This paper proposes a deep neural network (DNN) codebook approach for multi-user interference (MUI) mitigation in extremely large multiple-input multiple-output (XL-MIMO) systems operating in the near-field region. Unlike existing DNN-based nulling control beamforming (NCBF) methods that face scalability and complexity challenges, the proposed framework partitions the Fresnel region using correlation-based sampling and assigns a lightweight fully connected DNN model to each subsection. Each model is trained on beamforming weights generated using the linearly constrained minimum variance (LCMV) method, enabling accurate prediction of nulling control beam-focusing weights that simultaneously optimize the desired signal strength and suppress potential interference for both collinear and non-collinear user configurations. Simulation results show that the trained models achieve average phase and magnitude prediction errors of 0.085 radians and 0.52 dB, respectively, across 75 sample subsections. Full-wave simulations in Ansys HFSS further demonstrate that the proposed DNN codebook achieves interference suppression better than 31.64 dB, with a performance gap within 2 dB of the LCMV method, thereby validating its effectiveness in mitigating MUI while reducing computational complexity.

Extremely large aperture arrays (ELAAs) introduce additional distance-domain degrees of freedom (DoF) in the electromagnetic near field, enabling a base station (BS) to spatially multiplex users sharing the same angular direction. Although these DoF have been characterized for continuous-aperture (CAP) channel models, it remains unclear how to sample such models to realize the maximum distance-domain DoF in practical, discrete arrays. We model the BS as a two-dimensional (2D) transmit (Tx) aperture of arbitrary shape and multiple collinear users as a linear receive (Rx) array, and we develop joint Tx/Rx sampling strategies that maximize the effective distance-domain DoF—the usable spatial resource in the distance dimension. Our method starts by analyzing the band-limited structure of the kernel of the channel correlation matrix, leading to a two-step sampling scheme. First, we sample the Tx aperture to maximize the kernel bandwidth, yielding a deterministic pattern governed by each element’s squared distance from the array center. Second, we sample the Rx (user) locations under a spacing condition derived via Toeplitz matrix analysis so that the effective distance-domain DoF fully matches the kernel bandwidth. We further obtain a closed-form threshold for the optimal spacing between adjacent collinear users, showing that the optimum depends only on the extreme edges of the Tx aperture, not on its detailed shape. Finally, we analyze and simulate the effects of the proposed sampling on spatial-multiplexing gains and distance-aware channel estimation performance.

Extremely large apertures in the near-field region unlock additional spatial resources in the distance domain, which enables the spatial multiplexing of multiple users even when they share the same angular direction—a capability unattainable in the far-field regime. A fundamental question remains unanswered: "What is the spatial degree of freedom of spatial multiplexing in the distance domain?"To address this, we investigate the spatial degree of freedom (DoF) in the distance domain of a large continuous aperture by considering the line-of-sight (LoS) channel between the aperture and a linear array, whose elements lie in the same direction but are located at different distances relative to the aperture. For simplicity of analysis, we consider the case where the linear array is in the broadside of the aperture. By reformulating the channel as an integral operator with a Hermitian convolution kernel, we derive a closed-form expression for the spatial DoF via the Fourier transform. Analytical and simulation results reveal that the spatial DoF in the distance domain of the aperture is predominantly determined by the aperture’s extreme boundaries rather than its detailed shape.

This paper investigates performance improvement of full-duplex (FD) multiple-input multiple-output (MIMO) communication systems through the integration of position reconfigurable antennas (PRAs). Using weighted sum-rate as the evaluation metric, we analyze a system where a base station simultaneously serves both downlink and uplink users, with both transmitters and receivers equipped with PRAs. To maximize the weighted sum-rate, we formulate a highly non-convex optimization problem subject to constraints on the reconfigurable region size, minimum inter-antenna distance, and transmit power. To tackle this challenge, we propose an alternating optimization framework that decomposes the original problem into sub-problems and solves them iteratively. Within this framework, fractional programming techniques are employed to decouple optimization variables from logarithmic and ratio terms, while a block successive upper-bound minimization approach addresses the non-convexity of PRA positioning. Simulation results confirm the performance gain achieved by incorporating PRAs into FD-MIMO systems and demonstrate the advantages of the proposed optimization algorithm.

An extra-large antenna array prototype operating between 3.35 GHz and 3.6 GHz was developed to experimentally study near-field beamfocusing and its distance-dependent radiation patterns. Using a 1×24 uniform linear sub-array, a 3 dB beam depth of 0.6 m was achieved at a beamfocusing distance of 0.8 m from the array. When transmitting a 64QAM 20 MHz modulated signal, the near-field beamfocusing approach yielded 9.6 dB higher received in-band power and improved error vector magnitude by 6.4 dB compared to the conventional far-field beamforming vector, resulting in high-quality constellation diagrams. Extensive measurements were conducted in the anechoic chamber to evaluate the distance-dependent radiation patterns and beam depth of uniform linear arrays with varying aperture sizes. Simulation results incorporating spherical wavefront characteristics demonstrated good agreement with the experimental observations.

A wideband, dual-layer metamaterial-based patch antenna operating at 10 GHz is proposed for centimeter-wave full-duplex (FD) massive multiple-input multiple-output systems. Full-wave simulations reveal an effective and wide impedance bandwidth of over$19 \%(1.9 \text{GHz})$, spanning from 9.5 GHz to 11.4 GHz. The antenna achieves a gain of 5.5 dB with a radiation efficiency exceeding 98 %. The introduction of a specially designed L-shaped cutting slot enables high circular polarization (CP) purity, achieving an axial ratio of 0.6 dB at 10.25 GHz and better than 3 dB across a 1.3 GHz (13 %) bandwidth. The resulting pure CP antennas support the creation of cross-polarized Tx-Rx antenna element pairs for FD operation. An improvement in Tx-Rx isolation of better than$\text{1 0 ~ d B}$is observed, reducing mutual coupling to better than -40 dB with an element separation of 4.6 cm (1.5 wavelengths).

This study employs an array-of-subarrays (AoSA) hybrid beamforming (HBF) architecture in ultra-massive multiple-input multiple-output (UM-MIMO) systems to enhance the total achievable rate. Our primary objective is to mitigate the strong multi-user interference (MUI) through the design of null-space projection (NSP)-based HBF scheme, which involves two stages: (i) RF beamforming based on introducing beam perturbations to steer beams in the null space of interfering users, and (ii) baseband MU precoding stage based on the instantaneous effective channel to mitigate the residual MUI by a regularized zero-forcing (RZF) technique. To solve this challenging non-convex optimization problem, we propose a swarm intelligence-based sequential optimization solution that finds the optimal beam perturbations while adhering to the directivity degradation constraints for the beams in each user direction. The illustrative results depict the high achievable rate by using the proposed NSP scheme over maximum-directivity beamforming (MBF) irrespective of the users' angular locations, which can be a promising beamforming solution in future sub-Terahertz (sub- THz) UM-MIMO systems.

Full-duplex (FD) radios have the potential to double the spectral efficiency of communication systems by transmitting and receiving on the same time and frequency slot. However, realizing this potential gain is challenging due to the strong selfinterference (SI) between the transmit and receive arrays. In massive MIMO (mMIMO), traditional cancellation techniques such as analog cancellation become unfeasible due to the required hardware complexity. The large number of antenna elements in mMIMO enables spatial processing techniques, making spatial SI suppression a promising approach for FD communications. This paper proposes Regularized Joint Linearly Constrained Minimum Variance Beamforming (RJLCMV), a novel alternating method for spatial SI cancellation for FD subconnected multi-user mMIMO based on regularized LCMV. The beamformer design problem is not jointly-convex between the transmit and receive array beamformers and is susceptible high levels of self-nulling with standard alternating optimization algorithms. The proposed algorithm uses diminishing regularization to mitigate the self-nulling effect while still providing total SI isolation. We study the cases in which total SI rejection can be achieved and demonstrate that with a measured SI channel, a duplexing gain of 1.93 can be achieved with RJLCMV in the fully constrained case in which both multi-user interference and SI are nulled, supporting as many users as there are per antenna element in each subarray.

This study explores a multiuser massive multiple-input-multiple-output (MU-mMIMO) system that incorporates an autonomous aerial vehicle (AAV) as a decode-and-forward (DF) relay between the base station (BS) and multiple Internet of Things (IoT) devices. The primary goal is to maximize the overall achievable rate (AR) by introducing a novel framework that integrates joint hybrid beamforming (HBF) with AAV deployment in dynamic MU-mMIMO IoT systems. Specifically, considering the geometry-based millimeter-wave (mmWave) channel model for both links, the radio frequency (RF) stages are configured to minimize the number of RF chains by utilizing slow time-varying angular information, while the baseband (BB) stages are developed using reduced-dimension effective channel matrices. Subsequently, deep deterministic policy gradient (DDPG), a reinforcement learning (RL) algorithm with continuous action space, is developed to train the AAV for its deployment. By employing a customized reward function, the RL agent learns an optimal AAV deployment policy capable of adapting to both static and dynamic environments. Then, a novel low-complexity DDPG codebook-based AAV deployment (DDPG-C-AD) is proposed, consisting of an offline agent training phase, and an online AAV deployment prediction to achieve maximum AR. The illustrative results show that the proposed DDPG-C-AD can attain a performance close to the DDPG-based solution in both static and dynamic environments while reducing the runtime by 99%. This makes the DDPG codebook-based solution a promising implementation for real-time online applications in AAV-assisted MU-mMIMO IoT systems.

This paper proposes a nulling steering beamforming-based isolation optimization scheme to mitigate the Tx-Rx self-interference in full-duplex massive MIMO systems. By searching the optimal nulling angles in both the uplink and the downlink beamforming process, the simulation and experimental measurements demonstrate a considerable beam-level isolation improvement while maintaining the integrity of the original beamforming performance. Illustrative results with a 20 MHz modulated signal and the 8×8Tx/8×8Rx full-duplex array prototype in the anechoic chamber show an average Tx-Rx isolation improvement of 10.2 dB, resulting in an average Tx-Rx mutual coupling level of -75.7 dB. Within the 25 measured Tx/Rx beam pairs, the most substantial isolation improvement of 22.2 dB can be observed and the best beam-level isolation of 92.7 dB can be achieved.

A dual-polarized high-isolation dielectric resonator antenna for full-duplex massive multiple-input and multiple output is proposed. The design integrates cross-polarized dual ports, utilizing two distinct mechanisms, probe feeding and aperture feeding, to enhance isolation performance. The beamforming capability and Tx-to-Rx port and beam-level isolation with monostatic and bistatic array configurations are studied. The proposed antenna element demonstrates a realized gain of 8.6 dB and a remarkable radiation efficiency of 97.7% at 3.5 GHz. In sample 1×8 array configurations, the design delivers an effective beam steering range from -60 to +60 degrees, with an average directivity and 3dB-beamwidth of 14.9 dB and 16.2 degrees, respectively. When deployed in a monostatic full-duplex array, the proposed structure achieves an average Tx-Rx port isolation of 53.8 dB. Moreover, adopting a bistatic array configuration can further enhance the average beam-level isolation to 73.4 dB, with peak beam-level isolation reaching up to 94.4 dB.

This paper introduces a dual-function radar-communication (DFRC) system with cognitive radio capability to tackle the spectral scarcity problem in wireless communications. Particularly, a cognitive DFRC system operates on a spectrum owned by a primary system to simultaneously perform data communication and target tracking while maintaining its interference to the primary users (PUs) below a certain threshold. To achieve this, an optimization problem is formulated to jointly design the beamforming vectors for both the radar and communication functions in minimizing the mean square error (MSE) of the beam patterns between the designed and desired waveforms under three constraints: i) the signal-to-interference-plus-noise ratio (SINR) at each data communication user; ii) the perantenna transmit power; and iii) the interference imposed on each PU. The semidefinite relaxation technique is utilized to search for the optimal solution to the optimization problem. The simulation results indicate that our proposed cognitive DFRC approach can effectively protect the PUs while simultaneously perform its communication and radar functions.

This study employs a uniform rectangular array (URA) sub-connected hybrid beamforming (SC-HBF) architecture to provide a novel self-interference (SI) suppression scheme in a full-duplex (FD) massive multiple-input multiple-output (mMIMO) system. Our primary objective is to mitigate the strong SI through the design of RF beamforming stages for uplink and downlink transmissions that utilize the spatial degrees of freedom provided due to the use of large array structures. We propose a non-constant modulus RF beamforming (NCM-BF-SIS) scheme that incorporates the gain controllers for both transmit (Tx) and receive (Rx) RF beamforming stages and optimizes the uplink and downlink beam directions jointly with gain controller coefficients. To solve this challenging non-convex optimization problem, we propose a swarm intelligence-based algorithmic solution that finds the optimal beam perturbations while also adjusting the Tx/Rx gain controllers to alleviate SI subject to the directivity degradation constraints for the beams. The data-driven analysis based on the measured SI channel in an anechoic chamber shows that the proposed NCM-BF-SIS scheme can suppress SI by around 80 dB in FD mMIMO systems.

This study considers an unmanned aerial vehicle (UAV)-assisted multiuser massive multiple-input multiple-output (MU-mMIMO) systems, where a decode-and-forward (DF) relay in the form of an UAV facilitates the transmission of multiple data streams from a base station (BS) to multiple Internet of Things (IoT) users. A joint optimization problem of hybrid beamforming (HBF), UAV relay positioning, and power allocation (PA) to multiple IoT users to maximize the total achievable rate (AR) is investigated. The study adopts a geometry-based millimeter-wave (mmWave) channel model for both links and proposes three different swarm intelligence (SI)-based algorithmic solutions to optimize: 1) UAV location with equal PA; 2) PA with fixed UAV location; and 3) joint PA with UAV deployment. The radio frequency (RF) stages are designed to reduce the number of RF chains based on the slow time-varying angular information, while the baseband (BB) stages are designed using the reduced-dimension effective channel matrices. Then, a novel deep learning (DL)-based low-complexity joint HBF, UAV location, and PA optimization scheme (J-HBF-DLLPA) is proposed via fully connected deep neural network (DNN), consisting of an offline training phase, and an online prediction of UAV location and optimal power values for maximizing the AR. The illustrative results show that the proposed algorithmic solutions can attain higher capacity and reduce average delay for delay-constrained transmissions in a UAV-assisted MU-mMIMO IoT systems. Additionally, the proposed J-HBF-DLLPA can closely approach the optimal capacity while significantly reducing the runtime by 99%, which makes the DL-based solution a promising implementation for real-time online applications in UAV-assisted MU-mMIMO IoT systems.

In this paper, we propose a transmission mechanism for a reconfigurable intelligent surface (RIS)-assisted millimeter wave (mmWave) system based on cluster index modulation (CIM), named best-gain optimized cluster selection CIM (BGCS-CIM). The proposed BGCS-CIM scheme considers effective cluster power gain and spatial diversity gain obtained by the additional paths within the indexed cluster to construct an efficient codebook. We also integrate the proposed scheme into a practical system model to create a virtual path between transmitter and receiver where the direct link has been blocked. Thanks to the designed whitening filter, a closed-form expression for the upper bound on the average bit error rate (ABER) is derived and used to validate the simulation results. It has been shown that the proposed BGCS-CIM scheme outperforms the existing benchmarks thanks to its higher effective cluster gain, spatial diversity of indexed clusters, and lower inter-cluster interference.

This study considers a dual-hop massive multiple-input multiple-output (mMIMO) system, where a decode-and-forward (DF) relay in the form of an unmanned aerial vehicle (UAV) facilitates the transmission of multiple data streams from a base station (BS) to a gateway serving multiple Internet-of-Things (IoT) devices. To maximize the end-to-end throughput in a three-node wireless sensor network (WSN), we investigate a novel joint optimization problem of hybrid beamforming (HBF) and UAV relay positioning in a given deployment span. The study adopts a geometry-based millimeter-wave (mmWave) channel model for both links and utilizes particle swarm optimization (PSO) to optimize the UAV location. The radio frequency (RF) stage is designed to minimize the number of RF chains through the utilization of slow time-varying angular information, while the baseband (BB) stage is designed through singular value decomposition (SVD) of the reduced-dimension effective channel matrix. The illustrative results show that the proposed joint HBF approach enhances energy efficiency compared to full-digital beamforming, and the UAV DF relay, placed via the PSO-based deployment scheme, attains a higher capacity compared to fixed UAV deployment locations.

This study considers a novel full-duplex (FD) massive multiple-input multiple-output (mMIMO) system using hybrid beamforming (HBF) architecture, which allows for simultaneous uplink (UL) and downlink (DL) transmission over the same frequency band. Particularly, our objective is to mitigate the strong self-interference (SI) solely on the design of UL and DL RF beamforming stages jointly with sub-array selection (SAS) for transmit (Tx) and receive (Rx) sub-arrays at base station (BS). Based on the measured SI channel in an anechoic chamber, we propose a min-SI beamforming scheme with SAS, which applies perturbations to the beam directivity to enhance SI suppression in UL and DL beam directions. To solve this challenging nonconvex optimization problem, we propose a swarm intelligence-based algorithmic solution to find the optimal perturbations as well as the Tx and Rx sub-arrays to minimize SI subject to the directivity degradation constraints for the UL and DL beams. The results show that the proposed min-SI BF scheme can achieve SI suppression as high as 78 dB in FD mMIMO systems.

We consider the problem of dynamic platoon leader selection, user association, channel assignment, and power allocation on a cellular vehicle-to-everything (C-V2X) based highway, where multiple vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) links share the frequency resources. There are multiple roadside units (RSUs) on a highway, and vehicles can form platoons, which has been identified as an advanced use-case to increase road efficiency. The traditional optimization methods, requiring global channel information at a central controller, are not viable for high-mobility vehicular networks. To deal with this challenge, we propose a distributed multi-agent reinforcement learning (MARL) for resource allocation (RA). Each platoon leader, acting as an agent, can collaborate with other agents for joint sub-band selection and power allocation for its V2V links, and joint user association and power control for its V2I links. Moreover, each platoon can dynamically select the vehicle most suitable to be the platoon leader. We aim to maximize the V2V and V2I packet delivery probability in the desired latency using the deep Q-learning algorithm. Simulation results indicate that our proposed MARL outperforms the centralized hill-climbing algorithm, and platoon leader selection helps to improve both V2V and V2I performance.

This paper presents a reconfigurable access scheme for critical massive machine-type communication (mMTC) networks with mobile devices, where in-coverage devices directly connect to the access point (AP) and out-of-coverage devices transmit to the AP by two-hop relaying. Given that devices have regular and event-based alarm traffic, we combine grantfree (GF) and grant-based transmissions to acquire unknown event information and ensure high reliability to guarantee alarm packet delay constraints. Then, the average age of information (AoI) is minimized by maximizing AoI-weighted regular packet throughput. Simulation results show that the proposed algorithm outperforms random and greedy scheduling in terms of minimizing AoI and guarantees the delay constraints in contrast to the baselines without delay consideration or only using GF transmissions to obtain event information. Also, the proposed algorithm can adaptively distribute resources to serve alarm and regular packets based on the stringency of delay constraints.

This paper proposes a genetic-algorithm (GA)-based subcarrier allocation in orthogonal frequency division multiplexing (OFDM)-based hybrid beamforming multi-user massive multiple-input multiple-output (MU-mMIMO) systems. Our goal is to maximize the system sum-rate capacity under the total transmit power constraint through optimally selecting Kmaxusers out of K available users to be served over each sub-carrier. Considering the energy-efficient hybrid beamforming architecture deployed at the base station (BS), the non-convex optimization problem is solved in four steps: (i) designing a radio frequency (RF) beamformer using slow time-varying angle-of-departure (AoD) information of users to generate the beams for all subcarriers, (ii) designing a baseband (BB) precoder for each subcarrier using the corresponding low-dimensional effective channel state information (CSI) seen from the BB stage based on regularized zero-forcing (RZF) technique, (iii) optimizing subcarrier allocation using GA with equal power allocation (EQ-PA) among users (iv) performing GA-based power allocation over each subcarrier to further improve the system sum-rate. Illustrative results indicate that the proposed algorithm performs significantly better than the random and greedy subcarrier allocation schemes in terms of the achieved sum-rate.

A perturbation-based adaptive beamforming scheme employing particle swarm optimization is proposed to mitigate multi-user interference by creating deep nulls in the potential interference directions. The paper uses an 8x8 circularly-polarized antenna array prototype as an example and examines the variation in antenna element radiation patterns at different positions on the large array, as well as their impact on the adaptive beamforming performance. The proposed scheme considers the measured element patterns and uses weights generated by the Linearly Constrained Minimum Variance method as a starting point to search for new optimal steering weights, which create deep nulls with a slight tradeoff in the directivity. The illustrative results show the proposed algorithm can achieve normalized nulls lower than -48.1dB with an accurate nulling location control. Compared to the linear constraint adaptive beamforming scheme, the proposed approach demonstrates improved results, achieving on average 22.9dB deeper arbitrary nulls with a tradeoff of 0.7 dB drop in the beam directivity.

This work considers a spherical array (SA)-based dual-hop massive multiple-input multiple-output (mMIMO) system using an unmanned aerial vehicle (UAV) as an amplify-and-forward (AF) relay between the base station (BS) and Internet of Things (IoT) gateway. We propose a particle swarm optimization (PSO)-based UAV deployment technique to maximize the total achievable rate by considering joint optimization of UAV location, hybrid beamforming (HBF) at two terminal nodes, and analog beamforming/combining at the UAV relay. Additionally, we employ singular value decomposition (SVD) of the channel matrices to form the transmit and receive radio frequency (RF) stages of the UAV relay, and an orthogonal matching pursuit (OMP)-based algorithmic approach to the HBF for the BS and the gateway. The illustrative results show that our proposed joint beamforming scheme for two distinct SA configurations significantly improves spectral and energy efficiencies, and outperforms uniform rectangular arrays (URAs).

This paper proposes a novel dual-layer electromagnetic bandgap slotted circularly polarized patch antenna to design an 8x8 antenna array for massive MIMO operating at 3.5GHz. The antenna element structure achieves a bandwidth of 250 MHz (7.1% at 3.5GHz). It has a gain of 5.1dB, a directivity of 6.1dB, and a 3dB-beamwidth of 94 degrees. With EBG elements underneath, the radiating patch size can be reduced by 65% while the top EBG layer provides a high surface impedance and an isolation of 14.3dB between radiating patches with a separation distance of 0.32 wavelength. Simulation and measured results of the designed 8x8 antenna array on return loss, radiation patterns, mutual coupling, and beamforming performance in different scenarios are discussed and shown to be in good agreement.

This paper proposes a reconfigurable intelligent surface (RIS)-aided and angular-based hybrid beamforming (AB-HBF) technique for the millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. The proposed RIS-AB-HBF architecture consists of three stages: (i) RF beamformer, (ii) baseband (BB) precoder/combiner, and (iii) RIS phase shift design. First, in order to reduce the number of RF chains and the channel estimation overhead, RF beamformers are designed based on the 3D geometry-based mmWave channel model using slow time-varying angular parameters of the channel. Second, a BB precoder/combiner is designed by exploiting the reduced-size effective channel seen from the BB stages. Then, the phase shifts of the RIS are adjusted to maximize the achievable rate of the system via the nature-inspired particle swarm optimization (PSO) algorithm. Illustrative simulation results demonstrate that the use of RISs in the AB-HBF systems has the potential to provide more promising advantages in terms of reliability and flexibility in system design.

In this paper, a novel full-duplex non-coherent (FD-NC) transmission scheme is developed for massive multiple-input multiple-output (mMIMO) systems using analog beamforming (ABF). We propose to use a structured Grassmannian constellation for the non-coherent communications that does not require channel estimation. Then, we design the transmit and receive ABF via the slow time-varying angle-of-departure (AoD) and angle-of-arrival (AoA) information, respectively. The ABF design targets maximizing the intended signal power while suppressing the strong self-interference (SI) occurred in the FD transmission. Also, the proposed ABF technique only needs a single transmit and receive RF chain to support large antenna arrays, thus, it reduces hardware cost/complexity in the mMIMO systems. It is shown that the proposed FD-NC offers a great improvement in bit error rate (BER) in comparison to both half-duplex non-coherent (HD-NC) and HD coherent schemes. We also observe that the proposed FD-NC both reduces the error floor resulted from the residual SI in FD transmission, and provides lower BER compared to the FD coherent transmission.

This paper proposes a deep learning based power allocation (DL-PA) and hybrid precoding technique for multi-user massive multiple-input multiple-output (MU-mMIMO) systems. We first utilize an angular-based hybrid precoding technique for reducing the number of RF chains and channel estimation overhead. Then, we develop the DL-PA algorithm via a fully-connected deep neural network (DNN). DL-PA has two phases: (i) offline supervised learning with the optimal allocated powers obtained by particle swarm optimization based PA (PSO-PA) algorithm, (ii) online power prediction by the trained DNN. In comparison to the computationally expensive PSO-PA, it is shown that DL-PA greatly reduces the runtime by 98.6%-99.9%, while closely achieving the optimal sum-rate capacity. It makes DL-PA a promising algorithm for the real-time online applications in MU-mMIMO systems.

This paper presents a reconfigurable access scheme for critical massive machine-type communication networks where the access point (AP) is equipped with a large-scale antenna array, and devices can move and form clusters. Devices transmit alarm and regular packets to the AP by two-hop relay connections via cluster leaders. The delay constraints of alarm packets are guaranteed by considering jointly the effective bandwidth and effective capacity with the success probability threshold while the aggregate age of information (AoI) of regular packets is minimized by maximizing their AoI-weighted throughput. Simulation results show that the proposed algorithm outperforms random and round-robin scheduling in terms of minimizing AoI, and it guarantees the delay constraints in contrast to the baselines without delay consideration. Moreover, the proposed algorithm can dynamically distribute resources to serve alarm and regular packet transmissions based on the stringency of delay constraints.

