In this paper, we propose an energy efficient wireless communication system based on fluid antenna relay (FAR) to solve the problem of non-line-of-sight (NLoS) links caused by blockages with considering the physical properties. Driven by the demand for the sixth generation (6G) communication, fluid antenna systems (FASs) have become a key technology due to their flexibility in dynamically adjusting antenna positions. Existing research on FAS primarily focuses on line-of-sight (LoS) communication scenarios, and neglects the situations where only NLoS links exist. To address the issues posted by NLoS communication, we design an FAR-assisted communication system combined with amplify-and-forward (AF) protocol. In order to alleviate the high energy consumption introduced by AF protocol while ensuring communication quality, we formulate an energy efficiency (EE) maximization problem. By optimizing the positions of the fluid antennas (FAs) on both sides of the FAR, we achieve controllable phase shifts of the signals transmitting through the blockage which causes the NLoS link. Besides, we establish a channel model that jointly considers the blockage-through matrix, large-scale fading, and small-scale fading. To maximize the EE of the system, we jointly optimize the FAR position, FA positions, power control, and beamforming design under given constraints, and propose an iterative algorithm to solve this formulated optimization problem. Simulation results show that the proposed algorithm outperforms the traditional schemes in terms of EE, achieving up to 23.39% and 39.94% higher EE than the conventional reconfigurable intelligent surface (RIS) scheme and traditional AF relay scheme, respectively.

Federated Learning (FL) enhances data privacy by allowing clients to share only model parameters. However, it suffers from slow convergence speed and low accuracy under non-Independent and Identically Distributed (non-IID) data conditions due to the statistical heterogeneity across clients. To address this issue, Federated Transfer Learning (FTL) was introduced, which leverages pre-trained feature extraction layers from a subset of clients and transfers them to others, thereby improving convergence and reducing communication costs in lowly and moderately non-IID settings. Nevertheless, FTL remains ineffective under highly non-IID conditions, as the transferred representations struggle to generalize across diverse data patterns. In this paper, we systematically evaluate the performance of FL and FTL under varying non-IID levels and identify their limitations in extreme scenarios. We then propose two novel approaches: Multiple-round Federated Transfer Learning (MFTL) and Multiple-round Federated Transfer Continual Learning (MFTCL). MFTL employs iterative rounds of Transfer Learning (TL) to enhance robustness, while MFTCL integrates Continual Learning (CL) strategies by a replaying mechanism to mitigate catastrophic forgetting. Experimental results on a radar sensing image dataset, where varying numbers of clients are used during the pre-training phase to control feature learning under different levels of non-IID conditions, demonstrate that our methods outperform traditional FL and FTL in terms of convergence speed, accuracy and communication burden.

Real-time and contactless monitoring of vital signs, such as respiration and heartbeat, alongside reliable communication, is essential for modern healthcare systems, especially in remote and privacy-sensitive environments. Traditional wireless communication and sensing networks fall short in meeting all the stringent demands of eHealth, including accurate sensing, high data efficiency, and privacy preservation. To overcome the challenges, we propose a novel integrated sensing, computing, and semantic communication (ISCSC) framework. In the proposed system, a service robot utilises radar to detect patient positions and monitor their vital signs, while sending updates to the medical devices. Instead of transmitting raw physiological information, the robot computes and communicates semantically extracted health features to medical devices. This semantic processing improves data throughput and preserves the clinical relevance of the messages, while enhancing data privacy by avoiding the transmission of sensitive data. Leveraging the estimated patient locations, the robot employs an interacting multiple model (IMM) filter to actively track patient motion, thereby enabling robust beam steering for continuous and reliable monitoring. We then propose a joint optimisation of the beamforming matrices and the semantic extraction ratio, subject to computing capability and power budget constraints, with the objective of maximising both the semantic secrecy rate and sensing accuracy. Simulation results validate that the ISCSC framework achieves superior sensing accuracy, improved semantic transmission efficiency, and enhanced privacy preservation compared to conventional joint sensing and communication methods.

Semantic communication is envisioned as a key technology for future wireless networks due to its high communication efficiency. However, research combining semantic communication and advanced multiple access techniques, such as rate splitting multiple access (RSMA), is still lacking. In this paper, the problem of joint communication and computation resource allocation for probabilistic semantic communication (PSCom) with RSMA is investigated. In the considered model, the base station (BS) needs to transmit a large amount of data to multiple users with 1-layer RSMA. Due to limited communication resources, the BS is required to utilize semantic communication techniques to compress the original data. In this paper, we utilize knowledge graphs to represent semantic information and employ probabilistic graphs, which are shared between the BS and users, to further compress the knowledge graphs. The BS can use the probabilistic graph to compress the data to be transmitted, while the user can recover the compressed semantic information using the same shared probabilistic graph. The additional computation power required for semantic information compression inevitably results in a reduction in transmission power due to the limited total power budget. Considering the effect of semantic compression ratio, the semantic rate expression for RSMA is first obtained. Then, based on the obtained rate expression, an optimization problem is formulated with the aim of maximizing the sum of semantic rates of all users under total power, semantic compression ratio, and rate allocation constraints. To tackle this problem, an iterative algorithm is proposed, where the semantic compression ratio subproblem is addressed using a greedy algorithm, and the rate allocation and transmit beamforming design subproblem is solved using a successive convex approximation method. Numerical results validate the effectiveness of the proposed scheme.

In this paper, we propose an uplink multi-modal probabilistic semantic communication (PSCom) system that considers both communication and computation. In the considered PSCom model, the users and base station share the common knowledge, which is characterized by probability graph. Based on the shared probability graph, the original large-size semantic data is compressed into the small-size semantic information, which will introduce additional computation costs for semantic compression. Besides, semantic information recovered by the base station will also bring additional computation costs. Although this method incurs additional computation costs, it effectively reduces communication energy consumption. Based on the considered model, an optimization problem is formulated to minimize the total communication and computation energy consumption, considering latency, power budget, bandwidth, and semantic compression ratio constraints. To solve this mixed integer optimization problem, we first adopt a greedy algorithm for the semantic compression level selection of each model, thereby transforming the original problem into a continuous variable optimization problem. Then, derive the optimal solution of the communication power. Finally, the Lagrange multiplier method is used to determine the optimal bandwidth, which can result in a closed-form optimal solution. Simulation results validate the effectiveness of the proposed algorithm.

This paper investigates the secure resource allocation for a downlink integrated sensing and communication system with multiple legal users and potential eavesdroppers. In the considered model, the base station (BS) simultaneously transmits sensing and communication signals through beamforming design, where the sensing signals can be viewed as artificial noise to enhance the security of communication signals. To further enhance the security in the semantic layer, the semantic information is extracted from the original information before transmission. The user side can only successfully recover the received information with the help of the knowledge base shared with the BS, which is stored in advance. Our aim is to maximize the sum semantic secrecy rate of all users while maintaining the minimum quality of service for each user and guaranteeing overall sensing performance. To solve this sum semantic secrecy rate maximization problem, an iterative algorithm is proposed using the alternating optimization method. The simulation results demonstrate the superiority of the proposed algorithm in terms of secure semantic communication and reliable detection.

The unmanned aerial vehicle (UAV) swarms have shown great potential to serve next-generation communication networks with their extraordinary flexibility, affordability, and the ability to collaboratively and autonomously provide Line-of-Sight (LoS) services. However, autonomous collaboration under wireless dynamics is challenging. Distributed learning (DL) provides a chance for the UAV swarms to operate intelligently under sophisticated dynamics, such that they can be applied to wireless communication service scenarios, as well as applications including multidirectional remote surveillance, and target tracking. In this survey, we first introduce several popular DL frameworks that are capable of managing a UAV swarm, these include federated learning (FL), multiagent reinforcement learning (MARL), distributed inference (DI), and split learning (SL). We also present a comprehensive overview of how these DL frameworks manage UAV swarms in regard to trajectory design, power control, wireless resource allocation, user assignment, perception, and satellite–drone integration. Then, we present several state-of-the-art applications of UAV swarms in wireless communication systems, such as reconfigurable intelligent surfaces (RISs), virtual reality (VR), and semantic communications (SemComs), and discuss the problems and challenges that DL-enabled UAV swarms can solve in these applications. Finally, we describe open problems of using DL in UAV swarms and future research directions of DL-enabled UAV swarms. In summary, this survey provides a concise survey of various DL applications for UAV swarms in extensive scenarios.

In this work, the problem of training the secure federated learning (FL) algorithm over a multicell wireless network is investigated. FL is indeed a learning method that can protect users’ privacy, but it has also been shown to be vulnerable to gradient leakage attacks, which can leak users’ private data. Therefore, we propose a defense method to prevent gradient leakage attacks by uploading the selected layers to attend global model updates. Moreover, we add the differential privacy (DP) noise to the model during the local training to strengthen the defense further. Furthermore, to minimize the total privacy leakage for users, we formulate an optimization problem that minimizes this leakage by jointly optimizing resource block (RB) allocation, layer selection, and DP noise. To find a locally optimal solution to this problem, we divide it into two subproblems: 1) initially solving for RB allocation and 2) layer selection using successive convex approximation (SCA) for convex approximation, followed by optimizing DP noise. The simulation results demonstrate that our proposed optimization algorithm outperforms the other two benchmarks.

This article introduces an approach to harness digital twin (DT) technology in the realm of integrated sensing and communications (ISACs) in sixth-generation (6G) Internet of Everything (IoE) applications. We consider moving targets in a vehicular network and use DT to track and predict the motion of the vehicles. After predicting the location of the vehicle at the next time slot, the DT designs the assignment and beamforming for each vehicle. The real-time sensing information is then utilized to update and refine the DT, enabling further processing and decision making. In the DT, an extended Kalman filter (EKF) is used for the precise motion prediction. This model incorporates a dynamic Kalman gain, which is updated at each time slot based on the received echo signals. The state representation encompasses both the vehicle motion information and the error matrix, with the posterior Cramér-Rao bound (PCRB) employed to assess sensing accuracy. We consider a network with two roadside units (RSUs), and the vehicles need to be allocated to one of them. To optimize the overall transmission rate while maintaining acceptable sensing accuracy, an optimization problem is formulated. Since, it is generally hard to solve the original problem, the Lagrange multipliers and fractional programming are employed to simplify this optimization problem. To solve the simplified problem, this article introduces both the greedy and heuristic algorithms by optimizing both the vehicle assignments and predictive beamforming. The optimized results are then transferred back to the real space for ISAC applications. Recognizing the computational complexity of the greedy and heuristic algorithms, a bidirectional long short-term memory (LSTM)-based recurrent neural network (RNN) is proposed for efficient beamforming design within the DT. Simulation results demonstrate the effectiveness of the DT-based ISAC network. Notably, the LSTM-based RNN method achieves similar transmission rates as the heuristic algorithm but with significantly reduced computational complexity.

