We develop a low-overhead Channel State Information (CSI) feature that enables to reveal the physical geometry of a Non-Line-of-Sight (NLoS) environment with more accuracy than with the Channel Impulse Response (CIR). The proposed feature is based on statistical and representative information contained in the CIR, as well as Time Difference of Arrival (TDoA) information. In addition, we devise the CSI feature such that the required communication overhead and computational complexity, for applications such as localization, are lower compared to using the CIR. To that end, the proposed feature is used for fingerprint localization and compared with other state-of-the-art features. Simulation results show that the proposed feature, which reduces the communication overhead by 98.05% compared to using the CIR, achieves sub-meter localization accuracy, which is comparable to or higher than that of other state-of-the-art features with higher overhead.

Accurate fingerprint localization requires extensive numbers of measurements to build a comprehensive data set. We consider adaptive sampling to reduce the cost of measurement campaigns. Existing adaptive sampling algorithms target scalar or low-dimensional features and do not generalize to high-dimensional Channel State Information (CSI) features. To address this challenge, we create a radio environment map using Kriging interpolation. Sample points with high variances are identified in the 2D coverage area. The fingerprints of these points are obtained and used to adaptively update the data set. Simulation results demonstrate the effectiveness of proposed adaptive sampling strategy in improving localization accuracy while significantly reducing data collection effort. Compared to uniformly random sampling, the proposed adaptive sampling method enhances localization accuracy by 35% in root mean squared error when using weighted K nearest neighbor regression. An equivalent localization performance is achieved with 70% fewer training points compared to sampling uniformly at random.

We predict the physical distance between two users based on the Channel State Information (CSI) of wireless channels. The CSI of each user is measured at several multiantenna base stations. We consider a supervised metric learning framework using a neural network that ensures that the properties of a metric are fulfilled: zero distance between a point and itself, non-negativity, symmetry, and the triangle inequality. The training data set consists of CSI from pairs of points and their physical distance. As an example use case, we consider fingerprint localization, where creating large datasets is impractical. The metric can be learned from a small dataset because the number of training data pairs increases quadratically in the number of CSI-fingerprints. We use the learned metric for Weighted K-Nearest Neighbor (WKNN) localization, to find neighbors in the dataset and to compute the weighting vector. Simulation results show that the$80^{\text{th}}$percentile error can be improved by some 70 % using the learned metric as compared to the Euclidean distance for WKNN regression.

We consider uplink pilot allocation based on multipoint channel charting (CC) to mitigate pilot contamination in a multi-cell network with spatially correlated MIMO channels. The channel chart is created in an offline phase with full information, i.e. user channel covariance matrices are estimated at multiple base stations (BSs). In the online phase, we assume that only partial information about a user’s channel covariance is known, i.e., it is available only at the serving BS. A machine learning framework is developed to predict the CC locations in the online phase. Pilots are allocated to active users in the online phase based on weighted graph colouring. CC locations are used as proxies of user locations; similarity weights between users are constructed from CC distances. Simulation results show that the CC based approach with partial information in the online phase outperforms a solution based on full angle-of-arrival information, and performs closely to an algorithm with full covariance information. We also consider a partial information machine learning framework to predict the channel covariance matrices at other BSs, which slightly outperforms CC based approach, with the price of a larger communication overhead and computational complexity.

This paper considers a disaggregated Radio Access Network (RAN) architectural framework with the aim of eliminating cell boundaries and improving the bad performance of users near the cell-edge, which is an inherent limitation of cellular networks. The network consists of several Remote Radio Heads (RRHs), connected via a front-haul network to a number of Distributed Units (DUs), in turn connected to one Centralized Unit (CU). The achieved performance gains come from making each DU manage multiple overlapping clusters of RRHs, each tied to an orthogonal partition of the system bandwidth. This is done by assigning users to their preferred clusters, in such a way that each user has at least one cluster in which it does not suffer from low performance. For this, RRHs may be connected to multiple DUs, effectively extending the coverage area of each DU by adding new front-haul connections. After approximating the RRH clusters as having a circular shape, we formulate a geometric problem to guarantee that all users observe a predefined minimum ergodic rate. We minimize the number of partitions needed, so as to minimize the coordination traffic between the DUs and the CU. Additionally, we find the solution with the minimum number of extra front-haul connections needed. Simulation results show that the 5th percentile ergodic rate of users can be improved by 78%, compared to using a single partition.