We propose joint user association, channel assignment and power allocation for mobile robot Ultra-Reliable and Low Latency Communications (URLLC) based on multi-connectivity and reinforcement learning. The mobile robots require control messages from the central guidance system at regular intervals. We use a two-phase communication scheme where robots can form multiple clusters. The robots in a cluster are close to each other and can have reliable Device-to-Device (D2D) communications. In Phase I, the APs transmit the combined payload of a cluster to the cluster leader within a latency constraint. The cluster leader broadcasts this message to its members in Phase II. We develop a distributed Multi-Agent Reinforcement Learning (MARL) algorithm for joint user association and resource allocation (RA) for Phase I. The cluster leaders use their local Channel State Information (CSI) to decide the APs for connection along with the sub-band and power level. The cluster leaders utilize multi-connectivity to connect to multiple APs to increase their reliability. The objective is to maximize the successful payload delivery probability for all robots. Illustrative simulation results indicate that the proposed scheme can approach the performance of the centralized algorithm and offer a substantial gain in reliability as compared to single-connectivity (when cluster leaders are able to connect to 1 AP).

This work studies the joint design of hybrid pre-coding (HP) and optimal positioning of unmanned aerial vehicle (UAV) relay in a millimeter-wave (mmWave) multi-user massive multiple-input multiple-output (MU-mMIMO) systems to maximize the spectral and energy efficiencies. The UAV operates as a flying wireless relay, expanding a base station’s coverage and delivering capacity boost to a group of users/devices that are obscured by obstructions. We explore the geometry-based mmWave channel model for the UAV-User link and propose joint HP and UAV positioning scheme (JHPP). In particular, the RF beamformer is designed using singular value decomposition (SVD) of channel matrix by incorporating users’ angle-of-departure (AoD) information to reduce the number of radio frequency (RF) chains, and the baseband (BB) precoder is designed using regularized zero-forcing (RZF) technique to mitigate MU interference. Then, using a particle swarm optimization-based location algorithm (PSO-L), a constrained optimization problem with the goal of maximizing the achievable sum-rate (ASR) is constructed for the optimal UAV placement in the given search space. Illustrative results show that the integration of a UAV relay considerably enhances the performance of mmWave MU-mMIMO systems when the BS is remote. Moreover, compared to UAV random placement in the given flying span, PSO-L based UAV positioning has higher spectral/energy efficiency. Finally, the use of a hemispherical array (HSA) configuration at UAV relay can further increase the performance when compared to uniform rectangular array (URA).

We consider the problem of joint channel assignment and power allocation in underlaid cellular vehicular-to-everything (C-V2X) systems where multiple vehicle-to-network (V2N) uplinks share the time-frequency resources with multiple vehicle-to-vehicle (V2V) platoons that enable groups of connected and autonomous vehicles to travel closely together. Due to the nature of high user mobility in vehicular environment, traditional centralized optimization approach relying on global channel information might not be viable in C-V2X systems with large number of users. Utilizing a multi-agent reinforcement learning (RL) approach, we propose a distributed resource allocation (RA) algorithm to overcome this challenge. Specifically, we model the RA problem as a multi-agent system. Based solely on the local channel information, each platoon leader, acting as an agent, collectively interacts with each other and accordingly selects the optimal combination of sub-band and power level to transmit its signals. Toward this end, we utilize the double deep Q-learning algorithm to jointly train the agents under the objectives of simultaneously maximizing the sum-rate of V2N links and satisfying the packet delivery probability of each V2V link in a desired latency limitation. Simulation results show that our proposed RL-based algorithm provides a close performance compared to that of the well-known exhaustive search algorithm.

We consider the problem of joint user association, channel assignment, and power allocation for mobile robot application in factory automation system that require ultrareliable and low latency communications (URLLC). The aim is to deliver control commands from the controller to mobile robots with stringent requirements of latency and reliability. To achieve URLLC, we develop a two-phase communication scheme. The robots work close to each other in a factory environment and can form clusters for reliable device-to-device (D2D) communications. Within the latency requirements, the combined payload of a cluster is transmitted to the leader in Phase I. In Phase II, the leader broadcasts the payload to its members. Under this strategy, we use multi-agent reinforcement learning (MARL) for resource allocation. The cluster leaders in Phase I act as the agents and interact with the environment to optimally select the Access Point (AP) for connection along with the sub-band and power level. The objective is to maximize the successful payload delivery probability to all the robots. Illustrative simulation results indicate that the proposed scheme can offer average successful payload delivery probability close to that of centralized exhaustive search algorithm.

This paper develops a new genetic algorithm based resource allocation (GA-RA) technique for energy-efficient throughout maximization in multi-user massive multiple-input multiple-output (MU-mMIMO) systems using orthogonal frequency division multiplexing (OFDM) based transmission. We employ a hybrid precoding (HP) architecture with three stages: (i) radio frequency (RF) beamformer, (ii) baseband (BB) precoder, (iii) resource allocation (RA) block. First, a single RF beamformer block is built for all subcarriers via the slow time-varying angle-of-departure (AoD) information. For enhancing the energy efficiency, the RF beamformer aims to reduce the hardware cost/complexity and total power consumption via a low number of RF chains. Afterwards, the reduced-size effective channel state information (CSI) is utilized in the design of a distinct BB precoder and RA block for each subcarrier. The BB precoder is developed via regularized zero-forcing technique. Finally, the RA block is built via the proposed GA-RA technique for throughput maximization by allocating the power and subcarrier resources. The illustrative results show that the throughput performance in the MU-mMIMO-OFDM systems is greatly enhanced via the proposed GA-RA technique compared to both equal RA (EQ-RA) and particle swarm optimization based RA (PSO-RA). Moreover, the performance gain ratio increases with the increasing number of subcarriers, particularly for low transmission powers.

This paper presents an access scheme for machine-type communication (MTC) networks where the base station (BS) is equipped with a massive antenna array and devices have correlated traffic and delay constraints. We formulate an optimization problem to allocate resources and calculate the access probabilities to maximize the throughput with delay constraints. Since the traffic model parameters and event locations are not available to the BS and the throughput with delay constraints is hard to be derived, we propose a reinforcement-learning-based algorithm to solve the problem. Our simulation reveals that our proposed algorithm is superior to a random scheduling baseline both in terms of throughput and delay. More importantly, our proposed algorithm achieves comparable throughput and lower average delay compared to the algorithm that has full information of traffic model parameters and event locations but optimizes throughput without delay constraints.

This paper investigates the performance of different antenna array structures for hybrid massive-MIMO precoding schemes using few-bit DACs. Particularly, the proposed hybrid scheme includes two precoding stages: the RF-beamforming stage is designed via the slowly time-varying channel second-order correlation matrix, while the baseband multi-user (MU) precoding stage is constructed via the regularized zero-forcing (RZF) technique to mitigating the MU-interference. For the same system cost and complexity, we examine the achieved sum-rate and energy efficiency of various 2D antenna array structures, namely, uniform linear array (ULA), uniform rectangular array (URA), uniform circular array (UCA), and concentric circular array (CCA), in serving multiple users at different angular locations. The Monte Carlo simulation results indicate the higher achievable rate and energy efficiency of CCA by using low-resolution DACs as compared to various 2D array structures. We also show that only (3-5)-bit DACs are sufficient to provide comparable spectral and energy efficiencies.

This paper presents a semi-deterministic groupwise channel estimation method to generate UT-group CSI of user terminal (UT) zones in the service area for the angular-based hybrid precoding (AB-HP) in multi-user massive multiple-input multiple-output (MU-mMIMO) systems based on geospatial data and the fuzzy c-Means (FCM) clustering algorithm. The slow time-varying UT-level channel state information (CSI) between the base station (BS) and all possible UTs are generated by a ray tracing algorithm and grouped into clusters by a proposed FCM clustering. The service area is then divided into a number of non-overlapping UT zones, where each is characterized by a corresponding set of clusters used as UT-group CSI for RF beamformer to eliminate the required large online CSI acquisition overhead. Simulations are performed in both outdoor and indoor scenarios to evaluate the performance of the proposed channel estimation approach. Illustrative results show that the proposed method identifies clusters robust to imprecise UT-level CSI and provides RF beamformer with the UT-group CSI for different UT zones in the service area. Meanwhile, with the UT- group CSI, the AB-HP can successfully achieve a comparable sum-rate performance as the fully-digital precoding (FDP) system for UTs in specific zones without large dimensional CSI overhead.

This paper presents a learning-based multiple-access scheme for machine-type communication networks where the base station (BS) is equipped with a large-scale antenna array and devices have spatially correlated and event-based traffic. Each access time frame is divided into two segments: grant-based and grant-free, which are dynamically configured according to device traffic statistics to achieve high throughput. As device traffic statistical characteristics might not be available at the BS in reality, we propose a learning-based algorithm that exploits device traffic correlations to predict the probability of having data packets of each device in the next time frame based on its traffic history. The BS uses this predicted probability for its scheduling and access probability calculation algorithms to maximize the system throughput. Performance of the proposed learning-based multiple-scheme is evaluated and illustrative results show its superior achievable throughput as compared to both random scheduling and optimized random access schemes.

This work proposes a novel swarm intelligence based power allocation (PA) technique for multi-user massive multiple-input multiple-output (MU-mMIMO) systems. For the downlink transmission, we consider the geometry-based millimeter-wave (mmWave) channel model. The base station (BS) employs a three-dimensional angular-based hybrid precoding (3D-AB-HP) technique requiring low channel state information (CSI) overhead. The 3D-AB-HP architecture consists of three stages: (i) radio frequency (RF) precoder, (ii) baseband (BB) precoder, (iii) multi-user PA block. First, the RF precoder is built via the slow time-varying angle-of-departure information to reduce the CSI overhead size as well as the number of RF chains. It is designed via low cost phase-shifters, which induces the constant modulus constraint at the RF-stage design. Second, the BB precoder utilizes the regularized zero-forcing technique for mitigating the inter-user interference. Third, at the multi-user PA block, we develop a novel particle swarm optimization based PA (PSO-PA) algorithm to maximize the spectral/energy efficiency. Both the BB precoder and the multi-user PA block are constructed via the reduced-size effective channel seen from the BB-stage. Illustrative results reveal that the 3D-AB-HP with PSO-PA can remarkably improve the spectral/energy efficiency compared to the equal PA (e.g., up to 88% at the low/medium transmit power regime). Also, it is shown that the proposed 3D-AB-HP significantly decreases the number of RF chains (e.g., 94.2%) and the CSI overhead size (e.g., 87.1%), while providing higher energy efficiency than the conventional single-stage fully-digital precoding.

This paper studies the benefits of incorporating underlaid full-duplex (FD) device-to-device (D2D) communications into massive multiple-input-multiple-output (MIMO) downlink systems. Due to the nature of cellular downlink and FD D2D transmission, the performances of cellular and D2D services are severely impaired due to high interference caused by base-stations (BSs) and D2D transceivers. As a consequence, integrating a large number of D2D links into existing cellular networks might degrade the system performance. To overcome this challenge, utilizing the large uniform linear array (ULA) equipped at BSs, we propose a joint beamforming and power allocation design for average sum-rate maximization while considering the effects of interference to both cellular and D2D transmissions. The problem formulation leads to a nonconvex vector-variable optimization problem, where we develop an efficient solution using a fractional programming (FP) based approach. Numerical results show that, at sufficiently high self-interference cancellation (SIC) levels and numbers of active D2D links, the FD D2D transmission provides a significant sum-rate improvement as compared to the half-duplex (HD) counterpart and pure cellular systems in absence of D2D.

This paper studies a resource allocation problem where a set of users within a specific region is served by cloud radio access network (C-RAN) structure consisting of a set of base-band units (BBUs) connected to a set of radio remote heads (RRHs) equipped with a large number of antennas via limited capacity front-haul links. User association to each RRH, BBU and front-haul link is essential to achieve high rates for cell-edge users under network limitations. We introduce two types of optimization variables to formulate this resource allocation problem: (i) C-RAN user association factor (UAF) including RRH, BBU and front-haul allocation for each user and (ii) power allocation vector. The formulated optimization problem is non-convex with high computational complexity. An efficient two-level iterative approach is proposed. The higher level consists of two steps where, in each step, one of these two optimization variables is fixed to derive the other. At the lower level, by applying different transformations and convexification techniques, the optimization problem in each step is broken down into a sequence of geometric programming (GP) problems to be solved by the successive convex approximation (SCA). Simulation results reveal the effectiveness of the proposed approach to increase the total throughput of network, specifically for cell-edge users. It outperforms the traditional user association approach, in which, each user is first assigned to the RRH with the largest average value of signal strength, and then, based on this fixed user association, front-haul link association and power allocation are optimized.

This article considers a dual-hop Internet of Things communications network where sensor nodes transmit data to a gateway either directly or via other nodes using dual-hop communications. Each node employs separate transmission buffers to store its own sensing data and data received from other nodes. End-to-end delay quality-of-service constraints in terms of the maximum acceptable delay-outage probabilities are imposed. We investigate energy-efficient adaptive resource allocation problems (i.e., joint link scheduling, rate, and power allocation) to support minimum data rates of the nodes. A novel approach is proposed exploiting asymptotic delay analysis to first determine the achieved delay exponents of the queue length tail distributions to satisfy the delay-outage constraints. Next, the relation between the delay exponents and resource allocation variables are derived. Last, the solutions to the resulting constrained optimization problems are obtained using the Lagrangian approach and convex optimization. Illustrative examples demonstrate the effects of the rate requirements and delay constraint stringency on the power consumption and routing configuration.

This paper investigates the performance behaviours of various antenna array structures for hybrid massive-MIMO precoding schemes. In particular, the proposed hybrid scheme includes two cascaded stages: the RF-beamforming stage is designed via the eigen-decomposition of the massive-MIMO channel second-order correlation matrix while the baseband multi-user (MU) precoding stage is constructed via the regularized zero-forcing (RZF) technique to mitigating the MU-interference in the reduced-dimension effective MU-channel. A transfer block is introduced between the RF-beamforming and baseband precoding stages to significantly reduce the number of required RF chains. For the same number of antenna elements with half-wavelength spacing, we examine the achieved sum-rate performance of different 2D antenna array structures, namely, uniform linear array (ULA), uniform rectangular array (URA), uniform circular array (UCA), and concentric circular array (CCA), in serving multiple users in various angle-of-departure (AoD) settings. Simulation results indicate that, among the array structures, URA and CCA can offer both smaller array sizes and higher achieved sum-rate. Furthermore, for various user angular locations, the sum-rate of URA can vary about 2 bits/s/Hz while CCA can give an invariant sum-rate performance.

Hybrid Precoding (HP) has been introduced to reduce the complexity/costs due to a large number of RF chains in the fully-digital massive MIMO precoding. In a fully-connected (FC) HP, each RF chain is connected to all available antenna elements to exploit the full beamforming capability of the antenna array at the expense of large connectivity. To further reduce costs/complexity associated with this large connectivity, sub-connected (SC) HP considers that each RF chain connected to a subset of selected antenna elements of the antenna array at the costs of inferior performance. This paper aims to study the complexity and performance of both FC-HP and SC-HP. For comparison, we consider a common 2-stage HP scheme with the RF-beamforming (BF) stage designed via the slow time-varying angle-of-departure (AoD) information, using both orthogonal and non-orthogonal BF approaches, while the baseband-precoding stage uses a regularized zero-forcing (RZF) technique. This common HP scheme is used by a base-station equipped with a uniform rectangular large-scale antenna-array to serve multiple single-antenna users clustered in multiple groups. Three sub-array configurations (vertical, horizontal, square) are considered for SC- HP. Illustrative simulation results are provided to compare the performance of FC-HP and SC-HP in various scenarios and indicate that for a 64-element URA and 4 RF chains, SC-HP can achieve 91.46% sum-rate performance of FC-HP with only 25% complexity.

This paper proposes a three-dimensional angular-based hybrid precoding (3D-AB-HP) for multi-cell multi-user massive multiple-input multiple-output (MU-mMIMO) systems. By employing the angle-of-departure (AoD) information, the RF precoder in the proposed 3D-AB-HP is designed to decrease the channel estimation overhead and the number of RF chains. Then, the baseband (BB) precoder in the 3D-AB-HP is constructed via regularized zero-forcing technique using the effective channel seen at the BB. To further mitigate the inter-cell interference for the cell-edge users, we propose three cooperation strategies for the multi-cell downlink transmission in the case of cloud radio access networks (C-RAN): (i) non-cooperation (NC), (ii) partial-cooperation (PC) and (iii) full-cooperation (FC). Illustrative results indicate that FC and PC improve remarkably the sum-rate performance via utilization of C-RAN in comparison to the traditional RAN. Moreover, by means of the directional beamforming, the proposed 3D-AB-HP with PC and NC can outperform the corresponding single-stage fully-digital precoding requiring higher hardware cost/complexity and larger channel estimation overhead.

Hybrid precoding/combining (HPC) architecture is a promising candidate for millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. It is capable of reducing the hardware cost/complexity and power consumption compared to the full-digital precoding/combining (FDPC) while keeping the similar spectral efficiency. Most of the prior works on HPC consider the availability of full channel state information (CSI) to design both radio-frequency (RF) and baseband (BB) stages. In this work, an angular-based HPC (AB-HPC) design requiring low CSI overhead is proposed for mmWave massive MIMO systems equipped with low-resolution digital-to-analog converters (DAC) and analog-to-digital converters (ADC). Based on the 3D geometry-based mmWave channel model, the transmit and receive RF beamformers are first developed based on the slow time-varying angle-of-departure (AoD) and angle-of-arrival (AoA) parameters, respectively. Then, the transmit BB precoder and receive BB combiner are designed by employing the reduced-size effective CSI seen from the BB-stages. Considering the effect of low-resolution DACs/ADCs, the receive BB combiner is obtained by the minimum mean square error (MMSE) criterion. The numerical results reveal that the proposed AB-HPC technique can closely approach the achievable rate performance of FDPC while remarkably reducing the number of power-hungry RF chains and CSI overhead size (e.g., around 94.1% – 98.5%). Moreover, the quantization error occurred due to the low-resolution DACs/ADCs causes a performance floor. For a given signal-to-noise ratio (SNR), we also ask the required number of bits for the low-resolution DACs/ADCs for converging to the same achievable rate performance in full-precision DACs/ADCs.

In this paper, we propose a multiple access scheme for machine-type communications for which high spectral efficiency and massive connectivity are demanded. To meet these requirements, we enhance the proposed access scheme with the reconfigurability feature to properly divide each time frame to three segments of grant-based NOMA, grant-based OMA, and random access. To obtain the length of each segment, an optimization problem is formulated which is solved by dividing it into two sub-problems. The first sub-problem nominates devices for the NOMA transmissions, while the second sub-problem derives the length of each segment as well as the the parameter of the random access-based scheme.

This work studies a novel full-duplex (FD) cell-free massive multiple-input multiple-output (MIMO) network, where a very large number of multiple-antenna access points (APs) simultaneously serve many single-antenna uplink and downlink users in the same frequency band. The APs operate in the FD mode while the users in the half-duplex (HD) mode. The APs apply a simple conjugate beamforming/matched filtering scheme with the channel state information acquired via the uplink training with orthogonal pilots transmitted from the users. By an analysis with a large number of APs, residual self-interference (RI) is proved to be the main limitation of the cell-free massive MIMO systems. A simple power control method to mitigate this limitation is also proposed. The closedform expressions of uplink and downlink achievable rates are derived with a finite number of APs and the channel estimation error taken into account. Under considered parameter settings, numerical results show that when the RI is sufficiently low, the FD mode can achieve a spectral efficiency gain of 140% over the HD mode in the cell-free massive MIMO system. They also confirm that the FD cell-free massive MIMO systems outperform the FD collocated massive MIMO systems in terms of spectral efficiency.

This paper proposes a new angular-based 3D two- stage hybrid precoding scheme for multi-user massive MIMO systems using uniform rectangular arrays (URA), where users are partitioned into different groups based on the similarity of their angle-of-departure (AoD) information. At first using the user-group AoD ranges, the RF- beamforming stage is designed to reduce the inter- group interference, the number of RF chains, and the channel state information (CSI) overhead. Then, the digital baseband precoder stage is constructed via regularized zero-forcing (RZF) technique using the effective channel seen from baseband to reduce the intra-group interference between the users, considering three approaches: joint-group-processing (JGP), per-group-processing (PGP) and common-group-processing (CGP). Illustrative results indicate that the proposed two-stage hybrid precoding schemes with the reduced hardware cost/complexity and relaxed CSI estimation overhead can closely approach the sum- rate performance of the ideal single-stage fully- digital precoding. Moreover, their performance gap becomes negligible as the array size increases.

We consider the problems of user clustering and precoding in massive MIMO by exploiting the angles of departure (AoD) associated to the different users. First, we present a new clustering method that simultaneously finds the number of groups and associates the users to the appropriate group. In our approach, the users are modelled as a connected graph where the edges reflect the similarity in term of AoDs and we apply spectral analysis to form the clusters. Then a first precoder is obtained to point the energy in the direction of the user cluster with a second multi-user MIMO precoder to simultaneously serve the users within the same cluster. The direct application of the proposed method requires the number of RF chains to be equal to the degrees of freedom of the system. To reduce the number of RF chains down to the number of users, we propose a new hybrid precoder structure that approaches the performance of the traditional fully digital zero-forcing precoding.

In this paper, we present a reconfigurable MAC scheme where the partition between contention-free and contention-based regimes in each frame is adaptive to the network status leveraging reinforcement learning. In particular, to support a virtualized wireless network consisting of multiple slices, each having heterogeneous and unsaturated devices, the proposed scheme aims to configure the partition for maximizing network throughput while maintaining the slice reservations. Applying complementary geometric programming and monomial approximations, an iterative algorithm is developed to find the optimal solution. For a large number of devices, a scalable algorithm with lower computational complexity is also proposed. The partitioning algorithm requires the knowledge of the device traffic statistics. In the absence of such knowledge, we develop a learning algorithm employing Thompson sampling to acquire packet arrival probabilities of devices. Furthermore, we model the problem as a thresholding multi-armed bandit and propose a threshold-based reconfigurable MAC algorithm, which is proved to achieve the optimal regret bound.

This paper examines nonlinear hybrid precoding with minimum-mean-squared-error (MMSE)-vector perturbation (VP) for multi-cell massive multiple- input multiple-output (MIMO) systems. Two- timescale channel state information (CSI) is assumed, which consists of noisy observations of the short-term RF-beamformed channel, and perfect knowledge of long-term channel temporal and spatial correlation. By exploiting the low- dimensional effective CSI, we propose to estimate the instantaneous realization of the high- dimensional MIMO channel via Kalman filtering. The CSI estimate is then utilized for RF precoding in consideration of centralized MMSE-VP. In particular, robust baseband solutions are first derived which reduces the objective function to a simple form. By abstracting the effect of nonlinear baseband precoding, RF precoding is separately formulated as a solution to balance the error performance with the accuracy of channel tracking. To numerically optimize such a non- convex problem, we develop a Cayley transformation-based gradient descent search algorithm. Simulation results illustrate that the proposed hybrid scheme outperforms state-of-the- art two-timescale CSI-based baselines in terms of bit error rate. Moreover, the resilience of the proposed solution to channel estimation errors is demonstrated.

We investigate the uplink resource allocation problem for virtualized wireless networks (VWNs) supported by multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) and present a sensitivity analysis to imperfect successive interference cancellation (SIC) and various system parameters. The proposed algorithm for power and sub-carrier allocation is derived from the non- convex power minimization subject to rate and sub- carrier reservations, for which an optimal solution is NP-hard. To develop an efficient solution, the resource allocation is decomposed into separate power and sub-carrier allocation problems and an iterative algorithm based on successive convex approximation and complementary geometric programming is proposed. Simulation results demonstrate that compared to orthogonal multiple access, the proposed algorithm for MIMO NOMA can offer significant improvement in spectrum and power efficiency.

This paper utilizes the tools of stochastic geometry to derive closed-form approximations of coverage probabilities for both cellular and D2D links of a realistic underlaid full-duplex (FD) cellular D2D network. In the considered model, D2D users operate in FD mode under the adverse effect of realistic residual self-interference, and their locations are modeled by a homogeneous spatial Poison point process (PPP). The coverage probabilities involve multiple integrals due to the average over the distributions of transmit power, wireless fading, and link distances. Therefore, our solution is to first apply Laplace transforms and novel approximations that accurately approximate the expected values of fractional and exponential functions of random variables to obtain the distributions of signal to interference and noise ratios (SINRs) at the base station (BS) and D2D users. By further taking the average over the distributions of cellular and D2D link distances, we then arrive at the closed-form approximations of cellular and D2D coverage probabilities. Furthermore, based on the approximation of D2D link coverage probability, an analytical expression of the D2D link sum-rate is obtained, which can be effectively calculated. Our results show that the integration of FD in D2D provides significant sum-rate improvement over the half-duplex D2D counterpart.

Long-term evolution (LTE) operation in the unlicensed spectrum is a promising solution to address the scarcity of licensed spectrum for cellular networks. Although this approach brings higher capacity for LTE networks, the Wi-Fi performance operating in this band can be significantly degraded. To address this issue, we consider a coordinated structure, in which both networks are controlled by a higher level network entity. In such a model, LTE users can transmit in the assigned time-slots, while Wi-Fi users can compete with each other by using p-persistent carrier sense multiple access (CSMA) in their exclusive timeshare. In an unsaturated network, at each duty cycle, the timedivision multiple access (TDMA) scheduling for LTE users and p values for Wi-Fi users should be efficiently updated by the central controller. The corresponding optimization problem is formulated and an iterative algorithm is developed to find the optimal solution using complementary geometric programming and monomial approximations. Aiming to address the qualityof-service assurance for LTE users, an upper bound for average delay of these users is obtained. This analysis could be a basis for the admission control of LTE users in unlicensed bands. The simulation results reveal the performance gains of the proposed algorithm in preserving the Wi-Fi throughput requirement.