We propose a novel deep learning-based algorithm for channel prediction and energy efficiency (EE) optimisation in an intelligent reflecting surface (IRS) aided Terahertz communication system. Specifically, a multi-antenna base station with an IRS with massive reflecting elements is designed to serve multiple moving users. A deep learning-based prediction-optimisation scheme is presented where we first propose a transformer encoder with channel index embedding (TE-CIE) deep learning model for time-varying channel prediction. With the help of channel prediction, an EE optimisation algorithm is then designed to maximise the EE in advance based on the predicted channel state information (CSI). Finally, we combine both methods to construct a deep learning-based prediction-optimisation scheme. Specifically, the future CSI is predicted by TE-CIE and the IRS phase-shift and precoding matrices are optimised in advance. Simulation results demonstrate that our proposed channel prediction method achieves close-to-optimal performance in terms of low mean absolute error and a much faster speed than the two baseline models. We demonstrate that the proposed EE optimisation algorithm outperforms the baseline algorithms in terms of much better EE under diverse parameter settings. Finally, the proposed prediction-optimisation scheme achieves at least twice the EE improvement compared to the baseline methods in the literature.

Unmanned aerial vehicle (UAV) swarms have become a promising solution to enhance modern wireless communication in complicated environments. However, due to the existence of real-world malicious attacks, the performance of prediction and optimisation methods used for UAV swarms are easily degraded. In this paper, we propose a novel deep learning-based user mobility prediction, user assignment and drone position optimisation scheme for a UAV swarm-enabled wireless communication system in the presence of malicious Global Navigation Satellite System (GNSS) spoofing attackers. Specifically, a robust deep learning-based user mobility prediction model, namely denoising autoencoder recurrent transformer (DART), is designed. Additionally, two efficient user assignment and drone position optimisation methods are proposed. The proposed deep learning model forecasts user locations, on which we construct and solve assignment and position optimisation problems. Simulation results show that the proposed deep learning-based prediction-optimisation scheme can provide up to 30% higher overall sum rate compared with the adversarially trained long short-term memory (LSTM) baseline and almost double the overall sum rate compared with the vanilla LSTM baseline.

5G new radio (NR) technology is introduced to satisfy more demanding services. Ultra Reliable Low Latency Communication (URLLC) requires very low delay compared with the previous techniques. This is hard to achieve when hybrid automatic repeat request (HARQ) is applied and especially when the feedback channel is erroneous. In this work, we consider various delay components in incremental redundancy (IR) HARQ systems and minimize the average delay by applying asymmetric feedback detection (AFD) and find the optimal transmission length for each transmission attempt. A M/G/1 queuing model is used in this work to analyze the queuing delay in 5G NR when there are multiple uses in the system. Numerical results show that significant performance gains and lower outage probability can be achieved by applying AFD.

In wireless communication networks, relays are required when the quality of the direct link between the source and the destination is not high enough to support reliable transmission because of long distances or obstacles. Selecting the proper relay node (RN) to support hybrid automatic repeat request (HARQ) is of great importance in such a relay-assisted network. Different from previous works, whether to participate in the transmission is determined by each RN itself in this work, thus reducing the overhead. As RNs do not need to obtain channel state information about the whole network, there is no significant overhead in the system. Using the numbers of transmission attempts required by both channels calculated from the obtained channel state information, each RN sets a timer and forwards the packet when time-out occurs. Simulation results show that our proposed method significantly improves the performance of the system. When the channels are of relatively high quality, the performance of our method is close to the optimal relay selection which requires full information about the network.

This paper focuses on physical layer security issues in a multiple-user-multiple-input-multiple-output dual-functional radar-communication system (MU-MIMO-DFRC), which communicates with multiple downlink communication users (CUs) whilst detecting multiple targets, who are passive eavesdroppers that could intercept the confidential message. To establish secure integrated sensing and communication (ISAC) against numerous eavesdroppers, we use artificial noise (AN) for conventional receivers (Type-I). In the case of ISAC-dedicated receivers (Type-II), instead of AN, we employ sensing signals to confuse the eavesdroppers. We proposed secure ISAC beamforming designs for conventional and ISAC-dedicated receivers. For conventional receivers, we aim to minimise the summation of all eavesdroppers’ SNR. The optimisation problem is a fractional programming problem solved iteratively. For ISAC-dedicated receivers, we optimise the radar beam pattern towards the eavesdroppers, followed by a least-square minimisation between the ISAC beam pattern and the obtained radar beam pattern. The proposed design for ISAC-dedicated receivers is computationally efficient since the optimal solution is determined non-iteratively. The simulation results demonstrate that our design is able to find a compromise between sensing targets and communicating with the CUs while ensuring information confidentiality for both types of receivers.

In physical layer security, one interest of the community is the development of practical approaches to achieve reliable and secure communication, such as model-free approaches, in which the gradient of the channel model is required. This paper proposes a new model-free approach based on information-theoretic metrics. We train the encoder with deep reinforcement learning that uses a policy-based gradient descent algorithm whose loss function contains a feed-forward neural network. Simulation results show that our model is capable of retaining the eavesdropper’s BLER at a high level whilst ensuring the legitimate receiver’s BLER reduces to nearly zero.

Future connected and autonomous vehicles (CAVs) will operate at different autonomous levels for decades. Therefore, we propose a novel prioritisation scheme according to the autonomous levels of CAVs, and a corresponding multiple access mechanism, to be named full-duplex prioritised scheduling (FDPS) protocol, to enhance the delivery of high-priority packets in future New Radio (NR) enhanced vehicle-to-everything communication (eV2X) vehicular ad hoc networks (VANETs). Comparing to the standardised sensing-based semi-persistent scheduling (SB-SPS) protocol, this paper presents how collision detection is enabled during the broadcast, how collisions are resolved, how the priority of a packet from a colliding CAV is recognised, and how the broadcast of high-priority packets is enhanced. Both protocols are evaluated through mathematical formulations and simulations in terms of average successful packet delivery rate (PDR), collision duration, and latency. Simulation results have firstly validated the mathematical analysis, they have also shown the enhancement of packets delivery in future NR eV2X VANETs when the self-interference suppression (SIS) factor is better than 16%, which is achievable by deploying the existing SIS technologies.

Flying platforms, such as unmanned aerial vehicles (UAVs) are a promising solution for future small cell networks. UAVs can be used as aerial base stations (BSs) to enhance coverage, capacity and reliability of wireless networks. Also, with recent advances of self interference cancellation (SIC) techniques in full-duplex (FD) systems, practical implementation of FD BSs is feasible. In this paper, we investigate the problem of resource allocation for multi-small cell networks with FD-UAVs as aerial BSs with imperfect SIC. We consider three different scenarios: a) maximizing the DL sum-rate; b) maximizing the UL sum-rate; and finally c) maximizing the sum of UL and DL sum-rates. The aforementioned problems result in non-convex optimization problems, therefore, successive convex approximation algorithms are developed by leveraging D.C. (Difference of Convex functions) programming to find sub-optimal solutions. Simulation results illustrated validity and effectiveness of the proposed radio resource management algorithms in comparison with ground BSs, in both FD mode and its half-duplex (HD) counterpart. The results also indicate those situations where using aerial BS is advantageous over ground BS and reveal how FD transmission enhances the network performance in comparison with HD one.

In this paper, we propose a joint transceiver beamforming design method for hybrid full-duplex (FD) and half-duplex (HD) ad-hoc networks to cancel co-channel interference, thereby to improve system spectral efficiency. To characterize network performances, we derive a general expression of transmission capacity upper bound (TC-UB) plus its two compact versions by using a stochastic geometry model. Due to the proposed beamforming design and hybrid-duplex consideration, the exact TC and conventional methods to obtain TC-UBs are not applicable. This motivates us to exploit the UB of the largest eigenvalue of desired signals, Alzer’s inequality for the incomplete gamma function, and dominating interference region to formulate one general TC-UB and two of its compact versions. The numerical results show that the proposed beamforming method outperforms the existing beamforming strategies in terms of exact TC, especially when the number of transmit antennas is larger than the number of receiver antennas per node pair. In addition, the derived general TC-UB can provide relatively close TC performance as the exact ones, and its two compact versions can at least give order-wise TC performance. Moreover, we find the break-even points, where FD outperforms HD with different system configurations.

In this paper, the problem of maximizing the wireless users’ sum-rate for uplink rate splitting multiple access (RSMA) communications is studied. In the considered model, the message intended for a single user is split into two sub-messages with separate transmit power and the base station (BS) uses a successive decoding technique to decode the received messages. To maximize each user’s transmission rate, the users must adjust their transmit power and the BS must determine the decoding order of the messages transmitted from the users to the BS. This problem is formulated as a sum-rate maximization problem with proportional rate constraints by adjusting the users’ transmit power and the BS’s decoding order. However, since the decoding order variable in the optimization problem is discrete, the original maximization problem with transmit power and decoding order variables can be transformed into a problem with only the rate splitting variable. Then, the optimal rate splitting of each user is determined. Given the optimal rate splitting of each user and a decoding order, the optimal transmit power of each user is calculated. Next, the optimal decoding order is determined by an exhaustive search method. To further reduce the complexity of the optimization algorithm used for sum-rate maximization in RSMA, a user pairing based algorithm is introduced, which enables two users to use RSMA in each pair and also enables the users in different pairs to be allocated with orthogonal frequency. For comparisons, the optimal sum-rate maximizing solutions with proportional rate constraints are obtained for non-orthogonal multiple access (NOMA), frequency division multiple access (FDMA), and time division multiple access (TDMA). Simulation results show that RSMA can achieve up to 10.0, 22.2, and 81.2 percent gains in terms of sum-rate compared to NOMA, FDMA, and TDMA.