We consider fingerprinting-based localization in highly cluttered multipath environments with non-line-of-sight conditions, typical of indoor scenarios. Channel state information (CSI) from multiple Base Stations (BSs) is used to construct a fingerprint. We investigate the physical geometry of the$k$nearest neighbors found by feature distances, as well as possible enhancements to boost achievable positioning accuracy. We observe that the performance of Weighted K-Nearest Neighbor (WKNN) regression depends on the relation between the true position and its$k$nearest feature neighbors. Better accuracy is achieved when the true position is inside the convex hull of the$k$nearest neighbors, otherwise localization performance degrades. Consequently, we devise a neighborhood selection algorithm to increase the possibility of a point being inside the convex hull of the$k$nearest feature neighbors. WKNN localization is also affected by the weighting function used. To further improve performance, we consider a general framework to find the optimum weighting function, utilizing Laguerre polynomials. We benchmark performance against WKNN with exponential weight and deep neural network based localization. Simulation results show that the optimum weighting function with neighbor selection outperforms the benchmark algorithms.

We study performance and complexity of fingerprint localization based on 5G signaling. We concentrate on channel covariance and Channel Impulse Response (CIR) features, studying the effect of several factors on the localization performance such as the channel bandwidth, the number of Base Stations (BSs), the number of antennas at each BS, and the number of time samples. We consider Weighted K Nearest Neighbour (WKNN) as well as Deep Neural Network (DNN) localization. We adopt DNNs based on the Rel-18 3GPP Study Item AI/ML for positioning accuracy enhancement. Simulation results show that channel covariance features outperform CIR in terms of localization accuracy. Furthermore, covariance-based features are robust with respect to bandwidth reduction, allowing for more power-efficient implementations. However, a noticeable dependency on the number of BSs, BS antennas, and time samples, is found. Results also show that increasing sampling density is much more beneficial for improving performance with CIR-based features. Again this highlights the power saving virtues of using covariance based features as input. Finally, results show that WKNN performs better with covariance-based features, with noticeable degradation in performance, when CIR features are used instead.

Channel charting (CC) is an unsupervised machine learning framework for learning a lower-dimensional representation of Channel State Information (CSI), while preserving spatial relations between CSI samples. In this paper, we consider super-resolution features in the angle-delay domain in massive Multiple-Input Multiple-Output (MIMO) systems. We i) treat the angle and delay separately, ii) present the so-called “Normalized Polar Feature” utilizing the channel statistics of the CSI samples, iii) use the Euclidean distance to compute the dissimilarity matrix, and create the channel chart. Simulation results based on the DeepMIMO data-set show that the proposed super-resolution representation with the Euclidean distance leads to the state-of-the-art quality CC as compared to other CSI features and distances from the literature such as angle-delay-power features with earth mover distance.