This paper explores joint design of two-timescale hybrid RF-baseband precoding with minimum-mean-square-error (MMSE)-vector perturbation (VP) for multi-user massive multiple-input multiple-output systems, where users on the downlink are separated into geographical clusters, and each user cluster experiences identical transmit spatial correlation. Considering the perfect effective channel state information-based MMSE-VP at baseband, the spatial correlation-based RF precoder design is formulated as orthonormality-constrained stochastic optimization problems, where the objective functions cannot be characterized in closed form. RF eigen-beamforming is shown as an optimal solution for single-cluster transmission. In multi-cluster scenarios, mathematically tractable lower bounds are proposed and numerically optimized by trust-region Newton methods on Riemannian manifolds. Additionally, constant-modulus RF precoding based on the discrete Fourier transform (DFT) codebook is addressed. By recognizing the objective functions as a difference of increasing functions, branch-reduce-and-bound techniques are developed to find the globally optimal solutions to such combinatorial problems with reduced computational complexity. Simulation results demonstrate that the proposed nonlinear hybrid schemes deliver a superior bit error rate to other state-of-the-art baselines. The effectiveness of the suboptimal DFT-based RF solutions is also verified.

Due to the increasing demand for mobile traffic, the unlicensed band operation for LTE is proposed by mobile operators. Although by using this approach higher capacity can be achieved for LTE, performance of other wireless technologies operating in this band such as WiFi can be degraded significantly. In order to enable efficient LTE/WiFi coexistence, we consider a coordinated structure via a virtual network entity. LTE users can transmit in the assigned time-slots, while WiFi users can compete with each other by using p-persistent CSMA in their exclusive time-share. In an unsaturated network, at each duty cycle, the TDMA scheduling for LTE users and p values for WiFi users are updated to maximize the overall network throughput subject to a constraint on the minimum acceptable throughput for WiFi. The corresponding optimization problem is formulated and an iterative algorithm is developed to find the optimal solution using complementary geometric programming (CGP) and monomial approximations. The simulation results reveal the performance gains of the proposed algorithm in preserving the WiFi throughput requirement.

This paper presents a compact, highly-linear RF/Analog self-interference canceller (SIC) for a 2×2 MIMO full-duplex (FD) transceiver to suppress the existing strong selfinterference (SI) at the receiver input in order to prevent the receiver from saturation. Linearity requirements of RF/Analog SIC, as well as corresponding variable attenuator and delay/phase module, are derived and a RF/Analog SIC with good linearity for high power transmission is proposed. A prototype with compact size is fabricated on a multilayer PCB for a 2×2 MIMO inband FD transceiver, and is implemented with highly-linear, custom-made tunable attenuator and delay/phase-shift module to suppress nonlinear distortion. The measurement results in anechoic-chamber reveal that this RF/Analog SIC prototype integrated with a dual-polarized antenna can provide over 80dB cancellation for +30dBm and 20MHz OFDM signal centered at 2.27GHz. Typical 34dBm OIP3 of the prototype is measured. The simulation with following digital canceller reveals a negligible SNR degradation of 0.3dB due to the nonlinear distortion of this prototype.

This paper examines design of mixed-timescale hybrid precoding with vector perturbation (VP) to achieve minimum mean square errors (MMSE) for multi-user massive multiple-input multiple-output (MIMO) systems. In particular, equipped with the perfect effective channel state information (CSI)-based MMSE-VP solution at the baseband, we derive partial CSI-based linear radio frequency (RF) precoding as the solution to a stochastic programming problem. In the scenario of single-cluster transmission, RF eigen-beamforming in the transmit correlated subspace is proved to be optimality-achieving. In the multi-cluster case, by approximating the discrete constellation as uniformly distributed, we establish a closed-form lower bound for the objective function. A critical point to this bound is found by a trust-region method on a smooth Riemannian manifold. Simulation results demonstrate that in the former case, a limited number of RF chains suffices for the nonlinear hybrid scheme to outperform the fully digital linear solution, and approaches the performance of the traditional MMSE-VP solution in terms of bit error rate. In the latter case, the proposed solution delivers a superior error performance to the state-of-the-art baselines.

Focusing on reliable and low-complexity backhaul-limited uplink coordinated multi-point (CoMP) systems, we propose two hybrid automatic repeat request (HARQ)-based protocols using incremental MIMO (IMIMO) approach for both centralized processing (CP) and distributed processing (DP). Using outage probability and throughput metrics, we compare the proposed protocols and the other HARQ-based MIMO approaches with both Chase combining (CC) and incremental redundancy (IR) schemes. Zero-forcing (ZF) receivers and limited capacity of backhaul are considered as practical assumptions. Effects of user locations, backhaul capacity, number of antennas, and low SNR regime are highlighted. The results indicate that IMIMO outperforms MIMO at low SNR region in both non-CoMP/CoMP scenarios, and approaches the performance of MIMO-HARQ in CoMP scenario.

In this paper, we investigate the dynamic user-AP association with QoS-aware CSMA in a virtualized wireless network. In particular, to efficiently support slice isolation, we consider an enhanced CSMA frame with two phases: a contention-free A-phase using CSMA with deterministic back-off, followed by a contention-based C-phase using p-persistent CSMA. The user scheduling in the contention-free A-phase and determination of p values for the contention-based C-phase along with the user-AP association are formulated as an optimization problem, aiming to maximize the overall network throughput under the slice isolation constraints and channel conditions. Subsequently, complementary geometric programming (CGP) and monomial approximations are used to develop an iterative algorithm for solution. Illustrative simulation results show the efficiency of the developed solution and its effect on network performance while maintaining the isolation among slices.

In this work, we investigate the uplink resource allocation problem for virtualized wireless networks (VWNs) supported by multi-carrier non-orthogonal multiple access (MC-NOMA) and present a sensitivity analysis of such a system to imperfect successive interference cancellation (SIC) and various system parameters. The proposed algorithm for power and sub-carrier allocation is derived from the non-convex optimization minimizing power subject to rate and sub-carrier reservations, for which an optimal solution is NP-hard. To develop an efficient solution, we decompose the optimization into separate power and sub-carrier allocation problems and propose an iterative algorithm based on successive convex approximation and complementary geometric programming. Simulation results demonstrate that compared to orthogonal multiple access, for imperfect SIC with residual interference even up to 10%, the proposed algorithm for MC-NOMA can offer significant improvement in spectrum and power efficiency.

This paper investigates the wideband self-interference (SI) channel characteristics for a 2×2 MIMO full-duplex (FD) transceiver with dual-polarized antennas. The measurements, carried out at 2.45GHz with 500MHz span in various environments: anechoic chamber, laboratory- room, and corridor, show that the SI-channel can be represented by a multipath model consisting of two segments: a quasistatic internal SI-sub-channel due to the specific Tx/Rx antenna structure and a time-varying or dynamic external SI-sub-channel due to possible reflections from the surrounding environment. The quasi-static internal SI-sub-channel parameters can be derived from the Tx/Rx antenna structure specifications. The dynamic external SI-channel exhibits cluster reflection arrival features and can be modeled by a modified Saleh-Valenzuela (S-V) model, and the cluster power decays exponentially with cluster arrival delay. However, the path power-versus-arrival delay decay is exponential in a laboratory-room environment while it follows the power law in a corridor environment.

This paper studies joint design of mixed-timescale hybrid precoding and combining to maximize the effective capacity for wireless backhaul in massive multiple-input multiple-output (MIMO) systems. Specifically, radio frequency (RF) analog processing is adaptive to statistical channel state information (CSI) while digital baseband processing is updated with instantaneous effective CSI. Equipped with traditional MIMO solutions at the baseband, the issue of RF design for both unconstrained-modulus and constant-modulus elements is addressed. Under the jointly correlated channel model, the objective function does not have a closed-form expression. In the unconstrained case, we derive the optimal RF solution structures, which lead to a combinatorial eigenmode selection formulation. Such an NP-hard problem is solved to near-optimality by semi-definite relaxation. In view of the additional difficulty posed by the non-convex modulus constraint, we exploit the problem structure to construct the constant-modulus design from the unconstrained-modulus solution which is cast as a problem of joint matrix approximation and solved by low-complexity Jacobi-like algorithms. Numerical results show that under loose and stringent delay-outage constraints, the mixed-timescale hybrid designs deliver effective rates comparable with other perfect CSI-based state-of-the-art baselines.

This paper examines a coordinated multi-point transmission/reception system where multiple base-stations (BSs) employ coordinated beamforming to serve multiple mobile-stations (MSs). Under the dynamic point selection mode, each MS can be assigned to only one BS at any time. This paper then presents a solution framework to optimize the BS associations and coordinated beamformers for all MSs. With target signal-to-interference-plus-noise ratios at the MSs, the design objective is to minimize either the weighted sum transmit power or the per-BS transmit power margin. Since the original optimization problems contain binary variables indicating the BS associations, finding their optimal solutions is a challenging task. To circumvent this difficulty, we first relax the original problems into new optimization problems by expanding their constraint sets. Based on the nonconvex quadratic constrained quadratic programming framework, we show that these relaxed problems can be solved optimally. Interestingly, with the first design objective, the obtained solution from the relaxed problem is also optimal to the original problem. With the second design objective, a suboptimal solution to the original problem is then proposed, based on the obtained solution from the relaxed problem. Simulation results show that the resulting jointly optimal BS association and beamforming design significantly outperforms fixed BS association schemes.

A good self-interference (SI) cancellation strategy in full-duplex (FD) communication passes first by a proper understanding of the nature of this SI. In this paper, we investigate the basic causes of SI cancellation bottlenecks in FD. To that end, we first classify the known FD architectures based on where the reference signal is taken to cancel the SI. By combining the effects of transceiver impairment, estimation error and SI channel, our analysis reveals that the the main bottleneck to completely cancel the SI turns out to be either the quantization-noise, the phase-noise in the local oscillator or the estimation error, depending on the used architecture.

In this paper, we address a power-efficient resource allocation problem in virtualized wireless networks (VWNs) using non-orthogonal multiple access (NOMA). In this set-up, the resources of one base station (BS) are shared among different service providers (slices), where the minimum reserved rate is considered for each slice for guaranteeing their isolation. The formulated resource allocation problem aiming to minimize the total transmit power subject to the isolation constraints is non-convex and suffers from high computational complexity. By applying complementary geometric programming (CGP) to convert the non-convex problem into the convex form, we develop an efficient iterative approach with low computational complexity to solve the proposed problem. Illustrative simulation results on the performance evaluation of VWN using OFDMA and NOMA indicate significant performance improvement in the VWN when NOMA is used.

In this paper, we study the joint prioritized link scheduling and resource allocation for the OFDMA-based wireless network which serves two classes of user links, namely non-prioritized (low-priority) and prioritized (high-priority) links. Our design objectives are to maximize the number of non-prioritized links to be scheduled and to maximize the weighted sum rate of all scheduled links while guaranteeing the minimum rate requirements of all prioritized links. To solve this problem, we first transform the original problem into a singlestage optimization problem which is a Mixed Integer Nonlinear Program (MINLP). Then, we propose an iterative algorithm to solve the transformed problem where we sequentially perform modified power allocation and link removals. We prove the convergence and characterize important properties of the proposed algorithm. Numerical results show that the proposed algorithm significantly outperforms the greedy uniform power allocation and the rounding-based admission algorithm in term of the average number of scheduled non-prioritized links and the weighted sum rate.

This paper details novel methods to accurately estimate the achievable rates of channels with additive Gaussian mixture (GM) noise. Attention is paid to a Gaussian input and discrete inputs. Such discrete inputs represent a wide range of signaling strategies and include the capacity-achieving input as a special case. At first, we propose a simple technique to calculate the GM noise entropy. Specifically, when the noise level is high, a lower bound on the integrand of the noise entropy is established and the noise entropy can be estimated in closed-form. In the low noise region, the piecewise-linear curve fitting (PWLCF) method is applied to calculate the noise entropy. It is then demonstrated this can be estimated in both regions with a predetermined accuracy. We then extend this result to calculate the output entropy and the achievable rate when the input is Gaussian distributed, which is shown to be asymptotically optimal. Next, we propose a simple PWLCF-based method to estimate the output entropy for a given discrete input. In particular, the output entropy is evaluated by examining the output in high and low regions of amplitude using a lower bound on the integrand of the output entropy and PWLCF, respectively. It is demonstrated that the output entropy, and consequently, the achievable rates, can be computed to achieve any desired accuracy level.

In this paper, we study joint design of linear hybrid precoding and equalization for full-duplex (FD) massive multiple-input multiple-output (MIMO) systems such that the sum mean squared error is minimized across all mobile stations. To better resolve practical issues such as hardware complexity, power consumption, and overhead of channel estimation, hybrid processing, which consists of digital processing in the baseband and radio frequency (RF) analog processing, is employed at the base station for simultaneous transmission and reception. In particular, baseband processing is adjusted according to instantaneous channel variation while RF processing is only updated based on such long-term channel statistics as transmit and receive correlation. In the presence of self-interference (SI) and co-channel interference, joint power optimization is carried out in order to achieve balanced performance for both the uplink and the downlink. As demonstrated by numerical results, FD is able to outperform half-duplex under realistic SI. Furthermore, the employment of the proposed hybrid processing structure is justified by its near optimal performance when equipped with even only a small number of RF chains.

Millimeter-wave (mmWave) communication has emerged as one of the most promising technologies to deal with the increasing demand in data transmissions over wireless networks. However, due to the propagation characteristic at the mmWave band, much higher pathloss is observed compared to the commonly-used microwave band. Thus, antenna arrays become a necessary ingredient in mmWave systems because of their needed beamforming gains. Beamforming for multiple users, also known as multiuser precoding, can be utilized to further improve the spectral efficiency of mmWave systems. Unfortunately, fully digital precoding with large antenna arrays is difficult to implement due to the hardware cost and power constraint in mmWave systems. Recent works in literature have advocated the structure of hybrid analog/digital precoding for mmWave systems, in which only minor performance degradation is observed. In this work, we study hybrid precoding for multiuser mmWave systems. After reviewing recent works in literature on hybrid precoding designs, we then develop a new hybrid minimum mean-squared error (MMSE) precoder. The proposed precoder can be easily obtained by an orthogonal matching pursuit-based algorithm. Simulation results show significant performance advantages of the proposed precoder over known designs in various system settings.

This paper presents two self-interference (SI) cancellation methods using active signal injection (ASI) for full-duplex MIMO-OFDM systems. Specifically, the ASI approach considers adding an appropriate cancelling signal to the transmitted signal to reduce the SI at the receiver input to avoid overloading the receiver low-noise amplifier (LNA) and analog-to-digital converter (ADC) while ensuring proper signal detection. In the first method, the cancelling signal uses some reserved subcarriers that are not used for data transmission. In the second method, the constellation points are dynamically extended within the cancellation boundary in order to minimize the SI. The proposed techniques appear to be simple to implement and do not require any change on the receiver structure.

We consider Hybrid Automatic Repeat & reQuest with Chase-combining (HARQ-CC) for uplink spectral efficiency enhancement in a multicell system with base- stations (BSs) using large antenna arrays. Each BS uses a zero-forcing receiver and knows only the channel gains of users in its own cell. We first derive the outage probability and long-term average transmission rate (LATR) of the proposed HARQ-CC while limiting the number of retransmissions. We then formulate the optimal rate-selection problem for the proposed scheme and provide a method to find a solution. Since the optimal-rate selection method for the HARQ-CC scheme is computationally intensive, we develop a sub-optimal rate-selection technique that has a LATR performance close to that of the optimal method. Illustrative results show that the HARQ-CC requires less number of antennas than the non-HARQ scheme to achieve the same LATR performance.

We investigate multiple input multiple output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems that operate with insufficient cyclic prefix (CP). Using a CP shorter than the channel delay spread can enable a significant improvement in bandwidth utilization or range extension for OFDM networks at the cost of increased intersymbol interference (ISI) and inter-carrier interference (ICI). We first analyze the effect of ICI and ISI on the received signal. A bi-directional M-algorithm (BDMA) is then proposed for high performance trellis-based equalization to construct an iterative interference mitigation and detection process. Simulations show that, after only 2 iterations, the bit error rate (BER) of the proposed equalization scheme can converge to that of a sufficient-CP system even when the channel delay spread is 6 times longer than the insufficient CP.

This paper proposes hybrid TDMA-CSMA for virtualized wireless networks, aiming to meet their isolation requirements. In this scheme, high-load users with non-empty queues are proper and potential candidates for TDMA, while others can compete using p-persistent CSMA. At each superframe, AP decides on TDMA-CSMA scheduling by taking into account traffic parameters of users and slice reservations to maximize the network utilization, while maintaining slice isolation. The corresponding optimization problem is formulated to dynamically schedule users for TDMA phase and optimally pick p parameter for remaining CSMA users. Using complementary geometric programming (CGP) and monomial approximations, an iterative algorithm is developed to find the optimal solution. The simulation results reveal the performance gains of the proposed algorithm in improving the throughput and keeping isolation in a virtualized wireless network.

This paper investigates the resource allocation problem for a virtualized wireless network (VWN) in which each base station (BS) is equipped with a large number of antennas and due to the pilot contamination error, the perfect estimation of channel state information (CSI) is not available. In this case, the duration of pilot sequence transmission plays a critical role on the achieved VWN throughput. Therefore, we consider this parameter as a new optimization variable and propose a novel utility function for the resource allocation problem. The proposed optimization problem is non-convex with high computational complexity. To address this issue, by applying relaxation and variable transformation techniques, we propose a two-step iterative algorithm in which the allocation of power, sub-carrier and number of antennas is first established and then used to optimize the pilot duration. Simulation results reveal that proper pilot duration design improves the VWN performance.

This paper studies hybrid RF/baseband linear pre-coding design to minimize the mean square error (MSE) for millimeter-wave massive multiple-input multiple-output (MIMO) systems using optimal linear equalizer. Instead of dealing with the objective function of sum MSE, which involves matrix inverses, we approach this problem by minimizing the Euclidean distance between the hybrid precoder and the optimal minimum MSE precoder. In an effort to impose the optimal structure of channel diagonalization, we separate the design of modulus-constrained RF precoder from the design of unconstrained baseband pre-coder. Magnitude-least-squares approximation is introduced to formulate the RF precoder design problem, and is subsequently transformed into a simultaneous matrix diagonalization problem. Such transformation enables application of a simple and numerically stable Jacobi-like algorithm. The effective channel representing a cascade of the derived RF precoder and the MIMO channel, is diagonalized by the baseband precoder. The error performance of the proposed solution is examined by numerical results where the effectiveness is verified by its closeness to the optimal design and its noticeable gain over sparse approximation based schemes.

This paper proposes a delay-aware resource provisioning policy for virtualized wireless networks (VWNs) to minimize the total average transmit power while holding the minimum required average rate of each slice and maximum average packet transmission delay for each user. The proposed cross-layer optimization problem is inherently non-convex and has high computational complexity. To develop an efficient solution, we first transform cross-layer dependent constraints into physical layer dependent ones. Afterwards, we apply different convexification techniques based on variable transformations and relaxations, and propose an iterative algorithm to reach the optimal solution. Simulation results illustrate the effects of the required average packet transmission delay and minimum average slice rate on the total transmission power in VWN.

This paper studies stochastic power control problems over a fading channel, where the transmitter randomly harvests renewable energies from environment and stores them in a battery for future data transmissions. Moreover, data packets are assumed to arrive at the data queue of transmitter with constant rate μ. To incorporate delay quality-of-service guarantees, two delay constraint models are separately considered, namely average delay model with maximum average delay constraint and statistical delay model with maximum delay outage probability constraint. Under each delay constraint model, the stochastic power control problem aims at maximizing μ considering the randomness of channel fading and energy harvesting (EH) processes. The resulting optimization problems can be formulated as infinite-horizon Markov decision processes. Under average delay model, the optimal power control policy needs to keep track of current data queue-length state in addition to the battery state. On the other hand, under statistical delay model, a sufficiently large queue-length region is assumed, hence, the optimal policy does not depend on the data queue-length state. We study various structural properties of the optimal control policies and develop online power control algorithms that converge to the optimal solutions without requiring statistical knowledge of channel fading and EH processes. By defining and learning the so-called post-decision state-value functions, the proposed learning algorithms require less complexity and converge faster than the conventional reinforcement learning algorithms. Numerical results demonstrate the effectiveness of the online learning algorithms for different delay constraint models and EH settings.

In this paper, we detail effective methods to approximate the achievable rates of channels with additive Gaussian mixture (GM) noise for both real and complex channels to achieve any desired level of accuracy. Attention is paid to a Gaussian input, a discrete real input, and a complex input with discrete amplitude and independent uniform phase. Such discrete inputs represent a wide range of input distributions and they include the capacity-achieving inputs as special cases. At first, we propose a simple technique to accurately calculate the noise entropy. Specifically, when the noise level is high, a lower bound on the integrand of the entropy is established and the noise entropy can be estimated using a closed-form solution. In the low noise region, the piecewise-linear curve fitting (PWLCF) method is applied. We then extend this result to calculate the achievable rate when the input is Gaussian distributed, which is shown to be asymptotically optimal. Next, we propose a simple PWLCF-based method to approximate the output entropy for a real GM channel when the input is discrete, and for a complex GM channel when the input is discrete in amplitude with independent uniform phase. In particular, for the real channel, the output entropy is evaluated by examining the output in high and low regions of amplitude using a lower bound on the integrand of the output entropy and PWLCF, respectively. For the complex channel, the output entropy is approximated a similar manner but using polar coordinates and the Kernel function. It is demonstrated that the output entropy, and consequently, the achievable rates, can be computed to achieve any given accuracy level.

We consider a three-node buffer-aided relaying network with statistical quality-of-service (QoS) constraint in terms of maximum acceptable end-to-end queue-length bound outage probability. In particular, we study the adaptive link selection relaying problem that aims to maximize the constant supportable arrival rate μ to the source (i.e., the effective capacity). Fixed and adaptive source and relay power allocation are investigated. By employing asymptotic delay analysis, we first convert the QoS constraint into minimum QoS exponent constraints at the source and relay queues. We then derive the link selection and power allocation solutions as functions of the instantaneous link conditions and QoS exponents using Lagrangian approach. Solutions for various special cases of link conditions and QoS constraints are presented. Moreover, we compare the effective capacities of the proposed relaying schemes and other existing schemes under different link conditions and QoS constraints. Illustrative results indicate that the proposed schemes offer substantial performance gains, and power adaption outperforms fixed power allocation at low signal-to-noise power ratio (SNR) region or under loose QoS constraints.

In this paper, we study the non-orthogonal dynamic spectrum sharing for device-to-device (D2D) communications in the D2D underlaid cellular network. Our design aims to maximize the weighted system sum-rate under the constraints that: 1) each cellular or active D2D link is assigned one subband and 2) the required minimum rates for cellular and active D2D links are guaranteed. To solve this problem, we first characterize the optimal power allocation solution for a given subband assignment. Based on this result, we formulate the subband assignment problem by using the graph-based approach, in which each link corresponds to a vertex and each subband assignment is represented by a hyper-edge. We then propose an iterative rounding algorithm and an optimal branch-and-bound (BnB) algorithm to solve the resulting graph-based problem. We prove that the iterative rounding algorithm achieves at least 1/2 of the optimal weighted sum-rate. Extensive numerical studies illustrate that the proposed iterative rounding algorithm significantly outperforms the conventional spectrum sharing algorithms and attains almost the same system sum-rate as the optimal BnB algorithm.

We consider hybrid automatic repeat and request (HARQ) schemes in the uplink of a multicell multiuser single-input multiple-output system with large-scale antenna arrays at the base station (BS) for improving the spectral efficiency while limiting the number of retransmissions. Assuming a zero-forcing receiver at each BS and that each BS knows only the channel gains of users in its own cell, we derive the expressions for the outage probability and long-term average transmission rate (LATR) of the Type-I HARQ, HARQ with chase-combining (HARQ-CC), and HARQ with incremental redundancy (HARQ-IR). We then formulate the optimal rate-selection problems for the three schemes and provide methods to find a solution. Since the optimal-rate selection methods for the HARQ-CC and HARQ-IR schemes are computationally intensive, we propose sub-optimal rate-selection methods, which yield a closer LATR performance to that of the optimal methods. For the Type-I HARQ and HARQ-IR, we also present a parameterization-based method showing the approximate relation between the expressions of the optimized-LATR, optimal rate, and number of antennas. Illustrative results show that the HARQ-IR has significant gain over HARQ-CC and Type-I HARQ in terms of LATR, while Type-I HARQ yields practically similar performance to HARQ-CC as the number of antennas at the BS increase.

This paper studies optimal stochastic power control problem for a time-varying communication link, where the transmitter randomly harvests renewable energies from the environment. The harvested energies are stored in an energy buffer (or battery). Packets arrive at the transmitter data buffer with a constant rate μ. The objective is to maximize μ under the statistical delay and energy harvesting (EH) constraints. In order to study the optimal power control policy, we reformulate the problem as an infinite-horizon Markov decision process (MDP) using asymptotic delay analysis. The optimal policy and its structural properties are studied by employing the post-decision framework approach. We then propose an online power control algorithm, which converges to the optimal solution without requiring the statistical knowledge of the channel fading and EH processes. Numerical results demonstrate the effectiveness of the online algorithm for different delay constraints and EH settings.