This paper investigates the problem of resource allocation for a wireless communication network with distributed reconfigurable intelligent surfaces (RISs). In this network, multiple RISs are spatially distributed to serve wireless users and the energy efficiency of the network is maximized by dynamically controlling the on-off status of each RIS as well as optimizing the reflection coefficients matrix of the RISs. This problem is posed as a joint optimization problem of transmit beamforming and RIS control, whose goal is to maximize the energy efficiency under minimum rate constraints of the users. To solve this problem, two iterative algorithms are proposed for the single-user case and multi-user case. For the single-user case, the phase optimization problem is solved by using a successive convex approximation method, which admits a closed-form solution at each step. Moreover, the optimal RIS on-off status is obtained by using the dual method. For the multi-user case, a low-complexity greedy searching method is proposed to solve the RIS on-off optimization problem. Simulation results show that the proposed scheme achieves up to 33% and 68% gains in terms of the energy efficiency in both single-user and multi-user cases compared to the conventional RIS scheme and amplify-and-forward relay scheme, respectively.

This paper investigates the deployment of federated learning (FL) over an over-the-air computation (AirComp) and intelligent reflecting surface (IRS) based wireless network. In the considered system, devices transmit locally trained machine learning (ML) models to the base station (BS) which aggregates the received ML models and generates a shared global ML model. The devices can directly transmit ML models to the BS or using IRS. Meanwhile, AirComp is used to aggregate ML models that are transmitted from the devices to the BS. To minimize the energy consumption of devices, an energy minimization problem is formulated, which jointly optimizes the device selection, phase shift matrix, decoding vector, and power control. To seek the solution, the original optimization problem is divided into four sub-problems. Then the fractional program, greedy algorithm, matrix derivation, and weighted minimum mean square error methods are used to compute the phase shift matrix, device selection vector, decoding vector, and transmit power, respectively. Simulation results show that the proposed algorithm can reduce 11.2% energy consumption of devices compared to an FL algorithm that is implemented at a network without any IRSs.

In this paper, the problem of optimal passive beamforming design is studied for a reconfigurable intelligent surface (RIS) assisted full-duplex (FD) communication system. In the studied model, two devices communicate with each other using one RIS under the FD mode. Each of the device will receive not only the message from the other device but also the self-interference. The main problem of this work is to minimize the sum transmit power by jointly optimizing the reflection coefficients matrix and the transmit power of devices. To solve this problem, a dual method is proposed, where the dual problem is formulated as a semidefinite programming problem. After solving the dual problem, the phase beamforming of the RIS is obtained in the closed form. Simulation results show that the proposed scheme can reduce up to 66% sum transmit power compared to a conventional RIS assisted half-duplex mode.

This work considers downlink incremental redundancy Hybrid Automatic Repeat Request (IR-HARQ) over unreliable feedback channels. Since the impact of positive feedback (i.e., ACK) error is smaller than that of negative feedback (i.e., NACK) error, an asymmetric feedback detection scheme is proposed to protect NACK and further reduce the outage probability. We formulate the HARQ process as a Markov Decision Process (MDP) model to adapt to the transmission rate of each transmission attempt without enriched feedback and additional feedback cost. We aim to optimize the performance of HARQ process under certain outage probability requirements by finding optimal asymmetric detection thresholds. Numerical results obtained on the downlink Rayleigh fading channel and 5G new radio (NR) PUCCH feedback channel show that by applying asymmetric feedback detection and adaptive rate allocation, higher throughput can be achieved under outage probability limitations.

In this paper, we propose a novel scheduling protocol, named full duplex semi-persistent scheduling (FD-SPS) protocol, to support the communications between autonomous vehicles (AVs) in future 5G enhanced vehicle-to-everything (eV2X) ad hoc networks (VANETs). Different from other works, AVs are considered to operate at different autonomous levels. Besides, full duplex (FD) technology is incorporated to provide simultaneous sensing and broadcasting capability, so that AVs can detect signal collisions whilst broadcasting. Furthermore, compared to the standardised cellular V2X (C-V2X) Mode 4 technology, the sensing-based semi-persistent scheduling (SB-SPS) protocol, collision resolution capability is realised. Thus, collided messages can be re-broadcast. A corresponding prioritised re-broadcast mechanism is also proposed. We focus on the latency performance, because the delivery of a message with short delay is of paramount significance in safety-related communications. We evaluate the FD-SPS protocol through both analysis and simulations, and compare it with the benchmark SB-SPS protocol. It is shown that our proposed protocol has enhanced the latency performance, and can be considered to be deployed in future 5G eV2X VANETs. This paper also shows that latency can be enhanced by taken autonomous levels into consideration in future MAC design.

This paper exploits full-duplex (FD) technology and deep reinforcement learning (DRL) algorithm jointly and adaptively to enhance the performance of 5G-V2X networks that operate based on the 5G-V2X Mode 4 standard. Specifically, we propose a novel physical- (PHY) and medium access control- (MAC) layer cross-layer design, in which collision detection capability is enabled during broadcasting without introducing redundant signalling. Besides, the resource reservation scheme, collision resolution mechanism and scheduling policy are also designed. As the proposed adaptive method is fully decentralised, vehicular users adapt to the unknown and fast-changing environment autonomously without any help from gNBs. Simulation results demonstrate the superiority of our proposed design over the standardised sensing-based semi-persistent scheduling (SB-SPS) protocol. Therefore, the proposed cross-layer design can be considered as a solution for future 5G-V2X VANETs.

Abstract Demands for stable and efficient transmission are increasing due to the ongoing expansion of global wireless data traffic. One of the most promising ways to moderate burden of traffic load and decrease the queuing delay is to design cross‐layer protocols. This paper considers a utility maximization problem for multipath ad hoc wireless networks via joint cross‐layer congestion control, routing and scheduling design. In the formulated utility maximization problem, rate, scheduling and queuing delay constraints are involved under the condition of fixed channels and time varying channels. To solve this utility maximization problem, two scheduling methods are proposed, that is, perfect scheduling and distributed scheduling. In perfect scheduling, all nodes in the network contribute to the scheduling process, while distributed method only takes into account adjacent nodes. For both methods, the global convergence is proved. A comprehensive simulation evaluation is performed which shows that this proposed algorithms outperform existing cross layer protocols in terms of increasing source rate and reducing congestion price.

Supporting reliable and seamless connectivity for aerial users, such as Unmanned Aerial Vehicles (UAVs), is one of the key challenges for the next-generation cellular networks. To tackle this challenge, we propose a joint beamforming, user association, and UAV-height control framework for cellular-connected multi-UAV communications. Our objective is to maximize the minimum achievable rate for UAVs subject to co-existing terrestrial users' rate constraints. A hierarchical bi-layer iterative algorithm is devised to solve the problem. By using the projection gradient method in inner layer iterations and the geometric program modeling plus the convex-concave procedure in outer layer iterations, our proposed algorithm is proved to converge to a local optimum. We also examine our proposed algorithm under the practical condition where channel estimation is not perfect. Numerical results show that our proposed joint beamforming, user association, and UAV-height control framework outperforms the conventional nearest UAV association method in terms of UAVs' minimum achievable rate for both perfect and imperfect channel estimation cases. We also observe different UAVs' heights (i.e., between 100m and 300m) do not affect the UAVs' achievable rates. For the case with moving UAV, we also study the trade-off between UAVs' minimum achievable rate and the frequency of updating optimization variables.

In this paper, the sum-rate maximization problem is studied for wireless networks that use downlink rate splitting multiple access (RSMA). In the considered model, the base station (BS) divides the messages that can be transmitted to its users into a “private” part and a “common” part. Here, the common message is a message that multiple users want to receive and the private message is a message that is dedicated to only a specific user. The RSMA mechanism enables a BS to adjust the split of common and private messages so as to control the interference by decoding and treating interference as noise and, thus optimizing the data rate of users. To maximize the users' sum-rate, the network can determine the rate allocation for the common message to meet the rate demand, and adjust the transmit power for the private message to reduce the interference. This problem is formulated as an optimization problem whose goal is to maximize the sum-rate of all users. To solve this nonconvex maximization problem with a single-antenna BS, the optimal power used for transmitting the private message to the users is first obtained in closed form for a given rate allocation and common message power. Based on the optimal private message transmit power, the optimal rate allocation is then derived under a fixed common message transmit power. Subsequently, an iterative algorithm is proposed to obtain a suboptimal solution of common message transmit power. To solve this nonconvex maximization problem with a multiple-antenna BS, a successive convex approximation method is utilized. Simulation results show that the RSMA can achieve up to 15.6% and 21.5% gains in terms of data rate compared to non-orthogonal multiple access (NOMA) and orthogonal frequency-division multiple access (OFDMA), respectively.

This article investigates the problem of resource allocation for multiuser communication networks with a reconfigurable intelligent surface (RIS)-assisted wireless transmitter. In this network, the sum transmit power of the network is minimized by controlling the phase beamforming of the RIS and transmit power of the base station. This problem is posed as a joint optimization problem of transmit power and RIS control, whose goal is to minimize the sum transmit power under signal-to-interference-plus-noise ratio (SINR) constraints of the users. To solve this problem, a dual method is proposed, where the dual problem is obtained as a semidefinite programming problem. After solving the dual problem, the phase beamforming of the RIS is obtained in the closed form, while the optimal transmit power is obtained by using the standard interference function. Simulation results show that the proposed scheme can reduce up to 94% and 27% sum transmit power compared to the maximum ratio transmission (MRT) beamforming and zero-forcing (ZF) beamforming techniques, respectively.

