Recent advances in large language models (LLMs) have driven remarkable progress across diverse NLP benchmarks. However, the application of these models to sophisticated domains such as wireless communications raises new challenges. This paper addresses the evaluation gap for LLMs in telecommunications by introducing Last Dance of Telecommunications (LDOT) – a comprehensive benchmark suite for telecom-related tasks. LDOT encompasses a broad range of problem categories, including conceptual telecom questions, mathematical and logical reasoning problems, and complex network optimization scenarios, specifically designed to challenging LLMs’ high-level reasoning and domain-specific knowledge. Using LDOT, we rigorously assess state-of-the-art (SOTA) LLMs (both closed-source and open-source) on domain-specific tasks. Our results reveal that while general-purpose LLMs exhibit strong performance on basic telecom knowledge questions, they struggle with reasoning-intensive wireless problems. Notably, certain multi-step optimization and planning tasks in LDOT remain unsolved by even the best models, exposing performance gaps that are not apparent from existing saturated benchmarks. We provide a detailed failure analysis to pinpoint whether these limitations arise from insufficient telecom-specific knowledge or from inadequate reasoning capabilities. The LDOT dataset and our evaluation findings aim to facilitate the development of more robust domain-adapted LLMs for next-generation wireless communications.

Hierarchical Federated Learning (HFL) has emerged as a promising approach for scalable and communication-efficient model training in wireless networks. However, achieving energy efficiency while ensuring convergence remains challenging due to limited bandwidth resource and strict latency constraints. This paper addresses energy-efficient HFL under both statistical and system heterogeneity, aiming to minimize long-term energy consumption through adaptive and unbiased edge scheduling and resource allocation in dynamic environments. A convergence analysis is first conducted without relying on a convex assumption, explicitly characterizing the influences of the number of scheduled edges and scheduling probabilities. An iterative algorithm is then proposed to jointly optimize these variables: the scheduling probabilities are solved by using a Barrier Method (BM) with an Infeasible-Start Newton Method (ISNM), while the number of scheduled edges is derived in a closed form. To further enhance communication efficiency, Non-Orthogonal Multiple Access (NOMA) is employed at the user–edge layer. Then, a joint optimization of inter-edge bandwidth allocation and intra-edge local resource allocation is developed to balance computation and communication overhead. Extensive simulations demonstrate that the proposed framework significantly outperforms existing benchmarks in terms of energy consumption under Non-Independent and Identically Distributed (Non-IID) data and dynamic wireless environments.

Cell-free massive multiple-input multiple-output (MIMO) utilizes multiple access points (APs) distributed over a large coverage area as distributed antennas to simultaneously serve multiple user equipments (UEs). In order to guarantee its scalability, it is necessary to optimally select AP-UE association clusters, which is a combinatorial problem with high complexity. Although deep reinforcement learning (DRL) has notably emerged as a potential solution, its application to AP-UE association is still challenging due to the difficulty in exploring such large action spaces and slow convergence. Existing work has tackled this challenge by heavily punishing invalid actions in the reward function, which does not scale well. In this paper, an alternative action space shaping approach is proposed, where a tailored mapping algorithm is added to the policy network, transforming invalid actions to feasible ones, thereby ensuring that training time is not wasted in discovering invalid actions. Additionally, we propose to incorporate behavior cloning (BC) with a heuristic teacher to speed up learning. We show that the proposed algorithm can improve the spectral and energy efficiency compared to existing schemes.

With the explosive growth of data traffic and the ubiquitous connectivity of wireless devices, the energy demands of wireless networks have inevitably escalated. Reconfigurable intelligent surfaces (RIS) have emerged as a promising solution for 6G networks due to their energy efficiency (EE) and low cost, while cell-free massive multiple-input multiple-output (CF mMIMO) has been proposed as an innovative network architecture without fixed cell boundaries to enhance these measures even further. However, existing studies often assume consistently high traffic loads, neglecting the dynamic nature of user demand. This can result in underutilized access points (APs) and unnecessary energy expenditure during low-demand periods. To tackle the challenge of EE in CF mMIMO systems under low-load conditions, this paper proposes a novel energy-efficient transmission scheme that jointly coordinates active APs and multiple passive RISs. Specifically, a dynamic AP sleep-mode strategy is designed, where certain APs are selectively deactivated while nearby RISs assist in maintaining coverage. To maximize EE, we formulate the EE maximization as a fractional programming problem and adopt the Dinkelbach method in conjunction with alternating optimization (AO) to iteratively solve the coupled subproblems: (i) AP selection via a hybrid branch-and-bound (BnB) and greedy algorithm, and (ii) RIS phase-shift optimization using gradient projection. Additionally, transmit power is allocated to users through a heuristic zero-forcing strategy. Simulation results show that the proposed scheme achieves significantly higher EE than existing methods in both low and moderate user scenarios.

Large language models (LLMs) have shown great promise in many domains, yet their potential to transform wireless communications, where the escalating complexity of the network outpaces traditional model-based methods, remains largely untapped. Addressing this gap is critical for the next generation of intelligent and adaptive 6G systems. In this work, we develop a specialized dataset aimed at enhancing the evaluation and fine-tuning of LLMs specifically for wireless communication applications. The dataset includes a diverse set of multi-hop questions, including true/false and multiple-choice types, spanning varying difficulty levels from easy to hard. By utilizing advanced language models for entity extraction and question generation, rigorous data curation processes are employed to maintain high quality and relevance. Additionally, we introduce a Pointwise V-Information (PVI) based fine-tuning method, providing a detailed theoretical analysis and justification for its use in quantifying the information content of training data with 2.24% and 1.31% performance boost for different models compared to baselines, respectively. To demonstrate the effectiveness of the fine-tuned models with the proposed methodologies on practical tasks, we also consider different tasks, including summarizing optimization problems from technical papers and solving the mathematical problems related to non-orthogonal multiple access (NOMA), which are generated by using the proposed multi-agent framework. Simulation results show significant performance gain in summarization tasks with 20.9% in the ROUGE-L metrics. We also study the scaling laws of fine-tuning LLMs and the challenges LLMs face in the field of wireless communications, offering insights into their adaptation to wireless communication tasks. This dataset and fine-tuning methodology aim to enhance the training and evaluation of LLMs, contributing to advancements in LLMs for wireless communication research and applications.

Hierarchical Federated Learning (HFL) has been proposed to achieve large-scale model training and more efficient communication, surpassing conventional Federated Learning (FL). However, inappropriate aggregation frequency and edge association in HFL result in excessive energy consumption for users with poor channels or hinder its convergence performance due to stochastic gradient descent (SGD) and Non-Independent and Identical Distribution (NIID) data, which is particularly challenging for energy-limited users. Motivated by this, a joint aggregation frequency and edge association optimization problem is proposed to minimize the long-term energy consumption during HFL training process. The problem can be formulated by incorporating computation, communication model and convergence analysis together. Due to the coupling between control variables, we decompose it into two sub-problems and adopt an iterative algorithm to approximate their optimal solutions. Specifically, the aggregation frequency is optimized under a given edge association by convex optimization to trade-off the computation and communication energy consumption, considering the convergence characteristic and SGD noise. Then, Deep Reinforcement Learning (DRL) is adopted to optimize edge association based on data distribution, dynamic channels and the derived aggregation frequency. Simulation results demonstrate that our proposed strategy achieves the lowest energy consumption while attaining the required model accuracy, outperforming other benchmarks.

Cell-free massive MIMO has emerged as a key technology that is envisioned to play a central role in future wireless networks. It leverages the benefits of massive MIMO by utilizing a large number of access points (APs) distributed over a large coverage area to concurrently serve multiple user equipment (UEs). In its canonical form, each user is served by all the APs which is impractical. It is therefore important to carefully select groups of APs that will participate in serving each UE. In this work, we propose a method to form efficient UE-AP association clusters using matching theory, by modeling the problem as a many-to-many matching with externalities. We consider that the UEs exhibit partially altruistic behavior and therefore select APs in an empathetic way, aiming to improve their neighbor’s rate in addition to their own. Simulation results show that we can improve the system sum spectral efficiency, outage probability and the average energy efficiency compared to existing methods.

This paper investigates wireless federated learning in data heterogeneous scenarios, where device selection usually leads to a degradation in learning performance. This paper is motivated by the fact that while training deep learning networks using federated stochastic gradient descent (FedSGD) on non-independent and identically distributed (non-IID) datasets, device selection can generate gradient errors that accumulate, leading to potential weight divergence, which is further exacerbated with low device participation. To mitigate weight divergence, an age-weighted FedSGD algorithm is designed in this paper to scale local gradients according to the previous device selection results. Furthermore, by revealing the relationship between device participation and latency, an energy consumption minimization problem is formulated accordingly, which consists of resource allocation and sub-channel assignment. By transforming the resource allocation problem into convex and utilizing KKT conditions, we derive the optimal resource allocation solution. Moreover, this paper develops a matching based algorithm to generate the enhanced sub-channel assignment. Simulation results indicate that 1) age-weighted FedSGD is able to outperform conventional FedSGD in terms of convergence rate and achievable accuracy, and 2) the proposed resource allocation and sub-channel assignment strategies can significantly reduce energy consumption and improve learning performance by increasing device participation.

Hierarchical Federated Learning (HFL) has been introduced to enhance the communication efficiency and scalability of traditional Federated Learning (FL). In addition, the integration of Non-Orthogonal Multiple Access (NOMA) into the HFL framework serves to bolster system capacity and spectral efficiency. However, the formidable challenge of energy efficiency persists, particularly in energy-constrained scenarios, which can be further compounded by factors such as Non-Independent and Identical Distribution (NIID) data, varying channels across users, heterogeneous computation and communication resources, and the interference from weak users. Motivated by this, we aim to minimize the sum of the computation and communication energy consumption of all users in the NOMA-based HFL system. This is achieved through a joint optimization of User-Edge Association (UEA) and Resource Allocation (RA). Specifically, we utilize Deep Reinforcement Learning (DRL) to optimize UEA to achieve the objective from a long-term perspective. Subsequently, computation and communication resources are jointly optimized by Newton's Method to balance the computation and communication energy consumption while meeting a given latency requirement. Numerical results show that our strategy significantly improves energy efficiency of the system compared with other benchmarks.

Reconfigurable intelligent surfaces (RISs) have attracted extensive attention in millimeter wave (mmWave) systems because of the capability of configuring the wireless propagation environment. However, due to the existence of a RIS between the transmitter and receiver, a large number of channel coefficients need to be estimated, resulting in more pilot overhead. In this paper, we propose a joint sparse and low-rank based two-stage channel estimation scheme for RIS-assisted mmWave systems. Specifically, we first establish a low-rank approximation model against the noisy channel, fitting in with the precondition of the compressed sensing theory for perfect signal recovery. To overcome the difficulty of solving the low-rank problem, we propose a trace operator to replace the traditional nuclear norm operator, which can better approximate the rank of a matrix. Furthermore, by utilizing the sparse characteristics of the mmWave channel, sparse recovery is carried out to estimate the RIS-assisted channel in the second stage. Simulation results show that the proposed scheme achieves significant performance gain in terms of estimation accuracy compared to the benchmark schemes.

Communication networks are becoming increasingly content-centric, and reusable content like viral information and video on demand are often requested by multiple users. Caching at the user equipment enables device-to-device (D2D) communications to be used to deliver requested contents which will help reduce data traffic at base stations and also enhance the achievable data rates. In this paper, we propose a system whereby users are able to exchange valuable cached content with each other via a D2D link which underlays the reception of a downlink non-orthogonal multiple access (NOMA) signal. We formulated a sum rate maximization problem that is subject to minimum rate constraints and derived optimal solutions depending on which user is the D2D transmitter. Additionally, sub-optimal solutions based on a negligible self-interference assumption are also proposed. Simulation results demonstrate the significant performance gains of underlaid D2D communications as compared with optimal downlink cellular NOMA. Furthermore, results on the self-interference (SI) cancellation factor highlight that the sub-optimal power allocation solution offers sum rate performance close to the optimal case. The performance gains are further enhanced when SI cancellation is high.