In this paper, we study the energy-efficient resource allocation for device-to-device (D2D) communication underlying cellular networks. Specifically, we aim to maximize the minimum weighted energy-efficiency (EE) of D2D links while guaranteeing the minimum data rates of the cellular links. This design, therefore, guarantees fairness for D2D links and quality-of-service (QoS) for cellular links. Toward this end, we first characterize the optimal power allocation for cellular links based on which the original resource allocation problem can be transformed into the joint sub-channel and power allocation problem for D2D links. We then propose a dual decomposition based algorithm to solve the resource allocation problem in the dual domain. Theoretical analysis demonstrates that the proposed algorithm achieves strong performance guarantee. Numerical studies show that the proposed algorithm achieves nearly optimal performance, and it performs much better than the spectrum-efficient algorithm.

This work considers the relay selection and resource allocation problem (i.e., link scheduling, and rate allocation) for multi-source, multi-relay dual-hop wireless networks. The relays employ buffers to store the received data from the sources for future transmissions. End-to-end (E2E) delay of each traffic flow originated from a source or a relay is constrained in terms of maximum allowable delay-outage probability. To solve this problem, we first study the resource allocation problem to maximize the constant supportable arrival rate of a non-prioritized source under minimum rate requirements of the prioritized sources and relays for a given relay selection solution. Then, the optimal relay selection can be determined to support the largest rate of the non-prioritized source among all possible relay selection solutions. We derive the resource allocation solutions using asymptotic delay analysis and convex optimization techniques. We also develop an online allocation algorithm which does not require the knowledge of the fading statistics by using stochastic approximation theory. Numerical results are presented to demonstrate the usefulness of the proposed resource allocation design for relay selection under different delay and rate constraint regimes.

This paper addresses the problem of digital self-interference (SI) cancellation in full-duplex systems. Under practical transmitter imperfections, the received SI is affected by transmitter nonlinearities and propagation channel, which need to be estimated in order to cancel the SI. The proposed estimation method is based on subspace decomposition. The major detriment of subspace technique is the need of oversampling or multisensor receiver to obtain a nondegerate noise subspace. We modify the traditional subspace techniques by exploiting the covariance and the pseudo-covariance of the received signal. This enables us to increase the dimension of the received signal without resulting to oversampling or multisensor receiver. The different parameters are estimated, up to an ambiguity term, without any knowledge of the intended signal. We develop a joint detection and ambiguity identification procedure that requires a considerably smaller number of pilots than standard training-based methods. Simulation results show that the proposed algorithm can properly estimate the SI channel coefficients and the nonlinear parameters without any pilot symbol from the intended transmitter.

Neighbor-area network (NAN), also known as smart meter communication network, is one of the most important constitutive segments of smart grid communication network. Since almost all smart meters are deployed in hash outdoor environment, they could fail or wireless links between them could be fluctuating over time. These dynamics could hinder the network connectivity and degrade the reliability of data communications. However, the robustness of NANs in the case of network element failures has not received sufficient attention in existing work. This paper therefore proposes a cross-layer scheme that adaptively switches preferred parent nodes in order to help the routing protocol for low-power and lossy networks (RPL), the state-of-the-art implementation of self-organizing routing class, quickly deflect network traffic from points of failures in the NAN scenario. Operation and performance of the proposed scheme in IEEE 802.11-based wireless mesh NANs are studied by simulations.

This paper studies resource allocation for the device-to-device (D2D) underlying cellular system where we aim to maximize two different energy-efficiency metrics of D2D links while guaranteeing the minimum data rates for cellular links. Specifically, we formulate two resource allocation problems that optimize two different objective functions corresponding to System Energy-Efficiency (SEE) and Total Individual Energy-Efficiency (TIEE). To solve these problems, we propose elegant algorithms, which decompose the considered problems into power control and cellular-D2D matching sub-problems. We prove that our proposed iterative algorithms for the SEE and TIEE problems converge to their optimal solutions. Numerical results show that the energy-efficiency achieved by the proposed SEE and TIEE algorithms is much higher than that of the optimal spectrum-efficiency (SE) algorithm with slight degradation in the system sum rate.

This paper presents a solution framework to jointly optimize the base station association strategy and linear beamforming design for downlink transmission in a multicell system. Assuming each mobile station can only be assigned to one base station, our design objective is to minimize the sum transmit power across the base stations with a set of target signal-to-interference-plus-noise ratios at the mobile stations. Since the original optimization problem involves binary variables for base station associations, finding its optimal solution is a challenging task. To circumvent this difficulty, the original problem is relaxed into a new optimization problem by expanding its constraint set. Interestingly, it is shown that the relaxed problem can be solved optimally and its solution is also optimal to the original problem. We then propose two solution approaches to tackle the relaxed problem: one via its Lagrangian dual problem and the other via its dual uplink problem. Simulation results show that the resulting jointly optimal base station association and beamforming design can significantly outperform fixed base station association schemes.

This paper considers a 3-node buffer-aided relaying network with statistical delay quality-of-service (QoS) constraints imposed at the source and relay. To exploit the relay buffering capability and link fading diversity, an adaptive link selection relaying scheme is proposed. In a time slot, the relay (R) can adaptively select to receive from the source (S) or to transmit to the destination (D) based on the instantaneous conditions of the S-R and R-D links. The selection scheme aims to maximize the constant supportable arrival rate to the source, i.e., the effective capacity in consideration of the link fading distributions and the average signal-to-noise power ratios (SNRs) as well as the QoS constraints. We compare the capacities of the adaptive relaying and the fixed relaying where the relay employs fixed transmission and reception schedule, demonstrating the gain of the former, especially under loose QoS constraints. The capacities of the buffer-aided relaying and non-buffer relaying under similar end-to-end delay QoS constraint are also compared, showing the benefits of using buffer-aided relaying to support delay-sensitive applications.

This paper studies the characterization of the optimal input for impulsive ambient noise channels under average power constraint. Our focus is on the two-term Gaussian mixture complex noise model, which has been widely used to model impulsive noise arising in various communication channels. We first demonstrate that there exists a unique input distribution that achieves the channel capacity and the capacity-achieving input distribution has a uniformly distributed phase. By examining the Kuhn-Tucker conditions (KTC), we further show that if the optimal amplitude input distribution contains an infinite number of mass points on a bounded interval, the channel output must be Gaussian distributed. However, by using Bernstein's theorem to examine the completely monotonic condition, it is shown that the assumption of a Gaussian distributed output is not valid. As a result, there is always a finite number of mass points on any bounded interval in the optimal amplitude distribution. In addition, by applying a novel bounding technique on the KTC and using the Envelop Theorem, we demonstrate that the optimal amplitude distribution cannot have an infinite number of mass points. That gives us a unique solution of the optimal input having discrete amplitude with a finite number of mass points. Given such interesting results, we also develop an efficient way to compute the discrete optimal input and the corresponding capacity.

We study the stochastic user scheduling and power control problem for an uplink multi-user network over time-varying channels, where the users randomly harvest renewable energies from the environment. For each user, the renewable energies and arriving data packets with a constant rate are stored in energy (battery) and data buffers, respectively. Users have statistical packet delay constraints in terms of maximum acceptable delay-outage probabilities. We classify the users as prioritized and non-prioritized users. Our goal is to maximize the arrival rate of the non-prioritized user while supporting the minimum data rate requirements for the prioritized users. We reformulate the problem as an infinite-horizon Markov decision process (MDP) using asymptotic delay analysis and study the optimal scheduling and power control policy. Since the optimal policy requires centralized processing with high computational complexity, we develop a reduced-complexity distributed algorithm, which can be implemented at each individual user. Online algorithm is devised, which does not require the statistical knowledge of the channel fading and energy harvesting (EH) processes. Numerical results demonstrate the effectiveness of the centralized and distributed schemes for different delay constraints and EH settings.

We consider progressive hybrid precoder design for packet retransmissions in large-scale multiple-input multiple-output (MIMO) systems with perfect channel state information (CSI) knowledge at the transmitter. To exploit time diversity provided by packet retransmissions, we propose a two-step approach to optimize both the radio-frequency (RF) precoder and the corresponding baseband precoder with the objective of maximizing the mutual information. During each retransmission attempt, we choose the RF precoder matrix columns from the set of transmit array response vectors. For a given RF precoder, we show that the optimal baseband precoder is a function of the generalized eigen-matrix of the Gram matrices of the effective channel matrix and the RF precoding matrix. The optimal baseband precoder for each transmission round includes appropriate power loading, selection and reverse pairing of the singular values of the previous transmission attempts with the ratio of the elements obtained by the diagonalization of the Gram matrices of the effective channel matrix and the RF precoding matrix using a generalized eigen-matrix. Illustrative results show that the proposed progressive hybrid precoder design achieves a performance close to that of the optimal progressive digital precoder (OPDP).

This paper deals with the energy-efficient resource allocation for device-to-device (D2D) communication underlaid cellular networks. Specifically, our design objective is to maximize the weighted energy-efficiency (EE) of D2D links while guaranteeing the minimum data rate for each cellular link. To solve this problem, we first characterize the optimal power allocation solution for the cellular links so that the original resource allocation problem can be transformed into the joint subchannel and power allocation problem for D2D links. We then propose a relaxation-based algorithm to solve the transformed problem. We prove that the proposed algorithm converges to the optimal solution of the original problem if no D2D link utilizes the maximum transmit power. Extensive numerical results demonstrate that the proposed algorithm achieves the optimal performance and it outperforms existing algorithms.

This paper investigates the dynamic resource provisioning with queue stability in wireless virtualized networks (WVN). Aiming to maximize the total average rate of WVN over a transmission frame, a dynamic resource provisioning policy is proposed, while a minimum average required rate of each slice and a stable-queue constraint of WVN are preserved. Based on Lyapunov drift-plus-penalty algorithm and variable transformation techniques, an iterative algorithm is proposed for joint power and sub-carrier allocation. Performance of the proposed algorithm is evaluated by simulations performed investigate the effects of various system parameters on the average rate of WVN and queue stability.

In this paper, we focus on the self-interference (SI) cancellation in the presence of phase-noise in full-duplex systems. Reducing the SI needs the estimation of the SI channel and the phase-noise. In particular, the time-varying phase-noise process is approximated by an expansion over a basis. As the unknown intended signal received from the other transceiver can limit the estimation performance if considered as additive noise, we incorporate it in the joint estimation of the SI channel, the intended channel and the phase-noise process. The proposed algorithm maximizes the likelihood function by fully exploiting the known transmitted data and the second-order statistic of the intended signal.

This paper considers a buffer-aided full-duplex (FD) relaying network assuming imperfect self-interference (SI) cancellation. The residual SI level is modeled to be proportional to the relay transmit power. Moreover, for quality-of-service (QoS) provisioning, the end-to-end queue-length is statistically constrained in terms of maximum acceptable outage probability. We derive the optimal power allocation solution for the source and relay to maximize the constant supportable arrival rate μ to the source (i.e., the effective capacity) and study its properties. Illustrative examples are performed to compare the effective capacities of the proposed FD relaying scheme and other benchmark schemes under different settings and QoS constraints.

This paper presents an adaptive hybrid TDMA-CSMA MAC protocol to improve network performance and isolation among service providers (SPs) in a virtualized 802.11 network. Aiming to increase network efficiency, wireless virtual-ization provides the means to slice available resources among different SPs, with an urge to keep different slices isolated. Hybrid TDMA-CSMA can be a proper MAC candidate in such scenario benefiting from both the TDMA isolation power and the CSMA opportunistic nature. In this paper, we propose a dynamic MAC that schedules high-traffic users in the TDMA phase with variable size to be determined. Then, the rest of active users compete to access the channel through CSMA. The objective is to search for a scheduling that maximizes the expected sum throughput subject to SP reservations. In the absence of arrival traffic statistics, this scheduling is modeled as a multi-armed bandit (MAB) problem, in which each arm corresponds to a possible scheduling. Due to the dependency between the arms, existing policies are not directly applicable in this problem. Thus, we present an index-based policy where we update and decide based on learning indexes assigned to each user instead of each arm. To update the indexes, in addition to TDMA information, observations from CSMA phase are used, which adds a new exploration phase for the proposed MAB problem. Throughput and isolation performance of the proposed self-exploration-aided index-based policy (SIP) are evaluated by numerical results.

This paper presents a channel-aware access scheme for Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) aiming to take advantage of multi-user diversity and improve throughput, while supporting distributed and asynchronous operation. By dynamically adjusting the contention window of each station (STA) according to its channel state, this method prioritizes STAs that gain most from using a channel, and hence, enhances channel utilization in comparison with a simple random access scheme. To model the proposed Adaptive CSMA/CA (A-CSMA/CA) protocol, a three-dimensional Markov chain is developed. With the aid of such model, performance of the proposed A-CSMA/CA is analytically studied in terms of saturation throughput. Furthermore, illustrative results confirm that A-CSMA/CA significantly improves the throughput, specifically in a large network.

Full-duplex operation requires effective self-interference (SI) cancellation that in turn needs reliable SI channel estimation. In this paper, we develop two estimation algorithms suitable for a 2-stage SI cancellation structure. By exploiting the sparsity of the SI channel, we first derive a compressed sensing-based SI channel estimation algorithm to be used in the first SI cancellation stage at radio-frequency (RF) to reduce the SI. We then develop a subspace-based algorithm to jointly estimate the residual SI channel, the intended channel and the transmitter nonlinearities for the second SI cancellation stage at baseband. Including the intended received signal in the estimation process is the main advantage of the proposed algorithm as compared to previous works that assume it as additive noise. Simulation results show that the proposed algorithms outperform the least-square (LS) algorithm and offer higher signal-to-residual-interference-and-noise ratio (SINR) over a large received signal-to-noise ratio (SNR) range.

This paper deals with energy-efficient adaptive power allocation for an incremental multiple-input multiple-output (IMIMO) system employing hybrid automatic repeat request (HARQ) with Chase combining (CC), to minimize its rate-outage probability under a constraint on average energy consumption per data packet. We first provide the rate-outage probability expressions for the considered IMIMO system, and use Gauss-Legendre approximation to convert them into a tractable form and formulate a non-convex optimization problem that can be solved by an interior-point algorithm for finding a local optimum. Next, to further reduce the solution complexity, using an asymptotically equivalent approximation of the rate-outage probability expression, we approximate the non-convex optimization problem as a geometric programming problem (GPP), for which a solution can be obtained using convex optimization algorithms. Illustrative results indicate that the proposed power allocation (PPA) offers significant gains in energy savings as compared to the equal-power allocation (EPA), and the less complex GPP approach can provide a closer performance to the exact method at lower values of rate-outage probability.

This paper focuses on the design of vector perturbation (VP) precoding for multi-user multiple-input singleoutput downlink transmission under quantized channel state information. Each receiver decomposes its downlink channel vector in forms of channel direction information (CDI) and channel magnitude information (CMI) for feedback to the transmitter. Under quantized CDI and quantization error statistics, closed-form expressions to the mean-squared-error (MSE) between channel input and output when (i) perfect CMI available to the transmitter and (ii) only CMI statistics known at the transmitter, are derived. We then propose a unified approach to design the MSE minimization based VP precoders. Bit error rate simulation results indicate that the proposed VP precoder designs are less sensitive to quantization errors and CMI availability helps to improve the performance.

In this paper, the problem of joint access point (AP) selection and linear precoding for multiple-input multiple-output orthogonal frequency division multiplexing systems is studied in the framework of a non-cooperative game. This game is shown to be a potential game with the potential function being the sum rate achieved by successive interference cancellation. Due to the mixed-integer nature of the optimization variables, it is difficult, if not impossible, to directly characterize the maxima of the potential function, which are closely related to the Nash equilibrium (NE) of the game. Instead, we establish the existence and achievability of the maximum through non-decreasing and upper-bounded properties of the potential function as a direct result of the proposed update scheme. A distributed algorithm is designed where each player selfishly optimizes its AP selection and linear precoding strategy in a sequential manner. Convergence is a byproduct of the established properties of the potential function which are materialized by an iterative water-filling algorithm. Numerical results show that the algorithm is able to reach fast convergence, scale linearly with the number of users in terms of complexity, and provides a practical system sum rate at the NE nearing that of the optimal centralized solution.

This paper is concerned with linear precoding designs in a multiuser multiple-input single-output system. With the design objective of maximizing the system sum-rate, we take into consideration multiple linear power constraints at the base-station, including sum, per-antenna, and interference power constraints. We then propose two mean-squared error (MSE)-based precoders, namely minimum MSE (MMSE) and iterative minimization of weighted MSE (IWMMSE) precoders. Both proposed precoding designs are obtained by specialized iterative algorithms. To enforce the multiple power constraints, a certain set of auxiliary variables are introduced and updated iteratively at each algorithm. The proposed precoders are then given in closed-form at each iteration. Convergence of both proposed algorithms is then proved and verified by numerical simulations. Simulation results also show significant enhancements in sum-rate performance by the proposed precoding designs over zero-forcing precoding.

This paper studies joint resource provisioning and admission control in wireless virtualized networks (WVN), where one base station of an OFDMA-based wireless network is virtualized into two types of slices with resource-based and rate-based reservations. Aiming to maximize the total rate of WVN, first, the resource provisioning optimization problems are formulated by guaranteeing a minimum requirement for each slice. Via constraint relaxation and variable transformations, an iterative algorithm is developed for power and sub-carrier allocation. Due to the channel variations, WVN suffers from non-zero outage probability, i.e., slice requirements cannot always be met. To prevent this issue, we present an admission control algorithm in which slice requirements are dynamically adjusted based on channel state information. The simulation results demonstrate the effectiveness of our proposed algorithms.

This paper considers the use of a friendly full-duplex (FD) relay to increase the secrecy rate over a fading channel between the legitimate source and destination in the presence of a naive or informed eavesdropper. Naive eavesdropper can only decode the received signals either from the source or from the relay, while informed eavesdropper can overhear signals transmitted from both the source and relay. Accordingly, we compare the achievable secrecy rates of FD relay with traditional half-duplex (HD) relay in terms of the channel state information (CSI) between nodes, eavesdropper types, and the self-interference (SI) in FD-Relay. We consider the non-convex power allocation problems for the developed FD-relay to maximize the secrecy rate under the power constraints and develop an efficient iterative algorithm based on the difference-of-two-concave-functions (DC) programming. The analytical and simulation results confirm that FD relay offers significant improvements in the secrecy rate over the HD-Relay.

This paper studies the characterization of the optimal input and the computation of the capacity of additive quadrature Gaussian mixture (GM) noise channels under an average power constraint. The considered model can be used to represent a wide variety of channels with impulsive interference, such as the well-known Bernoulli-Gaussian and Middleton class-A impulsive noise channels, as well as multiple-access interference channels and cognitive radio channels under imperfect sensing. At first, we demonstrate that there exists a unique input distribution that achieves the channel capacity, and the capacity-achieving input distribution has a uniformly distributed phase. By examining the Kuhn-Tucker alignment conditions (KTCs), we further show that, if the optimal input amplitude distribution contains an infinite number of mass points on a bounded interval, the channel output must be Gaussian-distributed. However, by using Bernstein's theorem to examine the completely monotonic condition, it is shown that the assumption of a Gaussian-distributed output is not valid. As a result, there are always a finite number of mass points on any bounded interval in the optimal amplitude distribution. In addition, by applying a novel bounding technique on the KTC and using the envelop theorem, we demonstrate that the optimal amplitude distribution cannot have an infinite number of mass points. This gives us the unique solution of the optimal input having discrete amplitude with a finite number of mass points. Given this discrete nature of the optimal input, we then develop a simple method to compute the discrete optimal input and the corresponding capacity. Our numerical examples show that, in many cases, the capacity-achieving distribution consists of only one or two mass points.

In this paper, we examine the use of a friendly full-duplex (FD) relay to increase the secrecy rate over a fading channel between the legitimate source and the destination in the presence of residual self-interference (SI) and eavesdropper. In particular, we consider two different protocols based on the FD capability of relay: 1) FD transmission (FDT), in which the FD-Relay receives and sends data concurrently; 2) FD-Relay with jamming (FDJ), where first, the FD-Relay simultaneously receives data and sends jamming to the eavesdropper; then, it forwards the data, while the source jams the eavesdropper. We first develop the secrecy rate expressions for half-duplex transmission (HDT), half-duplex with jamming (HDJ), FDT, and FDJ relaying protocols, and then use them to derive their performance properties in terms of the channel gains between nodes, eavesdropper types, and more importantly, the SI level in FD-Relay. We further investigate the non-convex power allocation problems for the developed FDT and FDJ to maximize the secrecy rate under the power constraints. In particular, we develop an efficient iterative algorithm based on the difference-of-two-concave-functions programming. Analytical and simulation results show the strong influence of SI level on the achieved secrecy rate of the FDT and the FDJ. For sufficiently low SI, FDT achieves a much higher secrecy rate than FDJ, HDJ, and HDT. However, for higher SI, FDJ becomes more effective in enhancing the achieved secrecy rate. The results also indicate that adaptive power allocation can significantly improve the performance and confirm that the proposed FDT and FDJ outperform the HDT and the HDJ.

This paper presents a single- or multi-user Multiple-Input-Multiple-Output (MIMO) Full-Duplex (FD) precoding transceiver structure applicable for single-carrier and Orthogonal Frequency Division Multiplexing (OFDM) systems. The structure increases the dimensionality at the transmitter, which allow for the cancellation of self-interference and forward beamforming to be jointly processed using precoding at the transmitter. The FD Precoding (FDP) structure allows for various joint precoding algorithms and different optimization objectives. We present separate and joint precoding designs for sum-rate maximization and a theoretical analysis of when the separate design is optimal. The joint designs make use of Sequential Convex Programming (SCP). Extensive simulation results using both channel models and measured data show that the FDP structure can provide very significant performance gains over existing techniques for both SU- and MU-MIMO systems. In particular, the FDP structure provides between 1.6 and 1.8 times the spectral efficiency of optimized half-duplex for many of the tested SU-MIMO scenarios.

We consider the power allocation and relay selection for rate maximization in a two-path amplify-and-forward (AF) relay network with inter-relay interference (IRI) consideration. We first investigate the power allocation with only a pair of relays under both the individual and global power constraints. To find the global optimum solution to this nonconvex problem, we develop a three-step approach using the rate-profiling technique together with a reformulation of the sum-rate maximization problem as a set of power-minimization geometric programming problems (GPPs). For reduced complexity, we further convert the optimization problem into a set of GPPs in a single-step by using a high signal-to-interference-plus-noise ratio approximation. Next, we consider the relay pair selection and propose an algorithm in which the achievable rate of each pair of relays with the proposed power allocation is compared. This selection criterion outperforms the conventional selection scheme in terms of the achievable rate. We further propose two low-complexity selection criteria for low and moderate IRI. For moderate IRI, the ratio of the source-relay and relay-destination channel power product to the square of inter-relay channel power can be used for relay selection to achieve a performance close to that of the selection based on the proposed power allocation.

The successful implementation of Smart Grid (SG) requires an efficient communication infrastructure that is cost-effective, scalable and fault-tolerant. This paper aims to study and develop relevant networking techniques for an efficient and reliable SG Communication Network (SGCN). In particular, we propose a viable communication architecture for the interconnection of different radio access technologies along the separate segments of the SGCN. Specifically, WiFi mesh network at the Neighbor Area Network (NAN) level with LTE at the Wide Area Network (WAN) level. Based on this architecture, the performance, transmission latency and Packet Delivery Ratio (PDR), of geographic routing in the NAN segment is considered. Specifically, the scaling of system performance when per-smart-meter data rate, channel shadowing level and the number of smart meters per collector increases is investigated. The results presented in this study can then serve as important guidelines for the design and development of relevant communication infrastructures for SGs.

This paper deals with the dynamic spectrum sharing between underlaying device-to-device (D2D) and cellular links in a multi-carrier cellular network to maximize the weighted network sum-rate while guaranteeing the minimum individual cellular link data rates and proportional fairness among D2D links. In particular, we formulate an NP-hard non-orthogonal resource allocation problem, and develop an iterative algorithm that alternates between the sub-carrier assignment and power allocation in each iteration until convergence. It is shown that the sub-carrier assignment problem corresponds to an integer linear program while the power allocation is reformulated into a difference-between-two-concave-functions (DC) problem. We establish the important properties of the developed algorithms and prove their convergence behavior. Illustrative results indicate that the proposed non-orthogonal resource allocation algorithm significantly outperforms the orthogonal spectrum sharing counterpart.

This work studies the problem of joint multiuser downlink beamforming and admission control in multiple-input multiple-output (MIMO) heterogeneous networks. Considered is a network where a newly deployed femtocell base-station (FBS) has the coverage overlapped with that of an existing macrocell base-station (MBS). Our design objective is to serve as many femto-users (FUEs) as possible at their quality-of-service (QoS) requirements while maintaining the QoS requirements at the macro-users (MUEs). In the first part of this work, we consider the joint downlink beamforming and admission control problem as a joint optimization problem, which can be solved in a centralized manner with full coordination between the MBS and the FBS. In the second part, we propose a distributed algorithm in performing joint downlink beamforming and admission control at the femtocell with only limited MBS-FBS coordination. Specifically, after acquiring certain design parameters from the MBS, the FBS unilaterally determines its beamforming and admission control strategy while coordinating its induced interference to the macrocell. We then prove that the distributed algorithm will converge to a fixed-point where the QoS at the MUEs and admitted FUEs is guaranteed. Simulation results show that the distributed algorithm performs as well as the centralized one in terms of number of FUEs served with only a small penalty on the power usage at the MBS and the FBS.