We consider a Static Reflecting Surface (SRS) assisted communication system, with an SRS deployed to assist communication in an area of a cell with poor connection to a massive multiple-input multiple-output Base Station (BS). The SRS has a high number of reflecting elements, with a static phase shift matrix, optimized offline at installation for serving a user population in the coverage area. To find the phase shift matrix at the SRS and the BS beamformer, we formulate a joint optimization problem aiming to maximize the average spectrum efficiency in the area, assuming line-of-sight communication between SRS and BS, as well as between SRS and the area. To tackle the problem, we decouple the BS beamforming and SRS phase shifter design problems. We assume that the BS beamforming is optimized in the operation phase based on the instantaneous end-to-end channel. Based on this we formulate the phase shifter design problem considering an upper bound of the spectrum efficiency, and a collection of sample locations in the area. Projected gradient ascent and convex relaxation approaches are used to obtain the phase shifters. In addition we consider wide-beam designs for the SRS to steer energy evenly at the target area. For this, we numerically find an approximate ideal wide beam, as well as constant moudulus prolate spheroidal sequences. We evaluate the spectrum efficiency performance of the designed phase shifters both in a single- and multi-user scenario, considering a single and multiple SRSs, where each SRS is optimized to serve the area. In the simulated scenario, an SRS loses between 37% and 60% in average spectrum efficiency, as compared to a fully dynamic Reconfigurable Intelligent Surface (RIS) of the same size with real-time electronic control of the phase shifter. The performance gap between a dynamically optimized RIS and an SRS shrinks with an increasing number of simultaneous users.

We define covariance difference of arrival (CDOA) features derived from channel state information that can be used for machine learning based fingerprinting localization in non-line of sight (NLoS) conditions, with minimal communication overhead. Taking advantage of the uniqueness of the multipath channel between the base station (BS) and user equipment (UE) at different locations in the geographical region of interest. UEs compute CDOA features, consisting of pair-wise distances between covariance matrices of received signals from multiple BSs. Measured features are fed back to the network, where fingerprinting localization is performed. We consider both k-nearest neighbour and neural network localization, and investigate the trade-off between localization performance and communication overhead. In simulations of a NLoS 5G NR factory scenario with eight-antenna BSs, CDOA features provide a localization error less than 0.91 m in 80% of the cases, as compared to 0.78 m for a benchmark method where UEs feed back complete measured covariance matrices to the network, and 1.36 m for power difference of arrival features. Comparing to complete covariance feedback, CDOA features reduce communication overhead by 98%.

We consider cellular network caching with network-wide Orthogonal Multipoint Multicast (OMPMC) delivery. We apply a probabilistic model for content placement at the Base Stations (BSs). Content is delivered with multipoint multicast operating in file-specific orthogonal resources: all BSs caching a distinct file synchronously multicast it to requesting users in a dedicated resource. For a network modeled as a Poisson Point Process (PPP), an expression for the outage probability is derived. The outage-minimizing cache policy is found from a joint constrained optimization problem over cache placement and resource allocation. We devise principles by which the solution in one propagation environment can be generalized to another. To reduce computational complexity, we obtain a sub-optimal solution based on convex relaxation. We obtain an upper bound of the gap between the optimal and sub-optimal solutions. We compare the outage performance of OMPMC with delivery polices from the literature. Simulation results show that exploiting OMPMC with optimal cache placement and resource allocation outperforms single point cache delivery policies with a wide margin.

We propose a novel beam-tracking algorithm based on channel charting (CC) which maintains the communication link between a base station (BS) and a mobile user equipment (UE) in a millimeter wave (mmWave) mobile communications system. Our method first uses large-scale channel state information at the BS in order to learn a CC. The points in the channel chart are then annotated with the signal-to-noise ratio (SNR) of best beams. One can then leverage this CC-to-SNR mapping in order to track strong beams between UEs and BS efficiently and robustly at very low beam-search overhead. Simulation results in a mmWave scenario show that the performance of the CC-assisted beam tracking method approaches that of an exhaustive beam-search approach while requiring significantly lower beam-search overhead than conventional tracking methods.

We consider an optimal cache-placement-and-delivery-policy where traffic is offloaded from Single-Point Unicast (SPUC) service by using network-level Orthogonal Multipoint Multicast (OMPMC) scheme. The files are classified into two sets. The most popular files are cached at the BSs using a probabilistic approach and are served by OMPMC. The remaining files are fetched from the core network on demand and served by SPUC. Optimal compound scheme is analyzed, based on resource allocation between OMPMC and multi-antenna SPUC schemes. If a user is not able to successfully receive the requested file due to its experienced signal-to-interference-plus-noise ratio, its request is in outage. A closed-form expression is derived for the total outage probability based on stochastic geometry for the compound scheme. An optimization problem is formulated to design the caching policy for the compound scheme. The optimal solution to this problem is obtained by finding optimal cache placement, bandwidth allocation, and file classification. Simulation results show that the compound scheme outperforms other caching schemes in terms of the total outage probability.