In this paper, the problem of energy efficient transmission and computation resource allocation for federated learning (FL) over wireless communication networks is investigated. In the considered model, each user exploits limited local computational resources to train a local FL model with its collected data and, then, sends the trained FL model to a base station (BS) which aggregates the local FL model and broadcasts it back to all of the users. Since FL involves an exchange of a learning model between users and the BS, both computation and communication latencies are determined by the learning accuracy level. Meanwhile, due to the limited energy budget of the wireless users, both local computation energy and transmission energy must be considered during the FL process. This joint learning and communication problem is formulated as an optimization problem whose goal is to minimize the total energy consumption of the system under a latency constraint. To solve this problem, an iterative algorithm is proposed where, at every step, closed-form solutions for time allocation, bandwidth allocation, power control, computation frequency, and learning accuracy are derived. Since the iterative algorithm requires an initial feasible solution, we construct the completion time minimization problem and a bisection-based algorithm is proposed to obtain the optimal solution, which is a feasible solution to the original energy minimization problem. Numerical results show that the proposed algorithms can reduce up to 59.5% energy consumption compared to the conventional FL method.

Abstract Multi-homed devices such as smartphones, tablets and laptops are enabled with multiple heterogeneous interfaces available for transmission. Those interfaces can be utilized for simultaneous transmission of a single TCP flow using Multipath TCP (MPTCP). MPTCP is a protocol that is designed to increase end-to-end throughput and reliability of communications by splitting data through multiple parallel paths. Although delay in MPTCP enhanced significantly in the recent years, high number of data transmissions remains an issue. In this paper, we reduce MPTCP delay by reducing the number of transmissions using Opportunistic Routing (OR) technique. OR is a routing model used to increase the delivery rate and reliability of data transmission in wireless networks by using the broadcasting method. This enables each subflow data to be delivered by multiple relays. We adapted OR on a number of MPTCP protocols namely, traditional MPTCP, Multipath TCP Traffic Splitting Control (MPTCP-TSC) and Redundant MPTCP (ReMP TCP) in an Internet of Things (IoT) environment. The results show that OR-based MPTCP schemes outperform existing schemes. We further compared the OR-based MPTCP protocols in terms of startup delay and energy efficiency. We found that ReMP TCP is better than other schemes in all scenarios.

This paper investigates the problem of resource allocation for a wireless communication network with distributed reconfigurable intelligent surfaces (RISs). In this network, multiple RISs are spatially distributed to serve wireless users and the energy efficiency of the network is maximized by dynamically controlling the on-off status of each RIS as well as optimizing the reflection coefficient matrix of the RISs. This problem is posed as a joint optimization problem of transmit power and RIS control, whose goal is to maximize the energy efficiency under minimum rate constraints of the users. To solve this problem, an alternating algorithm is proposed by solving two sub-problems iteratively. The phase optimization sub-problem is solved by using a successive convex approximation method, which admits a closed-form solution at each step. Moreover, the RIS on-off optimization sub-problem is solved by using the dual method. Simulation results show that the proposed scheme achieves up to 27% and 68% gains in terms of the energy efficiency compared to the conventional RIS scheme and amplify-and-forward relay scheme, respectively.

Neuromorphic sensors, a.k.a. dynamic vision sensors (DVS) or silicon retinas, do not capture full images (frames) at a fixed rate, but asynchronously capture spikes indicating changes of brightness in the scene, following the principles of biological vision and perception in mammals. DVS sensing and processing produces a data representation where the scene can be represented with a very high time resolution with a limited number of bits (an inherent data compression is performed at the time of acquisition). Such representation can be used locally to derive actionable responses and selected parts can be transmitted and then processed in another network location. Due to these features, such sensors represent an excellent choice as visual sensing technology for next-generation Internet-of-Things, e.g. in surveillance, drone technology, and robotics. It is in fact becoming evident that in this framework acquiring, processing, and transmitting frame-based video is inefficient in terms of energy consumption and reaction times, in particular in some scenarios. Hence, we explore here the feasibility of advanced Machine to Machine (M2M) communications systems that directly capture, compress and transmit spike-based visual information to cloud computing services in order to produce content classification or retrieval results with extremely low power and low latency.

In this paper, the sum-rate maximization problem is studied for wireless networks that use downlink rate splitting multiple access (RSMA). In the considered model, each base station (BS) divides the messages that must be transmitted to its users into a “private” part and a “common” part. Here, the common message is a message that all users want to receive and the private message is a message that is dedicated to only a specific user. The RSMA mechanism enables a BS to adjust the split of common and private messages so as to control the interference by decoding and treating interference as noise and, thus optimizing the data rate of users. To maximize the users' sum-rate, the network can determine the rate allocation for the common message to meet the rate demand, and adjust the transmit power for the private message to reduce the interference. This problem is formulated as an optimization problem whose goal is to maximize the sum-rate of all users. To solve this nonconvex maximization problem, the optimal power used for transmitting the private message to the users is first obtained in closed form for a given rate allocation and common message power. Based on the optimal private message transmission power, the optimal rate allocation is then derived under a fixed common message transmission power. Subsequently, a one-dimensional search algorithm is proposed to obtain the optimal solution of common message transmission power. Simulation results show that the RSMA can achieve up to 19.6% and 23.5% gains in terms of data rate compared to non-orthogonal multiple access (NOMA) and orthogonal frequency-division multiple access (OFDMA), respectively.

In this paper, we employ Cooperative Rate-Splitting (CRS) technique to enhance the Secrecy Sum Rate (SSR) for the Multiple Input Single Output (MISO) Broadcast Channel (BC), consisting of two legitimate users and one eavesdropper, with perfect Channel State Information (CSI) available at all nodes. For CRS based on the three-node relay channel, the transmitter splits and encodes the messages of legitimate users into common and private streams based on Rate-Splitting (RS). With the goal of maximizing SSR, the proposed CRS strategy opportunistically asks the relaying legitimate user to forward its decoded common message. During the transmission, the eavesdropper keeps wiretapping silently. To ensure secure transmission, the common message is used for the dual purpose, serving both as a desired message and Artificial Noise (AN) without consuming extra transmit power comparing to the conventional AN design. Taking into account the total power constraint and the Physical Layer (PHY) security, the precoders and timeslot allocation are jointly optimized by solving the nonconvex SSR maximization problem based on Sequential Convex Approximation (SCA) algorithm. Numerical results show that the proposed CRS secure transmission scheme outperforms existing Multi-User Linear Precoding (MU-LP) and Cooperative Non-Orthogonal Multiple Access (C-NOMA) strategies. Therefore, CRS is a promising strategy to enhance the PHY security in Multi-antenna BC systems.

Reconfigurable intelligent surface (RIS) is a promising solution to improve spectrum efficiency and promote cost- effectively wireless communication in the future. In this paper, RIS is deployed between a single-antenna base station (BS) and multiple single-antenna users to assist downlink non-orthogonal multiple access (NOMA) transmission. Considering the fairness among users, our goal is jointly optimizing the power allocation, decoding order, and the phase shifts to maximize the minimum user rate under total power constraint. To solve this minimum rate maximization problem, the optimal power allocation and the optimal fair rate are first revealed with a given phase shift vector. Then, the phase shift vector is optimized via maximizing the worst channel gain, which can determine the lower bound of the fair rate. The phase shift vector optimization problem is relaxed to a convex semidefinite program (SDP) and an efficient algorithm is proposed to obtain a rank-one solution. Simulation results show that our proposed algorithm can enhance the fair rate compared to the conventional scheme.

Reconfigurable intelligent surface (RIS) is an emerging technology to achieve energy-efficient wireless communication. This technology has the potential of turning the wireless environment, which is highly probabilistic in nature, into a programmable and partially deterministic space. This paper focuses on the joint user clustering, passive beamforming and power allocation for the downlink RIS-assisted nonorthogonal-multiple-access (NOMA) system, with the target of maximizing energy efficiency. This is an optimization problem which is solved by optimizing three sub-problems iteratively. In particular, user clustering sub-problem is solved with a matching algorithm, power allocation sub-problem is solved with the difference of two convex functions (DC) programming, and passive beamforming sub-problem is solved with univariate search technique. Simulation results demonstrate that the downlink RIS-assisted NOMA system can improve the energy efficiency by 7.8%-23.1%, compared with NOMA system without RIS and traditional orthogonal-multiple-access (OMA) system without RIS.

In this paper, the optimization of the system energy efficiency (EE) is studied for a reconfigurable intelligent surface (RIS) assisted full-deplex (FD) communication system. In the studied model, two devices communicate with each other using one RIS under the FD mode. Each of the devices will receive not only the message from the other device but also the self-interference. The main problem of this work is to maximize EE by jointly optimizing the reflection coefficients matrix and the transmit power of devices. To solve this problem, a nonlinear fractional programming based algorithm is used to transform the fractional optimization problem into a subtractive problem. Then the transmit power and the RIS phase shifts matrix are optimized by an iterative method. Simulation results show that the proposed scheme can achieve up to 200% gain in terms of EE compared to a conventional RIS assisted half-duplex mode.

For crowded and hotspot area, unmanned aerial vehicles (UAVs) are usually deployed to increase the coverage rate. In the considered model, there are three types of services for UAV assisted communication: control message, non-realtime communication, and real-time communication, which can cover most of the actual demands of users in a UAV assisted communication system. A bandwidth allocation problem is considered to minimize the total energy consumption of this system while satisfying the requirements. Two techniques are introduced to enhance the performance of the system. The first method is to categorize the ground users into multiple user groups and offer each group a unique RF channel with different bandwidth. The second method is to deploy more than one UAVs in the system. Bandwidth optimization in each scheme is proved to be a convex problem. Simulation results show the superiority of the proposed schemes in terms of energy consumption.

We propose an ensemble machine learning (EML) based robust cooperative spectrum sensing framework in full-duplex cognitive radio networks (FD-CRNs), which is robust with accuracy against malicious attacks and interference. FD communication improves the spectrum awareness capability of secondary users (SUs) by allowing them to sense and transmit simultaneously over the same frequency band. However, it also complicates the sensing environment by introducing self-interference and co-channel interference. Meanwhile, the presence of malicious attacks such as Primary User Emulation and Spectrum Sensing Data Falsification (SSDF) attacks also degrades the sensing performance. To alleviate the influence of interference and attacks, we design an EML framework that provides robust and accurate fusion performance. In such a context, we analyse the spectrum waste probability, collision probability and secondary throughput in both FD Listen-Before-Talk and Listen-And-Talk protocols. Simulation results show that our proposed EML framework can provide lower and more robust false-alarm probability than single-model based fusion methods with the same detection probability constraint for any size of training sets. It also outperforms the conventional majority vote based fusion strategy in terms of spectrum waste probability, collision probability and secondary throughput for any number of SSDF SUs, only at the cost of slightly higher inference time.