In this paper, a federated learning (FL) based system is investigated with non-independent and identically distributed (non-IID) dataset, where multiple devices participate in the global model aggregation through a limited number of sub-channels. By analyzing weight divergence and convergence rate, a new metric is proposed based on age-of-information (AoI), which incorporates latency and can provide an advanced device selection standard. After that, device selection, sub-channel assignment and resource allocation are jointly designed in an overall AoI minimization problem under the maximum energy consumption constraint. The formulated problem is decoupled into two sub-problems. After analyzing the feasibility, the resource allocation problem is transformed to a convex problem, and the closed-from solution is obtained based on KKT conditions. By introducing virtual sub-channels, device selection and sub-channel assignment are jointly solved by a matching based algorithm. Simulation results indicate that the proposed scheme is able to outperform all baselines in terms of both test accuracy and sum AoI, and the developed strategies can achieve significant improvements for all schemes.

Cell-free massive MIMO is a promising technology to meet the requirements of future wireless networks. It uses multiple access points (APs) spread over a large coverage area to serve multiple User Equipment (UEs) simultaneously. In order to make it practical and ensure good performance, it is important to carefully determine which APs should serve particular users. In this work, we propose a method to efficiently select these clusters using matching theory - by modeling the problem as a many-to-many matching with externalities. We consider both the UEs and APs to be altruistic, and therefore select AP clusters with the goal of improving the network sum spectral efficiency, rather than their own rate. Simulation results show that we can improve the outage probability and the average energy efficiency compared to existing methods.

Hierarchical Federated Learning (HFL) has been proposed to achieve larger-scale model training and more efficient communications compared to conventional Federated Learning (FL). However, both inappropriate edge association strategy and aggregation frequency may consume massive energy in users with poor channel conditions or degrade the HFL convergence performance due to Non Independent and Identical Distribution (NIID) data, which is challenging to energy-limited users. Motivated by this, a dynamically joint edge association and aggregation frequency optimization problem is proposed from the perspective of minimizing long-term energy consumption. By incorporating the communication model and convergence analysis, the problem can be formulated to strike a balance between HFL convergence rate and energy consumed by all users within one global communication round. Then, a Deep Reinforcement Learning (DRL) agent is designed to approximate the optimal solution. Simulation results verify the convergence analysis and the proposed DRL-assisted joint strategy can consume the least energy while reaching the required target model accuracy compared to other benchmarks.

Federated Learning (FL) is widely regarded as a leading distributed machine learning paradigm, owing to its outstanding performance in preserving privacy and conserving communication resources. To use it efficiently in wireless communication networks, novel transmission schemes that jointly consider the model propagation and training features are required. In this paper, a novel joint user scheduling and resource allocation scheme is proposed to reduce the communication cost in terms of the weighted sum of energy and time consumption while ensuring the convergence of FL. The time-varying channels and unpredictable model loss in the system make it difficult to use conventional optimization methods for this problem. Furthermore, considering optimal transmission policy in FL is to train a qualified model in the dynamic iterative process, a deep reinforcement learning based Proximal Policy Optimization (PPO) approach is employed to train an automatic policy maker. Specifically, the dynamic policy is decided in each training round based on the observed model accuracy and the time-varying channel gains, aiming at minimizing the total cost. Simulation results verify the proposed scheme can reduce the defined communication cost and improve the training efficiency compared with the traditional greedy and random benchmarks.

Reconfigurable intelligent surface (RIS) is a promising technique that smartly reshapes wireless propagation environment in the future wireless networks. In this paper, we apply RIS to an unmanned aerial vehicle (UAV)-assisted non-orthogonal multiple access (NOMA) network, in which the transmit signals from multiple UAVs to ground users are strengthened through RIS. Our objective is to minimize the power consumption of the system while meeting the constraints of minimum data rate for users and minimum inter-UAV distance. The formulated optimization problem is non-convex by jointly optimizing the position of UAVs, RIS reflection coefficients, transmit power, active beamforming vectors and decoding order, and thus is quite hard to solve optimally. To tackle this problem, we divide the resultant optimization problem into four independent subproblems, and solve them in an iterative manner. In particular, we first consider the sub-solution of UAVs placement which can be obtained via the successive convex approximation (SCA) and maximum ratio transmission (MRT). By applying the Gaussian randomization procedure, we yield the closed-form expression for the RIS reflection coefficients. Subsequently, the transmit power is optimized using standard convex optimization methods. Finally, a dynamic-order decoding scheme is presented for optimizing the NOMA decoding order in order to guarantee fairness among users. Simulation results verify that our designed joint UAV deployment and resource allocation scheme can effectively reduce the total power consumption compared to the benchmark methods, thus verifying the advantages of combining RIS into the multi-UAV assisted NOMA networks.

Combining simultaneous wireless information and power transfer (SWIPT) and an intelligent reflecting surface (IRS) is a feasible scheme to enhance energy efficiency (EE) performance. In this paper, we investigate a multiuser IRS-aided multiple-input single-output (MISO) system with SWIPT. For the purpose of maximizing the EE of the system, we jointly optimize the base station (BS) transmit beamforming vectors, the IRS reflective beamforming vector, and the power splitting (PS) ratios, while considering the maximum transmit power budget, the IRS reflection constraints, and the quality of service (QoS) requirements containing the minimum data rate and the minimum harvested energy of each user. The formulated EE maximization problem is non-convex and extremely complex. To tackle it, we develop an efficient alternating optimization (AO) algorithm by decoupling the original nonconvex problem into three subproblems, which are solved iteratively by using the Dinkelbach method. In particular, we apply the successive convex approximation (SCA) as well as the semi-definite relaxation (SDR) techniques to solve the non-convex transmit beamforming and reflective beamforming optimization subproblems. Simulation results verify the effectiveness of the AO algorithm as well as the benefit of deploying IRS for enhancing the EE performance compared with the benchmark schemes.

Combining simultaneous wireless information and power transfer (SWIPT) and intelligent reflecting surface (IRS) is a feasible scheme to enhance the energy efficiency (EE) performance. In this paper, we investigate a multiuser IRS-aided multiple-input single-output (MISO) system with SWIPT. For the purpose of maximizing the EE of the system, we jointly optimize the base station (BS) transmit beamforming vectors, the IRS reflective beamforming vector and the power splitting (PS) ratios, while considering the maximum transmit power budget, the IRS reflection constraints and the quality of service (QoS) requirements containing the minimum data rate and the minimum harvested energy per user. As the proposed EE maximization problem is non-convex and extremely complex, we propose an efficient alternating optimization (AO) algorithm by decoupling the original problem into three subproblems which are tackled iteratively by using the Dinkelbach method. In particular, we apply the successive convex approximation (SCA) as well as the semi-definite relaxation (SDR) techniques to solve the non-convex transmit beamforming and reflective beamforming optimization subproblems. Numerical results confirm the effectiveness of the AO algorithm as well as the benefit of deploying IRS for enhancing the EE performance compared with the benchmark schemes.

Plane spiral orbital angular momentum (PSOAM) mode-groups (MGs) and multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) serve as two emerging techniques for achieving high spectral efficiency (SE) in the next-generation networks. In this paper, a PSOAM MGs based multi-user MIMO-NOMA system is studied, where the base station transmits data to users by utilizing the generated PSOAM beams. For such scenario, the interference between users in different PSOAM mode groups can be avoided, which leads to a significant performance enhancement. We aim to maximize the energy efficiency (EE) of the system subject to the constraints of the total transmission power and the minimum data rate. This designed optimization problem is non-convex owing to the interference among users, and hence is quite difficult to tackle directly. To solve this issue, we develop a dual layer resource allocation algorithm where the bisection method is exploited in the outer layer to obtain the optimal EE and a resource distributed iterative algorithm is exploited in the inner layer to optimize the transmit power. Besides, an alternative resource allocation algorithm with Deep Belief Networks (DBN) is proposed to cope with the requirement for low computational complexity. Simulation results verify the theoretical findings and demonstrate the proposed algorithms on the PSOAM MGs based MIMO-NOMA system can obtain a better performance comparing to the conventional MIMO-NOMA system in terms of EE.

The intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) communication system has emerged as a promising technology for coverage extension and capacity enhancement. Prior works on IRS have mostly assumed perfect channel state information (CSI), which facilitates in deriving the upper-bound performance but is difficult to realize in practice due to passive elements of IRS without signal processing capabilities. In this paper, we propose a compressive channel estimation techniques for IRS-assisted mmWave multi-input and multi-output (MIMO) system. To reduce the training overhead, the inherent sparsity of mmWave channels is exploited. By utilizing the properties of Kronecker products, IRS-assisted mmWave channel is converted into a sparse signal recovery problem, which involves two competing cost function terms (measurement error and sparsity term). Existing sparse recovery algorithms solve the combined contradictory objectives function using a regularization parameter, which leads to a suboptimal solution. To address this concern, a hybrid multiobjective evolutionary paradigm is developed to solve the sparse recovery problem, which can overcome the difficulty in the choice of regularization parameter value. Simulation results show that under a wide range of simulation settings, the proposed method achieves competitive error performance compared to existing channel estimation methods.

Coded multicasting is considered to be an effective approach to simultaneously serve multiple users in the same frequency/time/code resource, which is made possible by exploiting the information available in users’ caches. Recently, non-orthogonal multiple access (NOMA) has emerged as an alternative transmission technique for cache-aided network. In cache-aided NOMA system, cache-enabled interference cancellation (CIC) is employed to cancel the interference using the information in the cache, and thus enhance the user transmission rate, particularly for the weak users. Despite the enhanced spectral efficiency offered by both techniques, complexity issue arises from handling large number of users. Therefore, user pairing/clustering should be employed to limit the number of users served in the same time-frequency resource. However, the key question is which delivery technique performs better under different pairing scenarios. In this paper, the performance of NOMA and coded multicasting for two-user pairing are investigated in terms of probability of sum rate comparison and outage probability. In order to exploit the benefits of NOMA and coded multicasting, we also propose a hybrid delivery scheme, which select either NOMA or coded multicasting depending on the channel conditions of the paired users in each resource block (RB). A joint mode selection, power allocation and user pairing scheme is developed to enhance the performance of the hybrid scheme. Both analytical and simulation results demonstrate that NOMA outperforms coded multicasting when pairing users whose channel gains are highly distinctive, while coded multicasting is preferred when the paired users have similar channel gains. In addition, the hybrid scheme is demonstrated to offer enhanced sum rate performance in comparison to NOMA and coded multicasting.

In this paper, we propose a compressive channel estimation technique for IRS-assisted mmWave multi-input and multi-output (MIMO) system. To reduce the training overhead, the inherent sparsity in mmWave channels is exploited. By utilizing the properties of Kronecker products, IRS-assisted mmWave channel estimation are converted into a sparse signal recovery problem, which involves two competing cost function terms (measurement error and a sparsity term). Existing sparse recovery algorithms solve the combined contradictory objectives function using a regularization parameter, which leads to a suboptimal solution. To address this concern, a hybrid multi-objective evolutionary paradigm is developed to solve the sparse recovery problem, which can overcome the difficulty in the choice of regularization parameter value. Simulation results show that under a wide range of simulation settings, the proposed algorithm achieves competitive error performance compared to existing channel estimation algorithms.

Non-orthogonal multiple access (NOMA) is effective in enhancing the spectral efficiency and sum rate of a system as compared to an orthogonal scheme. In addition to this, as mobile users consume more rich content, the role of device-to-device communications (D2D) and wireless caching will become more prominent in further optimizing the usage of resources. In this paper, we propose a system in which users are able to exchange useful cached content with each other via a D2D link which underlays a downlink NOMA transmission. We formulated a sum rate maximization problem that is subject to minimum rate constraints and derived a sub-optimal power allocation solution based on a low self-interference assumption. Simulation results demonstrate the effectiveness of D2D communications in comparison with optimum conventional NOMA downlink, as well as highlighting the similarity in performance between the suboptimal power allocation and the optimum power allocation.