Optimal resource allocation is crucial for successful deployment of energy harvesting wireless sensor networks (EH-WSNs) such as Internet of Things (IoT) devices. Nonorthogonal multiple access (NOMA) can significantly improve the network throughput compared to orthogonal multiple access (OMA). This article considers optimal power management and data scheduling in multihop EH-WSN using NOMA. The EH-WSN consists of$M$sensor nodes aiming to transmit their data to a sink node. Assuming network connectivity, the multihop EH-WSN is represented by a directed graph. The resource allocation problem is formulated to efficiently utilize the available harvested energy to send the available data to the sink node with minimum cost. The resource allocation problem given the system dynamics is nonconvex due to the nonconvex constraints. Assuming high signal-to-interference and noise ratio (SINR), the nonconvex constraints are lower bounded by convex constraints. With the aid of variable transformation, the constrained nonconvex problem is approximated with a convex problem. The convex problem is solved using finite-horizon dynamic programming considering offline and online operations. The offline problem is formulated assuming noncausal information of the harvested energy and data arrival. The model predictive control (MPC) framework is used to obtain the solution of the online operation of the EH-WSN. A distributed MPC (DMPC) is proposed to overcome the computational complexity of solving the centralized MPC problem, assuming each sensor node is allowed to exchange information with its neighboring nodes. In the simulations, we use energy efficiency and average data transmitted to compare the performance of the EH-WSN using NOMA and OMA. Simulation results confirm that NOMA in multihop EH-WSN results in higher throughput compared to OMA.

We consider a Static Reflecting Surface (SRS) assisted communication system. Part of a cell served by a Base Station (BS) is blocked from Line-of-Sight (LoS), and an SRS is deployed to assist communication in that target area. The SRS has a high number of reflecting elements, with a static phase shift matrix, optimized offline at installation phase for a user population. To find the beamformer at the BS and phase shift matrix at the SRS, we formulate an optimization problem aiming to maximize the average data rate in the target area assuming LoS communication between SRS and BS, as well as between SRS and the users. A local optimum of the population-level problem is obtained using the interior point method. We furthermore consider a low complexity approach, where we divide the SRS & BS antennas into sub-blocks and the target area into subareas; each sub-block is designed to serve a user at the center of the corresponding subarea. Simulation results show that as compared to a fully dynamic Reconfigurable Intelligent Surface (RIS) of the same size, where there is real-time electronic control of the phase shifter, an SRS loses 30% in performance. Comparing to other SRS approaches, and broadcast approach from the literature, the population based approach provides higher average Spectrum Efficiency (SE), 5% SE and fairness index.

We consider optimal cache placement and delivery for Orthogonal Multipoint Multicasting (OMPMC) cellular systems. In OMPMC, all Base Stations (BSs) that cache a file transmit identical signals in a dedicated frequency resource. The simultaneous transmissions create artificial multipath propagation, which creates Inter-Block Interference (IBI) and Inter-Carrier Interference (ICI) in Orthogonal Frequency Division Multiplexing systems where the Cyclic Prefix (CP) is shorter than the maximum propagation delay. The placement of files at BS caches is based on a probabilistic model. A file request is in outage if the average signal-to-interference-and-noise ratio associated with a request is less than a threshold. We formulate the cache policy and bandwidth allocation as a joint optimization problem aiming to minimize the total outage probability, and considering the effect of IBI and ICI. Despite that the outage probability does not have a closed form expression, we are able to devise an algorithm to find the optimal solution based on predictor-corrector approach. Simulations results are used to demonstrate the capability of the proposed algorithm to find the optimum cache policy. Simulation results show that the effect of ICI/IBI has to be considered in designing OMPMC caching policy.