In this paper, the problem of maximizing sum-rate for uplink rate splitting multiple access (RSMA) communications is studied. In the considered model, each user transmits two messages to the base station (BS) with separate transmit power and the BS will use a successive decoding technique to decode the received messages. To maximize each user's transmission rate, the users must adjust their transmit power and the BS must determine the decoding order of the messages transmitted from the users to the BS. This problem is formulated as a sum-rate maximization problem with proportional rate constraints by adjusting the users' transmit power and the BS's decoding order. However, since the decoding order variable in the optimization problem is discrete, the original minimization problem with transmit power and decoding order variables can be transformed into a problem with only the rate splitting variable. Then, the optimal rate splitting of each user is determined. Given the optimal rate splitting of each user and a decoding order, the optimal transmit power of each user is determined. Next, the optimal decoding order is determined by an exhaustive search method. To further reduce the complexity of the optimization algorithm used for sum-rate maximization in RSMA, a user pairing based algorithm is introduced, which enables two users to use RSMA in each pair and also enables the users in different pairs to be allocated with orthogonal frequency. Simulation results show that RSMA can achieve up to 10.0%, 22.2%, and 83.7% gains in terms of rate compared to non-orthogonal multiple access (NOMA), frequency division multiple access (FDMA), and time division multiple access (TDMA).

This paper considers a full-duplex (FD) mobile-edge computation (MEC) system with non-orthogonal multiple access (NOMA) and energy harvesting (EH), where one group of users simultaneously offload task data to the base station (BS) via NOMA and the BS simultaneously receive data and broadcast energy to other group of users with FD. We aim at minimizing the total energy consumption of the system via power control, time scheduling and offloading data allocation. To solve this nonconvex problem, we first transform it into an equivalent problem with less variables. The equivalent problem is shown to be convex in each vector with the other two vectors fixed, which allows us to design an iterative algorithm with low complexity. Simulation results show that the proposed algorithm achieves better performance than the conventional methods.

In this paper we propose a cognitive radio scheme for full duplex (FD) device to device (D2D) communication between two vehicles in a vehicular network. The vehicles in proximity establish their connection over the unoccupied cellular network channels in a FD manner, and leave the channel as soon as they are informed of the presence of an incumbent, through error detection method. Channel sensing and identification is done in a cooperative manner prior to transmission. Probabilities of detection and false alarm, as well as the throughput of FD D2D communication in the proposed scheme have been formulated and performance of the proposed scheme and validity of corresponding analysis are verified via simulations.

Physical layer network coding (PNC) has been studied to serve wireless network MIMO systems with much lower backhaul load than approaches such as Cloud Radio Access Network (Cloud-RAN) and coordinated multipoint (CoMP). In this paper, we present a design guideline of engineering applicable PNC to fulfil the request of high user densities in 5G wireless RAN infrastructure. We show that the proposed design criteria guarantee that: 1) the whole system operates over binary system; 2) the PNC functions utilized at each access point overcome all singular fade states; and 3) the destination can unambiguously recover all source messages, while the overall backhaul load remains at the lowest level. We then develop a two-stage search algorithm to identify the optimum PNC mapping functions which greatly reduces the real-time computational complexity. The impact of estimated channel information and reduced number of singular fade states in different QAM modulation schemes are studied in this paper. Numerical results show that the proposed schemes achieve low outage probability with reduced backhaul load.

This paper investigates an uplink non-orthogonal multiple access (NOMA)-based mobile-edge computing (MEC) network. Our objective is to minimize a linear combination of the completion time of all users’ tasks and the total energy consumption of all users including transmission energy and local computation energy subject to computation latency, uploading data rate, time sharing and edge cloud capacity constraints. This work can significantly improve the energy efficiency and end-to-end delay of the applications in future wireless networks. For the general minimization problem, it is first transformed into an equivalent form. Then, an iterative algorithm is accordingly proposed, where closed-form solution is obtained in each step. For the special case with only minimizing the completion time, we propose a bisection-based algorithm to obtain the optimal solution. Also for the special case with infinite cloud capacity, we show that the original minimization problem can be transformed into an equivalent convex one. Numerical results show the superiority of the proposed algorithms compared with conventional algorithms in terms of completion time and energy consumption.

In this paper, we consider the sum power minimization problem via jointly optimizing user association, power control, computation capacity allocation, and location planning in a mobile edge computing (MEC) network with multiple unmanned aerial vehicles (UAVs). To solve the nonconvex problem, we propose a low-complexity algorithm with solving three subproblems iteratively. For the user association subproblem, the compressive sensing-based algorithm is accordingly proposed. For the computation capacity allocation subproblem, the optimal solution is obtained in closed form. For the location planning subproblem, the optimal solution is effectively obtained via one-dimensional search method. To obtain a feasible solution for this iterative algorithm, a fuzzy c-means clustering-based algorithm is proposed. The numerical results show that the proposed algorithm achieves better performance than the conventional approaches.

In this paper, we propose a joint transceiver beamforming design to simultaneously mitigate self- interference (SI) and partial inter-node interference for full-duplex multiple-input and multiple-output ad-hoc network, and then derive the transmission capacity upper bound (TC-UB) for the corresponding network. Condition on a specified transceiver antenna's configuration, we allow the SI effect to be cancelled at transmitter side, and offer an additional degree-of-freedom at receiver side for more inter-node interference cancellation. In addition, due to the proposed beamforming design and imperfect SI channel estimation, the conventional method to obtain the TC-UB is not applicable. This motivates us to exploit the dominating interferer region plus Newton-Raphson method to iteratively formulate the TC-UB. The results show that the derived TC-UB is quite close to the actual one especially when the number of receive-antenna is small. Moreover, our proposed beamforming design outperforms the existing beamforming strategies, and FD mode works better than HD mode in low signal-to-noise ratio region.

This paper presents a physical layer network coding (PNC) approach for network MIMO (N-MIMO) systems to release the heavy burden of backhaul load. The proposed PNC approach is applied for uplink scenario in binary systems, and the design guideline serves multiple mobile terminals (MTs) and guarantees unambiguous recovery of the message from each MT. We present a novel PNC design criterion first based on binary matrix theories, followed by an adaptive optimal mapping selection algorithm based on the proposed design criterion. In order to reduce the real-time computational complexity, a two-stage search algorithm for the optimal binary PNC mapping matrix is developed. Numerical results show that the proposed scheme achieves lower outage probability with reduced backhaul load compared to practical CoMP schemes which quantize the estimated symbols from a log-likelihood ratio (LLR) based multiuser detector into binary bits at each access point(AP).

In this paper, we consider joint antenna selection and optimal beamforming for energy efficient delay minimization. We assume multiple-input multi-output (MIMO) system with full duplex simultaneous wireless information and power transfer (FD-SWIPT) where each sensor is equipped with a power splitting (PS) system and can simultaneously receive both energy and information from the aggregator (AGG). We show that the antenna selection and beamforming power control policies are adaptive to the energy state information (ESI), the queue state information (QSI) and the channel state information (CSI). We develop an analytical framework for energy efficient delay-optimal control problem based on the theory of infinite horizon partially observable Markov decision process (POMDP). The infinite-horizon POMDP problem is transformed into an equivalent value Bellman program and solved by near-optimal point-based Heuristic Search Value Iteration (PB-HSVI) method under specific standard conditions. The proposed solution outcome is a set of sub-optimal antenna selection and beamforming control policies. Simulation results reveal an effective trade-off between the contradictory objectives (i.e. delay and power consumption) and show the enhancement in delay by using FD-SWIPT systems in comparison to Half Duplex (HD)-SWIPT systems.

In this paper, a multi-antenna assisted virtual full-duplex (FD) relaying with reliability-aware iterative decoding at destination node is proposed to improve system spectral efficiency and reliability. This scheme enables two half-duplex relay nodes, mimicked as FD relaying, to alternatively serve as transmitter and receiver to forward their received signals regardless of the decoding errors. Meanwhile, the inter-relay interference is cancelled with QR-decomposition. Then, by deploying the proposed reliability-aware iterative detection/decoding process at destination node, the inter-frame interference and error propagation effect can be simultaneously mitigated. Simulation results show that, without extra cost of time delay and signalling overhead, our proposed scheme outperforms the conventional selective decode-and-forward (S-DF) relaying schemes, such as cyclic redundancy check based S-DF relaying and threshold based S-DF relaying, by up to 8 dB in terms of bit-error-rate.

In this paper, a symbol-level selective transmission for full-duplex (FD) relaying networks is proposed to mitigate error propagation effects and improve system spectral efficiency. The idea is to allow the FD relay node to predict the correctly decoded symbols of each frame, based on the generalized square deviation method, and discard the erroneously decoded symbols, resulting in fewer errors being forwarded to the destination node. Using the capability for simultaneous transmission and reception at the FD relay node, our proposed strategy can improve the transmission efficiency without extra cost of signaling overhead. In addition, targeting on the derived expression for outage probability, we compare it with half-duplex relaying case and provide the transmission power and relay location optimization strategy to further enhance the system performances. The results show that our proposed scheme outperforms the classic relaying protocols, such as cyclic redundancy check-based selective decode-and-forward (S-DF) relaying and threshold-based S-DF relaying in terms of outage probability and bit error rate. Moreover, the performances with optimal power allocation are better than those with equal power allocation, especially when the FD relay node encounters strong self-interference and/or it is close to the destination node.

In this paper, we aim to minimize the average file transmission delay via bandwidth allocation and cache placement in two-tier heterogeneous networks with limited storage capacity, which consists of cache capacity and buffer capacity. For average delay minimization problem with fixed bandwidth allocation, although this problem is nonconvex, the optimal solution is obtained in closed form by comparing all locally optimal solutions calculated from solving the Karush-Kuhn-Tucker conditions. To jointly optimize bandwidth allocation and cache placement, the optimal bandwidth allocation is first derived and then substituted into the original problem. The structure of the optimal caching strategy is presented, which shows that it is optimal to cache the files with high popularity instead of the files with big size. Based on this optimal structure, we propose an iterative algorithm with low complexity to obtain a suboptimal solution, where the closed-from expression is obtained in each step. Numerical results show the superiority of our solution compared with the conventional cache strategy without considering cache and buffer tradeoff in terms of delay.