This paper considers an unmanned aerial vehicle (UAV)-aided millimeter Wave (mmWave) multiple-input-multiple-output (MIMO) non-orthogonal multiple access (NOMA) system, where a UAV serves as a flying base station (BS) to provide wireless access services to a set of Internet of Things (IoT) devices in different clusters. We aim to maximize the downlink sum rate by jointly optimizing the three-dimensional (3D) placement of the UAV, beam pattern and transmit power. To address this problem, we first transform the non-convex problem into a total path loss minimization problem, and hence the optimal 3D placement of the UAV can be achieved via standard convex optimization techniques. Then, the multiobjective evolutionary algorithm based on decomposition (MOEA/D) based algorithm is presented for the shaped-beam pattern synthesis of an antenna array. Finally, by transforming the original problem into an optimal power allocation problem under the fixed 3D placement of the UAV and beam pattern, we derive the closed-form expression of transmit power based on Karush-Kuhn-Tucker (KKT) conditions. In addition, inspired by fraction programming (FP), we propose a FP-based suboptimal algorithm to achieve a near-optimal performance. Numerical results demonstrate that the proposed algorithm achieves a significant performance gain in terms of sum rate for all IoT devices, as compared with orthogonal frequency division multiple access (OFDMA) scheme.

In this article, a two-user scenario of a non-orthogonal multiple access (NOMA)-based mobile edge computing (MEC) network is investigated. By treating the users and the MEC server as leader and follower, respectively, a Stackelberg game is formulated. More specifically, the leader tends to minimize the total energy consumption for task offloading and local computing by optimizing the task assignment coefficients and transmit power. On the other side, the follower aims to minimize the total execution time by allocating different computational resources for processing the offloaded tasks. In order to solve the formulated problem, the Stackelberg equilibrium is considered. Based on the given insights, a closed-form solution of the follower level problem is obtained and included in the leader level one. Furthermore, by analyzing the leader's strategies, the leader level problem is solved through the Karush-Kuhn-Tucker (KKT) conditions, and closed-form expressions for the optimal task assignment coefficients and offloading time, are derived. Finally, this work is extended to the multi-user scenario, where a matching-based user pairing algorithm is proposed to assign users into different sub-channels. Simulation results indicate that: i) the derived closed-form solutions and the proposed user pairing algorithm can significantly improve energy efficiency; ii) the different task assignment strategies can be dynamically implemented to handle the varying wireless environment.

This paper studies an unmanned aerial vehicle (UAV)-assisted wireless powered IoT network, where a rotary-wing UAV adopts fly-hover-communicate protocol to successively visit IoT devices in demand. During the hovering periods, the UAV works on full-duplex mode to simultaneously collect data from the target device and charge other devices within its coverage. Practical propulsion power consumption model and non-linear energy harvesting model are taken into account. We formulate a multi-objective optimization problem to jointly optimize three objectives: maximization of sum data rate, maximization of total harvested energy and minimization of UAV's energy consumption over a particular mission period. These three objectives are in conflict with each other partly and weight parameters are given to describe associated importance. Since IoT devices keep gathering information from the physical surrounding environment and their requirements to upload data change dynamically, online path planning of the UAV is required. In this paper, we apply deep reinforcement learning algorithm to achieve online decision. An extended deep deterministic policy gradient (DDPG) algorithm is proposed to learn control policies of UAV over multiple objectives. While training, the agent learns to produce optimal policies under given weights conditions on the basis of achieving timely data collection according to the requirement priority and avoiding devices' data overflow. The verification results show that the proposed MODDPG (multi-objective DDPG) algorithm achieves joint optimization of three objectives and optimal policies can be adjusted according to weight parameters among optimization objectives.

Non-orthogonal multiple access (NOMA) is a promising radio access scheme for future mobile networks due to its high spectral efficiency, but the weak user suffers poor performance due to the interference and poor channel quality. Full-duplex cooperative NOMA (FD C-NOMA) is an attractive solution by allowing the strong user to relay the weak user's signal simultaneously, but most existing works made an impractical assumption that the signals from the base station (BS) and the strong user can be perfectly resolved and combined at the weak user. Considering feasible successive interference cancellation (SIC) operation, we propose a two-phase FD C-NOMA system where the BS only transmits a new signal for the strong user in the second phase, such that the weak user can perform SIC to obtain its own signal. A sum-rate maximization for this proposed system is investigated and a low-complexity closed form solution is derived under the total BS transmission power and proportional rate constraints. Numerical simulation results show that the proposed FD C-NOMA system model attains better sum-rate performance compared to other schemes.

Resource allocation for Sparse Code Multiple Access (SCMA) systems is restricted by the codebook design for data transmission from different users simultaneously. The effect of the interference between users assigned to the same resource can result in severe degradation of performance. To alleviate this problem, a practical subcarrier allocation approach is proposed in this paper. The proposed scheme has a lower complexity than existing methods while achieving close to optimal performance. It is based on a modified greedy algorithm and exploits the properties of multiuser systems by assigning opposite codebook patterns to adjacent users. A comparison between different allocation graph designs is explored and we introduce a low complexity power allocation method with minimum rate constraints per user. The scheme is based on the water-filling principle adapted for multiuser sparse systems in the presence of interference. Simulation results show that the proposed scheme has close to optimal performance with lower computational complexity.

With increasing demand in rich content driving up the need for increased system capacity, novel transmission techniques are required for future mobile networks. In this paper, a novel cache-aided (CA) device-to-device (D2D) non-orthogonal multiple access (NOMA) system employing cache-enabled interference cancellation (CIC) is proposed to increase the system sum rate performance. Utilising the uplink channels of a pair of users, the proposed system allows both users to exchange previously cached content with each other over a D2D link instead of receiving them only from the base station in conventional approaches. The sum rate of the proposed approach is derived and analysis shows an exact region in which it outperforms CANOMA. Simulation results verify the analytical results that when the users are close together, CA-D2D NOMA is the preferred choice of transmission technique over CA-NOMA. The results also show that the best sum rate performance is obtained when the system switches between the two NOMA schemes based on the derived regions.

Non-orthogonal multiple access (NOMA) is one of the most significant technologies to meet the demand of high spectral efficiency (SE) in the fifth generation (5G) cellular networks. The utilization of simultaneous wireless information and power transfer (SWIPT) contributes to prolonging the battery life of the mobile users (MUs) and enhancing the system energy efficiency (EE), especially in the NOMA scenario where the inter-user interference can be reused for energy harvesting (EH). In this paper, we study the achievable data rate maximization problem for the downlink multi-carrier NOMA (MC-NOMA) network with power splitting (PS)-based SWIPT, in which power allocation and PS control are jointly optimized with the limitation of available power budget as well as the requirement for EH. The considered non-convex optimization problem is arduous to tackle, resulting from the presence of the coupled variables and the inter-user interference. To cope with the problem, a decoupled approach is developed, in which the power allocation and PS control are separated and the corresponding sub-problems are respectively solved through Lagrangian duality method. Furthermore, an alternative approach based on deep learning is proposed, which is capable of effectively obtaining the approximate optimal solution according to the empirical data. Simulation results confirm the effectiveness of the proposed schemes, and demonstrate the superiority of the combination of PS-based SWIPT with MC-NOMA over SWIPT-aided single-carrier NOMA (SC-NOMA) and SWIPT-aided orthogonal multiple access (OMA).

Transmission rate and harvested energy are well-known conflictive optimization objectives in simultaneous wireless information and power transfer (SWIPT) systems, and thus their trade-off and joint optimization are important problems to be studied. In this paper, we investigate joint power allocation and splitting control in a SWIPT-enabled non-orthogonal multiple access (NOMA) system with the power splitting (PS) technique, with an aim to optimize the total transmission rate and harvested energy simultaneously whilst satisfying the minimum rate and the harvested energy requirements of each user. These two conflicting objectives make the formulated problem a constrained multi-objective optimization problem. Since the harvested power is usually stored in the battery and used to support the reverse link transmission, we transform the harvested energy into throughput and define a new objective function by summing the weighted values of the transmission rate achieved by information decoding and transformed throughput from energy harvesting, defined as equivalent-sum-rate (ESR). As a result, the original problem is transformed into a single-objective optimization problem. The considered ESR maximization problem which involves joint optimization of power allocation and PS ratio is nonconvex, and hence challenging to solve. In order to tackle it, we decouple the original nonconvex problem into two convex subproblems and solve them iteratively. In addition, both equal PS ratio case and independent PS ratio case are considered to further explore the performance. Numerical results validate the theoretical findings and demonstrate that significant performance gain over the traditional rate maximization scheme can be achieved by the proposed algorithms in a SWIPT-enabled NOMA system.

In this paper, we investigate joint power allocation and time switching (TS) control for energy efficiency (EE) optimization in a TS-based simultaneous wireless information and power transfer (SWIPT) non-orthogonal multiple access (NOMA) system. Our aim is to optimize the EE of the system whilst satisfying the constraints on maximum transmit power, minimum data rate and minimum harvested energy per-terminal. The considered EE optimization problem is formulated and then transformed according to the duality of broadcast channels (BC) and multiple access channels (MAC). The corresponding non-linear and non-convex optimization problem, involving joint optimization of power allocation and time switching factor, is difficult to solve directly. In order to tackle this problem, we develop a dual-layer algorithm where a convex programming-based Dinkelbach's method is proposed to optimize the power allocation in the inner-layer and an efficient search method is then applied to optimize the TS factor in the outer-layer. Numerical results validate the theoretical findings and demonstrate that significant performance gain over orthogonal multiple access (OMA) scheme in terms of EE can be achieved by the proposed algorithm in a SWIPT-enabled NOMA system.

Non-orthogonal multiple access (NOMA) scheme is considered as a promising technology for 5G networks due to its ability to increase the spectral efficiency. In this paper, by combining the principle of NOMA and full-duplex (FD) transmission, a downlink FD cooperative NOMA (C-NOMA) scheme is proposed to enhance the system performance. We consider a practical approach where the weak user's signal is only forwarded in the second timeslot after it is decoded, while at the same time the base station (BS) retransmits the weak user's signal again and new information to the strong user. The analytical expressions for the sum rate using maximal ratio combining (MRC) and maximum ratio transmission (MRT) are formulated to investigate the performance of the proposed schemes. Also, in contrary to most existing C-NOMA methods, we assume that the signal from the BS and from the strong user cannot be separated for diversity combining. Hence, accurate analytical sum rates for half-duplex (HD) C-NOMA with MRC and MRT are derived. Numerical simulation results demonstrate that the proposed FD C-NOMA system using MRT offers better sum rate performance than FD C-NOMA using MRC and the HD C-NOMA schemes. Furthermore, the proposed scheme attains a close sum rate performance compared to NOMA and a superior fairness for the weak user.

In this paper, we investigate the energy efficiency (EE) optimization for a downlink orthogonal frequency division multiple access (OFDMA) system with overlaying Device-to-Device (D2D) communications. Joint EE optimization is highly complex while a two-stage solution will utilize most of the bandwidth for cellular users and not providing sufficient bandwidth for D2D users. Using resource efficiency (RE) optimization approach that balances the bandwidth usage and EE, we propose a two-stage solution that optimizes RE for base station (BS) and EE for D2D pairs. To achieve higher EE, D2D communications operate in non-orthogonal mode, where each resource block (RB) not being assigned to the cellular users are reused by multiple D2D pairs. By exploiting a range of optimization tools including fractional programming, Dinkelbach approach, Lagrange dual decomposition, difference of convex functions, and concave-convex procedure, the original non-convex problem is transformed and we present an iterative two-stage RE-EE solution. Simulation results demonstrate that the proposed resource allocation scheme provides comparable EE performance to a two-stage EE-EE solution with significant gain on EE for D2D users, and achieves much higher EE compared to a sum rate maximization scheme.

Simultaneous wireless information and power transfer (SWIPT) is anticipated to have great applications in 5G communication systems and the Internet of Things. In this paper, we address the energy efficiency (EE) optimization problem for SWIPT multiple-input multiple-output broadcast channel (BC) with time-switching (TS) receiver design. Our aim is to maximize the EE of the system whilst satisfying certain constraints in terms of maximum transmit power and minimum harvested energy per user. The coupling of the optimization variables, namely transmit covariance matrices and TS ratios, leads to an EE problem which is nonconvex, and hence very difficult to solve directly. Hence, we transform the original maximization problem with multiple constraints into a suboptimal min-max problem with a single constraint and multiple auxiliary variables. We propose a dual inner/outer layer resource allocation framework to tackle the problem. For the inner-layer, we invoke an extended SWIPT-based BC-multiple access channel (MAC) duality approach and provide two iterative resource allocation schemes under fixed auxiliary variables for solving the dual MAC problem. A subgradient searching scheme is then proposed for the outer-layer in order to obtain the optimal auxiliary variables. Numerical results confirm the effectiveness of the proposed algorithms and illustrate that significant performance gain in terms of EE can be achieved by adopting the proposed extended BC-MAC duality-based algorithm.

