (PA) methods that ensure signal-to-interference-and-noise-ratio (SINR) across all users are equal, under the power constraints at both BSs and relay station (RS). In the optimal PA scheme, the PA is done at both BSs and RS. In the sub-optimal scheme, to reduce the computational complexity of optimal PA significantly, the PA is done only at BSs, and at the RS a power scaling is performed to satisfy the RS power constraint. The simulation results show that by using optimal and sub-optimal PAs, the iterative ZF-based scheme outperforms the iterative THP-based scheme by an average of 0.4 dB at the cost of a four times higher complexity.

Interference among multiple base stations that co-exist in the same location limits the capacity of wireless networks. In this paper, we propose a method to design a spectrally efficient cooperative downlink transmission scheme employing precoding and beamforming for multi-user multiple-input-multiple-output (MIMO) systems. 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 are chosen to cancel the remaining interference. 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 multi-user MIMO systems, we develop algorithms that provide equal signal-to-interference-plus-noise-ratio (SINR) across all users. The users are also ordered 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.

Interference among multiple base stations that co-exist in the same location limits the capacity of wireless networks. In this paper, we propose a nonlinear multi-user MIMO cooperative downlink transmission scheme. 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 part. The uplink-downlink duality principle is used to calculate transmit-receive antenna weights. The proposed scheme is then extended to work when the receiver does not have complete Channel State Informations (CSIs). 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 under the same configuration.

For a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system, time-varying multipath fading of channel destroys the orthogonality among subcarriers and leads to serious intercarrier interference (ICI). The system performance degrades more severely as normalized Doppler frequency increases. In order to mitigate the effect of time-varying fading, a low-complexity iterative receiver with joint ICI cancellation and pilot-assisted channel estimation is proposed. The initial channel state information (CSI) is estimated by performing time-domain interpolation and least-square (LS) method on the received pilot symbols. The soft outputs are obtained from the decoders after low-complexity linear minimum mean-square error (LC-LMMSE) detection. In the following stages, the soft outputs are feedback to update the CSI estimation. Furthermore, a ¿linear statistics combining¿ (LSC) technique is used to improve the performance of the proposed equalizer by combining the outputs of LC-LMMSE and parallel interference canceler (PIC) with weighting coefficients estimated by maximizing the signal to interference-plus-noise ratio (SINR) at the output of LSC. The complexity of system is significantly reduced by restricting the interference to neighboring subcarriers and employing the LC-LMMSE by limiting the frequency-domain CSI into diagonal region. The simulation results show that the proposed iterative receiver with estimated CSI approaches the ICI-free bound even at very high mobility scenarios.

Interference among multiple base stations that coexist in the same location limits the capacity of wireless networks. In this paper, we propose a method to design a spectrally efficient cooperative downlink transmission scheme employing precoding and beamforming. The algorithm eliminates 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 are chosen to cancel the remaining interference. A novel iterative method is applied to generate the transmit-receive antenna weights. The convergence behaviour of the iterative process is investigated. To achieve SER fairness among different users and improve the performance of the system, we develop algorithms that provide equal signal to-interference-plus-noise-ratios (SINR) across all users under both per base station (BS) and total BSs power constraints. Per BS and total BSs power constraints are constraints where the power for each BS and all BSs is limited to a particular value. The simulation results show that the proposed scheme outperforms existing cooperative transmission schemes in terms of the SER performance and complexity and closely approaches an interference free performance under the same configuration.

The interference among multiple access points (APs) that co-exist in the same location, limits the capacity of co-working wireless local area networks (WLANs). In this paper, we propose a practical cooperative transmission scheme to mitigate the interference in co-working WLANs. In particular, we combine 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 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 non-cooperative and cooperative transmission schemes and is only 2 dB away from an interference-free channel under the same configuration.

We consider relay broadcast channels (RBCs) with multiple antennas at all nodes. A practical precoding, relaying and combining scheme is proposed. Under an overall power constraint, we derive the optimal power allocation solution in a closed form. The conditions for an effective RBC scheme are identified with a qualitative analysis to the relationship between the relay gain and the relative strengths of the direct and relay links. Simulation results are shown for various channel configurations, which verify the analysis and conclude that the first hop in the relay chain is a determining factor to the overall performance of the RBC.

In this paper, 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 maximise the signal to noise plus interference ratio (SNIR) by adjusting its weights dynamically. The improved SNIR is then used by AM as an input 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 are evaluated through the simulations of BER and spectral efficiency (SE) against SIR. The simulation results for joint AMA-AM also show that joint AM and receive beamforming produce the most effective solution in co-working OFDM-WLANs