The distributed spatial modulation (DSM) protocol, which allows relays to forward the source's data while simultaneously allowing the relays to transmit their own data, has been proposed by Narayanan et al. In this paper, we introduce two new protocols for enabling the DSM, consisting of single-antenna network nodes, with simultaneous wireless information and power transfer capability: power splitting-based DSM (PS-DSM) and energy recycling-based DSM (ER-DSM). More specifically, the PS-DSM relies on power splitters at the relay nodes to harvest energy transmitted from the source. On the other hand, the ER-DSM, by exploiting the inactive cooperating relays in DSM-based protocols, recycles part of the transmitted energy in the network, without relying on power splitters or time switches at the relays to harvest energy. This leads to an increase in the average harvested energy at the relays with reduced hardware complexity. Both the PS-DSM and the ER-DSM also retain all the original features of DSM. Due to its particular operating principle and specific advantages, we select the ER-DSM as the candidate for further mathematical analysis. More specifically, by considering a multi-state battery model, we propose an analytical framework based on a Markov chain formulation for modeling the charging/discharging behavior of the batteries at the relay nodes in the ER-DSM. Furthermore, based on the derived Markov chain model, we introduce a mathematical framework for computing the error probability of the ER-DSM, by explicitly taking into account, the effect of finite-sized batteries. The frameworks are substantiated with the aid of Monte Carlo simulations for various system setups.

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In this paper we propose a novel adaptive scheme for full duplex communication of secondary users (SUs) in a cognitive radio network. The secondary network operates in three modes; Cooperative Sensing (CS), Full Duplex Transmit and Sensing (FDTS), and Full Duplex Transmit and Receive (FDTR). In the CS mode, SUs detect the activity of primary users (PUs) through a novel cooperative MAC protocol and will decide the system's mode of operation in the subsequent spectrum hole. In the FDTS mode one of the SUs senses the PUs' activity continuously whilst transmitting to another node. In the FDTR mode, SUs communicate bidirectionally in an asynchronous full duplex manner, with decreased collision durations. It is shown that the performance of the proposed protocol in terms of average and maximum collision duration and network throughput outperforms the traditional methods.

We propose a new caching concept in which wireless nodes in Device-to-Device (D2D) network can cache the initialsegments of the popular video files and may share to users in their proximity upon request. We term this approach as Bootstrapping-D2D (B-D2D) system. Our findings suggest that caching only initial-segments creates a large pool of popular files, thus improving the probability of availability of initial-segments within the vicinity of the requesting users. Our proposed B-D2D system is based on a popular clustering algorithm called density-based spatial clustering of applications with noise (DBSCAN), that groups the users into dense and arbitrary shape clusters based on two predefined thresholds. We then formulate an optimization problem to maximize the probability of availability of initialsegments within the transmission range of requesting wireless devices. The simulation results demonstrate that, caching the initial video segments could substantially improve the availability of segments within the proximity of users without requiring any extra hardware infrastructure cost, and D2D services could be utilized to deliver the video contents with lower startup delays in cellular networks.

Peer-assisted content delivery networks have recently emerged as an economically viable alternative to traditional content delivery approaches: the feasibility studies conducted for several large content providers suggested a remarkable potential of peer-assisted content delivery networks to reduce the burden of user requests on content delivery servers and several commercial peer-assisted deployments have been recently introduced. Yet there are many technical and commercial challenges which question the future of peer-assisted solutions in industrial settings. This includes among others unreliability of peer-to-peer networks, the lack of incentives for peers’ participation, and copyright issues. In this paper, we carefully review and systematize this ongoing debate around the future of peer-assisted networks and propose a novel taxonomy to characterize the research and industrial efforts in the area. To this end, we conduct a comprehensive survey of the last decade in the peer-assisted content delivery research and devise a novel taxonomy to characterize the identified challenges and the respective proposed solutions in the literature. Our survey includes a thorough review of the three very large scale feasibility studies conducted for BBC iPlayer, MSN Video and Conviva, five large commercial peer-assisted CDNs - Kankan, LiveSky, Akamai NetSession, Spotify, Tudou - and a vast scope of technical papers. We focus both on technical challenges in deploying peer-assisted solutions and also on non-technical challenges caused due to heterogeneity in user access patterns and distribution of resources among users as well as commercial feasibility related challenges attributed to the necessity of accounting for the interests and incentives of Internet Service Providers, End-Users and Content Providers. The results of our study suggest that many of technical challenges for implementing peer-assisted content delivery networks on an industrial scale have been already addressed in the literature, whereas a problem of finding economically viable solutions to incentivize participation in peer-assisted schemes remains an open issue to a large extent. Furthermore, the emerging Internet of Things (IoT) is expected to enable expansion of conventional CDNs to a broader network of connected devices through machine to machine communication.

Spectrum scarcity and dramatically increasing demand for high data rate and high‐quality video live streaming are of future cellular network design challenges. As a solution to this problem, cache‐enabled cellular network architecture has been recently proposed. Device‐to‐device (D2D) communications can be exploited for distributed video content delivery, and devices can be used for caching of the video files. This can increase the capacity and reduce the end‐to‐end delay in cellular networks. In this study, the authors propose a new scheme for video distribution over cellular networks by exploiting full‐duplex (FD) radios for D2D devices in two scenarios: (i) two nodes exchange their desired video files simultaneously and (ii) each node can concurrently transmit to and receive from two different nodes. In the latter case, an intermediate transceiver can serve one or multiple users’ file request(s) whilst capturing its desired file from another device in the vicinity. Mathematical expressions along with extensive simulations are used to compare their proposed scheme with a half‐duplex scheme to show the achievable gains in terms of sum throughput, active links, and delay. They will also look into the energy cost for achieving the improvements provided by operation in FD mode.

In asynchronous cognitive networks (CNs), where there is no synchronization between primary users (PUs) and secondary users (SUs), spectrum sensing becomes a challenging task. By combining cooperative spectrum sensing and full-duplex (FD) communications in asynchronous CNs, this paper demonstrates improvements in terms of the average throughput of both PUs and SUs for particular transmission schemes. The average throughputs are derived for SUs and PUs under different FD schemes, levels of residual self-interference, and number of cooperative SUs. In particular, we consider two types of FD schemes, namely, FD transmit-sense-reception (FDr) and FD transmit-sense (FDs). FDr allows SUs to transmit and receive data simultaneously, whereas, in FDs, the SUs continuously sense the channel during the transmission time. This paper shows the respective trade-offs and obtains the optimal scheme based on cooperative FD spectrum sensing. In addition, SUs’ average throughput is analyzed under different primary channel utilization and multichannel sensing schemes. Finally, new FD MAC protocol design is proposed and analyzed for FD cooperative spectrum sensing. We found optimum parameters for our proposed MAC protocol to achieve higher average throughput in certain applications.

In this paper, we propose a cooperative automatic repeat request (ARQ) scheme for cognitive radio networks with full duplex (FD) capability. Cognitive system takes advantage of opportunities arising during primary's ARQ retransmission rounds for transmission of data whilst cooperating with primary network at the same time. This requires self-interference cancellation as well as known interference cancellation capabilities of destination nodes in the secondary network which are achievable with recent advances in FD technology. It is shown that the proposed protocol will achieve non-trivial throughput gain for the secondary network during primary's ARQ rounds without any degradation of the primary's performance. The validity of this method has been investigated in terms of data link layer Packet Error Rate (PER) as an important and practical figure of merit. Performance of the proposed protocol is analyzed mathematically and its effectiveness is verified via simulations.

We consider a cognitive radio network of nodes with full duplex capability where Self-Interference Suppression (SIS) is not perfect. We have proposed a new scheme for sensing and transmission of Secondary Users (SUs) with two modes of operation; Cooperative Sensing (CS) mode and Full Duplex Transmit and Receive (FDTR) mode. Cooperative sensing is applied to reduce misdetection probability and to avoid collision with primary users. Upon successful detection of a spectrum hole, both SUs start bidirectional communication in the FDTR mode in an asynchronous manner to decrease average collision duration while doubling throughput. Analytical closed forms for collision probability, average collision duration, and throughput of SUs are derived and impact of imperfect SIS and SUs' frame duration on network performance are investigated.

We propose an optimal radio resource allocation (RRA) scheme for scalable H.264/SVC multi-user video transmission over downlink orthogonal frequency division multiple Access (OFDMA)-based cognitive radio (CR) networks. Our framework adopts a new probabilistic approach to mitigate the total imposed interference by cognitive users on the licensed spectrum. We consider two fundamental network service objectives, i.e., the number of satisfied users and the video quality. We devise the 3-dimensional scalable quality of the H.264/SVC video transmission for an OFDMA-based CR network and develop efficient suboptimal algorithms to solve the probabilistic constrained mixed discrete-continuous non-linear programming (MDCNLP) problem. Simulation results indicate that the proposed quality-aware scheme can achieve up to 1.3 dB increase in the average peak signal-to-noise ratio (PSNR) per user over the conventional non-quality-aware RRA algorithms.

In this paper, a cross-layer resource allocation algorithm is proposed in the context of orthogonal frequency division multiple access (OFDMA)-based cognitive radio (CR) video application systems. User video quality and channel awareness are incorporated in the design, towards an optimal subcarrier and power allocation scheme, subject to minimum secondary receiver (SRx) video quality and primary receiver (PRx) interference threshold constraints. First, the relationship between PRx interference margin and power limits at the secondary transmitter (STx) is analytically derived. Then, to provide PRx with satisfactory quality of service (QoS), we propose a new probabilistic approach to mitigate the total imposed interference of the STx on PRx, considering imperfect STx to PRx channel state information (CSI). The effect of PRx interference limit violation probability, interference threshold, and error variance of imperfect CSI are evaluated through mathematical analysis and computer simulations. Results indicate that significant improvement in SRx total video quality is achieved through our proposed quality-aware (QA) algorithm over other state-of-the-art non-quality-aware (NQA) designs in the literature. The enhanced performance was obtained whilst guaranteeing SRx minimum quality and PRx prescribed QoS constraints.