In this paper, a fundamental study of energy efficiency (EE) optimization for coordinated multi-point (CoMP) simultaneous wireless information and power transfer (SWIPT) heterogeneous networks (HetNets) is provided. We aim to optimize the EE while satisfying certain quality-of-service requirements in regard to transmission rate and energy harvesting at both the macro cell and small cells. The corresponding joint beamforming and power allocation in the presence of intra- and inter-cell interference constitutes an EE maximization problem that is non-convex, and hence, very challenging to solve. In order to solve this problem, we propose to separate the beamforming design and power allocation processes. First, we adopt linear zero-forcing (ZF) beamforming to suppress the multi-user interference from both the energy harvesting users (EH-UEs) as well as the information decoding UEs (ID-UEs), thus transforming the HetNet under consideration to a virtual point-to-point system. An efficient power allocation algorithm is then developed to maximize the corresponding EE. On the other hand, the ZF strategy does not utilize the notion that interference benefits the EH-UEs. As a result, we propose a partial ZF approach by differentiating the EH-UEs and ID-UEs in order to further improve the EE. Our findings show that the EE can be significantly improved through the integration of CoMP-SWIPT in HetNets.

Non-orthogonal multiple access (NOMA) has attracted a lot of attention recently due to its superior spectral efficiency and could play a vital role in improving the capacity of future networks. This paper considers resource allocation for a downlink, multi-user (MU) MIMO-NOMA system that aims at maximizing the sum rate with interference alignment (IA) technique. Using singular decomposition value (SVD) based IA, we propose IA based NOMA system in which a number of users are grouped together while the others are aligned to the null space as interference. The targeted group of users employ NOMA with a low complexity hierarchical power allocation scheme for sum rate maximization. In addition, an optimization problem is formulated to maximize the sum rate under the total power and proportional fairness constraints. A low complexity sub-optimal solution for two-user scenario is obtained and then extended to the multi-user case by a hierarchical pairing scheme. Another approach is proposed to allocate the transmission power of each user using an iterative subgradient method. Simulation results show that the proposed schemes provide better performance than an existing scheme and perform close to the optimal one. In addition, the simulation scenario considers the case where two users share the data streams while performing IA as compared to the case where all users are sharing it without IA. Simulation results verify that applying IA with NOMA could improve the achievable sum rate and offers simplicity in terms of successive interference cancellation (SIC) application.

This paper investigates the resource allocation scheme to maximize the energy efficiency for a downlink nonorthogonal multiple access (NOMA) system. An optimization problem is formulated taking into account the total power and the minimum user rate requirements to balance the system energy efficiency and the total system throughput. Due to the complexity of the objective function, we used the Dinkelbach approach to convert the non-linear fractional programming problem into a simpler subtractive form. Then, the equivalent subtractive form-objective function problem is solved by using iterative programming. A subgradient based resource allocation algorithm is proposed to allocate the power for each user. Simulation results justify the effectiveness of the proposed method and show how it approaches the optimal solution. It also shows that the proposed schemes for NOMA provide better performance than the orthogonal frequency division multiple access (OFDMA) in terms of the energy efficiency and sum rate.

The pilot contamination problem is one of the major obstacles that limit the performance of time-division duplex (TDD) multi-cell massive multiple-input multiple-output (MIMO) systems. Pilot contamination results from the re-use of the same set of pilot sequences in the different cells of the system. In this paper, we compare between two different scenarios of pilot signals allocation with respect to the impact of pilot contamination. We derive lower bounds on the achievable rates and study the performance of both scenarios under different system settings. Our results show that although increasing the number of base station (BS) antennas improves the system performance, it does not eliminate the effect of pilot contamination. Thus, when the pilot contamination is high, it should be countered by allocating more system resources for the training phase. This can be achieved by increasing the number of pilot sequences to guarantee an orthogonal pilot sequence for each user in the system. Further, we show that the pilot sequences allocation strategy also depends on the characteristics of the communication environment: a low mobility environment has better performance when orthogonal sequences are allocated to all users while the opposite is true for a high mobility environment.

Device-to-Device (D2D) communication is one of the technologies for next generation communication system. Unlike traditional cellular network, D2D allows proximity users to communicate directly with each other without routing the data through a base station. In this paper, we propose a resource allocation scheme for energy efficiency (EE) optimization in cellular network with overlaying D2D communication. The objective of this work is to maximize the overall EE of the network while satisfying the rate and power constraints for all users. We decompose the main problem into two subproblems; resource efficiency (RE) optimization for cellular user in the first stage and EE optimization for D2D pair in the second stage. The RE optimization problem is solved using the bisection method while Dinkelbach and interior point method are implemented to solve the EE optimization problem. Simulation results demonstrate that the proposed scheme outperforms the cellular mode and dedicated mode of communication and the performance is close to the global optimal solution.

In this paper, we provide joint subcarrier assignment and power allocation schemes for quality- of-service (QoS)-constrained energy-efficiency (EE) optimization in the downlink of an orthogonal frequency division multiple access (OFDMA)-based two-tier heterogeneous cellular network (HCN). Considering underlay transmission, where spectrum- efficiency (SE) is fully exploited, the EE solution involves tackling a complex mixed-combinatorial and non-convex optimization problem. With appropriate decomposition of the original problem and leveraging on the quasi-concavity of the EE function, the problem can be efficiently solved. On the other hand, the inherent inter-tier interference from spectrum underlay access may degrade EE particularly under dense small-cell deployment and large bandwidth utilization. We therefore develop a novel resource allocation approach based on the concepts of spectrum overlay access and resource efficiency (RE) (normalized EE-SE trade-off). Specifically, the optimization procedure is separated where the macro- cell optimal RE and the corresponding bandwidth is first determined, then the EE of small-cells utilizing the remaining spectrum is maximized. Simulation results confirm the theoretical findings and demonstrate that the proposed resource allocation schemes can approach the optimal EE with each strategy being superior under certain system settings.

In this paper, we investigate joint antenna selection and spatial switching (SS) for energy efficiency (EE) optimization in a multiple-input multiple-output (MIMO) simultaneous wireless information and power transfer (SWIPT) system. A practical linear power model taking into account the entire transmit-receive chain is accordingly utilized. The corresponding fractional-combinatorial and non-convex EE problem, involving joint optimization of eigen-channel assignment, power allocation, and active receive antenna set selection, subject to satisfying minimum sum-rate and power transfer constraints, is extremely difficult to solve directly. In order to tackle this, we separate the eigen-channel assignment and power allocation procedure with the antenna selection functionality. In particular, we first tackle the EE maximization problem under fixed receive antenna set using Dinkelbach-based convex programming. We then provide a fundamental study of the achievable EE with antenna selection and accordingly develop dynamic optimal exhaustive search and Frobenius-norm-based schemes. Simulation results confirm the theoretical findings and demonstrate that the proposed resource allocation algorithms can efficiently approach the optimal EE.

In this paper, we address the energy efficiency (EE) optimization problem for SWIPT multiple-input multiple-output broadcast channel (MIMO-BC) with time-switching (TS) receiver design. Our aim is to maximize the EE of the system whilst satisfying certain constraints in terms of maximum transmit power and minimum harvested energy per user. The coupling of the optimization variables, namely, transmit covariance matrices and TS ratios, leads to a EE problem which is non-convex, and hence very difficult to solve directly. Hence, we transform the original maximization problem with multiple constraints into a min-max problem with a single constraint and multiple auxiliary variables. We propose a dual inner/outer layer resource allocation framework to tackle the problem. For the inner- layer, we invoke an extended SWIPT-based BC-multiple access channel (MAC) duality approach and provide an iterative resource allocation scheme under fixed auxiliary variables for solving the dual MAC problem. A sub-gradient searching scheme is then proposed for the outer-layer in order to obtain the optimal auxiliary variables. Numerical results confirm the effectiveness of the proposed algorithms and illustrate that significant performance gain in terms of EE can be achieved by adopting the proposed extended BC-MAC duality-based algorithm.

Non-orthogonal multiple access (NOMA), which has attracted a lot of attention recently due to its superior spectral efficiency, could play a vital role in improving the capacity of future networks. In this paper, a resource allocation scheme is developed for a downlink multi-user NOMA system. An optimization problem is formulated to maximize the sum rate under the total power and proportional rate constraints. Due to the complexity of computing the optimal solution, we develop a low complexity sub-optimal solution for a two-user scenario and then extend it to the multi-user case by proposing a user-pairing approach as well as a number of power allocation techniques that facilitate dealing with a large number of users in NOMA system. Simulation results support the effectiveness of the proposed approaches and show the close performance to the optimal one. In addition, we propose a new hybrid multiple access technique that combines the properties of NOMA and the orthogonal frequency division multiple access. Simulation results show that the proposed hybrid method provides better performance than NOMA in terms of the overall achievable sum rate and the coverage probability.

Mobile traffic demand has been increasing exponentially over the last few years and forecasts show that this trend will continue in the foreseeable future. As a result, operators are forced to densify and upgrade their networks to meet this demand. This has created concerns, such as increasing greenhouse gas emissions, high capital expenditures, and associated energy costs. This paper uses tools from stochastic geometry to analyze and formulate energy-efficient deployment strategies for multi-tier heterogeneous networks (HetNets) using various user association schemes. We use simple approximations to combine the required base station (BS) density and associated transmit power per tier subject to both coverage probability and average user rate constraints. In this paper, this combination is called the deployment factor and it can be expressed in closed form for unbiased HetNets. We then formulate area power consumption (APC) minimization framework, which optimizes the deployment factor to derive specific optimal BS density and transmit power values. Furthermore, we perform a comprehensive study of the effect of biasing on the APC performance of biased HetNets. Our results show that for HetNets using the maximum average-biased-received-power association scheme, significant energy savings are possible with appropriate biasing.

In this paper, we investigate joint antenna selection and spatial switching for quality-of-service-constrained energy efficiency (EE) optimization in a multiple-input multiple-output simultaneous wireless information and power transfer system. A practical linear power model taking into account the entire transmit-receive chain is accordingly utilized. The corresponding fractional-combinatorial and non-convex EE problem, involving joint optimization of eigenchannel assignment, power allocation, and active receive antenna set selection, subject to satisfying minimum sum-rate and power transfer constraints, is extremely difficult to solve directly. In order to tackle this, we separate the eigenchannel assignment and power allocation procedure with the antenna selection functionality. In particular, we first tackle the EE maximization problem under fixed receive antenna set using Dinkelbach-based convex programming, iterative joint eigenchannel assignment and power allocation, and low-complexity multi-objective optimization-based approach. On the other hand, the number of active receive antennas induces a tradeoff in the achievable sum-rate and power transfer versus the transmit-independent power consumption. We provide a fundamental study of the achievable EE with antenna selection and accordingly develop dynamic optimal exhaustive search and Frobenius-norm-based schemes. Simulation results confirm the theoretical findings and demonstrate that the proposed resource allocation algorithms can efficiently approach the optimal EE.

We devise a framework using tools from stochastic geometry and queuing theory for the study of irregular cellular networks when user traffic varies randomly in time and space. We consider a typical wireless cell with a guard zone surrounded by an interference environment comprised of a dominant node at the guard-edge plus an outer-bound Poisson field of sources. A systematic approach is presented to characterize the flow rate in the presence of elastic data traffic with closed-form expressions of the intended signal power and bounded aggregate interference statistics over Nakagami-m fading channels accordingly derived. We then formulate and solve an optimization problem for the computation of the traffic capacity defined as the maximum elastic data flow intensity for which the system remains unsaturated.

Non-orthogonal multiple access (NOMA) is a promising multiple access scheme for next generation wireless networks. This paper investigates the sum rate maximization problem for NOMA system over a frequency selective fading channel with its users being paired according to their channel powers. A hierarchical power allocation process is proposed, whereby the users are divided into two groups such that a closed form power allocation solution can be applied. This divide-and allocate approach is repeated until all users are allocated with a transmission power. The proposed architecture facilitates a large number of users for NOMA system. Simulation results show that the proposed scheme achieves comparable performances to the optimal one based on numerical solution, and outperforms other approaches.