As a key enabler for massive machine-type communications (mMTC), spatial multiplexing relies on massive multiple-input multiple-output (mMIMO) technology to serve the massive number of user equipments (UEs). To exploit spatial multiplexing, accurate channel estimation through pilot signals is needed. In mMTC systems, it is impractical to allocate a unique orthogonal pilot sequence to each UE as it would require too long pilot sequences, degrading the spectral efficiency. This work addresses the design of channel features from correlated fading channels to assist the pilot assignment in multi-sector mMTC systems under pilot reuse of orthogonal sequences. In order to reduce pilot collisions and to enable pilot reuse, we propose to extract features from the channel covariance matrices that reflect the level of orthogonality between the UEs channels. Two features are investigated: covariance matrix distance (CMD) feature and CMD-aided channel charting (CC) feature. In terms of symbol error rate and achievable rate, the CC-based feature shows superior performance than the CMD-based feature and baseline pilot assignment algorithms.

We consider a downlink scenario where a multiantenna base station in a sectorized cellular system creates multiple logical cells in each sector, applying Adaptive Sector Splitting (ASS). In ASS, a population of User Equipments (UEs) is grouped based on radio Channel State Information (CSI), groups are assigned to cells, and the virtual antennas serving the cells are optimized based on CSI. Grouping UEs based on covariance matrix similarity may result in considerable spatial overlap of the UE groups, and a need for frequent handovers for mobile UEs. To reduce handovers, an improved grouping strategy that takes into account UE physical locations is needed. We use Channel Charting (CC) to learn the radio map of the cell from uplink CSI, and consider UE grouping based on CC locations aiming to maximize the mean distance of UEs to virtual cell borders without the need to know the physical locations of the UEs. Simulation results show that ASS groups based on CC are more compact than angle-of-arrival and covariance matrix based groupings from the literature.

We consider a reinforcement learning (RL) based joint cache placement and delivery (CPD) policy for cellular networks with limited caching capacity at both Base Stations (BSs) and User Equipments (UEs). The dynamics of file preferences of users is modeled by a Markov process. User requests are based on current preferences, and on the content of the user’s cache. We assume probabilistic models for the cache placement at both the UEs and the BSs. When the network receives a request for an un-cached file, it fetches the file from the core network via a backhaul link. File delivery is based on network-level orthogonal multipoint multicasting transmissions. For this, all BSs caching a specific file transmit collaboratively in a dedicated resource. File reception depends on the state of the wireless channels. We design the CPD policy while taking into account the user Quality of Service and the backhaul load, and using an Actor-Critic RL framework with two neural networks. Simulation results are used to show the merits of the devised CPD policy.

Channel charting is a method for creating radio-maps of a cell that capture the neighborhood relationships between User Equipments (UEs) in the cell based on machine learning techniques. In this paper, we leverage channel charting for predicting the best Base Station (BS) beam to serve a given UE in a massive-MIMO 5G network. Because of the autonomous beamforming at the UE in 5G networks, the BS cannot determine the best beam for transmission to a UE by measuring the UE transmissions in all the BS beams. To address this issue, we propose a framework to predict the best BS beam for a mobile UE in the next transmission instant by utilizing the channel charts of the cell that the UE is currently in. We evaluate the prediction accuracy of the framework using simulated channels from QuaDRiGa channel generator. We compare the performance of channel chart and physical location based predictors. While the prediction accuracy attained using channel charting is less than that of the prediction using physical locations, there remain several ways to improve the performance.