Advanced Distribution Automation (ADA) is one of the key applications of the Smart Grid (SG). Since Narrow-band Power Line Communications (NB-PLC) can offer a cost-effective communication infrastructure supporting data acquisition and automation, it has appeared to be one of the most promising technologies to enable ADA. However, the applicability of NB-PLC needs to be investigated carefully since this technology still exhibits some limitations related to throughput, signal attenuation/distortion and reliability in ADA scenarios. This paper attempts to give a reasonable data rate estimation by considering realistic ADA infrastructures/applications and IEC 61850 as the primary data modeling and communications standard for ADA. Effects of channel contention at Medium Access Control (MAC) layer on the achievable throughput of existing NB-PLC technologies are investigated. The data rate estimation and throughput analysis are then used to evaluate the feasibility of NB-PLC in supporting ADA applications.

Neighbor area network (NAN) is one of the most important segments of smart grid communications network (SGCN) since it is responsible for the information exchanges between the utility and a large number of smart meters (SMs) in order to enable various important smart grid (SG) applications. Greedy perimeter stateless routing (GPSR) and the routing protocol for low-power and lossy networks (RPL) have been considered as the most promising layer-3 protocols for wireless mesh NANs. This paper compares the system performance and investigates the applicability of these two protocols in practical NAN scenarios. Specifically, transmission reliability, latency and routing path details of GPSR and RPL are studied by extensive simulations. The advantages and disadvantages of each protocol with respect to the characteristics and required features of NAN are discussed in details. The effects of wireless channel characteristics and network offered load levels are also investigated.

This paper proposes two Sequential Convex Programming (SCP) algorithms, namely Difference of Convex functions (DC)-based and Sequential Convex Approximations for Matrix-variable Programming (SCAMP), for solving the non-convex matrix-variable sum-rate maximization problem in Full-Duplex (FD) Single-User Multiple-Input-Multiple-Output (SU-MIMO) systems. The two proposed algorithms result in different approximations of the objective function and hence, depending on the environment, one may be favorable than the other. Numerical results show that SCP can significantly increase the sum-rate over existing techniques for the SU-MIMO scenario. In particular, for the SU-MIMO scenario, the DC-based algorithm outperforms the SCAMP.

We investigate the signal-to-residual-interference ratio (SIRout) in a full-duplex transceiver with analog self-interference cancellation in consideration of three major sources of imperfection: (i) self-interference channel estimation error, (ii) quantization error in the receiver analog-to-digital converter (ADC), and (iii) quantization error in the digital-to-analog converter (DAC) used to generate the self-interference replica. In particular, we first derive the Cramér-Rao lower bound on the variance of the self-interference channel estimation error, and use it to further develop a closed-form expression of the SIRout. The resulting SIRoutexpression facilitates a study of the limit of a full-duplex system and determines the minimum required resolution for the ADC and DAC in order to meet a given performance. The expression reveals that, with a sufficiently high number of bits, the effects of ADC and DAC are negligible, but the cancellation performance is limited by the thermal noise and, in the best case, we can obtain a SIRoutequal to the received signal-to-thermal-noise ratio (SNR).

This paper presents an analysis of the achievable effective capacity of a dual-hop network with buffer-aided source and relays using a concurrent relay selection protocol. The source and relays are subject to constraints on the maximum allowable delay violation probabilities. The effective capacity under such statistical delay constraints is derived as a function of the source and relay signal-to-noise power ratios (SNRs), delay exponents, number of relays, and fading distributions. Illustrative results show the effective capacity under various SNR regions and statistical delay constraints. It is seen that more stringent delay constraints significantly reduce the effective capacity of dual-hop networks.

This paper investigates the error performance of a full-duplex (FD) amplify-and-forward (AF) single-relay system under the effect of residual self-interference whose variance is proportional to the λ-th power of the transmitted power (0≤λ ≤1). Our focus is on the cooperative FD linear relaying (LR) protocol that makes use of direct source-destination link. At first, a closed-form expression of the pairwise error probability (PEP) is derived for the considered system. This expression allows us to analyze the diversity behavior in high transmission power regions. Thanks to the use of the direct link, it is shown that the FD LR system can attain the same diversity function as its half-duplex (HD) counterpart as long as a suitable precoder is applied. Different from previous works that either ignore the direct link or treat it as a source of interference, a non-zero diversity order is thus achieved and the error floor behavior can be eliminated despite the existence of self-interference in FD. More interestingly, it is then demonstrated that transmitting a superposition of all symbols in HD mode maximizes the asymptotic coding gain. Although HD relaying is hence asymptotically optimal, simulations results reveal that FD is advantageous at practical bit error rate (BER) levels when λ is sufficiently small.

This paper proposes a jointly cooperative relay and jamming protocol based on full-duplex (FD) capable relay to increase the source-destination secrecy rate in the presence of different types of eavesdroppers. In this so called FD-Relay with jamming (FDJ) protocol, the FD-Relay, first, simultaneously receives data and sends jamming to the eavesdropper, and, then, forwards the data, while the source jams the eavesdropper. Achievable secrecy rates of the proposed FDJ in the presence of different eavesdropper types and self-interference (SI) are derived and compared with those of the traditional half-duplex (HD) relay. The adaptive power allocation for secrecy rate maximization in a multi-carrier scenario for both proposed FDJ and HD-Relay is formulated as a non-convex optimization problem and corresponding iterative solution algorithm is developed using the difference-of-two-concave-functions (DC) programming technique. The simulation results confirm that FDJ offers significant improvements in the secrecy rate over the HD-Relay.

In a dense WLAN deployment, the interfering wireless access points (APs) need to efficiently share the spectrum, and hence, avoid low-performance experience of users due to high collision rates and long backoff overheads. In this paper, we aim to propose a channel assignment scheme in which APs can self-configure their channel choices to mitigate interference and thereby maximize network throughput. Aiming to minimize interference sum utility, a channel assignment problem is formulated as a non-convex optimization problem and solved using difference-of-convex-functions (DC) programming. Subsequently, two distributed algorithms are developed in which each AP independently adapts its channel selection to the optimal values over time by measuring the received interference in different channels. The convergence, complexity, and efficiency of the developed algorithms are studied by simulation results.

This paper focuses on the channel estimation for residual self-interference cancellation at the baseband in a full-duplex transceiver. In particular, we analyze and develop a semi-blind maximum-likelihood algorithm to jointly estimate both the residual self-interference channel and intended signal channel based on the perfectly known transmitted symbols from its own transmitter, and both known pilot and unknown data symbols sent from the other intended transmitter. We first derive a closed-form solution for the channel estimate, and subsequently develop an iterative procedure to improve the estimation performance of the closed- form approach at high SNR. The iterative algorithm is guaranteed to converge to the ML solution when properly initiated. Simulation results show that, with a modest complexity, the proposed algorithm can offer good channel estimation MSE that follows well the Cramer-Rao bound (CRB), and good cancellation performance for a large SNR range.

In this paper, the dynamic power allocation problem over the multiple-access channel (MAC) against overhearing of eavesdroppers is investigated with the objective to maximize the users' secrecy rate under transmitted power constraints. Lyapunov drift approach is applied to derive the sub-optimal solution for this inherent non-convex optimization problem. Convergence condition and performance of the developed algorithm are investigated. Simulation results indicate that it outperforms the difference- of-two-convex-functions (DC) programming with much less computation time, and its achieved secrecy rate is close to the global optimum solution.

This paper studies quality-of-service (QoS)-aware frequency provisioning schemes for a downlink multi-user multi-carrier system in a frequency-selective fading environment with diverse user-QoS requirements in terms of target delay and effective capacity (EC). Since a jointly optimal power and sub-carrier allocation requires an exponential-time exhaustive search, we explore an alternative simpler approach with two steps: (i) frequency provisioning to allocate the available subcarriers to the demanding users, followed by (ii) power allocation for the set of subcarriers assigned to each user. The single-user EC-based power allocation can be directly applied to step (ii). Furthermore, its results can also be used to develop a low-complexity knowledge-based frequency provisioning algorithm for step (i). The proposed iterative frequency provisioning algorithm starts with an initial rate-based guess and analyzes the incremental cost of power with respect to each user's QoS requirement to modify the allocated numbers of subcarriers that can further reduce the power consumption, if possible, in each subsequent iteration. Illustrative results show that the initial rate-based guess can be very effective in many situations, whereas, when the demanding users have similar required ECs but widespread delay requirements, the initial rate-based guess can be far from the optimum solution and therefore, more iterations are needed.

In this paper, we consider the uplink subchannel and power allocation problem for device-to-device (D2D) and cellular links in the Orthogonal Frequency Division Multiple Access (OFDMA)-based D2D cellular network. This resource allocation problem aims to maximize the weighted sum throughput of D2D links while guaranteeing the minimum rate of each cellular link. The proposed formulation allows non-orthogonal spectrum sharing between the cellular and the D2D links to enhance the total D2D throughput. We develop an iterative algorithm that decouples the bandwidth and power allocation in two different steps and improves the objective function over iterations. For the power allocation sub-problem, we exploit the DC (difference between concave functions) structure of the objective function in the underlying problem and transform it into the convex optimization problem. We establish the convergence of the proposed algorithm. Numerical results confirm that our resource allocations algorithm outperforms other orthogonal spectrum sharing schemes.

This paper is concerned with linear precoding designs in a multiuser downlink system. We consider a multiple-input single-output system with multiple single-antenna user-equipments (UE) experiencing non-homogeneous user conditions, including the channel strength and the background noise power. Assuming perfect knowledge of channel state information and noise power at the base-station (eNB), we propose a new regularized zero-forcing (RZF) precoder, which takes advantage of the non-homogeneous user conditions. Given in a closed-form solution, the proposed RZF precoder outperforms other well-known linear precoders, while achieving a close performance to the locally optimal iterative weighted minimization of mean-squared error precoder, in terms of the achievable network sum-rate. We then propose a greedy user selection algorithm in conjunction with the proposed RZF precoder when the number of UEs exceeds the number transmit antennas at the eNB.

This work studies the effective capacities of dualhop networks with relay selection. The source uses buffer to store the arrival packets. For non-buffer relays, they forward the packets received from the source to the destination in the next time slot in the same transmission frame. For buffer-aided relays, they can store the received packets and forward them in future transmission frame(s). Two different buffer-aided relays can be selected for packet reception and packet forwarding in a frame to exploit the relay selection diversity in both relay and access links. As a result, higher throughput can be achieved as compared to the case of non-buffer relays at the expense of increased buffering delay. To provision delay QoS guarantees, the source and buffer-aided relays operate under statistical delay guarantees in terms of maximum delay violation probabilities. The effective capacities (i.e., maximum constant arrival rates to the source) are characterized as function of the signal-to-noise ratios (SNRs) of the source and relays, delay parameters, and fading distributions.

A novel iterative receiver is proposed for relay‐assisted multiuser communications in which multiple users transmit to a destination with the help of a relay and using fast frequency‐hopping modulation. Each user employs a channel encoder to protect its information and facilitate interference cancellation at the receiver. The signal received at the relay is either amplified, or partially decoded with a simple energy detector, before being forwarded to the destination. The proposed iterative receiver exploits the soft outputs of a channel decoder to successively extract the maximum‐likelihood symbols of the users and perform interference cancellation. Under the same spectral efficiency, simulation results demonstrate superior performance of the proposed receiver when compared to the performance of an interference cancellation scheme that was previously proposed for multiuser communications and is extended to multiuser amplify‐and‐forward relaying considered in this study, as well as performance of the maximum‐likelihood multiuser detection for uncoded transmission.

This paper investigates the error and diversity performances of full-duplex (FD) amplify-and-forward (AF) singlerelay systems under the effect of residual self-interference. The variance of this interference is assumed to be proportional to the λ-th power of the transmitted power (0 ≤ λ ≤ 1). The study considers the cooperative linear relaying protocol with direct source-destination link and the dual-hop scheme without direct link, both under uncoded and coded frameworks. At first, closed-form pairwise error probability expressions are derived for the uncoded systems, which are then used to obtain tight bounds to the bit error rate (BER) of the coded systems. To shed an insight on the diversity behavior, asymptotic expressions at high transmission powers are also presented. Different from previous works that treat the direct link as interference, this paper shows that FD linear relaying systems with a suitable precoder can attain the same diversity function as their half-duplex (HD) counterparts. However, further analysis shows that HD orthogonal AF using a superposition constellation is asymptotically optimal in terms of maximum coding gain. In addition, it is shown that the diversity of FD dual-hop systems is a decreasing function of λ and is equal to zero when λ = 1. Although HD relaying is asymptotically optimal under the considered protocols and interference model, illustrative results show that FD relaying is advantageous at practical BER levels when λ is sufficiently small.

This paper presents an energy-efficient power allocation strategy for Nakagami-m flat-fading channels with a delay-outage probability constraint. The operating input transmit power value is limited to Pmax. The energy efficiency (EE), expressed in units of b/J/Hz, is represented as the ratio of the effective capacity to the sum of transmission power (Pt) and circuit power (Pc). Since the EE-maximization objective function is quasi-concave, a unique global maximum exists. By using fractional programming, we develop an EE-optimal power allocation strategy that consists of two steps: 1) obtaining the power level P̅un, at which the maximum EE can be achieved, and 2) distributing the power optimally based on the minimum of Pmaxand P̅un. We prove that while P̅unmonotonically increases with Pc, the maximum achievable EE is a monotonically decreasing function of Pc. The analysis further allows us to derive the EE of three important cases: non-fading channels, extremely stringent delay-limited systems, and systems with no delay constraints. Simulation results confirm analytical derivations and further show the effects of the circuit power, fading duration, and fading severeness on the achievable EE and effective capacity of a delay-limited fading channel.

In this paper, we propose a joint subchannel and power allocation algorithm for the downlink of an orthogonal frequency-division multiple access (OFDMA) mixed femtocell/macrocell network deployment. Specifically, the total throughput of all femtocell user equipments (FUEs) is maximized while the network capacity of an existing macrocell is always protected. Towards this end, we employ an iterative approach in which OFDM subchannels and transmit powers of base stations (BS) are alternatively assigned and optimized at every step. For a fixed power allocation, we prove that the optimal policy in each cell is to give each subchannel to the user with the highest signal-to-interference-plus-noise ratio (SINR) on that subchannel. For a given subchannel assignment, we adopt the successive convex approximation (SCA) approach and transform the highly nonconvex power allocation problem into a sequence of convex subproblems. In the arithmetic-geometric mean (AGM) approximation, we apply geometric programming to find optimal solutions after condensing a posynomial into a monomial. On the other hand, logarithmic and \underline{d}ifference-of-two-\underline{c}oncave-functions (D.C.) approximations lead us to solving a series of convex relaxation programs. With the three proposed SCA-based power optimization solutions, we show that the overall joint subchannel and power allocation algorithm converges to some local maximum of the original design problem. While a central processing unit is required to implement the AGM approximation-based solution, each BS locally computes the optimal subchannel and power allocation for its own servicing cell in the logarithmic and D.C. approximation-based solutions. Numerical examples confirm the merits of the proposed algorithm.

This paper is concerned with the maximization of the weighted sum-rate (WSR) in the multicell MIMO multiple access channel (MAC). We consider a multicell network operating on the same frequency channel with multiple mobile stations (MS) per cell. Assuming the interference coordination mode in the multicell network, each base-station (BS) only decodes the signals for the MSs within its cell, while the inter-cell transmissions are treated as noise. Nonetheless, the uplink precoders are jointly optimized at MSs through the coordination among the cells in order to maximize the network weighted sum-rate (WSR). Since this WSR maximization problem is shown to be nonconvex, obtaining its globally optimal solution is rather computationally complex. Thus, our focus in this work is on low-complexity algorithms to obtain at least locally optimal solutions. Specifically, we propose two iterative algorithms: one is based on successive convex approximation and the other is based on iterative minimization of weighted mean squared error. Both solution approaches shall then reveal the structure of the optimal uplink precoders. In addition, we also show that the proposed algorithms can be implemented in a distributed manner across the coordinated cells. Simulation results show a significant improvement in the network sum-rate by the proposed algorithms, compared to the case with no interference coordination.

This paper studies a multiuser multicell system where block-diagonalization (BD) precoding is utilized on a per-cell basis. We examine and compare the multicell system under two operating modes: competition and coordination. In the competition mode, the paper considers a strategic non-cooperative game (SNG), where each base-station (BS) greedily determines its BD precoding strategy in a distributed manner, based on the knowledge of the inter-cell interference at its connected mobile-stations (MS). Via the game-theory framework, the existence and uniqueness of a Nash equilibrium in this SNG are subsequently studied. In the coordination mode, the BD precoders are jointly designed across the multiple BSs to maximize the network weighted sum-rate (WSR). Since this WSR maximization problem is nonconvex, we consider a distributed algorithm to obtain at least a locally optimal solution. Finally, we extend our analysis of the multicell BD precoding to the case of BD-Dirty Paper Coding (BD-DPC) precoding. We characterize BD-DPC precoding game for the multicell system in the competition mode and propose an algorithm to jointly optimize BD-DPC precoders for the multicell system in the coordination mode. Simulation results show significant network sum-rate improvements by jointly designing the BD or BD-DPC precoders across the multicell system in the coordination mode over the competition mode.

In this paper, we analyze the capacity of the static half-duplex single-relay amplify-and-forward (AF) system under both per-node and joint power constraints. Different from multiple-input multiple-output systems, the channel matrix of the cooperative AF system is a function of the parameters to be optimized and hence water-filling over the square of the singular values is no longer optimal. Furthermore, given that the mutual information of the AF system is not a concave function, conventional optimization methods cannot be applied. Instead, by deriving and comparing all local solutions, we characterize the optimal input covariance matrix at the source and the optimal power allocation scheme at the relay that maximize the achievable rate. First, for the individual power constraint scenario, it is shown that the capacity of the AF system is achieved by either a direct transmission (DT) scheme, a non-orthogonal AF (NAF) beamforming (BF) protocol with a unit-rank covariance matrix, or a NAF system using a specific full-rank covariance matrix. Then, for the global power constraint scenario, it is shown that only a DT or a NAF-BF protocol can achieve the capacity. In both cases, orthogonal transmission is strictly suboptimal. The capacity of the AF system is finally analyzed for some concrete examples, such as under asymptotically high and low transmission powers and for several network models.

This paper presents an adaptive access scheme for Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) aiming to take advantage of multi-user diversity and improve throughput, while supporting distributed and asynchronous operation. By assigning channel-adaptive access probabilities to different users, this method prioritizes users who gain most from using a channel, and hence, improves channel utilization in comparison with a simple random access scheme. Furthermore, in this method, access probabilities are designed to achieve long-term fairness by keeping a same average access probability for all users. Performance of the proposed adaptive CSMA/CA is evaluated in terms of collision probability and saturation throughput by analysis and simulation. Illustrative and analytical results show that A-CSMA/CA significantly improves the throughput by controlling contention among users and decreasing the collision probability, specifically in a large network.

Full-Duplex (FD) wireless communication systems have recently been proposed as a potential technique for increasing the spectral efficiency of wireless systems. FD communication systems suffer from the self-interference generated from one's own transmitter to one's own receiver in the same time and frequency slot. The self-interference signal is typically significantly larger than the received signal. The majority of existing work focuses on the suppression of self-interference without focusing on the effect on the forward channel. This paper presents a self-interference pricing based approach to suppress the self-interference without overly penalizing the forward channel. Simulation results show that even in the case of high self-interference, the proposed approach can provide significant data-rate improvements.

In this paper, we maximize the parameterized energy efficiency (EEP) of a multi-channel fading communication link that simultaneously transmits data corresponding to delay-sensitive and delay-insensitive applications subject to a constraint on the minimum effective capacity (EC) of delay-sensitive traffic. EEP, in b/s/Hz, is defined as the difference between the spectral efficiency (SE) and the transmit power scaled by a parameter which represents the penalty on the transmit power. We provide an optimal power adaptation policy to this problem which is either given by the global optimum, if feasible, or given by the boundary point where the objective function intersects the constraint. Further, we apportion the total transmit power into delay-sensitive power, which is the minimum power required to meet the EC constraint and delay-insensitive power, which is the residual power used to maximize the EEP of the link. Simulation results show that delay-sensitive power is an exponentially increasing function of minimum EC while delay-insensitive power decays to zero for higher values of minimum EC. Further, delay-insensitive power increases while delay-sensitive power exponentially decreases with number of subchannels in the system.

We evaluate a hybrid automatic repeat request (HARQ) scheme adopting incremental redundancy (IR) type under a finite-length codeword condition in AWGN channels. In the IR-type HARQ scheme, a long codeword is divided into L blocks, so that L becomes the maximum number of HARQ rounds. Although a finite-length codeword has a transmission rate loss from the channel capacity, IR-type HARQ schemes can significantly reduce the loss due to an early-termination effect. We find the sub-optimal coding rate of a codeword for given parameters such as signal-to-noise ratio (SNR), error probability constraint, and L. In addition, we scale the gap between the channel capacity and the average transmission rate of the IR-type HARQ and show that the gap decreases in the order of 1/L when a specific condition is satisfied while the gap of the non-HARQ case decreases in the order of 1/√L.

A novel iterative receiver is proposed for relay-aided multiuser communications in which multiple users transmit to a destination with the help of a relay and using fast frequency-hopping modulation. Each user employs a channel encoder to protect its information data and to help interference cancellation at the receiver. The received signal at the relay is partially decoded with a simple energy detector. At the destination, the iterative receiver exploits the soft outputs of the channel decoders to successively extract the maximum likelihood symbols of the users and perform interference cancellation. Under the same spectral efficiency, simulation results demonstrate the excellent performance of the proposed receiver when compared to the performance of decoding without interference cancellation as well as performance of the maximum likelihood multiuser detection previously developed for uncoded transmission.

This paper presents an optimal energy-efficient power allocation scheme for a point-to-point multicarrier link over frequency-selective fading channel subject to a delay-outage probability constraint. For a target delay-outage limit, the energy efficiency (EE) objective function is formulated as the ratio of the achieved link effective capacity to the total expenditure power, expressed in units of b/J/Hz. We first prove that this objective function is quasi-concave in the transmission power, and, hence, the global maximum solution of the underlying optimization problem can be obtained using fractional programming. Subsequently, we develop a two-step optimal power allocation algorithm by first obtaining the average sum power level corresponding to the maximum achievable EE, followed by jointly distributing this obtained average power over time and frequency. Analytical results show that the EE-based power allocation has a structure similar to that of the QoS-driven spectral-efficient scheme, but with a different cut-off threshold below which no transmission power is allocated. Simulation results show that the proposed joint optimal power allocation scheme provides significant EE gains over the simple independent subcarrier optimization scheme, where these performance advantages become more pronounced with tighter delay constraints and in fading channels with more severe frequency selectivity.

This paper is concerned with the maximization of the weighted sum-rate (WSR) in a multicell multiple-input multiple-output (MIMO) broadcast channel (BC). Studied is the multicell network operating on the same frequency channel with multiple mobile stations (MS) per cell. With interference coordination (IC) between the multiple cells, the base-station (BS) at each cell only transmits information signals to the MSs within its cell using the dirty paper coding (DPC) technique, while coordinating the inter-cell interference (ICI) induced to other cells. The main focus of this work is to jointly optimize the encoding covariance matrices at the BSs in order to maximize the network-wide WSR. Since this optimization problem is shown to be nonconvex, obtaining its globally optimal solution is highly complex. By applying a successive convex approximation technique, this work proposes a distributed algorithm that efficiently achieves a locally optimal solution. Simulations then show that the proposed algorithm can significantly improve the network-wide sum-rate, compared to the schemes with linear precoding or no interference coordination between the BSs.

This work studies the joint scheduling - admission control (SAC) problem over a fading channel. In particular, the optimal trade-off between maximizing the throughput and minimizing the queue size (or average congestion) is investigated. The SAC problem is formulated as a constrained Markov decision process (MDP) to maximize a utility defined as a function of the throughput and the queue size. The structural properties of the optimal policies are subsequently derived. When the statistical knowledge of the traffic arrival and channel processes is not available, we propose an online learning algorithm for the optimal policies. The analysis and algorithm development are relied on the reformulation of the Bellman's optimality dynamic programming equation using suitably defined value functions which can be learned using online time-averaging.

In this paper, achievable rates and power allocation (PA) schemes are studied for a two-way (TW) amplify-and-forward (AF) relaying system over Rayleigh fading channels in high and low signal-to-noise ratio (SNR) regimes. We consider three different AF techniques: i) the channel inversion (CI) scheme where the relay has full channel side information (CSI) of the two incoming links; ii) the fixed-gain scheme where the relay has only channel distribution information (CDI); and iii) a mixed (MX) scheme where the relay has CSI of one link but only CDI of the other. First, focusing on high SNR regimes, approximations to the achievable rates of the three systems are derived. The approximations are shown to be tight and can be used to analyze the systems under consideration. In particular, it is demonstrated that the CI system provides the best sum rate performance, followed by the MX and CDI techniques. A suboptimal yet effective PA scheme to maximize the sum rate at high SNR is then proposed. In low SNR regimes, we first derive rate approximations for the considered AF systems. It is then shown that the three systems achieve a similar sum rate. An asymptotically optimal PA is then proposed to maximize the sum rate at low SNRs. Finally, the sum rate of the direct transmission (DT) scheme is compared to that of the TWAF system. Although the TWAF is proved to be inferior at low SNRs, it outperforms the DT at high SNRs in various network configurations.