We present an analytical model for the design and analysis of dense cellular networks (DenseNets) where load-awareness is explicitly incorporated in the system performance. New bounded expressions of aggregate interference and average rate are developed considering spatially-correlated heterogeneous sources. Subsequently, an optimization problem for pinpointing the optimal network density which minimizes the total energy expenditure is formulated and tackled. The validity of our framework and its advantages over the existing fully-loaded and interference- thinning methods are depicted via Monte-Carlo simulations. Based on state-of-the-art system parameters, a homogeneous pico deployment is revealed to be the most energy-efficient solution in future dense urban environments.

We propose an analytical stochastic geometry-based model for multiple-input multiple-output (MIMO) heterogeneous cellular networks (HetNets) with zero-forcing (ZF) precoding at transmitting base stations (BSs) and partial zero-forcing (PZF) beamforming at receiving user equipments (UEs). The user and area spectral efficiencies are characterized using a non-direct moment- generating-function (MGF) methodology with closed- form expressions of the intended signal power and aggregate network interference statistics accordingly developed. The impact of different cellular network deployments, antenna configurations, and transmission schemes on achievable performance are examined through theoretical and simulation studies. The results confirm the promising potential of multi-antenna communications and small-cell solution in emerging wireless environments.

The application of stochastic geometry theory for the study of cellular networks has gained huge popularity recently. Most existing works however rely on unrealistic assumptions concerning the underlying user traffic model. This paper aims to make a step in this direction by devising a new model for the performance analysis and optimization of heterogeneous cellular networks (HetNets) with irregular BS deployment and flow- level traffic dynamics. We provide a unified methodology for the evaluation of the flow rate with closed-form expressions of the useful signal power and aggregate network interference over Nakagami-m fading channels. The problem of computing the optimal loading factors which result in the greatest sustainable traffic whilst the system remains stable is formulated and tackled.

This paper presents a stochastic geometry-based framework for the design and analysis of downlink multi-user multiple-input multiple-output (MIMO) heterogeneous cellular networks with linear zero-forcing transmit precoding and receive combining, assuming Rayleigh fading channels and perfect channel state information. The generalized tiers of base stations may differ in terms of their Poisson point process spatial density, number of transmit antennas, transmit power, artificial-biasing weight, and number of user equipments served per resource block. The spectral efficiency of a typical user equipped with multiple receive antennas is characterized using a non-direct moment-generating-function-based methodology with closed-form expressions of the useful received signal and aggregate network interference statistics systematically derived. In addition, the area spectral efficiency is formulated under different space-division multiple-access and single-user beamforming transmission schemes. We examine the impact of different cellular network deployments, propagation conditions, antenna configurations, and MIMO setups on the achievable performance through theoretical and simulation studies. Based on the state-of-the-art system parameters, the results highlight the inherent limitations of baseline single-input single-output transmission and conventional sparse macro-cell deployment, as well as the promising potential of multi-antenna communications and small-cell solution in interference-limited cellular environments.

Traffic demand is increasing exponentially and operators will need to densify their networks with small cells to meet this demand. This has created economic and ecological concerns due to the high cost of energy and the associated greenhouse gas emissions. In this paper, we apply tools from stochastic geometry to design a deployment strategy for multi-tier heterogeneous networks (HetNets). Using appropriate approximations, we derive simple expressions that allow insights into the energy efficiency (EE) performance of biased and unbiased HetNets. We perform a comprehensive analysis on the effect of biasing on the EE performance of HetNets. We also formulate an optimization framework that minimizes the area power consumption (APC) of the HetNet subject to appropriate performance constraints. Our results show that significant energy savings can be made by appropriately biasing the HetNet and optimizing the BS densities and their transmit powers subject to the performance constraints.

This paper proposes an adaptive scheduling strategy to improve the energy efficiency of massive MIMO. The proposed technique combines a novel adaptive discontinuous transmission (ADTx) and antenna optimization that can optimize the number of active antennas and transmission period such that data can be delivered to users with the minimum power. The proposed ADTx utilizes a DTx principle and adaptive precoding technique selection. On the other hand, antennas optimization is used to relax the number of active antennas once the required period has been calculated. Although massive MIMO is initially designed to utilize the multiplexing users found in dense users scenario, this paper discusses the impact of the proposed technique to low density scenarios, in particular suburban scenario, because major parts of cellular coverage are low density. It will be shown that the proposed scheme provides significant energy efficiency (EE) and QoS improvement that can support green cellular network.

This paper addresses energy efficiency (EE) of large scale antenna systems (LSAS) or massive MIMO based cellular networks. We propose a joint technique that combines a novel successive discontinuous transmission (SDTx) scheme with antenna array optimization (AAO). The SDTx utilizes information regarding the users' requirements and properties to deliver data efficiently and without compromising the users' service. On the other hand, AAO further reduces power consumption through minimizing the number of active antenna elements. Users requirements include bit rate and latency requirement while the users properties include pathloss between users and macro base station (MBS) and users' movement. It will be shown that, relative to conventional LSAS, the proposed scheduling significantly improves the EE of LSAS network for various network load under practical users' requirement in dense urban environment making it a potential candidate for green cellular networks.

Data traffic continues to increase exponentially and operators are continuously upgrading their networks to meet this demand. The resulting capital and operational expenditures have limited revenues and the associated energy costs and CO2emissions have raised economic and ecological concerns. In this paper, we use the stochastic geometry approach to investigate different sleep mode mechanisms that can address both capacity and energy efficiency (EE) objectives in dense small cell networks. We derive a multi-user connectivity model that facilitates the study of sleep mode mechanisms and manages the blocking rate of the network. We formulate an optimization framework that minimizes area power consumption using appropriate constraints. Numerical results show that sleep mode mechanisms enhance the EE of dense small cell networks and that the selection criterion of sleep mode candidate base stations is very important.

We provide new results on the signal-to-interference-plus-noise ratio (SINR) statistics considering a Poisson point process (PPP)-based heterogeneous interference field. In particular, closed-form expressions for the density functions of the reciprocal of the aggregate interference and signal-to-interference ratio (SIR) are developed. We prove that the effect of PPP-based interference on useful transmission is mathematically equivalent to the severe impact from a one-sided Gaussian fading channel. As an application example, the proposed approach is used to design and analyze the average SINR performance of a typical user in heterogeneous cellular networks (HetNets).

Heterogeneous network (HetNet) deployment is considered a de facto solution for meeting the ever increasing mobile traffic demand. However, excessive power usage in such networks is a critical issue, particularly for the mobile operators. Characterizing the fundamental energy efficiency (EE) performance of HetNets is therefore important for the design of green wireless systems. In this paper, we address the EE optimization problem for downlink two-tier HetNets comprised of a single macro-cell and multiple pico-cells. Considering a heterogeneous real-time and non-real-time traffic, transmit beamforming design and power allocation policies are jointly considered in order to optimize the system energy efficiency. The EE resource allocation problem under consideration is a mixed combinatorial and non-convex optimization problem, which is extremely difficult to solve. In order to reduce the computational complexity, we decompose the original problem with multiple inequality constraints into multiple optimization problems with single inequality constraint. For the latter problem, a two-layer resource allocation algorithm is proposed based on the quasiconcavity property of EE. Simulation results confirm the theoretical findings and demonstrate that the proposed resource allocation algorithm can efficiently approach the optimal EE.

Non-orthogonal multiple access (NOMA) can increase the spectral efficiency and could play an important role in improving the capacity of 5G networks. In this paper, an optimization problem is formulated to maximize the overall sum rate in a sub-carrier based NOMA system. The optimal transmission power of each user is obtained based on the users' instantaneous channel state information under the total power and the minimum rate constraints. Moreover, two closed-form suboptimal solutions are also proposed for a two-user scenario to reduce the complexity of the optimal solution. The suboptimal approaches are also extended to multiuser scenario by pairing users for subbands transmission. Simulation results show that the derived sub-optimal solutions provide better performance than the orthogonal frequency division multiple access (OFDMA) in terms of coverage probability and sum rate. Moreover, the results show that the proposed sub-optimal solutions achieve a comparable results to the optimal one with lower complexity.

The cooperative diversity technique, combined with distributed space-time coding (DSTC), can efficiently improve the secrecy capacity and transmission reliability of wireless communication systems. In this paper, a multicast cooperative relaying system, based on distributed Alamouti space-time coding, is proposed, as a method of preventing an eavesdropper from accessing source data. A maximum likelihood (ML) decoder at the destination was chosen to receive the data, however, the ML decoder was unable to receive data at the eavesdropper. An analysis was carried out to synchronize DSTC, a Rayleigh fading channel, a Rician fading channel, and a combination thereof. It was assumed that channel state information (CSI) for the relay-destination link was known, although this was unknown for the relay-eavesdropper link. The theoretical results for the secrecy outage probability for the proposed scheme are then compared with existing results.

Forecasts show that traffic demand will continue to grow exponentially over the foreseeable future and mobile operators will have to upgrade and densify their networks to support this traffic demand. However, due to the spatiotemporal variation of traffic, some base stations (BSs) may remain idle during low traffic conditions. In this paper, we use tools from stochastic geometry to analyze the capacity and energy efficiency (EE) performance of a dense heterogeneous network (HetNet). We derive a multi-user connectivity model based on the LTE standard to determine realistic BS density configurations. We implement conventional sleep mode and investigate the effect of biasing on the capacity and EE performance of the HetNet. Our results show that the prevailing user density influences the HetNet performance, and that conventional sleep mode significantly enhances the EE performance. Appropriate biasing of the HetNet results in further EE performance enhancement.

Mobile network operators are currently faced with exponentially-increasing data traffic demand which necessitates significant investment in network expansion and upgrades. The required capital expenditure (CAPEX) and operational expenditure (OPEX) have reduced revenues and the associated CO2 emissions continue to raise ecological concerns. In this paper, we investigate the effect of varying user densities on the energy efficiency (EE) performance of the network. Using Poisson point process (PPP) theory, we derive simple analytical approximations that allow important insights to be made on the spectral efficiency (SE) and EE performance of a typical dense small cell network. We also study the impact of the sleep mode power consumption on the network EE especially in dense networks where numerous base stations may be idle simultaneously.

Femtocell technology is widely known for its ability to improve cellular services while reducing the capital and operation expenditure of cellular providers. However, this can lead to more interference and accessibility issues. There are three types of femtocell access schemes currently, which are open, closed and hybrid access. While open access improves the overall network performance, closed access is a much more preferred by the end-user since it guarantees access to the femtocell owner. For this reason, hybrid access is a compromise which releases the femtocell access to the visitor when there is surplus resource left by the owner. This paper proposes a comprehensive method to solve the femtocell access issue by designing a joint multilevel priority scheduling and femtocell access policy based on a Matrix of Conflict RRM. It will be shown through computer simulation that the proposed scheduling and access policy provide network flexibility, for the femtocell owner and cellular provider, which can be utilized to achieve more benefits for both.

Spectral efficiency (SE) and energy efficiency (EE) are the main performance metrics for designing green radio (GR) networks; however they are conflicting criteria. Consequently, instead of separately focusing on either SE or EE, characterizing the fundamental trade-off between EE and SE of MIMO broadcast channels (BC) is significant for the development of green wireless communications. This paper investigates the fundamental EE-SE relationship in a multiple-input multiple-output (MIMO) broadcast channel, which is important for facilitating a desirable balance between energy savings and spectrum utilization. Through our investigation, we prove that EE-SE relationship for MIMO-BC is a quasiconcave function. Furthermore, EE is proved to be either strictly decreasing with SE or first strictly increasing and then strictly decreasing with SE. Based on these findings, we propose a two-layer resource allocation algorithm in order to tackle the comprehensive EE-SE trade-offs problem. The key of the proposed method lies in the inner-layer algorithm which is solved by applying the principle of multiple access channel - broadcast channel (MAC-BC) duality. The algorithm in its dual form is solved using sub-gradient method and bisection searching scheme. Simulation results confirm the theoretical findings and demonstrate that the proposed resource allocation algorithm can efficiently approach the optimal EE-SE trade-off for MIMO-BC.