Radio resource allocation (RRA) algorithms are developed to enhance the spectral efficiency of downlink adaptive modulation and coding (AMC)-based orthogonal frequency-division multiple access (OFDMA) cognitive radios (CRs) under average transmit and peak interference power constraints. We consider the practical case of noisy channel-state-information (CSI) between cognitive transmitter (CTx) and primary receiver (PRx), and design novel interference management techniques to model and control the CR-inflicted interference on licensed spectrum. An expression for the cumulative density function (cdf) of the CRs' received signal-to-interference-plus-noise ratio (SINR) is developed to evaluate the resultant average spectral efficiency. Simulation results reveal that by adopting the joint resource allocation and interference management framework a considerable performance gain in the AMC-based OFDMA CR network is achieved over the conventional optimization methods.

Link adaptation using adaptive modulation and coding (AMC) is a popular physical layer technique for efficient use of time‐varying fading channels. Using a cross‐layer approach, AMC and truncated automatic repeat request scheme at data link layer are jointly considered. For a convolutionally‐coded M‐ary quadrature amplitude modulation system under block fading channel condition, a variable rate transmission scheme that uses imperfect channel state information is derived. First, a closed‐form expression for approximating packet error rate (PER) is found as a function of coded information rate. It is then shown that by solving an optimisation problem using this expression, optimum transmission rate can be determined in a way that maximises the spectral efficiency while satisfying an average PER constraint. Hence, the parameters in physical and medium access control layers are incorporated in the analysis.

Delay-sensitive video transmission over IEEE 802.11 wireless local area networks (WLANs) is analyzed in a cross-layer optimization framework. The effect of delay constraint on the quality of received packets is studied by analyzing “expired-time packet discard rate”. Three analytical models are examined and it is shown that M/M/1 model is quite an adequate model for analyzing delay-limited applications such as live video transmission over WLAN. The optimal MAC retry limit corresponding to the minimum “total packet loss rate” is derived by exploiting both mathematical analysis and NS-2 simulations. We have shown that there is an interaction between "packet overflow drop" and "expired-time packet discard" processes in the queue. Subsequently, by introducing the concept of virtual buffer size, we will obtain the optimal buffer size in order to avoid "packet overflow drop". We finally introduced a simple and yet effective real-time algorithm for retry-limit adaptation over IEEE 802.11 MAC in order to maintain a loss protection for delay-critical video traffic transmission, and showed that the average link-layer throughput can be improved by using our adaptive scheme.

In CDMA systems, outer loop power control (OLPC) determines the target value of SNR at the receiver, mostly by using look-up tables to map bit error rates (BERs) to SNRtargets. In this contribution, transmission delay and packet loss rate constraints in the data link layer (DLL) are invoked in order to determine the optimum outer loop SNR-target setpoint analytically, according to the number of active users in cell. Optimality is, in this sense, the maximization of system throughput. Using the optimum SNR-target, the optimal spreading factor is determined. Subsequently, the joint optimization of outer loop SNR-target and variable spreading factor (VSF), at the physical(PHY)-layer, with truncated automatic repeat request (ARQ) error control mechanism at the data link layer are proposed. Our scheme is compared with 'constant SNR-target' and 'PHY-layer based variable SNR-target' cases under continuous power and rate variation to show (analytically and by simulations) the achievable gain through the coupling of physical and data link layers parameters.

Joint optimization of outer loop power control (OLPC) signal to noise ratio-target (SNR-target) and variable spreading factor (VSF), at the physical(PHY)-layer, with truncated automatic repeat request (ARQ), at data link layer (DLL), in accordance to the number of active users in the cell is considered. This investigation is on a single cell conventional cellular DS-CDMA communication system with frequency-selective fading channels, where the number of active users is modelled through a one-dimensional discrete Markov chain. The optimally is in the sense of maximizing the sum-throughput, given a coherent RAKE receiver is employed with maximum ratio combining (MRC). We determine the optimal spreading factor using OLPC SNR-target at the PHY-layer, which satisfies the QoS imposed by packet error rate-target (PER-target) as a function of maximum number of allowed ARQ retransmissions at the DLL. The channel model considers independent paths with Nakagami fading characteristics. Total and truncated channel inversion techniques are used within the inner loop power control (ILPC) to adapt the transmission power to short time channel variations. Sum-throughput performance of the optimized system and non optimized system, where the SNR-target is assumed to be constant, under various multipath fading conditions are studied. A considerable gain in sum-throughput is achieved through coupling of PHY-layer and DLL parameters.

This study proposes the use of wavelet transform in long-term evolution (LTE) cellular systems. Mathematical expressions are derived to represent data rate in LTE downlink transmission based on Wavelet and Fourier Transforms. Furthermore, a comparison between these two systems is provided. Simulation results show the proposed orthogonal wavelet division multiplexing (OWDM) approach outperforms the traditional orthogonal frequency division multiplexing-based systems. The data rate can also be increased by the amount of CyclicPrefix/SymbolTime%, as there is no need for a channel prefix in an OWDM-based system.

In CDMA systems, outer loop power control (OLPC) determines the target value of SNR at the receiver, mostly by using look-up tables to map bit error rates (BERs) to SNR-targets. In this contribution, transmission delay and packet loss rate constraints in the data link layer (DLL) are invoked in order to determine the optimum outer loop SNR-target setpoint analytically, according to the number of active users in cell. Optimality is, in this sense, the maximization of system throughput. Using the optimum SNR-target, the optimal spreading factor is determined. Subsequently, the joint optimization of outer loop SNR-target and variable spreading factor (VSF), at the physical(PHY)-layer, with truncated automatic repeat request (ARQ) error control mechanism at the data link layer are proposed. Hence, we show that quality of service (QoS) requirements at these layers can be simultaneously satisfied while maximizing throughput. Total and truncated channel inversion strategies are employed in the inner loop to adapt transmit power to short-time channel variations. We propose a system where the number of users in a cell is modeled by a one-dimensional discrete Markov chain, and design the adaptive continuous power and rate mechanism for the worst case packet error rate (PER) condition. The corresponding theoretical throughput, which can be regarded as upper-bound for discrete spreading factor case, is obtained numerically for various settings of system parameters. We have also provided simulation results for a practical channel condition. Our scheme is compared with "constant SNR-target" and "PHY-layer based variable SNR-target" cases under continuous power and rate variation to show the achievable gain through the coupling of physical and data link layers parameters.

In this paper, we consider a dynamic spectrum sharing system consisting of a primary user, whose licensed spectrum is allowed to be accessed by a secondary user as long as it does not violate the prescribed interference limit inflicted on the primary user. Assuming the Nakagami-m block-fading environment, we aim at maximizing the performance of secondary user's link in terms of average spectral efficiency (ASE) and error performance under the specified packet error rate (PER) and average interference limit constraints. To this end, we employ a cross-layer design policy which combines adaptive power and coded discrete M-QAM modulation scheme at the physical layer with a truncated automatic repeat request (ARQ) protocol at the data link layer, and simultaneously satisfies the aforementioned constraints. Numerical results affirm that the secondary link of spectrum sharing system combining ARQ with adaptive modulation and coding (AMC) achieves significant gain in ASE depending on the maximum number of retransmissions initiated by the ARQ protocol. The results further indicate that the ARQ protocol essentially improves the packet loss rate performance of the secondary link.

In this paper, new adaptive scheduling and resource allocation algorithms for frequency-selective time-varying downlink channels are proposed. The proposed methods are particularly useful in LTE-based systems. Analytical adaptive power optimization formulae have been obtained in order to maximize channel spectral efficiency for continuous and discrete rate schemes. The formulae are derived for single-user scenario with a limited number of time- frequency grids. Optimum rate adaptation in each grid is first considered and subsequently extended to a multi-user case while maintaining the fairness among all the users. Further, three different algorithms are proposed for resource scheduling according to the obtained analytical results. Finally, simulation results confirm that the proposed dynamic time and frequency sub-carrier allocation and scheduling algorithms provide fairness and improve system capacity.

We find a low-complicity and accurate model to solve the problem of optimizing MAC-layer transmission of real-time video over wireless local area networks (WLANs) using cross-layer techniques. The objective in this problem is to obtain the optimal MAC retry limit in order to minimize the total packet loss rate. First, the accuracy of Fluid and M/M/1/K analytical models is examined. Then we derive a closed-form expression for service time in WLAN MAC transmission, and will use this in mathematical formulation of our optimization problem based on M/G/1 model. Subsequently we introduce an approximate and simple formula for MAC-layer service time, which leads to the M/M/1 model. Compared with M/G/1, we particularly show that our M/M/1-based model provides a low-complexity and yet quite accurate means for analyzing MAC transmission process in WLAN. Using our M/M/1 model-based analysis, we derive closed-form formulas for the packet overflow drop rate and optimum retry-limit. These closed-form expressions can be effectively invoked for analyzing adaptive retry-limit algorithms. Simulation results (network simulator-2) will verify the accuracy of our analytical models.

An analytical model is employed to solve a cross-layer optimization problem in IEEE 802.11 wireless local area networks (WLANs). Closed-form expressions for the optimum retry limit, packet overflow drop rate and overall loss rate are derived using M/M/l queuing model, and subsequently an adaptive MAC retry limit scheme is studied. Furthermore simulation results (network simulator-2) will verify the accuracy of our analytical model.

We propose a technique for enhancing the achievable spectral efficiency of multiuser direct-sequence code-division multiple-access (DS-CDMA) fading channels in the presence of additive white Gaussian noise (AWGN) and multiple access interference (MAI). The proposed scheme involves the joint optimization of outer loop power control (OLPC) and rate control using variable spreading factors (VSFs). The optimality is in the sense of average spectral efficiency maximization. The optimum outer loop target signal-to-noise ratio (SNR-target) and the corresponding spreading factor are derived jointly, in terms of the number of active users. Along with transmit power adaptation in the inner loop, this leads to maximized average spectral efficiency. Total and truncated channel inversion strategies are used in the inner loop. The average spectral efficiency of the joint optimization scheme is derived for the conventional matched-filter and the multiuser decorrelating detectors. Average transmit power and instantaneous bit error rate (BER) constraints are considered and the performance is evaluated over Nakagami-m flat-fading channels. In low SNRs, the proposed scheme can provide a considerable gain in bits/s/Hz for either of the detectors, compared to a VSF-assisted system that does not exploit OLPC and thus the optimum SNR-target.