We consider full-duplex (FD) bidirectional communication between a pair of nodes and investigate the impact of residual self-interference on sum-rate performance. We first analyze a situation where channel state information is available only at receiver (CSIR). For this case, we derive an exact expression and a lower bound to the sum-rate performance of FD and hence characterize the effect of residual self-interference. The study shows that, FD sum-rate performance is limited by the effective signal-to-residual self-interference power ratio (effective SIR). In particular, for a fixed effective SIR, FD achieves almost twice the sum-rate of half-duplex (HD) in low signal-to-noise ratio (SNR) regimes whilst FD performance is surpassed by HD in high SNR regions. A closed-form approximation to this crossover SNR is derived. We then investigate the sum-rate of FD assuming channel state information is available to both transmitter and receiver (CSIT). Comparison of FD sum-rates of CSIR and CSIT shows that, in low SNR regions, a significant benefit can be achieved with CSIT while the gain is small in high SNR levels.

In this paper, we propose an optimal resource allocation algorithm that maximizes the energy efficiency (EE) of a downlink (DL) multi-user orthogonal frequency division multiplexing (OFDM) system subject to a constraint on the total transmit power of the base station (BS). We show that the optimal EE is obtained by first allocating each subcarrier to the user equipment (UE) with the best channel power gain and then adapting the transmit power for each subcarrier using water-filling (WF) policy. Moreover, we propose a fast algorithm with linear complexity to compute the optimal WF level. We perform simulations to illustrate the effectiveness of the proposed algorithm and investigate the impact of maximum transmit power on the optimal EE. Simulation results indicate that an energy-efficient system behaves like a spectral-efficient system when the maximum transmit power of the BS is set to lower values.

To improve the spectral efficiency of amplify-and-forward (AF) half-duplex relaying schemes, we propose a multidestination AF relaying protocol, in which the source sends two different data streams in two time-slots to two destinations with the aid of two relay nodes. In contrast to the conventional singledestination AF relaying protocol, the proposed multi-destination AF relaying protocol allows mutual interference in the transmission of the two data streams due to non-orthogonal resource allocation. Two relay nodes are opportunistically selected from a set of available relay nodes to maximize the minimum achievable rate of two data streams. We take into account the mutual interference and derive the ergodic capacity of the proposed protocol by deriving closed-form approximation. We show that the proposed protocol outperforms the conventional opportunistic AF relaying when the average power of interference is less than a crossover point and the proposed scheme has larger performance gain as the number of relay nodes increases in terms of the ergodic capacity.

This paper presents an Energy-efficient and Load- balancing Cluster-based (ELC) routing algorithm for CSMA-based wireless sensor networks. In particular, both distance and residual energy are taken into consideration in developing the cluster-head selection procedure. Furthermore, in addition to distance, cluster size is also used in formulating the cost function for cluster forming in order to balance load and energy consumption among the nodes, and hence, to enhance the network lifetime. Besides, ELC employs multi-hop inter-cluster routing based on a lowest-cost path approach that considers both energy efficiency and load balancing. Illustrative simulation results show that, for the same amount of delivered data, ELC consumes less energy and offers longer network lifetime as compared to other cluster-based routing algorithms such as LEACH-C and CBCDACP.

Spectral efficiency (SE) is one of the key performance indicators of wireless communications, and energy efficiency (EE) is an urgent need to tackle the challenges raised by the high demands of wireless traffics and energy consumption. However, these two important design criteria conflict with each other and a careful study of their trade-off is mandatory for designing future wireless communication systems. In this paper, we introduce an optimization problem to maximize the ergodic SE of a point-to-point communication link with a constraint on its minimum ergodic EE. We prove that, at optimality, the constraint on minimum EE is met with equality, and use it to provide a closed-form expression for finding the optimal water-filling level in a Nakagami-m fading channel with integer values of m. We exploit this formulation to investigate the relationship between SE and EE as a function of circuit power, power amplifier (PA) efficiency and channel power gain. We observe that the SE and EE always contradict with each other, however, the trade-off curve is non-linear. The curve is steeper at the extremities as compared to the middle region. Hence, a small sacrifice in EE from its maximum value may map into a significant gain in SE. Our simulations show that this gain in SE is a decreasing function of the circuit power and channel power gain while an increasing function of the PA efficiency.

This paper proposes a distributed joint subchannel and power allocation algorithm for the downlink of an orthogonal frequency-division multiple access (OFDMA) mixed femtocell/macrocell network deployment. Specifically, the total throughput of all femtocell user equipments (FUEs) is maximized while the network capacity of an existing macrocell is always protected. To this end, we employ an iterative process in which subchannels and transmit powers of base stations (BS) are alternatively assigned and optimized at every step. For a fixed power allocation, we prove that the optimal policy is to give each subchannel to the user with the highest data rate, or equivalently the highest signal-to-interference-plus-noise ratio (SINR), on that subchannel. For any given subchannel assignment, we apply the difference-of-concave- functions (d.c.) approach and transform the highly nonconvex program into a sequence of convex power allocation subproblems. We show that the developed iterative scheme converges to an optimal point. Importantly, we implement the devised solution by a decentralized algorithm, wherein each BS computes the optimal subchannel and power allocation for its own servicing cell. Numerical results confirm the merits of our proposed approach.

This work studies the dynamic scheduling problems in wireless networks with delay-sensitive loss-tolerant users. The users' traffic satisfies some statistical delay constraints. Moreover, the traffic can be dropped but the dropping rates do not exceed some thresholds. We consider two scheduling scenarios. First, we study the problem to minimize the total transmission power while maintaining the minimum rates for the users. Then, we study the problem to maximize the minimum rate(s) of the users while constraining the maximum total power. We derive the optimal solutions for both scheduling problems. When the fading statistics are available, using the dual-gradient method, the optimal policies can be computed. When the fading statistics are unknown, this work proposes online scheduling algorithms using online time-averaging. The convergence and optimality of the proposed algorithm are guaranteed by the results in stochastic approximation theory.

In this paper, we establish the achievable sum-rate of a half-duplex single-relay two-way amplify-and-forward network in which the relay uses channel knowledge of the two incoming links to cooperate with the two sources. Specifically, by assuming that the relay can acquire full channel knowledge and that Gaussian codebooks are used at the source nodes, the optimal power adaptation scheme at the relay that maximizes the achievable sum-rate under a long-term average power constraint is derived. While the maximization of the sum-rate is shown to be a convex optimization problem, obtaining optimal solutions are challenging. By using the Karush-Kuhn-Tucker conditions, we first show that finding the optimal relay adaptation scheme is equivalent to finding the root of a quartic polynomial. The closed-form optimal solutions are then obtained. Important insights on the proposed adaptation scheme are also presented and discussed. Numerical results reveal that the derived relay adaptation technique outperforms the conventional fixed-gain and variable-gain amplification coefficients at low signal-to-noise ratio regimes, thanks to the benefit of dynamic power allocation.

This paper characterizes the multicell precoding game where block-diagonalization (BD)-based precoding is utilized on a per-cell basis for downlink transmissions. Sharing the same frequency band, the base-station (BS) at each cell wishes to maximize the sum-rate for its connected mobile-stations (MS) with BD precoding. In this context, the paper considers a strategic non-cooperative game (SNG), where each BS greedily determines its precoding strategy in a distributed manner, based on the knowledge of the inter-cell interference (ICI) at its connected MSs. Via the game-theory framework, the existence and uniqueness of a Nash Equilibrium (NE) of this multicell game are subsequently studied. It is shown that there always exists at least one pure NE in the game, whereas the uniqueness of the NE is guaranteed under a certain condition on the ICI. The paper also characterizes the multicell precoding game where BD-Dirty Paper Coding (BD-DPC) is utilized at each BS on a per-cell basis. Simulation results then confirm our analysis on the NE's uniqueness in the BD and BD-DPC multicell precoding games.

This work studies the power optimal dynamic scheduling problem in multi-channel multi-user wireless access networks. Users have quality-of-service (QoS) requirements on the minimum rates with statistical delay guarantees. Only one user is allowed to transmit over a channel in a given time slot. This work considers two scenarios: homogeneous and heterogeneous users. For the former scenario, the optimal scheduling policy can be derived, and an online scheduling algorithm for the optimal policy is proposed using online time-averaging without requiring a-priori known fading statistics. For the latter scenario, the optimal scheduling problem is combinatorially hard; hence, even when the fading statistics are available, computing the optimal policy is intractable. Consequently, this work develops a sub-optimal online scheduling algorithm with linear complexity which does not require a-priori known fading statistics. Moreover, the scheduling algorithm satisfies the QoS constraints for the users. Illustrative results demonstrate the performance of the proposed scheduling algorithms in various settings.

Challenges of the existing power grid demand the integration of information and communication technologies into the next-generation electric grid, namely the Smart Grid (SG). This paper focuses on the critical communications segment corresponding to the consumer-premise, the Neighbor Area Network (NAN). For this segment, Greedy Perimeter Stateless Routing (GPSR) protocol is considered for its low complexity and high scalability. In order to provide guidelines for SG communications system designers and network engineers, the performance of GPSR in terms of throughput, latency and scalability is investigated with parametric sweeps of transmission range, data rate and the number of Smart Meters (SMs) per Data Aggregation Point (DAP). The simulation results show that network throughput of hundreds of times the rate required for basic SG applications (e.g., meter reading, service switch, …) can be achieved while the end-to-end delay can always be maintained below 100 ms. However, the converge-cast nature of the uplink traffic severely limits the SM-to-DAP ratio. Thus, with GPSR at the NAN level, emerging SG applications such as smart metering, real-time pricing, demand response, etc., can be supported.

This study is concerned with the downlink beamforming designs in a coordinated multicell system with dynamic base‐station (BS) assignment. At each cell, a multiple‐antenna BS employs linear beamforming to send multiple data streams to its assigned mobile‐stations (MSs). Exploiting multicell coordination, the multiple BSs jointly optimise the beamformers and the BS‐MS assignments to enhance the overall system performance. With per‐BS power constraints, considered are the coordinated beamforming problems under the following two design criteria: (i) minimising the transmit power margin at the BS with a set of target signal‐to‐interference‐plus‐noise ratios (SINR) at the MSs and (ii) jointly maximising the minimum SINR margin at the MSs. As the original problem formulations are shown to be non‐convex integer programs, which are combinatorially hard, the authors propose an efficient convex relaxation approach to solve the problems with low complexity. Simulations show that the convex relaxation‐based assignment schemes significantly outperform heuristic fixed assignment schemes.

This paper presents an energy-efficient power allocation for a multicarrier link over a frequency-selective fading channel with a delay-outage probability constraint. The power adaptation maximizes the system energy efficiency (EE), formulated as the ratio of the achieved effective capacity (EC) to the total expenditure power, including both transmission power and rate-independent circuit power. We prove that this objective function is quasi-concave in the transmission power, and derive the global optimum solution using fractional programming. Based on the obtained solution, we develop a power adaptation algorithm consisting of two steps: (i) establishing the optimum average power level corresponding to the maximum achievable EE with no transmit power constraint, and then (ii) for a given power constraint, jointly distributing the power over time and frequency based on the constraint and the optimum power level found in the first step. Analytical results show that the proposed EE-based power allocation has a structure similar to the allocation that maximizes the EC, but with a different cut-off threshold. Our proposed joint EE-optimal power allocation provides significant EE gains over both the joint spectral-efficient and independent-subcarrier EE-based power allocation schemes, where the rate-energy tradeoff becomes more pronounced with higher frequency selectivity.

This work studies the joint scheduling- admission control (SAC) problem for a single user over a fading channel. Specifically, the SAC problem is formulated as a constrained Markov decision process (MDP) to maximize a utility defined as a function of the throughput and queue size. The optimal throughput- queue size trade-off is investigated. Optimal policies and their structural properties (i.e., monotonicity and convexity) are derived for two models: simultaneous and sequential scheduling and admission control actions. Furthermore, we propose online learning algorithms for the optimal policies for the two models when the statistical knowledge of the time-varying traffic arrival and channel processes is unknown. The analysis and algorithm development are relied on the reformulation of the Bellman's optimality equations using suitably defined state-value functions which can be learned online, at transmission time, using time-averaging. The learning algorithms require less complexity and converge faster than the conventional Q-learning algorithms. This work also builds a connection between the MDP based formulation and the Lyapunov optimization based formulation for the SAC problem. Illustrative results demonstrate the performance of the proposed algorithms in various settings.

This paper presents a CSMA-based opportunistic spectrum access for secondary users (SUs) from a game-theoretic perspective. A key requirement for efficient design of CSMA-based access schemes for SUs is to address competition among SUs. Thus, to enable contention control in consideration of competition among SUs, an adaptive SU access approach based on a modified CSMA scheme is presented in which each SU accesses multiple idle frequency-slots of a licensed frequency band with different probabilities. The problem of finding optimal access probabilities of SUs is cast in a game-theoretic framework to highlight the issues of competition and cooperation among SUs. Subsequently, the existence, uniqueness and efficiency of Nash Equilibrium (NE) are investigated. To improve the efficiency of the unique NE in the competitive design, the game is transformed into a more cooperative framework exploiting a pricing mechanism. Finally, an algorithm based on the best response dynamics is developed in which each SU independently updates its access probabilities until convergence to the unique NE.

Inter-line interference, known as crosstalk, is the most significant source of performance degradation for Digital Subscriber Line (DSL) systems. Vectored DSL is an effective technique for he elimination of crosstalk; however, there are many scenarios in which Vectored DSL is not implementable or practical. Interference alignment is presented as a technique to solve these issues. In particular, the combination of partial vectoring and interference alignment is proposed. The practical advantages of partial vectoring and interference alignment over Vectored DSL are discussed. Finally, simulation results are provided o demonstrate the potential performance gains of the proposed approach.

In this paper, we obtain an energy-efficient power allocation technique for a Rayleigh block-fading channel with delay-limited applications. In particular, we consider a probabilistic delay constraint as the user quality-of-service (QoS) requirement, and incorporate the concept of effective capacity to obtain the maximum arrival rate, at which, the delay constraint is satisfied. We obtain the energy efficiency (EE), which is formulated as the ratio between the effective capacity and the total expenditure power, of this system and derive the power allocation strategy that maximizes the EE. Numerical results are conducted to corroborate our theoretical results. In addition, for comparison reasons, we plot the maximum achievable EE under two well-known power allocations schemes, namely, water-filling (wf) and constant power allocation (cons) when considering delay constraints. The results show that in stringent delay limited systems, adaptive power allocation improves the maximum achievable EE significantly.

This paper studies the problem of channel estimation in amplify-and-forward (AF) multi-relay transmissions over time- and frequency-selective (doubly selective) channels. To avoid two separate channel estimation processes of source-to-relay and relay-to-destination links, a cascaded doubly selective channel model is formulated to characterize the source-to-relay-to-destination (SRD) channel. Time-varying SRD channel gains are projected onto different basis expansion functions to attain dimension reduction in the formulated channel model. In estimating the cascaded SRD channel responses at the destination, the presence of multiple relays gives rise to the ambiguity problem due to the use of a single pilot signal transmitted from the source. To circumvent this problem, time-variant amplifying factors at relays are introduced to be used in maximum-likelihood-based cascaded multi-relay channel estimation. Simulation results show that basis expansion models (BEMs) and time-variant amplifying factors can efficiently facilitate a single estimation process of different cascaded doubly selective SRD channels in a multi-relay system. Furthermore, for a fixed pilot overhead, a relationship between space diversity gains (with multiple relays) and channel estimation accuracy is also numerically illustrated.

In this paper, we rigorously analyze the optimal input distribution and capacity of an additive Bernoulli-Gaussian (BG) impulsive noise (IN) channel in high and low input power regimes. First, we obtain an input distribution for which the channel output is Gaussian distributed. This distribution, if valid, shall result in the capacity of the channel. At an asymptotically high input power level, we then show that the derived input is always valid and in fact, it resembles a Gaussian distribution. As such, the Gaussian channel input is considered approximately optimal. Using the monotonicity property of the characteristic function (CF), we then develop a necessary condition for the existence of the derived optimal input for a finite level of input power. The condition indicates that a sufficiently high input power is usually required. Then focusing on the low power region, we first derive an upper bound on the channel capacity assuming full knowledge of noise state. A closed-form expression of the mutual information (MI) achieved by Gaussian inputs, which is considered as a lower bound on the channel capacity, is then developed. By comparing these two bounds, it is shown that a Gaussian input asymptotically results in the capacity. Interestingly, it is also demonstrated that such a capacity is the same as the capacity of an erasure channel in low power regimes.

In this paper, we develop optimal power adaptation schemes by means of power amplification coefficients at the relay for the half-duplex single-relay non-orthogonal amplify-and-forward (NAF) system with channel side information (CSI) available at the relay. In particular, assuming that the relay has full knowledge of the channel gains, optimal power adaptation schemes are established in closed-form using both the mutual information (MI) and pairwise error probability (PEP) criteria with Gaussian inputs at the source. The proposed solutions can be understood as a multi-dimensional (multi-D) distributed water-filling in time and space. Numerical results show that the proposed power amplification methods provide a significant improvement over conventional schemes using either channel distribution information (CDI) or channel inversion (CI). The gain is observed with regard to both the MI with Gaussian inputs and the bit error rate (BER) performance using finite constellations such as quadrature amplitude modulation (QAM).

In this paper, we establish in closed-form the capacity and characterize the optimal input covariance matrix at the source and the optimal power allocation scheme between source and relay for a half-duplex single-relay amplify-and-forward (AF) system with static channel gains. Different from multiple-input multiple-output (MIMO) systems, the channel matrix of the AF system is a function of the parameters to be optimized and hence water filling over the square of the singular values of this matrix is no longer optimal. Furthermore, given that the mutual information of the AF system is not a convex function, conventional optimization methods cannot be applied to find the optimal input covariance, the power allocation, and the capacity. Instead, by analyzing all local maximizers, it is shown that the capacity of the AF system is achieved by either the direct transmission (DT) scheme, the orthogonal AF (OAF) protocol, or the non-orthogonal AF (NAF) protocol using a non-diagonal covariance matrix. The choice of protocol depends on the signal-to-noise ratio (SNR) and network configuration. By further analyzing the asymptotic mutual information, it is shown that the DT scheme is dominant in low and high SNR regimes. The capacity of the AF system is also provided for comparison among several network models. Specifically, it is demonstrated that in a general linear network model, the NAF protocol can provide significant gains over the other schemes at medium SNRs.

To protect digital subscriber loop (DSL) systems against impulse noise (IN), interleaving combined with Reed-Solomon (RS) coding has been used. Nevertheless, interleaving introduces a long delay. This paper considers retransmission instead of interleaving to reduce such delay and proposes an approach using the available RS decoding failure status to trigger retransmission request. Retransmission probability, average delay and bit error rate (BER) are derived and validated by simulation. Illustrative results show that retransmission can offer a much shorter delay than interleaving while efficiently avoiding transmission errors in various channel conditions.

Reed-Solomon (RS) coding and interleaving are usually combined to protect digital subscriber line (DSL) systems against impulse noise (IN) but introducing a long delay. It is proved that interleaving delay can be significantly reduced by error and erasure decoding (EED). This paper proposes an erasure marking technique to enhance the reliability of the erasure information in order to completely exploit the capability of EED for interleaving delay reduction. Selection of proper parameters for the proposed erasure marking technique is analyzed. Simulation results show that the proposed technique can guarantee efficient erasure marking and reduce the interleaving delay by a factor of two.

In order to guarantee dual quality-of-service (QoS) measures, namely, packet error rate (PER) and delay constraint, in a spectrum-sharing channel, we propose a cross-layer resource allocation approach in this paper. In particular, we assume an underlay cognitive radio scenario, in which, a secondary user (SU) is granted access to the spectrum as long as its average interference power, imposed on the primary-user (PU) receiver is below a predefined threshold. The SU employs adaptive modulation and coding (AMC) at the physical layer and automatic repeat request (ARQ) at the link-layer. An adaptive power and rate allocation scheme is proposed for the SU transmitter to meet both the PER requirement and the statistical delay constraints. To this end, we use the effective capacity concept and obtain closed-form expressions for the power allocation and capacity of the SU's link in Nakagami-m fading channels.

In this paper, we investigate the capacity limit of a single secondary user (SU) communication link in a cognitive radio system. An SU transmitter establishes the communication with its receiver by dynamically hopping in a frequency spectrum pool, and by sensing the spectrum to exploit the temporal communication opportunities. We characterize the performance of sensing by false-alarm and miss-detection probabilities. Firstly, focusing on a high channel input power region, we develop an upper bound on the SU capacity by assuming a Gaussian distributed output. A lower bound on the SU capacity is also derived using a Gaussian input. We then show that the lower bound closely approaches the upper bound at a high channel input power level. This means that the Gaussian input is nearly optimal in this case. Furthermore, we characterize the impact of primary user activities and sensing performance on the SU capacity by developing a closed-form tight approximation. Secondly, paying attention to a low channel input power region, we propose a genie-aided upper bound and a lower bound using the Gaussian input. By comparing these two bounds, a closed-form approximation to the capacity is developed and the near optimality of the Gaussian input is demonstrated. Finally, numerical results are provided to complement the theoretical discussion.

In this paper, we consider the problem of energy-efficient resource and power allocation in the uplink of multiuser multi-channel Orthogonal Frequency Division Multiplexing (OFDM) based systems subject to constraints on user equipment (UE) transmit power. This problem is non-deterministic polynomial-time hard and an optimum solution for a system with U users and N resource units requires a complexity of at least O(NUN). Using an iterative solution approach, we propose two sub-optimal, yet efficient, scheduling algorithms that maximize the energy efficiency (EE) considering both UE circuit power (Pc) and rate-dependent transmit power with an upper limit of Pmax. Simulation results show that the proposed algorithms provide near-optimal solutions with much lower computational burden of O(UN) and O(UN2/2). Further performance studies indicate that the proposed algorithms can offer an EE of more than 2 times with a throughput reduction of less than 13% as compared to the spectral-efficient greedy algorithm. Our studies also reveal that the EE is quickly increased with Pmaxwhen ≪ Pcand then reach saturation as Pmax approaches Pc.

This paper presents a study on opportunistic spectrum access for secondary users (SUs) from a game-theoretic learning perspective. In consideration of the random return of primary users, it is assumed that a SU dynamically hops over multiple idle frequency-slots of a licensed frequency band, each with an adaptive activity factor. The problem of finding optimal activity factors of SUs is cast in a game-theoretic framework and is formulated as a potential game. Subsequently, the existence, feasibility and optimality of Nash Equilibrium (NE) are investigated analytically. Furthermore, an algorithm is developed in which each SU independently adjusts its activity factors based on the best response dynamics by learning other SUs' behavior from locally available information. Aiming to establish stability for the proposed algorithm, the convergence with probability 1 to an arbitrarily small neighborhood of the globally optimal solution is investigated with analysis and simulation.

This paper investigates various designs of 4-element miniaturized Planar Inverted-F Antennas (PIFA) with pattern and polarization diversity at 2.45 GHz. The antennas are only 13mm x 7mm in area and built on a small ground plane size of 105 mm x 55 mm. Diversity performance criteria namely the Envelope Correlation Coefficient (ECC) and Mean Effective Gain (MEG) are computed for different fading channel scenarios using the simulated far-field radiation patterns. Then the Effective Diversity Gain (EDG) of all the configurations under the Maximal Ratio Combining (MRC) scheme are generated. The results show that the antenna systems can give a maximum EDG of 16.31 dB as compared to the theoretical limit of 19.12 dB.

This paper addresses the critical problem of interference management in two-tier networks, where the newly-deployed femtocell users (FUEs) operate in the licensed spectrum owned by the existing macrocell. A Pareto-optimal power-control algorithm is devised that jointly maximizes the utilities of both macrocell and femtocell networks while robustly guaranteeing the macrocell's quality-of-service (QoS) requirements. After effectively enforcing the minimum signal-to-interference-plus-noise ratios (SINRs) prescribed by the macrocell users (MUEs) with the use of a penalty function, the Pareto- optimal boundary of the strongly-coupling SINR feasible region is characterized. Based upon the specific network utility functions and also the target SINRs of the MUEs, a unique operating SINR point is determined, and transmit power adapted to achieve such a design objective. We prove that the developed algorithm converges to the global optimum, and more importantly, it can be distributively implemented at individual links. Effective mechanisms are also available to flexibly designate the access priority between macrocells and femtocells, as well as to fairly share the system resources among different users. The merits of the proposed approach are verified by numerical examples.

In this paper, we investigate the capacity and optimal power allocation (PA) scheme between the source and relay for a dual-hop amplify-and-forward (AF) system over non-symmetric Rayleigh fading channels with channel information available at the relay. At first, a closed-form expression of the mutual information (MI) between the input and output of the considered channel is obtained. Since only the exponential integral is involved, the derived expression is useful in finding the optimal PA to achieve the capacity. By further considering high and low signal-to-noise ratio (SNR) regimes, we present tight yet simple approximations to this MI, which can be used to show the advantage of knowing channel information at the relay. Then, focusing on the problem of optimal PA, we first derive a closed- form derivative of the MI. A simple bisection method is then proposed to find the optimal PA scheme. While uniform PA is shown to achieve the capacity at any SNR over the symmetric channel, its optimality can only be observed at low SNRs over a non- symmetric channel. In other SNR regimes, numerical results reveal that uniform PA experiences a significant loss. A comparison between the dual-hop and direct transmission scheme is also made, where we show that the dual-hop scheme using the optimal PA can provide impressive rate increases in medium SNR ranges in various network configurations.

Geographic routing algorithms conventionally use one-hop greedy forwarding as their primary routing technique, which might lead to routing voids. Secondary routing schemes used to circumnavigate such routing voids are unfortunately not efficient in terms of throughput and energy consumption. Moreover, node residual energy and link quality are not considered during the routing process. This paper presents Two-hop Information based Energy- efficient Geographic Routing (TIEGeR) scheme to achieve effective energy balancing throughout the network, while preventing routing voids by proactively avoiding "local maxima" nodes. Distance to reach destination, node connectivity, link quality, and node residual energy are employed to formulate the routing metric for the TIEGeR. Besides, secondary routing scheme dealing with routing voids is supplemented by the reverse progress mode. Simulations verify the advantages of TIEGeR against conventional geographic routing schemes.