We present a new unified model for the design and analysis of load-aware downlink heterogeneous networks (HetNets) where interferers are inherently spatially-correlated. A closed-form expression for the aggregate network interference statistics generated by correlated load-proportional tiers of base stations (BSs) over Nakagami-m fading interfering channels is developed. This approach allows for relaxation of several major limitations in the existing state-of-the-art models, in particular the always-on-BSs, uncorrelated interferers, and Rayleigh fading with no shadowing assumptions. The validity and advantages of the proposed load-aware framework over the heavily-adopted fully-loaded model and the more recent interference-thinning-based approximation are confirmed via extensive Monte-Carlo (MC) simulations. The results reveal several important trends and design guidelines for the practical deployment of HetNets.

Heterogeneous network (HetNet) deployment is considered a de facto solution for meeting the ever increasing mobile traffic demand. However, excessive power usage in such networks is a critical issue, particularly for mobile operators. Characterizing the fundamental energy efficiency (EE) performance of HetNets is therefore important for the design of green wireless systems. In this paper, we address the EE optimization problem for downlink two-tier HetNets comprised of a single macro-cell and multiple pico-cells. Considering a heterogeneous real-time and non-real-time traffic, transmit beamforming design and power allocation policies are jointly considered in order to optimize the system energy efficiency. The EE resource allocation problem under consideration is a mixed combinatorial and non-convex optimization problem, which is extremely difficult to solve. In order to reduce the computational complexity, we decompose the original problem with multiple inequality constraints into multiple optimization problems with single inequality constraint. For the latter problem, a two-layer resource allocation algorithm is proposed based on the quasiconcavity property of EE. Simulation results confirm the theoretical findings and demonstrate that the proposed resource allocation algorithm can efficiently approach the optimal EE.

Characterizing the fundamental energy efficiency (EE) performance of multiple-input–multiple-output interfering broadcast channels (MIMO-IFBC) is important for the design of green wireless system. In this paper, we propose a new network architecture proposition based on EE maximization for Multi-Cell MIMO-IFBC within the context of interference alignment (IA). Particularly, EE is maximized subject to maximum power and minimum throughput constraints. We propose two schemes to optimize EE for different signal-to-noise ratio (SNR) regions. For high-SNR operating regions, we employ a grouping-based IA scheme to jointly cancel intra- and inter-cell interferences and thus transform the MIMO-IFBC to a single-cell MIMO scenario. A gradient-based power adaptation scheme is proposed based on water-filling power adaptation and singular value decomposition to maximize EE for each cell. For moderate SNR cases, we propose an approach using dirty paper coding (DPC) with the principle of multiple access channel and broadcast channel duality to perform IA while maximizing EE in each cell. The algorithm in its dual form is solved using a subgradient method and a bisection searching scheme. Simulation results demonstrate the superior performance of the proposed schemes over several existing approaches. It also shows that interference-nulling-based IA approaches outperform hybrid DPC-IA approach in high-SNR region, and the opposite occurs in low-SNR region.

We derive new results for the higher order moments of signal-to-interference-plus-noise ratio (SINR) in the presence of an arbitrary Poisson point process (PPP)-based heterogeneous interference field. The analysis leverages on a moment-generating-function (MGF) methodology, which only requires the statistics of intended signal and aggregate interference, thus eliminating the need for the exact distribution of SINR. We extend the existing results on interference statistics by deriving a generalized closed-form expression of the interference MGF considering Nakagami-m fading channels with exclusion region. In certain special cases, explicit expressions for the averages of different functions of SINR are found, which also lead to closed-form solutions for the probability distributions of aggregate interference reciprocal and signal-to-interference ratio. We prove that in such cases the effect of total PPP-based interference power on useful transmission is mathematically equivalent to the severe fluctuations from a one-sided Gaussian fading channel. As an application example, the proposed methodology is used together with stochastic geometry theory to characterize the average SINR and rate in heterogeneous cellular networks. The validity of our analytical derivations is confirmed via Monte Carlo simulations for various system settings. We show that with cellular network densification there exists a tradeoff between the average SINR and rate performance.

Characterizing the fundamental metric of energy efficiency (EE) of multiple-input multiple-output interfering broadcast channels (MIMO-IFBC) is important for the development of green wireless communications. In this paper, we address the EE optimization problem for multi-cell MIMO-IFBC within the context of interference alignment (IA). We employ grouping-based IA scheme to cancel both inter-cell interference (ICI) and inter-user inference (IUI), and thus transform the MIMO-IFBC to a single cell single user MIMO scenario. A gradient-based optimal power adaptation scheme is proposed which utilizes water-filling approach and singular value decomposition (SVD) to maximize EE for each cell. Simulation results confirm the theoretical findings and demonstrate that the proposed resource allocation algorithm can efficiently approach the optimal EE.

This paper proposes the use of an overloaded hybrid MC-CDMA system to improve the spectral efficiency of Cognitive Radio (CR) network. The overlay transmission in the unused parts of the spectrum is full-loaded MC-CDMA signals with scrambling. The underlay signal, using a separate scrambling code from the overlay signals, utilizes the whole spectrum taking into account of the interference threshold of the Primary User (PU). On the receiver side, chip-level MMSE is used for overlay detection, interference reconstruction and cancellation. Symbollevel MMSE is then used to detect the underlay. Since overlay is performing chip-level equalization and data detection, there is no need for other users' signatures. Therefore, the proposed system does not add complexity to the overlay system in comparison with the conventional MMSE receivers. Simulation results show that the underlay transmission does not affect the overlay performance while achieving good performance for different PU occupancy levels. It is also shown that it is less sensitive to PU interference level and significantly outperforms previous methods.

This paper proposes a new pairing cooperative technique to achieve spatial diversity and constructive interference exploitation in uplink orthogonal frequency-division multiple access (OFDMA) systems. The underlying principle is to pair users such that their symbols are combined into a single constellation, according to a predefined rule, so that the same symbol is transmitted by each pair of users so their signals can be coherently summed at the receiver, hence maximizing the received signal power. To achieve such constructive combination of both signal copies, single-dimensional OFDMA is utilized in the inphase and quadrature dimensions. It will be shown that such transmission method not only maximizes the received power but also greatly reduces sensitivity to timing misalignment between the cooperating users. Both time synchronous and asynchronous transmission scenarios will be considered. In the asynchronous case, interference reduction will be applied at the base station to reinstate the pairs' orthogonality and hence minimize co-channel interference. The performance of this technique is evaluated through simulations and mathematical analysis. It will be shown that the results from both models closely match and that the proposed technique provides significant bit-error-rate reductions, particularly at low signal-to-noise ratio, and throughput improvements relative to existing cooperative transmission techniques.

We develop a unified framework for the performance analysis of arbitrary-loaded downlink heterogeneous networks (HetNets) in which interfering sources are inherently spatially-correlated. Considering a randomly-deployed multi-tier cellular network comprised of a diverse set of large-and small-cells, we incorporate the notion of load-awareness and spatial-correlations in characterizing the activities of base stations (BSs) using binary decision variables. A stochastic geometry-based approach is accordingly employed to systematically develop a bounded expression of ergodic rate with different cellular association and load-balancing strategies. Employing the proposed unified framework hence allows for relaxation of several major limitations in the existing state-of-the-art models, in particular the always-transmitting-BSs, uncorrelated interferers, and Rayleigh fading assumptions. We elaborate on the usefulness of adopting this methodology by providing detailed analysis of the aggregate network interference generated by interdependent load-proportional sources over Nakagami-m fading interfering channels. The analytical formulations are validated through Monte-Carlo (MC) simulations for various scenarios and system settings of interest. We observe that the heavily-adopted fully-loaded model as well as the more recent interference-thinning-based approximations are significantly limited in capturing the actual performance curve. The proposed bounded load-aware model and MC trials reveal several important trends and design guidelines for the practical deployment of HetNets.

Cognitive radio opportunistically accesses the spectrum while the licensed user is idle. A spectrum sensing procedure to monitor primary users' existence is therefore vital to cognitive radios. In this paper, we investigate the energy detection based sequential cooperative spectrum sensing technique in time varying channels. By utilizing past local observations from previous sensing slots, cognitive radio nodes can aggregate the current and previously received energy values to improve the detection performance. We propose the weighted sequential energy detector (SED) in which a fixed number of past observations are taken for decision making. In addition, we propose two adaptive schemes, namely the Two-Stage SED and the Differential SED, where a cognitive radio user uses previously received energy values until it detects a change in primary user's activity. The probability of false alarm and detection for the Weighted SED and Two-Stage SED schemes are derived. The results show that the proposed schemes achieve better detection performance over the conventional cooperative energy detection technique. In particular, the Two-Stage SED and Differential SED schemes can satisfy the IEEE 802.22 standard's requirement of 10% false alarm and 90% detection in scenarios that conventional techniques cannot accommodate.

Spectral efficiency (SE) and energy efficiency (EE) are the main metrics for designing wireless networks. Rather than focusing on either SE or EE separately, recent works have focused on the relationship between EE and SE and provided good insight into the joint EE-SE tradeoff. However, such works have assumed that the bandwidth was fully occupied regardless of the transmission requirements and therefore are only valid for this type of scenario. In this paper, we propose a new paradigm for EE-SE tradeoff, namely the resource efficiency (RE) for orthogonal frequency division multiple access (OFDMA) cellular network in which we take into consideration different transmission-bandwidth requirements. We analyse the properties of the proposed RE and prove that it is capable of exploiting the tradeoff between EE and SE by balancing consumption power and occupied bandwidth; hence simultaneously optimizing both EE and SE. We then formulate the generalized RE optimization problem with guaranteed quality of service (QoS) and provide a gradient based optimal power adaptation scheme to solve it. We also provide an upper bound near optimal method to jointly solve the optimization problem. Furthermore, a low-complexity suboptimal algorithm based on a uniform power allocation scheme is proposed to reduce the complexity. Numerical results confirm the analytical findings and demonstrate the effectiveness of the proposed resource allocation schemes for efficient resource usage.

Cognitive Radio (CR) aims to access the spectrum opportunistically mainly by two access strategies, overlay and underlay. Overlay can utilize the spectrum whenever the primary system is off. On the other hand, underlay can utilize the spectrum at any time with considering the interference limit of the primary system. However, by using hybrid methods and performing proper equalization technique, spectrum can be utilized more efficiently. In this paper, we propose a hybrid MC-CDMA system which utilizes the whole spectrum for underlay transmission rather than solely the occupied parts. Due to the interference rejection capability, MC-CDMA can minimize the interference from the Primary User (PU) and exploit more diversity. Furthermore, overlay utilizes the spectrum holes while maintaining orthogonality to the underlay by Orthogonal Variable Spreading Factor (OVSF) codes. Chip and symbol- level MMSE-based modified equalizers are proposed for the system and the underlay performance of the system is evaluated. Simulation results show that BER performance improves significantly with the modified MMSE equalizers and for all PU occupancy levels.

Data traffic demand in cellular networks continues to increase exponentially leading to high capital expenditure and operational costs such as electricity cost. Femtocells have been proposed as a solution to enhance network capacity without significantly increasing energy consumption and associated network deployment costs. In this paper, we analyze the network capacity and energy consumption aspects of a joint macrocell and femtocell network based on the LTE standard. Femtocells and macrocells are operated in the same frequency band to enhance spectral efficiency. Femtocell users are secondary users who only access the channel opportunistically when macrocell users are idle. We propose the use of cooperative spectrum sensing to manage adverse interference emanating from the co-channel operation of both macrocells and femtocells. Simulation results show that cooperative sensing is very effective at managing this cross-tier interference to maximize the throughput of both the macrocell and femtocell layers. This throughput maximization allows the lowest energy consumption ratio of such a heterogeneous network.

Cognitive Radio (CR) is proposed to maximize spectrum utilization. When the licensed user is not using the spectrum, CR will then use it to communicate among each other. Hence, spectrum sensing, a procedure to observe primary user's existence, is important to CR. In this paper, we investigate the energy detection based cooperative spectrum sensing technique as it offers low complexity and significantly improves the sensing performance for CR. Conventional soft decision combining (SDC) scheme assumes that CR's local observation is perfectly forwarded to the fusion center. This forwarding procedure requires bandwidth, and increasingly so with more sensing nodes. By carefully applying quantization to this local observation, we could significantly reduce the number of bits required for this communication and thus reduce the overall system overhead. Uniform and non-uniform quantization approaches for SDC are investigated. We further propose a low complexity scheme based on an approximation of the cumulative density function. Simulation results show that these schemes can achieve a good detection performance comparable to the conventional SDC scheme, with reduced communication overhead.