Bit error rate (BER)-based constrained optimization of average spectral efficiency for adaptive Multi-level Quadrature Amplitude Modulation (MQAM) with service rate and power constraint is studied under composite Gamma-Lognormal fading channel condition. We obtain the corresponding optimal schemes for power and rate adaptation for continuous and discrete rate MQAM. The optimal power for continuous rate QAM is shown to be a combination of two functions; one of which with water-filling nature and the other proportional to log(1/BER), whereas for discrete rate QAM the solution is proportional to log(1/BER) over each rate region. We have also shown that transmission rate is a monotonically increasing function of BER.

This paper first develops a simple closed form expression for the bit error rate (BER) of low density parity check coded (LDPC) M-ary quadrature amplitude modulation (MQAM) signals as a function of the signal constellation, received signal to noise ratio (SNR), and LDPC code parameters. Based on this new expression, joint optimization of the SNR target and transmission rate is studied for LDPC coded MQAM signals to maximize the average spectral efficiency (ASE) subject to an average transmit power constraint over Rayleigh flat fading channels. Total or truncated channel-inversion strategies are exploited for the inner-loop power control, since data rate and outer-loop SNR target are adapted either to coded BER (CBER) or to the SNR with instantaneous CBER constraint. It is shown that the use of BER-based adaptation instead of SNR-based adaptation is even more beneficial in coded systems than in un-coded systems in terms of ASE performance. Indeed some of our numerical examples show that adaptation based on CBER has up to 2-2.5 dB power saving over SNR-based adaptation on the ASE of coded systems.

An iterative reconstruction method that was proposed for clipping noise cancellation in orthogonal frequency domain multiplexing (OFDM) systems is applied to multilevel quadrature amplitude modulation-based multi-carrier (MC) code division multiple access signals in downlink. The iterative method uses a known distortion function, maybe a nonlinear one such as a successive clipping and filtering process, in the iterations to give an approximation of inverse of the distortion process and then the iterative method removes distortion under a convergence condition. The authors show that MC signal with properly chosen clipping threshold satisfies convergence conditions of the iterative method. In contrast to some of the other reconstruction-based techniques, this method requires no extra bandwidth and side information and it can be implemented with reasonable complexity. Furthermore, the authors show that the proposed iterative scheme can be enhanced by using an extra frequency bandwidth. Exploiting extra bandwidth improves the performance of the reconstruction-based methods in case of using successive clipping and filtering. Simulation results will be used to demonstrate achievable bit-error-rate improvement by the proposed enhanced iterative scheme.

Optimisation of average spectral efficiency for adaptive multilevel quadrature amplitude modulation (MQAM) with service outage and instantaneous bit error rate constraints is studied over fading channels with variations in two time-scales. The authors obtain the corresponding optimal schemes for power and rate adaptation based on signal-to-noise ratio for continuous and discrete rate conditions. For continuous rate MQAM they show that the optimal power allocation scheme is a combination of generalised water-filling and channel inversion. For discrete rate MQAM the optimal power will be piecewise channel inversion. The authors also show that using discrete-rate adaptation with five different rates around 1 dB power is lost compared with continuous rate adaptation scenario. In the optimisation problems, they consider block flat fading channels with Nakagami-distributed variations within each block.

The impact of inaccurate channel state information at the transmitter for a variable rate variable power multilevel quadrature amplitude modulation (VRVP-MQAM) system over a Rayleigh flat-fading channel is investigated. A system model is proposed with rate and power adaptation based on the estimates of instantaneous signal-to-noise ratio (SNR) and bit error rate (BER). A pilot symbol assisted modulation scheme is used for SNR estimation. The BER estimator is derived using a maximum a posteriori approach and a simplified closed-form solution is obtained as a function of only the second order statistical characterization of the channel state imperfection. Based on the proposed system model, rate and power adaptation is derived for the optimization of spectral efficiency subject to an average power constraint and an instantaneous BER requirement. The performance of the VRVP-MQAM system under imperfect channel state information (CSI) is evaluated. We show that the proposed VRVP-MQAM system that employs optimal solutions based on the statistical characterization of CSI imperfection achieves a higher spectral efficiency as compared to an ideal CSI assumption based method.

We investigate the tradeoff between uncoded adaptive modulation (AM) in the physical layer, and automatic repeat re-quest (ARQ) in the data link layer, on the system throughput. Most of the applicable research has assumed fixed maximum-number-of-retransmissions (MNR); however, we show that applying adaptive MNR (referred to as adaptive ARQ in this paper) based on channel condition, enhances the throughput performance of the system. In the second part of this paper the effect of finite length queuing, AM, and ARQ on system packet loss is studied. As today's networks and applications generate the traffics that are bursty and self-similar, using Poisson processes to model the packet arrivals proves to be an error-prone assumption. Instead, we will apply generalized exponential (GE) and Pareto distributions, which allow for the modeling of a wide range of bursty distributions, to model the packet arrivals. Closed form expressions for the packet loss and the packet dropping probabilities are derived as functions of MNR and transmission rate. We show that employing adaptive ARQ decreases the packet loss rate while improving the throughput.

We propose a technique for enhancing the achievable spectral efficiency of multiuser direct-sequence code-division multiple-access (DS-CDMA) channels in the presence of additive white Gaussian noise (AWGN) and multiple access interference (MAI). The proposed scheme is the joint optimization of outer loop power control and variable spreading factors (VSFs). The optimum SNR-target, the SNR that results in optimal spectral efficiency, is set in the outer loop. Accordingly, appropriate spreading factor is selected, and along with the transmit power adaptation in the inner loop, it leads to maximized spectral efficiency. Total and truncated channel inversion strategies are used in the inner loop. Analytical expressions are derived for the spectral efficiency of the joint optimization scheme for the conventional matched-filter and the multiuser decorrelating detectors. Average transmit power and instantaneous BER constraints are considered and the performance is evaluated over Nakagami-m flat fading channels. We show that our scheme achieves higher spectral efficiency than a system that uses VSF transmission but without outer loop optimal SNR-target. The gain is realized at no transmit power cost and without sacrificing the BER.

The performance of multi-user detectors in the presence of multiple frequency offsets under a Rayleigh fading channel environment is analysed, and techniques to estimate and remove multiple frequency offsets (FOs) for successive interference cancellation (SIC) and parallel interference cancellation (PIC) receivers are also proposed. The closed form expressions derived for bit error rate (BER) of SIC and PIC schemes in the presence of multiple FOs have been verified using extensive simulation results. The PIC is shown to be less sensitive to frequency offsets as compared to SIC. It is demonstrated through analytical and simulation results that the proposed frequency offset estimation and correction techniques provide approximately 8 dB gain in the BER performance over conventional SIC and PIC schemes in the presence of multiple frequency offsets.

Joint optimization of signal-to-noise ratio (SNR) target and transmission rate adaptation is examined for multilevel quadrature amplitude modulation (MQAM) over flat-fading channels, to maximize the spectral efficiency subject to an average transmit power constraint. We propose an adaptive transmission scheme in which the outer-loop SNR target and data rate are adapted to bit-error rate (BER), where total or truncated channel-inversion strategies are exploited for the (fast) inner-loop power control. We obtain the optimal solutions for both continuous and discrete rate adaptation, and consider cases where diversity combining is performed in the receiver. We show that by using this BER-based adaptive scheme, spectral efficiency can be improved compared with optimal SNR-based variable-rate variable-power MQAM. We also show that for continuous rate adaptation, the optimal SNR target monotonically increases with BER, while it descends within a BER range with constant rate

In this paper, we generalize the algorithms of our previously proposed Bayesian estimation based variable rate variable power multilevel quadrature amplitude modulation (VRVP-MQAM) system to incorporate for the first time a maximum a posteriori (MAP) channel predictor and a MQAM scheme adopted with practical constellation sizes. Based on a pilot symbol assisted modulation (PSAM) scheme, we evaluate the performance of our proposed VRVP-MQAM system over a Rayleigh flat-fading channel. We demonstrate in our simulation results that the proposed rate and power algorithms that are derived based on a Bayesian bit error rate (BER) estimation and the second order statistical characterization of the channel state information (CSI) outperforms in terms of spectral efficiency and average BER. This improvement is confirmed by comparison with an alternative rate and power algorithm which exploits an ideal CSI assumption

In this contribution we examine the joint optimization of the outer-loop power control (OLPC) and spreading factor in order to improve the spectral efficiency in the uplink of wideband code-division multiple-access (WCDMA) systems, in the presence of additive white Gaussian noise (AWGN) and multiple access interference (MAI), and while maintaining an average transmit power constraint. The variable spreading factors (VSF) and the target signal-to-noise ratio (SNR) -set by the OLPC - are adapted according to the interference-to-noise ratio (INR) and the target instantaneous bit error rate (BER). We use the standardized orthogonal VSF (OVSF) codes used for the uplink dedicated physical data channel (UL-DPDCH) and evaluate the performance over Nakagami flat fading channels. It is shown that the proposed scheme improves the spectral efficiency compared to systems that use constant spreading factor and OLPC, for a wide range of practical SNRs and signal-to-interference ratios (SIRs)

Performance (QoS and capacity) improvement by use of interference cancellation detection is examined for an uplink W-CDMA system with imperfect power control. Link- and system-level simulation results are given along with analytical expressions to investigate the case.

In this paper the application of a linear-quadratic processor is proposed for detection of each user's signal in a direct sequence code division multiple access system. In this method, some statistical knowledge of interfering transmitters' signals is used to detect the desired user's signal without needing exact "a priori" knowledge of the interfering signal parameters such as spreading sequences and signal powers. Parameters of the proposed processor can be obtained by solving a system of linear equations for which many effective techniques exist. A model for this detection procedure is developed and it is shown that a good compromise between complexity and quality of performance can be achieved. Finally, the problem of interference cancellation is also treated from an estimation point-of-view in which a different linear-quadratic processor is used to estimate the interference for further cancellation. Estimated error and probability of error are derived as measures of estimation performance.