This paper considers opportunistic spectrum access for secondary users (SUs) from an adaptive learning perspective. A SU dynamically hops over multiple idle frequency-slots of a licensed frequency band, each with an adaptive activity factor. Aiming to determine the optimal activity factors of SUs, an algorithm is developed, in which each SU independently adjusts its activity factors by learning other SUs' behavior from locally available information. Due to the error-prone learning procedure, the proposed algorithm is interpreted as a stochastic gradient descent method. In order to establish stochastic stability for the proposed algorithm, the convergence with probability of 1 and also convergence rate are investigated with analysis and simulation.

The effects of electromagnetic interference (EMI) on operations of sensitive medical devices have been recognized as a critical concern related to safety in hospitals and healthcare institutions. This paper proposes an adaptive and distributed routing protocol that attempts to reduce the EMI introduced by a medical wireless sensor network (MWSN). The proposed algorithm, namely EMI-aware routing protocol (EMIR), assigns to each node a potential value which is dynamically calculated in such a way that network traffic tends to be deflected from nodes that are radiating high EMI and/or locating far away from gateways. Experiments in a real-life IEEE 802.15.4-based WSN implemented with the EMIR demonstrate that, compared to the shortest path routing, the proposed algorithm can significantly suppress the level of the EMI in the surrounding area where the WSN is deployed. Besides, the EMIR is scalable to the network size because it only requires one-hop neighbor information.

This paper is concerned with the maximization of the weighted sum-rate in the multicell MIMO multiple access channel (MAC). Considered is the multicell network operating on the same frequency channel with multiple mobile stations (MS) per cell. Assuming the interference coordination mode in the multicell network, each base-station (BS) only decodes the signals for the MSs within its cell. However, the uplink covariance matrices at the MSs are jointly designed between the BSs in order to optimize the network-wide weighted sum-rate. Since this optimization is shown to be a nonconvex problem, obtaining its globally optimal solution is very hard. By applying successive approximation technique, a distributed algorithm is then proposed to efficiently achieve a locally optimal solution. Nonetheless, simulations then show that the proposed algorithm can significantly improve the network-wide sum-rate, compared to the case with no interference coordination between the BSs.

In this paper, we investigate the achievable rate and ergodic capacity of a general non-symmetric half-duplex non-orthogonal amplify-and-forward (NAF) single-relay channel in Rayleigh fading environments, assuming that the channel state information is only available at the destination. The considered channel, which captures pathloss and shadowing effects over the transmission links, includes the symmetric one as a special case. At first, for a given power allocation scheme, simple and closed-form expressions of the upper and lower bounds on the achievable rate are derived. As shown by various numerical examples, the gap between the upper and lower bounds is small in the entire range of SNRs, which makes them useful in finding the optimal power allocation solution to achieve the capacity. Focusing on the two extreme cases of low and high SNRs, we then provide relatively tight approximations of the achievable rate. Using these approximations, it is then revealed that at both high and low SNR regimes, the ergodic capacity is achieved when the relay is inactive. Equivalently, NAF relaying does not yield any advantage over direct transmission at low and high SNRs. The results and observations in this paper therefore provide some further important insights on NAF relaying.

This paper studies the mutual information improvement attained by rotated multidimensional (multi-D) constellations via a unitary precoder G in Rayleigh fading. At first, based on the symmetric cut-off rate of the N-D signal space, we develop a design criterion with regard to the precoder G. It is then demonstrated that the use of rotated constellations in only a reasonably low dimensional signal space can significantly increase the mutual information in high-rate regimes. Based on parameterizations of unitary matrices, we then construct good unitary precoder G in 4-D signal space using a simple optimization problem, which involves only four real variables and it is applicable to any modulation scheme. To further illustrate the potential of multi-D constellation and to show the practical use of mutual information improvement, we propose a simple yet powerful bit-interleaved coded modulation (BICM) scheme in which a (multi-D) mapping technique employed in a multi-D rotated constellation is concatenated with a short-memory high-rate convolutional code. By using extrinsic information transfer (EXIT) charts, it is shown that the proposed technique provides an exceptionally good error performance. In particular, both EXIT chart analysis and simulation results indicate that a turbo pinch-off and a bit error rate around 10-6happen at a signal-to-noise ratio that is well below the coded modulation and BICM capacities using traditional signal sets. For example, with code rates ranging from 2/3 to 7/8, the proposed system can operate 0.82 dB-2.93 dB lower than the BICM capacity with QPSK and Gray labeling. The mutual information gain offered by rotated constellations can be therefore utilized to design simple yet near Shannon limit systems in the high-rate regions.

This paper develops a detection scheme based on the implicit pilot-symbol-assisted architecture of M-ary frequency-shift-keying (FSK) modulation in amplify-and-forward (AF) multiple-relay networks. It is shown that, at the expense of the low spectral efficiency inherent in FSK modulation, every transmitted symbol from the source to the destination can be used as a pilot symbol. In the proposed detection scheme, the destination combines the signals from the source and the relays to perform detection by using the estimated channels obtained from the implicit pilot symbols. Using the Gaussian approximation for the effective noise at the destination, an upper bound of the approximated bit-error-rate (BER) is provided. Simulation results reveal that the proposed scheme can significantly improve the BER performance when compared to previously proposed schemes in a temporally-correlated fading environment. The simulation results also verify the obtained upper bound on the approximated BER.

This paper presents an adaptive hopping transmission strategy for secondary users (SUs) to access temporarily idle frequency-slots of a licensed frequency band in consideration of the random return of primary users (PUs), aiming to maximize the overall SU throughput. A SU dynamically hops over multiple idle frequency-slots, each with an adaptive activity factor to avoid high-risk data loss due to possible PU return. SU activity factor optimization problems are formulated to develop the optimal opportunistic spectrum access (OSA) algorithms for SUs based on the Lagrange dual decomposition method. Subsequently, a fully distributed learning-based OSA algorithm is developed in which each SU independently adapts its activity factors to the optimal values over time by learning other SUs' behavior from locally available information. The convergence and convergence rate that characterize its asymptotic behavior and efficiency are analyzed. It is shown that the proposed learning-based OSA algorithm converges with probability of 1 to the optimal solution. Illustrative results confirm its effectiveness and performance gain as compared to existing OSA schemes.

This paper proposes two Pareto-optimal power control algorithms for a two-tier network, where newly-deployed femtocell user equipments (FUEs) operate in the licensed spectrum owned by an existing macrocell. Different from homogeneous network settings, the inevitable requirement of robustly protecting the quality-of-service (QoS) of all prioritized macrocell user equipments (MUEs) here lays a major obstacle that hinders the successful application of any available solutions. Directly targeting at this central issue, the first algorithm jointly maximizes the total utility of both user classes. Specifically, we adopt the log-barrier penalty method to effectively enforce the minimum signal-to-interference-plus-noise ratios (SINRs) imposed by the macrocell, paving the way for the adaptation of load-spillage solution framework. On the other hand, the second algorithm is applied to the scenario where only the sum utility of all FUEs needs to be maximized. At optimality, we show that the MUEs' prescribed SINR constraints are met with equality in this case. With the search space for Pareto-optimal SINRs substantially reduced, the second algorithm features scalability, low computational complexity, short converging time, and stable performance. We prove that the two developed algorithms converge to their respective global optima, and more importantly, they can be implemented in a distributive manner at individual links. Effective mechanisms are also available to flexibly designate the access priority to MUEs and FUEs, as well as to fairly share radio resources among users. Numerical results confirm the merits of the devised approaches.

This paper proposes distributed joint power and admission control algorithms for the management of interference in two-tier femtocell networks, where the newly-deployed femtocell users (FUEs) share the same frequency band with the existing macrocell users (MUEs) using code-division multiple access (CDMA). As the owner of the licensed radio spectrum, the MUEs possess strictly higher access priority over the FUEs; thus, their quality-of-service (QoS) performance, expressed in terms of the prescribed minimum signal-to-interference-plus-noise ratio (SINR), must be maintained at all times. For the lower-tier FUEs, we explicitly consider two different design objectives, namely, throughput-power tradeoff optimization and soft QoS provisioning. With an effective dynamic pricing scheme combined with admission control to indirectly manage the cross-tier interference, the proposed schemes lend themselves to distributed algorithms that mainly require local information to offer maximized net utility of individual users. The approach employed in this work is particularly attractive, especially in view of practical implementation under the limited backhaul network capacity available for femtocells. It is shown that the proposed algorithms robustly support all the prioritized MUEs with guaranteed QoS requirements whenever feasible, while allowing the FUEs to optimally exploit the remaining network capacity. The convergence of the developed solutions is rigorously analyzed, and extensive numerical results are presented to illustrate their potential advantages.

We consider the analysis and design of space-time trellis codes (STTCs) for a cooperative relay channel operating in amplify-and- forward (AF) mode assuming the source and destination nodes are equipped with multiple antennas but the relay node has single antenna. We derive a pairwise error probability (PEP) expression for the performance of STTCs in this type of channels. A simple upper- bound on PEP is then derived and is maximized to find the optimum STTCs. We show that the designed STTCs based on the derived criterion achieve full diversity in the AF relay channels especially at high signal-to-noise-ratios (SNRs). The maximum achievable diversity in relay channels with single-antenna relay is bounded by min(M,N) where M and N are respectively the number of antennas in source and destination nodes. Simulation results confirm that the proposed codes achieve the maximum diversity and also provide an appealing coding gain.

This paper presents an adaptive hopping transmission strategy for secondary users (SUs) to access temporarily idle frequency-slots of a licensed frequency band in consideration of the random return of primary users (PUs), aiming to maximize the overall SU throughput. A SU dynamically hops over multiple idle frequency-slots, each with an adaptive activity factor so that possible PU return in a frequency-slot may destroy only a small fraction of the SU transmission that can be recovered by erasure-correction coding. SU activity factor optimization problems are formulated to develop adaptive SU access algorithms for both centralized and distributed structures. Numerical results confirm the effectiveness and demonstrate performance gains of the proposed approach as compared to existing schemes.

The goal of this paper is to investigate the capacity of a symmetric Digital Subscriber Line (DSL) channel under Gaussian interference and thermal noise. Previous efforts only considered various analytical worst-case channel models to calculate the capacity of the DSL channel. Assuming a statistically averaged system, it is shown that the ergodic capacity of the system can be lower- bounded using Jensen's inequality. This lower-bound is compared to the performance of some state-of-the- art spectrum management techniques for both the measured data and the American National Standards Institute (ANSI) model. Results indicate that the measured data obtain significantly higher data-rates than the ANSI model prediction. As well, it is found that the lower-bound derived in this paper could be used as an indication of achievable data-rates for scenarios where the channel statistics (expected values of transfer functions) are known. This lower- bound could be useful for system operators when estimating the capacity of their networks.

Crosstalk is the limiting factor of performance in Digital Subscriber Line systems. The use of Dynamic Spectrum Management (DSM) can reduce the effects of crosstalk. For DSM algorithms, there is an important trade-off between the complexity of an algorithm and its performance. This paper introduces a method of applying cluster theory to Autonomous Spectrum Balancing using Multiple Reference Lines in order to gain a favorable trade-off more consistently. It is shown that the achievable performance is very close to that of existing state-of-the-art DSM algorithms, while requiring significantly fewer time-consuming interference measurements.

This paper is concerned with optimal downlink beamforming designs in a coordinated multicell system with per-base-station power constraints. At each cell, a multiple-antenna base-station (BS) employs linear beamforming to send multiple data streams to its remote mobile-stations (MS). With the coordination between the cells, the multiple BSs jointly optimize their beamformers to enhance the overall system performance. Under per-BS power constraints, considered are the two following design criteria: (i) minimizing transmit power with guaranteed signal-to-interference-plus-noise ratio (SINR) at each each MS and (ii) jointly maximizing the minimum SINR margin at the MS. The two optimization problems are reformulated as second-order conic programs (SOCP), and simple and fast converging numerical algorithms to efficiently solve them are proposed.

This paper develops a detection scheme based on implicit pilot-symbol-assisted architecture for non-coherent amplify-and-forward (AF) relay networks. The networks use M-ary frequency-shift-keying (FSK) modulation and multiple relays to assist communication from a source to a destination. Built on the fact that every transmitted symbol from the source to the destination can be considered as a pilot symbol, the destination combines the signals from the source and the relays to perform detection by using the estimated channels obtained from the implicit pilot symbols. A tight upper bound on the bit-error-rate (BER) of the developed scheme is obtained and used to show that the proposed scheme achieves a full diversity order. Moreover, simulation results reveal that the proposed scheme can significantly improve the BER performance when compared to previously proposed schemes in a temporally-correlated fading environment.

This paper studies the problem of pilot-aided cascaded channel estimation in amplify-and-forward (AF) relay transmissions over time- and frequency-selective channels. Different basis expansion models (BEMs) are used to represent the doubly selective cascaded AF relay channel. Then, a maximum-likelihood (ML) algorithm is developed for the destination to estimate the BEM coefficients of the cascaded doubly selective channel response from source to destination. Numerical results demonstrate that the use of BEMs can alleviate performance degradation due to the conventional block-fading assumption in cascaded channel estimation for AF relay networks with moving nodes.

This paper proposes a precoding scheme over multiple cooperative frames to increase the diversity order of a bandwidth efficient Bit Interleaved Coded Modulation (BICM) system over a Non-orthogonal Amplify-and-Forward (NAF) half-duplex single-relay channel. By deriving a union bound on the bit error probability, it is shown that the diversity gain function of the considered system is (Nf· dH)-th power of that of uncoded cooperative systems, where Nfis the number of precoded cooperative frames and dHis the minimum Hamming distance of the outer code. An optimal class of precoders is then derived to optimize the asymptotic coding gain. It is then shown that the source should transmit a superposition of all symbols in the broadcasting phases, while being silent in all cooperative phases for best asymptotic performance. By further analyzing the first iteration performance, a design criterion is then developed to find optimal superposition angles for good convergence behavior. A pragmatic approach is then proposed to find good rotation angles. Analytical and simulation results show that the proposed scheme provides a significantly higher order of time and cooperative diversities and better coding gains than previous precoding schemes.

In this paper, we consider a joint cooperative scheduling and power control problem in interference-limited wireless ad hoc networks. In particular, we investigate the scenario where multiple pairs of users wish to communicate with their corresponding partners and each communication requires that its Signal-to-Interference-plus-Noise Ratio (SINR) is greater than a predetermined value for QoS guarantees. We propose to employ a decode-and-forward cooperative protocol jointly with the distributed Foschini-Miljanic power control algorithm to maximize the number of scheduled users in the network. In addition, we develop efficient handshaking and interference-aware relay selection mechanisms to achieve good throughput-overhead tradeoff for the proposed distributed protocol. We show that the proposed cooperative scheduling and power control algorithm is guaranteed to achieve better throughput than a non-cooperative scheduling algorithm based on direct communications and illustrate the performance gain through numerical studies.

This paper aims to devise a power control solution for femtocell networks that can be implemented distributively and, more importantly, is optimal in Pareto sense. For this, a complete characterization of the Pareto-optimal boundary of the signal-to-interference-plus-noise ratio (SINR) feasible region is first derived. The complicated interdependency between the macrocell and femtocell networks, whose access priorities and design objectives are inherently distinct, is also revealed. Our result confirms that the Pareto-optimal SINRs of the femtocell network can only be achieved conditionally upon the guaranteed performance of macrocell users. Through a suitable parametrization, we show that all SINR points on the aforementioned boundary can be realized. A unique operating SINR point is chosen among those infinite possible solutions, based upon the specific utility of femtocell users as well as the minimum SINR requirements of the macrocell network. Distributed transmit power adaptation is then performed to attain such optimal design target. We prove that the developed algorithm converges to the global optimum, wherein the performance of femtocell users is optimized while macrocell users being robustly protected with their minimum required SINRs maintained at all times. The merits of our approach are illustrated by numerical results.

We investigate the design of optimal threshold for energy detection of primary-user (PU) beacon signals in spectrum sensing. The weighted sum of the PU and secondary-user (SU) link rates is used as the objective function with constraint on the guaranteed minimum PU link rate. SU with wideband sensing capability selects the idle frequency-slot with the lowest sensed power to be the candidate to access. Numerical results show that the wideband sensing capability with the proposed optimal threshold and accessing strategy can help the SU to meet the stringent PU rate constraints while achieving much higher SU link rate and network capacity.

This paper considers a two-tier cellular network wherein femtocell users, who communicate with their home-owner-deployed base stations, share the same frequency band with macrocell users by code-division multiple access (CDMA) technology. Since macrocell users have strictly higher priority in accessing the available radio spectrum, their quality-of-service (QoS) performance, expressed in terms of the minimum required signal-to-interference-plus-noise ratio (SINR), should be maintained at all times. Femtocell users, on the other hand, are allowed to exploit residual network capacity for their own communications. In this work, we develop a joint power- and admission-control algorithm for interference management in such two-tier networks. Specifically, throughput-power tradeoff optimization is achieved for femtocell users while all macrocell users being supported with guaranteed QoS requirements whenever feasible. Importantly, the proposed algorithm makes power and admission control decisions in an autonomous and distributive manner with minimal coordination signaling, a desirable feature in two-tier networks where only limited exchange of signaling information can be afforded on backhaul links. Under certain practical conditions, the developed scheme is shown to converge to a stable solution. An effective technique is also proposed to improve the efficiency of such equilibrium in lightly-loaded networks. The performance of our proposed algorithm is demonstrated by numerical results.

This paper studies the precoding design in a multicell multiuser (MU) system with universal frequency-reuse using a game-based approach. Considered is a multicell system, where the MU downlink transmissions in each cell are facilitated by a multi-antenna base-station (BS). In particular, the BS wishes to maximize the transmission sum-rate to its connected mobile-stations (MS) by the means of zero-forcing (ZF) precoding. In this context, the paper considers a strategic non-cooperative game (SNG), where each BS greedily determines its optimal power allocation in a distributed manner, based on the knowledge of the out-of-cell interference (OCI) at its connected MSs. Via the game theory framework, we study the existence and uniqueness of a Nash equilibrium (NE) of this multicell game. It is shown that a NE of the game always exists, whereas the NE uniqueness is guaranteed under a certain condition on the OCI. Numerical results confirm with the analysis that a small OCI level almost always leads to the NE's uniqueness. Simulations also show that the multicell game using known OCI knowledge provides additional sum-rate gains over the scheme with no OCI information.

In this paper, we design a capacity-approaching coded modulation scheme for half-duplex non-orthogonal amplify-and-forward (NAF) single-relay channel. We apply the idea of multi-dimensional (multi-D) mapping employed in precoded multiple cooperative frames, concatenated with a simple outer binary code. Using union bounding techniques, it is first shown that for any unitary and full-diversity rotation G, the optimal multi-D labeling for NAF relaying shall maximize the average Euclidean distance between all pairs in the multi-D rotated constellation whose labels differ in only one bit. The extrinsic information transfer (EXIT) charts are then used to match the outer code, the multi-D mapping, and the precoder for near-capacity performance. It is demonstrated that the proposed scheme is promising for NAF relaying, in the sense that it can operate below the achievable rate obtained by using a conventional modulation scheme, i.e., the constrained capacity. In particular, for various spectral efficiencies, we obtain the bit error rate (BER) of 10-5or lower at a signal-to-noise ratio (SNR) that is 0.45dB-1dB below the traditional achievable rate and within 1.55dB-1.95dB from the ergodic capacity with Gaussian inputs.

This paper studies the mutual information improvement attained by rotated multidimensional (multi-D) constellations via a unitary precoder G ∈ CN×Nin Rayleigh fading. At first, based on the symmetric cut-off rate of the N-D signal space, we develop a design criterion with regard to the precoder G. It is then demonstrated that the use of rotated constellations in only a reasonably low dimensional signal space can significantly increase the mutual information in high-rate regimes. By considering the QPSK system, we then propose a class of good unitary rotation G in 4-D signal space using parameterization approach, which are shown to provide remarkable improvement. To further illustrate the potential of multi-D constellation and to show the practical use of mutual information improvement, we propose a simple yet powerful coded modulation scheme in which a (multi-D) mapping technique employed in a multi-D rotated constellation is concatenated with a short-memory high-rate convolutional code (CC). By using extrinsic information transfer (EXIT) charts, it is demonstrated that the proposed technique provides an exceptionally good error performance. For example, by using the derived 4×4 rotation, together with QPSK constellation, and a simple rate-3/4 outer convolutional code, it is shown that the proposed system can operate 1.39 dB lower than the traditional coded modulation capacity at the bit error rate (BER) level around 10-6.

In this paper, the exact symbol error probability expression for a decode-and-forward cooperative relaying scheme using quadrature amplitude modulation (QAM) in the presence of Rayleigh fading and Bernoulli-Gaussian impulsive noise is derived. Different from, we only consider the practical receiver structure at the destination without requiring any knowledge of impulsive noise. Also, the source retransmission when the relay fails to detect the source signal is discussed. Analytical results show that cooperative relaying in impulsive noise environment still achieves space diversity and better performance than direct transmission under same bandwidth efficiency and power consumption for any impulse power and impulse rate. However, the source retransmission-assisted cooperative relaying brings negligible improvement.

An incremental relaying protocol based on the adaptive decode-and-forward relaying scheme is presented for a cooperative wireless network with binary frequency-shift keying modulation. To reduce error propagation and satisfy bit-error-rate (BER) requirement, the proposed protocol employs two thresholds. One threshold is used to select reliable relays: if a relay is requested to re-trnsmit, it will do so if its decision variable is larger than the threshold; otherwise, it remains silent. The other threshold is used at the destination as follows: the destination sends a request to re-transmit if the decision variable corresponding to a received signal is smaller than the threshold, otherwise, the destination sends a request to stop re-transmissions. The destination combines the signals from the requested relays and from the source to make the final decision. Very-tight closed-form upper bounds for both the average BER and throughput are derived for the proposed protocol. Based on the obtained BER and throughput expressions, the problem of choosing optimal thresholds to maximise the throughput while the BER meets a given constraint is investigated. Simulation results show that our proposed protocol leads to a considerable improvement in the performence of cooperative diversity systems.

We investigate spectrum sensing by energy detection based on two different objective functions: a Bayesian sensing cost or the network weighted sum capacity. The Bayesian cost is a traditional detection measure which aims at minimizing a combination of the miss-detection and false-alarm probabilities, while the capacity objective is a communication measure which aims at maximizing the network throughput. Fading-dependent optimal sensing thresholds for each objective are derived in closed-form for different cases of location side information. To make sensing more robust to channel fading, we also propose fading-independent sub-optimal thresholds. Results show that location side information helps improve performance when using the threshold designed for that performance measure. However, the Bayesian-based threshold does not utilize the side information well in improving the network sum capacity. On the other hand, the capacity-based threshold captures the benefit of side information in both the capacity and Bayesian cost measures. Furthermore, it helps to significantly improve the network throughput. The proposed sensing schemes with location side information can also be generalized to a network with multiple cognitive users in a simple and distributed manner.

This paper proposes efficient cooperative cyclostationary spectrum sensing schemes in which each secondary-user (SU) performs single-cycle (SC) cyclostationary detection for fast and simple implementation, while collaboration between SUs in final decision on the presence or absence of the primary-user (PU) is explored to improve its performance. As the SUs simultaneously measure the spectral correlation functions at different cycle-frequencies (CF) and exchange their information regarding the measured results, a sufficient number of CFs are effectively examined with parallel searching, which makes the proposed cooperative spectrum sensing more reliable. This paper presents another look at performance evaluation of cyclostationary detectors in terms of deflection coefficients. Outage probability of deflection coefficient is defined as a measure to compare the performance of different cyclostationary detectors in a fading channel. Furthermore, performance of the proposed schemes in terms of false-alarm and detection probabilities is evaluated by analysis and simulation in AWGN and fading channels. Illustrative and analytical results show that the proposed schemes outperform both SC and multi-cycle (MC) cyclostationary detectors, especially in fading channels.

Abstract This paper studies and develops efficient traffic management techniques for downlink transmission at the base station (BS) of multi‐service IP‐based networks by combining quality‐of‐service (QoS) provision and opportunistic wireless resource allocation. A delay‐margin‐based scheduling (DMS) for downlink traffic flows based on the delays that each packet has experienced up to the BS is proposed. The instantaneous delay margin, represented by the difference between the required and instantaneous delays, quantifies how urgent the packet is, and thus it can determine the queuing priority that should be given to the packet. The proposed DMS is further integrated with the opportunistic scheduling (OPS) to develop various queueing architectures to increase the wireless channel bandwidth efficiency. Different proposed integration approaches are investigated and compared in terms of delay outage probability and wireless channel bandwidth efficiency by simulation. Copyright © 2010 John Wiley & Sons, Ltd.

ABSTRACT This paper proposes the idea of precoding over multiple cooperative frames with multi‐dimensional (multi‐D) mapping for a bit interleaved coded modulation system over an ergodic non‐orthogonal amplify‐and‐forward (NAF) half‐duplex single‐relay channel. The benefits of multiple‐frame precoding and multi‐D labeling are analyzed in two different regions: the error‐floor region to exploit diversity and the turbo pinch‐off region for near‐capacity performance. In the error‐floor area, it is shown that the diversity gain function of the considered system is th power of that of uncoded cooperative systems, where Nf is the number of precoded cooperative frames and dH is the minimum Hamming distance of the outer code. To optimize the asymptotic coding gain, it is then shown that the source and relay must transmit orthogonally for best asymptotic performance. In the turbo pinch‐off region, we demonstrate that the proposed system concatenated with a simple outer binary code can be employed to achieve near‐capacity performance. Using union bounding techniques, we show that the optimal multi‐D labeling for NAF relaying shall maximize the average Euclidean distance between all pairs in the multi‐D rotated constellation whose labels differ in only 1 bit. The extrinsic information transfer charts are then used to match the outer code, the multi‐D mapping, and the precoder. It is demonstrated that the proposed system is also promising for NAF relaying in the turbo pinch‐off region, in the sense that it can operate below the achievable rate achieved by a conventional coded modulation scheme. Copyright © 2011 John Wiley & Sons, Ltd.