We consider an optimal cache-placement-and-delivery-policy using Network-level Orthogonal Multipoint Multicasting (OMPMC) for wireless networks. The placement of files in caches of Base Station (BS) is based on a probabilistic model, with controlled cache placement probabilities. File delivery is based on multipoint multicast and network-based orthogonal transmission; all BSs in the network caching a file transmit it synchronously in dedicated radio resources. If the average signal-to-noise ratio associated to a file at a requesting user is less than a threshold, the request is in outage. We derive a closed-form expression for the outage probability for a network modeled as a Poisson Point Process. An optimal caching policy is solved from an optimization problem, and compared to a threshold-based policy, suboptimal partial solutions, and single-point cache delivery. Simulation results show that exploiting OMPMC with optimal cache and bandwidth allocation significantly improves the overall outage probability as compared to single point delivery.

In this paper, we consider power control and data scheduling in an energy-harvesting (EH) multi-hop wireless-sensor-network (WSN). The network consists of M sensor nodes aiming to send their data to a sink node. Each sensor node has a battery of limited capacity to save the harvested energy and a buffer of limited size to store both the sensed and relayed data from neighboring nodes, each sensor node can exchange information within its neighborhood using single-hop transmission. Our goal is to develop a distributed algorithm that adaptively changes the transmitted data and power according to the traffic load and available energy such that the sensed data are received at the sink node. In this sense, a differential-game (DG) framework is proposed to efficiently utilize the available harvested energy and balance the buffer of all sensor nodes. The open-loop receding horizon Nash equilibrium is used as a solution to the proposed EH-WSN DG. Simulation results demonstrate the merits of the proposed approach.

In this paper, model-predictive-control (MPC) framework is proposed for an online power control and data scheduling of energy-harvesting (EH) multi-hop wireless-sensor-networks (WSNs). The network consists ofMsensor nodes transmitting their sensed information to a sink node through multi-hop transmission. Each sensor node has a battery of limited capacity to save the harvested energy and a buffer of limited size to store both the sensed and relayed data. The dynamic resource allocation problem is formulated using the MPC framework aiming to efficiently utilize the available harvested energy and transmit the data of all sensor nodes. The solution of the proposed MPC framework is compared with an offline solution, which is obtained assuming prior knowledge of the sensed data and harvested energy. Simulation results demonstrate the merits of the proposed approach.

Joint data and off-grid narrow-band interference (NBI) recovery is investigated in orthogonal-frequency-division-multiplexing (OFDM) systems using compressive-sensing (CS) framework. The joint recovery problem is formulated as a convex optimization problem of three weighted norms. A reduced computational complexity and scalable algorithm is proposed to solve the recovery problem based on the alternating-direction-method-of-multipliers (ADMM). Simulation results, show that the average-run-time of solving the joint recovery problem using the proposed ADMM algorithm is much less than the average-run-time of the interior point method.

In this paper, we propose a novel approach to recover off-grid narrow-band-interference (NBI) in orthogonal-frequency-division-multiplexing (OFDM) systems using a compressive-sensing (CS) framework. NBI degrades the performance of OFDM systems which motivates the need for mitigation techniques to reduce its effect. NBI is a sparse signal in the frequency-domain (FD). However, frequency-grid mismatch destroys the sparsity of NBI in the FD. Therefore, the received off-grid NBI is characterized by a nonlinear model which is parametrized by two vectors; the first vector represents the frequency-grid-mismatch and the second vector represents the FD sparse NBI. A CS- based particle-swarm-optimization (CS-PSO) evolutionary algorithm is proposed based on a weighted sum of l_2 andl_0 norms fitness function to jointly recover the support of the FD sparse vector and the frequenc ygrid- mismatch vector. Simulation results demonstrate the merits of the proposed approach.

In this paper, a novel approach is proposed to jointly recover the transmitted signal and mitigate narrow-band interference (NBI) in OFDM systems using a compressive sensing (CS) framework. NBI degrades the performance of OFDM systems which motivates the need for mitigation techniques to reduce its effect. The main idea behind our approach is to represent the transmitted data and the NBI signal as sparse atoms and then to solve a joint compressive sensing (JCS) problem. The recovery problem is formulated as a weighted optimization problem of two atomic norms and then solved using convex programming. The solution aims to recover the transmitted signal and NBI jointly. The off-grid problem of NBI signal is avoided using the atomic norm. The bit error rate (BER) performance of our proposed JCS recovery approach outperforms the BER performance of the conventional CS-based approach as demonstrated by simulation results.