Cyclostationary feature detection enables a cognitive radio system to reliably sensing the existence of licensed users. A statistical test based on the second-order cyclostationary features has been widely used. This paper evaluates the detection performance of this 2nd-order feature based technique subject to Nakagami fading. The analytical upper and lower bounds on the detection probability are derived by exploiting some available series expansion and exponential-type bound of the generalized Marcum Q-function. As a result, the preliminary detection performance can be acquired without relying on Monte Carlo simulations.

Cooperative diversity exploits the broadcast nature of wireless channels and uses relays to improve link reliability. Most of the cooperative communication protocols are assumed to be synchronous in nature, which is not always possible in vehicle-to-vehicle (V2V) communication due to fast moving nature of the nodes. Also the relay nodes are assumed to be half duplex which in turn reduces the spectral efficiency. In this paper, we propose an asynchronous cooperative communication protocol exploiting polarization diversity, which does not require synchronization at the relay node. Dual polarized antennas are employed at the relay node to achieve full duplex amplify-and-forward (ANF) communication. Hence the transmission duration is reduced which results into an increased throughput rate. Capacity analysis of the proposed scheme ascertains the high data rate as compared to conventional ANF. Bit error rate (BER) simulation also shows that the proposed scheme significantly outperforms both the non-cooperative single-input single-output and the conventional ANF schemes. Considering channel path loss, the proposed scheme consumes less total transmission energy as compared to the conventional ANF and non-cooperative scheme. Thus the proposed scheme is suitable for high rate and energy efficient relay-enabled communication.

Cognitive radio is proposed to opportunistically access the spectrum while the licensed user is idle. As a result, spectrum sensing procedure to observe primary user's existence is vital to cognitive radio. In this paper, we propose two adaptive sequential cooperative spectrum sensing techniques in time varying channel, namely the Two Stage Sequential Energy Detector and Differential Sequential Energy Detector. By utilizing past local observations from previous sensing slots, cognitive radio nodes can aggregate the current and previously received energy values to improve the detection performance. We explore the adaptive schemes, where cognitive radio user takes in previously received energy values when it thinks that the primary user's activity remains unchanged. Simulation results show that both proposed schemes provide better detection over conventional energy detection based cooperative technique. Additionally, both proposed schemes can satisfy the IEEE 802.22 standard's requirement of 10% false alarm and 90% detection in scenario that conventional techniques can not accommodate.

Cognitive radio is proposed to opportunistically access the spectrum while the licensed user is idle. As a result, spectrum sensing procedure to observe primary user's existence is vital to cognitive radio. In this paper, we investigate the energy detection based sequential cooperative spectrum sensing technique in time varying channel. By utilizing past local observations from previous sensing slots, cognitive radio nodes can aggregate the current and previously received energy values to improve the detection performance. We explored the moving-average technique using equal and exponential weighting. Simulation results show that this technique provides better detection performance compared to conventional energy detection based cooperative technique. Equal weighting provides the best performance when primary user's activity is not varying. If the primary users often change states, the proposed exponential weighting approach provides better detection performance.

Layered space time processing in spatial multiplexing systems requires the number of receive antennas to be equal to or larger than the number of transmit antennas. However, it is impractical for small-sized mobile units to accommodate a large number of antennas. To loosen this stringent requirement, a novel concept of virtual receive antennas (VRA) for overloaded MIMO system is presented. The VRA system architecture consists of two major parts: fractional timing offset in the transmitter and oversampling at the receive matched filter. This procedure expands the received signal dimension and thus creates virtual receive antennas. Due to these created virtual receive antennas, the minimum number of physical receive antennas could be reduced. In order to explore the potential of VRA, this paper evaluates its system capacity and performance bound. Results show that the VRA system can achieve higher ergodic capacity and outage rate than conventional overloaded MIMO system. Performance analysis also suggests a potential performance gain for the VRA system. To eliminate the created inter-symbol interference in the VRA, time domain and frequency domain equalization are utilized and evaluated.

Nodes in most cooperative networks are powered by batteries and some of which are even non-rechargeable. Therefore, power allocation schemes must be developed to save the transmit power and improve the life-time of the system. In this context, we present a novel power allocation scheme for multiple relay nodes that results in efficient cooperative communication. Considering channel path loss, the total transmission energy is distributed between the source and the relay nodes. The energy distribution ratio between the relay and direct link is optimized such that the quality of received signal is maintained with minimum total transmission energy consumption. We calculate the energy distribution ratio analytically and verified it through computer simulation. With the new power allocation scheme, the system also obtains an increased channel capacity as compared to cooperative scheme with conventional equal power allocation. Optimal relay positioning with proposed energy allocation scheme is also explored to maximize the capacity.

Multi-carrier code division multiple access (MC-CDMA) allows multiuser communication with frequency diversity. To increase the system data rates, spatial multiplexing for multiple-input multiple-output (MIMO) MC-CDMA has been investigated. This study proposes a chip level layered space–time (LST) receiver architecture for coded downlink MIMO MC-CDMA systems. As the conventional chip level ordered successive interference cancellation (OSIC) receiver is unable to overcome multiple access interference and performs poorly in multiuser scenarios, the proposed receiver cancels both spatial and multiuser interference in an ordered LST detection process by requiring only the knowledge of the desired user's spreading sequence. Simulation results show that the proposed receiver not only performs better than the existing linear detectors but also outperforms both the chip and symbol level OSIC receivers. In this study the authors also compare the error rate performance between the proposed system and MIMO orthogonal frequency division multiple access (MIMO OFDMA) system and they justify the comparisons by deriving and analysing the pairwise error probability (PEP) for both systems. MIMO MC-CDMA demonstrates a better performance over MIMO OFDMA under low system load. If all users’ spreading sequences are known, multiuser interference can be reduced and MIMO MC-CDMA performs better than MIMO OFDMA at all system loads.

Energy efficiency has become increasingly important in wireless communications nowadays. Saving energy will not only reduce operating cost but also reduces greenhouse gas emissions, which is important for combating climate change. The authors propose energy-efficient user grouping algorithms to provide power minimisation of grouped multi-carrier code division multiple access (MC-CDMA) and space–time block coding MC-CDMA systems in a cellular environment. Depending on the channel fading conditions, power control is utilised to minimise the total transmitted power under a bit error rate constraint. When the allocation is performed without a fair data rate requirement, the authors provide the optimal solution to the minimisation problem. However, when some fairness is considered, the optimal solution requires high computational complexity. Thus, the authors solve the problem by proposing two suboptimal algorithms. Simulation results illustrate a significantly reduced power consumption in comparison with other techniques.

In this paper, we propose user grouping algorithms to provide power minimization in grouped STBC MC-CDMA systems. Depending on the channel fading conditions, power control is utilized to minimize the total transmitted power under a BER constraint. When the allocation is performed without a fair data rate requirement, we provide the optimal solution to the minimization problem. However when some fairness is considered, the optimal solution requires high computational complexity. We solve this problem by proposing two suboptimal algorithms. Simulation results illustrate a significantly reduced power consumption in comparison with other techniques.

Multiple Input Multiple Output (MIMO) systems use multiple transmit and receive antennas to achieve higher data rates by transmitting multiple independent data systems. Transmission errors can be reduced by using Hybrid Automatic Repeat request (HARQ) combining techniques with MIMO systems. We propose two novel MIMO HARQ combining methods which are based on using pre-combining only and a joint post and pre-combining techniques. In addition to conventional direct transmission, HARQ schemes for MIMO Multi-hop relay systems are also investigated. A novel approach is proposed to deal with the parallel HARQ processes in MIMO relay scenario. The simulation results show that the proposed methods can enhance the overall throughput performance of MIMO Single-hop and multi-hop relay systems.

Cooperative communication exploits the broadcast nature of wireless channel and uses relay nodes to provide better reliability and higher data rates without increasing power and bandwidth. In this paper, the energy analysis of the asynchronous polarized cooperative (APC) scheme is performed. APC employs multiple antennas at the relay and destination nodes to achieve full duplex amplify-and-forward (ANF) communication. Hence the transmission duration is reduced which results into an increased spectral efficiency. Considering channel path loss, the APC scheme consumes less total transmission energy as compared to ANF and non-cooperative scheme over more practical distance between the nodes. Thus the APC scheme is both spectral and energy efficient, and is suitable for the cooperative communication systems.

In this paper, we propose user grouping algorithms to provide power minimization in grouped MC-CDMA systems. Depending on the channel fading conditions, power control is utilized to minimize the total transmitted power under a bit error rate (BER) constraint. When the allocation is performed without a fair data rate requirement, we provide the optimal solution to the minimization problem. However when some fairness is considered, the optimal solution requires high computational complexity. Thus, we solve the problem by proposing two suboptimal algorithms. Simulation results illustrate a significantly reduced power consumption in comparison with other techniques.

Cooperative communication achieves diversity through spatially separated cooperating nodes, which are battery powered in most applications. Therefore the energy consumption must be minimized without compromising the transmission quality (bit error rate). In this context, we present a novel power allocation scheme that results in efficient cooperative multiple-input multiple-output (MIMO) communication. Considering channel path loss, the total transmission energy is distributed between the source and the relay nodes. The energy distribution ratio between the relay and direct link is optimized such that the quality of received signal is maintained with minimum total transmission energy consumption. We calculate the energy distribution ratio analytically and verified it through computer simulation. With the new power allocation scheme, the system also obtains an increased channel capacity as compared to cooperative scheme with conventional equal power allocation and non-cooperative scheme. Optimal relay positioning with proposed energy allocation scheme is explored to maximize the capacity.

In our earlier work, a novel concept of virtual receive antennas (VRA) was proposed and is a viable solution for single carrier spatial multiplexing system with less number of physical receive antennas. In this paper, we investigate the VRA system in spatially multiplexed orthogonal frequency-division multiplexing (OFDM) system, which is a promising scheme for next generation of downlink communication. To create VRA in OFDM, various fractionally spaced timing offset is imposed to each transmit substream, and oversampling at the receiver filter output. To explore the potential of the VRA-OFDM system, this paper analyzes its system capacity and error rate performance. Results show that the VRA-OFDM system can achieve higher ergodic capacity and outage rate, and lower outage probability than conventional MISO-OFDM system. Due to the created virtual antennas, ordered successive interference cancellation (OSIC) can be used in the VRA-OFDM system, which favours the use of VRA in downlink communication due to lower complexity. Meanwhile, if maximum likelihood (ML) detection is used, the VRA-OFDM system shows better BER performance than the conventional overloaded MISO-OFDM ML receiver.

In this paper, we analyze and compare the error rate performance of downlink coded multiple-input multiple-output multi-carrier code division multiple access (MIMO MC-CDMA) and coded MIMO orthogonal frequency division multiple access (MIMO OFDMA) systems under frequency selective fading channel conditions. In particular, the pairwise error probabilities (PEP) for both systems are derived. Simulation results illustrate that when the number of users, hence the system load is low, MIMO MC-CDMA outperforms MIMO OFDMA. However when the system load increases, the performance of MIMO MC-CDMA deteriorates and becomes worse than MIMO OFDMA. This can be explained by the PEP analysis of MIMO MC- CDMA which shows that when the number of users is small, the multiuser interference is also small and frequency diversity is better exploited. Conversely at high system load, MIMO OFDMA outperforms MIMO MC-CDMA as it is insensitive to multiuser interference. Nevertheless if the other users' spreading sequences are available at the receiver of the desired user, the impact of multiuser interference can be minimised and MIMO MC-CDMA outperforms MIMO OFDMA at all system loads.

In cooperative wireless network, the users exploit spatial diversity by cooperating with each other. This alleviates the detrimental effects of fading and offers reliable data transfer. In this paper, we present a novel asynchronous cooperative multi-input multi-output (MIMO) communication scheme in the presence of polarization diversity which does not require synchronization at the relay node. Utilizing dual-polarized antennas, the relay node achieves full duplex amplify-and-forward (ANF) communication. Hence the transmission duration is significantly reduced which in turn results into an increased throughput rate. Capacity analysis of the proposed system ascertains the high data rate as compared to the conventional ANF protocol. Bit error rate simulation also shows that the proposed scheme significantly outperforms both the non-cooperative single-input single-output and the conventional ANF schemes.