In this paper, narrow-band interference (NBI) mitigation is addressed for two-way (TW) relaying amplify and forward (AF) orthogonal frequency division multiplexing (OFDM) systems. Based on the channel gains between the interferer, relay and the terminal nodes two copies of the NBI signal are received at each terminal node in addition to the desired signal. Hence, NBI can degrade the performance of TW-AF-OFDM relaying systems which motivates the need for mitigation techniques to reduce its effect. NBI is a sparse signal in the frequency domain, hence, the compressive sensing (CS) framework can be used to recover NBI and cancel it before detecting the transmitted signal. However, frequency-grid-mismatch destroys the sparsity of the received NBI signal at the terminal nodes. Hence, we propose a block-structured-dictionary-mismatch formulation to estimate the frequency-grid-mismatch and recover the sparsity of the NBI. The block orthogonal matching pursuit (B-OMP) algorithm is proposed to solve the formulated optimization problem because of its reduced computational complexity. Simulation results demonstrate the merits of the proposed approach.

In this paper, narrow-band interference (NBI) mitigation is addressed in amplify-and-forward orthogonal-frequency-division-multiplexing (AF-OFDM) cooperative communication systems. Based on the channel gains between the interferer, destination and the relay nodes, three copies of the NBI are received at the destination node in addition to the desired signal. Hence, NBI degrades the performance of AF-OFDM systems which motivates the need for mitigation techniques to reduce its effect. NBI is a sparse signal in the frequency-domain, hence, compressive sensing (CS) framework can be used to estimate and cancel the NBI before detecting the transmitted signal. However, frequency-grid-mismatch destroys the sparsity of NBI in the frequency domain at the destination terminal. A structured-dictionary-mismatch formulation is proposed to approximate the received NBI vector by two sparse vectors. An £2,1 norm minimization problem is solved to recover the sparse vectors. The recovered NBI is then canceled from the received signal before detection. Simulation results demonstrate the merits of the proposed approach.

Joint allocation of resources, namely subcarrier assignment and power profiles at the source and relay nodes, is considered for both relaying schemes amplify and forward (AF) and decode and forward (DF) with multiple users using orthogonal frequency division multiple access (OFDMA). The objective is to allocate the resources to maximize the weighted sum rate subject to individual power constraint on each node. We formulate such a problem as subcarrier based resource allocation that seeks joint optimization of subcarrier assignment and power profiles at the source and relay nodes. Using high signal to noise ratio (SNR) approximation, two algorithms are proposed based on channel gains' ordering. In the first algorithm, the ordered subcarriers are split into two partitions; the first partition of the ordered subcarriers are assigned to one of the users, whereas, the second partition is assigned to the remaining users. The algorithm is applied repeatedly in a nested fashion by splitting each second partition into two partitions until the last partition is split between the last two users. The partitions that maximize the weighted sum rate are then sought. The proposed algorithm is of a reduced computational complexity compared to the exhaustive search and the dual approaches. A heuristic algorithm is also proposed to reduce the computational complexity further; it is a two-stage algorithm; a basic stage, and a transfer stage. In the basic stage, each user is assigned a subset of the subcarriers based on an ordering function. In the transfer stage, subcarriers can be transferred between the users to maximize the weighted sum rate. The merits of the proposed algorithms are demonstrated by numerical simulations.