Multiple antennas wireless systems can achieve large capacity at the expense of high hardware cost associated with the radio frequency chains. Antenna selection schemes make use of more antennas than RF chains and select a subset of antennas to effectively reduce the hardware cost and power consumption without much capacity loss. In this paper, the problem of receive antenna selection in V-BLAST systems is investigated. Two performance based selection criteria are proposed, namely the min-max MSE and min-first stage MSE criteria. The min-max criterion achieves the best performance at the expense of high computational complexity. Complexity reduced algorithms for this selection scheme are discussed. Analytical proof is provided to show the suboptimality of the capacity based selection to the performance based selection. The hybrid scheme, which combines the performance and capacity based selection approaches is proposed, it provides a good trade-off between performance and complexity. Computer simulations demonstrate that the complexity reduction algorithms and the hybrid scheme perform better and also with a lower complexity than the capacity based selection algorithm under most system configurations. Finally, robustness of the min-max MSE criterion under channel estimation errors is evaluated via computer simulations.

Multiple-input multiple-output (MIMO) antenna systems are capable of providing very high spectral efficiency due to the use of multiple antennas at transceivers. The problem of the expensive hardware cost associated with MIMO systems can be effectively mitigated by performing antenna selection at either or both ends. However, most works on antenna selection consider uncoupled antennas only, which might not be practical when implemented in a small-sized mobile unit. In this paper, effects of mutual coupling between antenna elements are included in the system model. We evaluate the system error performance of different antenna selection schemes in correlated channels when mutual coupling effects exist. Simulation results show that when the separation between two adjacent antennas is small, antenna selection schemes can significantly improve the performance degradation caused by the highly correlated and coupled receive signals. It could even outperform the full system, which is not possible under ideal channel conditions. In particular, the performance based selection scheme has the best performance in this scenario over capacity based selection and RF pre-processing.

Layered space time processing in spatial multiplexing systems has a stringent requirement that the number of receive antennas must be equal to or larger than the number of transmit antennas. A novel concept of virtual receive antennas (VRA) proposed in our previous work can loosen this requirement. In this paper, we present a low complexity frequency domain equalizer (FDE) and show the potentials of VRA by analysing the system capacity. In addition, we consider a more practical pulse shaping filter for the VRA system. Results show that the VRA system can achieve higher ergodic capacity and outage rate than the conventional overloaded MIMO system. With the proposed low complexity FDE receiver, VRA allows layered processing for spatial multiplexing system with less number of physical antenna.

In this paper, a chip level ordered successive spatial and multiuser interference cancellation (OSSMIC) receiver architecture is presented for downlink multiple-input multiple- output multi-carrier code division multiple access (MIMO MC-CDMA) systems. The proposed receiver performs ordered layer space-time detection. Unlike the existing ordered successive interference cancellation (OSIC) receiver, the proposed receiver cancels both spatial & multiuser interference in the SIC process. This allows detection in multiuser scenario, which is not possible in conventional chip level OSIC receiver. Simulation results show that the proposed receiver significantly outperforms both the existing chip level OSIC detector and the chip level linear receiver. As the proposed receiver only requires the knowledge of the desired user's spreading sequence, it is a viable solution for downlink MIMO MC-CDMA communications.

Recent research show that antenna selection schemes can reduce the hardware cost and power consumption of multiple antenna systems by using less radio frequency (RF) chains. The selected subset of antennas can achieve the diversity of a full system and a significant portion of the full capacity. In this paper, a new interpretation of the multiple-input multiple- output (MIMO) capacity formula is derived in term of mean square error (MSE) of different detection stages for V-BLAST specifically. Performance based and capacity based antenna selection schemes are compared in terms of computational complexity as well as their optimality in minimizing the system error probability. In addition, a hybrid scheme is proposed to combine the strength of both performance based and capacity based approaches. Simulation results show that performance based selection has the best performance yet with a higher complexity than the other two schemes for most combinations of antennas. The hybrid scheme, which has the least complexity among all the three schemes has a slightly worse block error rate (BLER) than the performance based selection, but better than the capacity based scheme. Robustness of selection schemes under channel estimation errors is investigated and verified via computer simulations.

Vertical Bell laboratory layered space-time (V-BLAST) is one of the promising multiple input multiple output (MIMO) architectures. Layered space time processing in V-BLAST requires the number of receive antennas to be equal to or larger than the number of transmit antennas. However, it is impractical for small-sized mobile units to accommodate all the required antennas. Focusing on this problem, we have presented a novel concept of virtual receive antennas (VRA) for spatial multiplexing system in flat fading channels. In that work, time domain equalization is utilized to eliminate inter-symbol interference, which is generated in the VRA. However, the employed time domain equalizer has a high computational complexity. In this paper, the VRA system with low complexity frequency domain equalizer (FDE) is investigated. Moreover, as the noise in the VRA system is correlated due to oversampling, we also present the noise decorrelating filter design. Through complexity and performance evaluation, FDE is demonstrated to be more suitable for the VRA system.

Spatial multiplexing with layered space-time receiver is one of the multiple input multiple output (MIMO) schemes that can achieve the linearly increasing capacity. However, layered space-time processing requires the number of receive antennas to be the same or larger than the number of transmit antennas. It is difficult to design a small sized mobile unit with such number of antennas, and hinders the broad dissemination of this promising technology. The authors investigate this problem and present the novel concept of virtual receive antennas for spatial multiplexing system. Each substream is transmitted with a specific timing offset, and the received matched filter output signal is oversampled. The oversampled signals have certain properties such that they can be considered as signals from correlated antennas. Hence, virtual receive antennas are created and the number of physical receive antennas can be reduced. Simulation results are presented to demonstrate the system performance.

Multiple antennas wireless systems can achieve large capacity at the expense of high hardware cost associated with the radio frequency (RF) chains. Antenna selection schemes make use of more antennas than RF chains and select a subset of antennas. It is an effective approach to reduce the hardware cost without much capacity loss. Antenna selection schemes in layered space-time system are mostly investigated in terms of capacity, and for zero-forcing (ZF) linear receivers. In this paper we analyse and compare the performance of antenna selection for zero-forcing and minimum mean square error (MMSE) based V-BLAST receivers, and derived a new performance bound for MMSE receivers. A novel performance based selection criterion for MMSE V-BLAST system is proposed, and named the minmax MSE criterion. Computer simulation shows that this new criterion can obtain the diversity of a full system and outperforms other previously proposed schemes with a near-optimal performance

Antenna selection is an effective approach to reduce RF (radio frequency) hardware cost and implementation complexity of multiple-input multiple-output systems. A subset of antennas is selected out of all available ones based on a certain criterion. Many researches on antenna selection for spatial multiplexing system focused on capacity based selection criterion, which do not necessarily optimise the system performance. In this paper, two novel error rate performance based criteria for receive antenna selection in layered space-time system are proposed. These criteria are namely the max-min and max-first layer criteria. A reduced complexity algorithm is also proposed for the max-min selection criterion. Computer simulations demonstrate that our proposed algorithms can obtain the diversity of a full system. In particular, the proposed max-min algorithm outperforms capacity based selection algorithms, while the max-first layer algorithm provides a low complexity solution to the problem

By transmitting different substreams in different antennas simultaneously, a multiple element antenna array system provides increased capacity that grows linearly with the number of transmit antennas. Layered space-time processing that performs nulling, detection and cancellation for each substream can be used for reception, with a linear growth in receiver complexity. This paper considers this multi-input multi-output system over a slow time-varying frequency selective Rayleigh fading channel environment. With the equivalent channel tap delay line model, each delayed tap in every transmit-receive antenna pair can be considered as an imaginary antenna transmitting a delayed version of the substreams. Based on this idea, we propose a layered maximum likelihood detection (L-MLD) scheme which performs layered processing and maximum likelihood detection for each substream and its delayed elements. To further improve the performance, a group maximum likelihood detection (G-MLD) scheme is also proposed by grouping the substreams and performing layered processing in groups and maximum likelihood detection within the group. However, both schemes increase the required number of receiving antennas, which increases hardware cost and size. To reduce this requirement, we propose the use of oversampling technique to increase the dimension of the received signal. Simulation results show that the L-MLD scheme achieves frequency diversity and outperforms existing schemes such as the V-BLAST system with OFDM and multi-input multi-output decision feedback equalizers (MIMO-DFE). Moreover, the G-MLD scheme performs better than L-MLD with an increased detection complexity. In addition, it was showed that further increasing the oversampling rate beyond the minimum requirement does not improve the performance.

Space-time trellis code (STTC) in frequency selective fading channel using maximum likelihood equalization and detection (MLED), and orthogonal frequency division multiplexing (OFDM) are compared in this paper for channels with arbitrary delay profile. The performance bound for both schemes are first derived and their performances are compared both analytically and through simulations. Code design, receiver complexity, interleaver design and the robustness issues are addressed in these comparisons

An iterative receiver for a space-time trellis coded system in frequency-selective fading channel is proposed. It performs channel gain estimation and sequence detection by using the expectation-maximization (EM) algorithm. Channel order estimation is included in the receiver to avoid unnecessary trellis computations by using the conditional model order estimator (CME). In addition, three modifications to the original CME criterion are proposed to improve the estimation accuracy. Simulation results show that the proposed receiver has a slight degradation in frame error rate performance to the known channel maximum likelihood receiver. Moreover, it outperforms the conventional fixed long-tap length EM receiver with a lesser complexity. Furthermore, the proposed modifications to the CME criterion improve the channel order estimation accuracy, thus minimizing unnecessary computations.

This paper considers V-BLAST (vertically layered Bell Laboratories layered space-time) over a frequency selective fading channel environment. By considering the delayed elements in each transmit-receive antenna pair as imaginary antennas transmitting delayed versions of the substreams, we proposed a layered maximum likelihood detection (L-MLD) scheme in So and Cheng (2002). This scheme performs ordered nulling, detection and cancellation to each substream and its delayed elements. However, it increases the required number of receiving antennas, thus increasing hardware cost and size. We propose the use of oversampling technique to increase the dimension of the received signal, hence reducing the minimum requirement on the number of receiving antennas. Simulation results show that the L-MLD scheme achieves frequency diversity and outperforms the V-BLAST system with OFDM. It also shows that further increasing the oversampling rate of L-MLD does not improve the performance.

This paper studies the performance of two major space-time trellis code (STC) transmission and detection schemes over frequency selective fading channels: (1) maximum likelihood equalization and detection (MLED), and (2) orthogonal frequency division multiplexing (OFDM). Their performance is evaluated and compared both analytically and empirically. It shows that the maximum achievable diversity order for MLED and OFDM approaches are both identical as NML, where N, M is the number of transmit and receive antennas respectively, and L is the channel tap length. To achieve a certain amount of diversity order, the MLED approach requires a weaker code, but the decoding trellis complexity for both MLED (with combined trellis detector) and OFDM are the same. For codes that do not achieve the maximum diversity order, MLED always achieves higher diversity order than OFDM. Moreover, the use of interleaver in STC OFDM is essential in avoiding rank deficiency caused by clustering of path arrivals, which reduces the diversity order of the system. Simulation results support these arguments, and show that the MLED approach outperforms OFDM when the same maximum diversity order achievable code is used.

V-BLAST (Vertically-layered Bell Laboratories Layered Space-Time) is a multiple transmit and receive antenna system that provides very high capacity compared to single antenna systems. However, most research in this area had assumed a flat-fading channel environment. This paper considers a frequency selective Rayleigh fading channel environment for multi-input and multi-output (MIMO) systems. Three different detection techniques with ordered successive interference cancellation are proposed: (1) zero-forcing maximal ratio combining (ZF-MRC); (2) layered maximum likelihood detection (L-MLD); and (3) group maximum likelihood detection (G-MLD). All these schemes consider the delayed elements in each transmit-receive antenna pair as imaginary antennas transmitting delayed versions of the substreams. Constraints on the number of receive antennas are also discussed, with G-MLD requiring the least elements. Simulation results show that G-MLD provides the best performance with ZF-MRC performing the worst.