We develop auction-based algorithms for joint allocation of resources, i.e., power profiles at the source and relay nodes and subcarrier assignment profile for multiple-user amplify-and-forward (AF) orthogonal frequency-division multiple-access (OFDMA) systems. The first algorithm is based on sequential single-item auction, where each user submits a bid based on either the marginal increase or the relative marginal increase in the data rate using the subcarrier. The first bidding strategy maximizes the sum data rate, whereas the second bidding strategy maximizes the fairness index. In both cases, the subcarrier is assigned to the user who submits the highest bid. The algorithm proceeds in a sequential fashion until all subcarriers are assigned. To reduce the synchronized interactions between the base station and the users, we propose a one-shot auction algorithm, where each user submits bids for all subcarriers at once based on the Shapley value, a well-known cooperative-game theoretic concept. The user evaluates each subcarrier based on an estimation of the Shapley value. The subcarriers are then assigned based on the submitted bids using an iterative algorithm that maximizes the fairness index. The throughput and fairness indices are used to evaluate the performance of the proposed algorithms. Numerical results are used to show the merits of each algorithm.

Joint power and bandwidth allocation of an improved amplify and forward (AF) cooperative communication scheme is proposed to utilize the spectrum efficiently. Each user adapts to a mixed strategy transmission using frequency division multiplexing (FDM), where part of the data is transmitted using AF relaying with diversity, and the other part is transmitted using direct transmission without diversity. The resource allocation problem aimed at maximizing the sum rate of a multi-user up-link scenario using the improved scheme is formulated. Both flat and frequency-selective fading channels are addressed. The resource allocation problem of the power and bandwidth profiles is non-convex and difficult to solve. In this sense, a two-step iterative algorithm is proposed, which alternates between solving either power allocation sub-problem for a given bandwidth profile or a bandwidth allocation sub-problem for a given power profile. The convergence and optimality of the proposed algorithm are discussed using game theory framework. Numerical simulations show the merits of the proposed improved AF cooperative communication scheme and the proposed iterative algorithm.

We develop an auction-based algorithm for joint allocation of resources, namely power profiles at the source and relay nodes and subcarrier assignment profile for multiple users amplify and forward (AF) orthogonal frequency division multiple access (OFDMA) system. The proposed algorithm is based on sequential single item auction, where each user submits a bid based on either the marginal increase or the relative marginal increase of the data rate after using that subcarrier with optimal power profiles at the source and relay nodes. The first bidding strategy maximizes the sum data rate, whereas the second bidding strategy maximizes the fairness index. In both cases, the subcarrier is assigned to the user who submits the highest bid. The algorithm proceeds in a sequential fashion until all subcarriers are assigned. The system throughput and fairness indices are used to evaluate the performance of the proposed algorithm. Numerical results are used to show the merits of the proposed algorithm.

A low complexity algorithm is proposed to jointly allocate the resources, namely subcarrier assignment and power profiles at the sources and relay nodes for two users decode and forward (DF) relaying scheme using orthogonal frequency division multiple access (OFDMA). The objective is to allocate the resources to maximize the weighted sum rate subject to individual power constraint on each node. We formulate such a problem as subcarrier based resource allocation that seeks joint optimization of subcarrier assignment and power profiles at the source and relay nodes. Using high signal-to-noise- ratio (SNR) approximation, the proposed algorithm splits the subcarriers into two partitions based on channel gains' ordering, where each partition is assigned to one user. The computational complexity of finding the subcarrier assignment that maximizes the weighted sum rate for N subcarriers DF-OFDMA scenario is of O(N). The merits of the proposed algorithm is demonstrated by numerical simulations.

A low complexity algorithm is proposed for joint allocation of power profiles at the source and relay nodes and subcarrier assignment for single user amplify and forward (AF) cooperative communication using orthogonal frequency division multiplexing (OFDM) system. The relay uses AF cooperative relaying to transmit messages on a set of available subcarriers with diversity. The remaining subcarriers are used for direct transmission without diversity. The objective is to allocate the resources to maximize the sum rate subject to individual power constraint on each node. We formulate such a problem as subcarrier based resource allocation that seeks joint optimization of subcarrier assignment and power profiles at the source and relay nodes. The algorithm is based on an ordering of the channel gain's ratio and splitting them into two partitions, then searching for the partition that maximizes the sum rate. Simulation results demonstrate the merit of the proposed algorithm.