This paper evaluates the performance of a decode-and-forward scheme using orthogonal frequency-division multiplexing (OFDM) over both time-selective and frequency-selective fading channels. We consider a simple detection based on the zero-forcing (ZF) principle to eliminate inter-carrier interference (ICI) caused by high mobility and to combine signals received from the source and the relay for high spatial diversity at the destination. The symbol error rate (SER) of the ZF-based cooperative relaying scheme is derived and validated by computer simulations. Various results show the superiority of cooperative relaying to direct transmission under same transmission power and bandwidth efficiency for any relay position, signal-to-noise ratio (SNR), path-loss exponent, and normalized Doppler frequency.

Crosstalk is the limiting factor of performance in Digital Subscriber Line systems. The use of Dynamic Spectrum Management (DSM) can reduce the effects of crosstalk. For DSM algorithms, there is an important trade-off between the complexity of an algorithm and its performance. The Autonomous Spectrum Balancing (ASB) algorithm uses a virtual reference line (representative of a typical victim in the network) to allow for a favorable trade-off in some scenarios; however, it can often lead to poor performance in many other scenarios. This paper introduces a method of applying ASB with multiple reference lines in order to more consistently gain a favorable trade-off. It is shown that the achievable performance is very close to that of existing state-of-the-art DSM algorithms while requiring significantly fewer interference plus noise measurements.

In cognitive radio settings with highly dynamic primary activities and with small opportunities for secondary access, the requirement to fairly distribute the temporarily available spectral ranges among the unlicensed users turns out to be of particular relevance. The current paper addresses this issue by presenting a new design formulation that aims to optimize the performance of an orthogonal-frequency-division-multiple-access (OFDMA) ad-hoc cognitive radio network, by means of joint subcarrier assignment and power allocation. Besides important constraint on the tolerable interference induced to primary network, to efficiently implement spectrum-sharing fairness, the optimization problem considered here strictly enforces upper and lower bounds on the total amount of temporarily available bandwidth to be granted to individual secondary users. Specifically, the system throughput is maximized via the application of Lagrangian duality theory. More importantly, the dual decomposition framework also gives rise to the realization of distributed solution. As the proposed distributed protocol requires very limited cooperation among the participating network elements, it is especially applicable for the ad-hoc networking environment under investigation, to which any central processing or control is certainly inaccessible. While the computational complexity of the devised algorithm is affordable, its performance in practical scenarios also attains the actual global optimum. The potential of the proposed approach is verified through asymptotic complexity analysis and via numerical examples.

This paper is concerned with the game-theory approach in designing the multiuser downlink beamformers in multicell systems. Sharing the same physical resource, the base station of each cell wishes to minimize its transmit power subject to a set of target signal-to-interference-plus-noise ratios (SINRs) at the multiple users in the cell. In that process, each base station determines its optimal downlink beamformer strategy in a distributed manner, without any coordination between the cells. Via the game-theory framework, we examine the conditions guaranteeing the existence and uniqueness of the Nash Equilibrium (NE). We establish the best response strategy of a cell, given the beamforming strategies from other cells. Such best response strategy is shown to be a standard function, which then guarantees the uniqueness of the NE and the convergence of the distributed algorithm. A sufficient condition for the existence of the NE is also presented.

This paper considers the precoder design for a bandwidth-efficient bit-interleaved coded modulation (BICM) over non-orthogonal amplify-and-forward (NAF) single-relay channels with an arbitrary length of cooperative frame 2N. Based on the tight union bound on the bit error probability (BEP), we first derive an asymptotic design criterion with regard to a general 2N × 2N rotation matrix. This expression allows us to develop a class of precoder that not only achieves full cooperative diversity but also optimizes the asymptotic error performance. Interestingly, the developed class of optimal precoder indicates that the source should be kept silent in the cooperative phase. In the broadcasting phase, it is shown that power is distributed equally to 2N information symbols at the source. By further examining the structure of the optimal class of 2N × 2N precoders, we then reveal that precoding over a group of at least 2 information symbols is sufficient to fully exploit diversity and coding advantages. Such precoding technique, which is referred to as symbol grouping, therefore significantly reduces the system complexity without degrading the error performance.

The paper proposes efficient cooperative cyclostationary spectrum sensing schemes in which each secondary user (SU) performs single-cycle cyclostationary detection for fast and simple implementation, while collaboration between SUs in final decision on the presence or absence of the primary user (PU) is explored to improve its performance. As the SUs simultaneously measure the spectral correlation functions at different cycle frequencies (CF) and exchange their information regarding the measured results, a sufficient number of CFs are effectively examined in a short period of time because of parallel searching, which makes the proposed cooperative spectrum sensing more reliable and faster. Performance of the proposed schemes in terms of false-alarm and detection probabilities and deflection coefficients is evaluated by analysis and simulation in AWGN, fading and shadowing channels. Illustrative results show that the proposed schemes outperform both single-cycle (SC) and multi-cycle (MC) cyclostationary detectors, especially in fading and shadowing channels.

This paper proposes an opportunistic multicast scheduling scheme using erasure-correction coding to jointly explore the multicast gain and multiuser diversity. The proposed scheme sends only one copy to all users in the multicast group at a transmission rate based on a SNR threshold selected using only the knowledge of the average SNR and fading type of the fading environment. Analytical framework is developed to establish the optimum selection of the SNR threshold and coding rate for given channel conditions in a Nakagami-m fading environment to achieve the best throughput. Numerical results show that the proposed scheme outperforms both the worst-user (WU) and best-user (BU) schemes for a wide range of average SNR and multicast group size. Without the needs of perfect knowledge of the instantaneous channel responses of the user links, the proposed scheme can significantly reduce the overhead required for channel information feedback and is suitable for a fast time-varying fading environment. Another advantage of the proposed scheme is that its achievable normalized throughput is independent of the multicast group size.

This paper studies the problem of scheduling, preceding and limited feedback design for the emerging 3GPP-LTE systems over time-and frequency-selective (doubly selective) channels. In particular, greedy scheduling with zeroforcing (ZF) precoding is considered for the doubly selective multiuser multiple-input single-output (MISO) orthogonal frequency division multiplexing (OFDM) downlink channels. In limited feedback design, the discrete prolate spheroidal basis expansion model (DPS-BEM) is used as a fitting parametric model for capturing the time-variation of the doubly selective channels and reducing the number of the channel parameters. The resulting dimension reduction in the channel representation, in turn, translates into a reduced feedback load of channel state information (CSI). To exploit the considerable reduction in CSI feedback load, vector quantization (VQ) of DPS-BEM parameters is performed at users' receivers under the assumption that perfect BEM parameter estimation has been established by existing algorithms. The output indices of the quantized BEM parameter vectors are, then, sent to the base station (BS) via error-free limited feedback links. With the channel state information (CSI) at transmitter (CSIT), greedy scheduling and ZF precoding are deployed for multiuser transmission in each subcarrier of OFDM symbols in a LTE frame. Numerical results show that the ZF-based multiuser transmission scheme with the suggested BEM quantization and limited feedback design offers significant sum-rate gains and stable performance with high robustness against time-varying channels.

This paper considers an adaptive iterative water-filling (IWF) algorithm to deal with the near-far problem in Digital Subscriber Lines (DSL) networks. In order to protect the far-end users, the paper proposes a new power back-off strategy by a pro-active configuration of the spectral mask at the near-end users. Instead of reducing the total power of the near-end users as in IWF, the proposed algorithm allows the near-end users to only back off the power in certain frequency bands. Simulation shows a significant performance advantage of the proposed algorithm over the traditional IWF algorithm. Moreover, the performance of the proposed algorithm approaches the optimal performance obtained by centralized algorithm with full channel information requirement while retaining the low-complexity and distributed implementation of the IWF.

A relaying technique based on single relay selection is studied for multiple-relay networks using hierarchical modulation for unequal error protection. A single relay selection scheme achieves a higher bandwidth efficiency while maintaining the same diversity order as when all the relays are selected in a multiple-relay network. Specifically, a cooperative network with one source, K relays, and one destination is considered in which two different protection information classes are modulated by a hierarchical 2/4-amplitude shift keying (ASK) constellation at the source. After selecting a relay to cooperate with the source, based on the instantaneous received SNR, the selected relay decides to retransmit both classes by using a hierarchical 2/4-ASK constellation, or the more protection class by using a 2-ASK constellation, or remains silent. The approximated bit error rate (BER) of each information class is derived. Optimal thresholds are chosen to minimize the BER of one class while satisfying the BER constraint of the other. Analytical and simulation results show that the optimal thresholds can improve the error performance significantly.

This paper investigates the error performance and precoder design for a bandwidth-efficient bit-interleaved coded modulation (BICM) system over a non-orthogonal amplify-and-forward (NAF)half-duplex single-relay channel. A tight union bound on the bit error probability (BEP) is first derived for an arbitrary block length of 2N using a 2N × 2N precoder. This bound provides an useful tool to predict the error performance. Attention is then devoted to the system using 2×2 precoder, where a closed-form expression of the bound is obtained. Based on this expression, an optimal 2×2 precoder applicable to any modulation scheme is developed. Different from the optimal precoders designed for uncoded NAF systems, the derived precoder indicates that the source only needs to send the superposition of signals in the first time slot and being silent in the second time slot in order to achieve the best asymptotic performance. Analytical and simulation results show that the proposed precoder not only exploits full cooperative diversity but also offers a significant coding gain over optimal precoders for uncoded NAF systems.

This paper presents a study on interference caused by Secondary Users (SUs) due to miss-detection and its effects on the capacity-outage performance of the Primary User (PU) in a cognitive network for two scenarios of beacon transmitter placement: beacon transmitter located at PU transmitter or at PU receiver. Interference analysis shows that aggregate interference power from SUs has a Gamma distribution when beacon transmitter is located at PU receiver, while it can be approximated as a shifted-Gamma distributed random variable for the case of beacon transmitter located at PU transmitter. Based on statistical model for the interference distribution, closed-form expressions of the capacity-outage probability of the PU are developed to examine the effects of various system parameters on the performance of the PU in presence of interference from SUs. Simulation results confirm the validity of the developed analytical models. It is shown that beacon transmitter at PU receiver offers lower interference and hence better capacity-outage probability to the PU than beacon transmitter at PU transmitter.

This paper studies the problem of pilot-aided joint carrier frequency offset (CFO) and channel estimation using a Bayesian approach in multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) transmissions over time- and frequency-selective (doubly selective) channels. Unlike the joint CFO and channel impulse response (CIR) estimation over block-fading channels, the joint CFO and time-variant CIR estimation gives rise to the identifiability problem where the number of observations (received samples) is smaller than that of both CFO and time-variant CIR parameters to be estimated. To reduce a large number of the time-variant CIR parameters to be estimated, various basis expansion models (BEMs) are deployed as fitting parametric models for capturing the time variation of the MIMO channels. As the main purpose of using BEMs, the resulting dimension reduction in the time-variant channel representation helps to avoid the identifiability issue in the joint estimation problem. Under Bayesian estimation, CFO and BEM coefficients are treated as random variables to be estimated by the maximum-a-posteriori (MAP) technique. Numerical results demonstrate that the deployment of BEMs is able to alleviate performance degradation in the considered estimation technique using the conventional assumption of block fading over time varying channels.

A relaying technique based on single-relay selection is studied for multiple-relay networks when hierarchical modulation is employed for unequal error protection. A single-relay selection scheme achieves a higher bandwidth efficiency while maintaining the same diversity order as when all the relays are selected in a multiple-relay network. Specifically, a cooperative network with one source, K relays, and one destination is considered in which two different protection information classes are modulated by a hierarchical 2/4-amplitude shift keying (ASK) constellation at the source. After selecting a relay to cooperate with the source, based on the instantaneous received signal-to-noise ratio, the selected relay decides to retransmit both classes by using a hierarchical 2/4-ASK constellation, or the more protection class by using a 2-ASK constellation, or remains silent. The approximated bit error rate (BER) of each information class is derived. Optimal thresholds are chosen to minimise the BER of one class while the BER of the other class satisfies a requirement. Numerical and simulation results are provided to validate the analysis. The results also show that the optimal thresholds can improve the error performance significantly.

This paper proposes a capacity-approaching, yet simple scheme for multi-input multiple-output (MIMO) channels. The proposed scheme is based on a concatenation of a mixture of short memory-length convolutional codes or repetition codes and a short, and simple rate-1 linear block code, followed by either 1-dimensional (1-D) anti-Gray or Gray mapping of quadrature phase-shift keying (QPSK) modulation. By interpreting the rate-1 code and the 1-D mapping as a multi-D mapping performed over multiple transmit antennas, the error performance is analyzed in two regions. In the error-floor region, a tight union bound and the corresponding design criterion on the asymptotic performance are derived. The bound provides a useful tool to predict the error performance at relatively low bit error rate (BER) values. Based on the obtained design criterion, an optimal rate-1 code for each 1-D mapping is then constructed to achieve the best asymptotic performance. In the turbo pinch-off region, by using extrinsic information transfer (EXIT) charts, the most suitable mixed codes are selected for both symmetric and asymmetric antenna configurations. It is demonstrated that the simple concatenation scheme can achieve a near-capacity performance over the MIMO channels. Furthermore, its error performance is shown to be comparable to that obtained by using well-designed irregular LDPC and RA codes, and therefore, the proposed scheme significantly outperforms a scheme employing a parallel concatenated turbo code. Simulation results in various cases are provided to verify the analysis.

Abstract This paper considers a coded cooperative relaying scheme in which all successfully decoded signals from multiple sources are simultaneously forwarded by a multi‐antenna relay to a common multi‐antenna destination to increase bandwidth efficiency. Iterative decoding with hard interference cancellation is used at destination to recover user information. By using orthogonal transmission from sources to avoid their mutual interference, the multi‐antenna relay offers receive space diversity that greatly enhances the decoding performance at the relay. This makes the source‐relay transmission more robust, less sensitive to the source‐relay link SNR, and hence increases the contribution of the relay in cooperative transmission. Simulation results show that the proposed scheme significantly outperforms direct transmission under the same transmit power and bandwidth efficiency. Copyright © 2009 John Wiley & Sons, Ltd.

This paper proposes a capacity-approaching, yet simple scheme over a multiple-input single-output (MISO) wireless fading channel, which is very common in the downlink of a cellular system. The proposed scheme is based on a concatenation of a mixture of short memory-length convolutional codes or repe

The paper introduces a turbo (iterative) receiver design for joint channel estimation, synchronization and soft decoding in convolutional-coded multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems over time- and frequency-selective (doubly selective) channels. Employing the complex-exponential basis expansion model (CE-BEM) for representing doubly selective channels, a maximum likelihood (ML) objective function of carrier frequency offset (CFO) and MIMO time-varying channel responses (BEM coefficients) is formulated to develop a semi-blind ML framework for joint time-variant channel estimation and synchronization. To reduce the overhead of pilot signals without sacrificing estimation accuracy, the soft bit information from a soft-input soft-output (SISO) decoder is exploited in computing soft estimates of data symbols to be functioned as pilots for further enhancing the estimation accuracy after CFO and channel acquisition phase (initial coarse estimation) using pilots. In other words, the resulting semi-blind ML estimation scheme operates in conjunction with soft decoding process in a (iteratively) progressive manner to exploit remarkable gains of turbo processing (iterative extrinsic information exchange). Simulation results show that the proposed turbo joint channel estimation and synchronization scheme offers high estimation accuracy that approaches Cramer-Rao lower bounds (CRLBs) over a wide range of CFO values under low signal-to-noise ratio (SNR) conditions.

We study Bayesian detection based cognitive sensing and analyze its impact on the capacity in various cases of location information. In a network of one primary and one cognitive users, the cognitive transmitter relies on information about the locations of the primary transmitter and the two receivers to design its optimal sensing threshold. Results show that this location-aware threshold can significantly improve the cognitive user's capacity, while imposing almost no detrimental effect on the primary user's capacity. Combined with a priori knowledge of the primary transmission probability, the location information is shown to be beneficial to the cognitive user's capacity when the primary user is likely to be active. Without the knowledge of the primary transmission probability, location information is beneficial for all range of primary activity.

This paper studies a relaying technique in cooperative networks using hierarchical modulation. Hierarchical modulation is useful in applications that require different protection classes of the information. In particular, a cooperative network with one source, one relay, and one destination is considered. Two different protection classes are modulated by a hierarchical 2/4-amplitude shift keying (ASK) constellation at the source. Based on the instantaneous received signal-to-noise ratio (SNR) at the relay, the relay decides to retransmit both classes by using a hierarchical 2/4-ASK constellation, or the more protection class by using a 2-ASK constellation, or remains silent. Optimal thresholds are chosen to minimize the bit-error-rate (BER) of the less protection class while the BER of the more protection class meets a given requirement. Numerical and simulation results are provided to verify the analysis. The results show that the optimal thresholds improve the performance significantly.

In this paper, we study the joint power allocation and relay selection problem for multi-user amplify-and-forward (AF) cooperative networks. To increase the system's spectral efficiency under the orthogonal transmission assumption, each source-destination pair is constrained to be assisted by a small subset of a set of available relays. The aim of this work is to establish a framework that determines which relays to help which users and with how much power. In particular, we propose the joint schemes under two design criteria: i) maximization of user rates, and ii) minimization of the total transmit power at the relays. As the original problem formulations are shown to be nonconvex integer optimization problems, and thus, are combinatorially hard, we also propose an efficient convex relaxation approach to solve the problems with low complexity. Numerical results demonstrate the effectiveness of the proposed approaches.

This paper proposes a simple yet capacity-approaching concatenation of a mixture of short memory length convolutional codes and simple rate-1 block code followed by either complex 1-dimensional (1-D) anti-Gray or Gray mapping over multiple antenna channels with quadrature phase-shift keying (QPSK). By interpreting rate-1 code together with 1-D mapping as a multi-D mapping employed over multiple transmit antennas, the error performance is analyzed in two regions, the error-floor and turbo pinch-off regions. In the former one, a tight union bound and design criterion on the asymptotic performance are first derived, which provide an useful tool to predict the error performance. Based on the design criterion, an optimal rate-1 code for each 1-D mapping is then constructed to achieve the best asymptotic performance. In the turbo pinch-off area, by using extrinsic information transfer (EXIT) chart, the most suitable mixed codes are selected for both symmetric and asymmetric antenna setups. It is demonstrated that the simple concatenation scheme can achieve near-capacity. Furthermore, its error performance is comparable to that obtained by using well-designed irregular low-density parity-check (LDPC) and repeat accumulate (RA) codes, and thereby, outperforms a scheme employing a parallel concatenated turbo code.

This paper considers the problem of multiuser resource allocation for multicasting stream video traffic over a wireless time-division multiplexing system. Most of previous work attempts to guarantee the reliability of data transmission for every user by limiting the multicast rate to the lowest instant sustainable rate of the worst user. Such rate allocation schemes may seriously penalize users with better channel conditions. Our proposed approaches attempt to exploit the multiuser diversity by dynamically selecting the rate which is not necessarily the lowest sustainable rate in order to maximize the total good-put. Simulation results conclusively demonstrate that the proposed approaches can improve the wireless channel efficiency, especially when there are more users in the multicast group.

This paper proposes a pilot-aided joint channel estimation and synchronization scheme for burst-mode orthogonal frequency division multiplexing (OFDM) systems over time- and frequency-selective (doubly selective) channels. Based on the basis expansion model (BEM) for representing doubly-selective channels, a least-square (LS) cost function of carrier frequency offset (CFO) and BEM coefficients is formulated for the joint estimation problem. By applying the first-order Taylor series expansion, an approximately linearized estimation error is obtained to facilitate a recursive least-square (RLS)-based joint CFO and BEM estimation with the aid of pilot OFDM symbols. Simulation results demonstrate that, over a wide range of Doppler spreads, the proposed estimation scheme offers a high robustness against the time variation of fast fading channels and outperforms the linear minimum mean-square (LMMSE) algorithm using CFO estimates provided by the CFO estimation technique in.

Optimal power allocation for multi-user amplify- and-forward wireless relay networks in which multiple source-destination pairs are assisted by a set of relays is investigated. Two relay power allocation strategies based on maximization of either i) the minimum rate among all users or ii) the weighted sum of rates are developed. A distributed implementation of the maximum weighted-sum-rate power allocation strategy is also studied. Numerical results demonstrate the efficiency of the proposed strategies and reveal their interesting throughput-fairness tradeoff in resource allocation.

This paper proposes a near-capacity yet simple bit-interleaved coded modulation with iterative decoding (BICM-ID) scheme by employing a multi-dimensional (multi-D) mapping technique in a multi-D constellation carved from a rotated lattice. Using extrinsic information transfer (EXIT) charts, it is shown that the proposed technique fits well with simple convolutional codes in terms of the area property, for which turbo pinch-off can happen at a low Eb/N0value. In particular, both EXIT chart analysis and simulation results indicate that by using just a simple convolutional code together with a 4-D mapping, a turbo pinch-off and a bit error rate (BER) close to 10-6happen at a signal-to-noise ratio (SNR) that is even lower than the BICM constraint capacity limit with a uniform input. The proposed BICM-ID scheme can be considered as an attractive alternative to other bandwidth-efficient coded modulation techniques using powerful turbo-like codes such as turbo or low-density parity-check (LDPC) codes over a Rayleigh fading channel.

This paper proposes a pilot-aided joint channel estimation and synchronization scheme for burst-mode orthogonal frequency division multiplexing (OFDM) systems over time- and frequency-selective (doubly selective) channels. By exploiting the basis expansion model (BEM) for representing doubly-selective channels, a maximum likelihood (ML) cost function of carrier frequency offset (CFO) and BEM coefficients is formulated to develop a ML framework for the joint synchronization and channel estimation problem. Inheriting the properties of the ML estimation, the proposed estimator is unbiased and its mean-squared-error (MSE) performance achieves Cramer-Rao lower bounds (CRLB) asymptotically (for a large data records). Simulation results demonstrate that, over a wide range of Doppler spreads, the proposed estimation scheme offers a high robustness against the time variation of fast fading channels and outperforms the linear minimum mean square (LMMSE) algorithm. In addition, the ML-based algorithm achieves CRLB at very low signal-to-noise ratio (SNR) regimes.

For the decode-and-forward (DF) strategy, smart relaying has been shown to achieve the maximal spatial diversity order over Rayleigh fading channels even when erroneous detection is committed at the relays. Due to the importance of Nakagami and Hoyt statistical models in describing channel fading in land, mobile and satellite communications, this paper performs diversity analysis of the smart relaying systems under Nakagami and Hoyt generalized fading channels. Performance analysis proves that, at high signal-to-noise ratio (SNR), the maximal diversity order of the smart relaying system under the Nakagami channel is mSD+ min{mSR, mRD}, where mSR, mRDand mSDare the fading figures of the source-relay (S-R), relay-destination (R-D) and source-destination (S-D) links. Under the Hoyt fading channel, the diversity order is 2.

An analytical technique is provided to estimate queue-length and delay distributions for multi-queue systems using generalised processor sharing discipline with time-correlated variable service rates, based on two-dimensional multi-level decoupling. First, temporal decomposition is used to convert the time-correlated queuing problem into a set of sub-problems over several timescales. Subsequently, queue decomposition exploits the queue weight dependencies to convert a multi-queue problem into a set of single-queue problems. The core of the analysis lies in estimating the multi-scale service rate models for each of these queues. The authors show the hierarchy of this estimation and the dependency of the queue service rate on the unused capacity of the other queues and their weights. Simulation and analytical results on queue and delay survivor functions are in good agreement.

The maximum likelihood detection problem in many underdetermined linear communications systems can be described as an underdetermined integer least squares (ILS) problem. To solve it efficiently, a partial regularisation approach is proposed. The original underdetermined ILS problem is first transformed to an equivalent overdetermined ILS problem by using part of the transmit vector to do the regularisation. Then the overdetermined ILS problem is solved by conventional sphere decoding algorithms. Simulation results indicate that this approach can be much more efficient than other approaches for any square constellation higher than 4QAM.

A coded cooperative transmission scheme based on turbo encoding/decoding, in which only newly generated parity bits of the partner are sent if the user successfully decodes its partner's information in order to improve bandwidth efficiency is proposed. The proposed encoding structure introduces correlation between users' data over multiple frames, which offers effectively longer codes and facilitates high-performance iterative multi-user decoding at the destination. Additionally, the iterative decoding over multiple frames can provide time diversity besides spatial diversity inherent in user cooperation even for flat block fading channels. Simulation results show that the proposed scheme significantly outperforms direct transmission for the same transmitted power and bandwidth efficiency.

Signal transmission with the help of relay(s) in wireless networks can achieve spatial diversity without the need of having multiple attennas at the source and/or destination. Among various signal processing techniques proposed for the relays, the adaptive decode-and-forward (DF) relaying strategy, recently proposed by Wang et al. and generally referred to as smart relaying, has been shown to achieve the maximal spatial diversity even when imperfect detection is committed at the relays. The work by Wang et al., however, only considers Rayleigh fading channels. This paper extends the diversity analysis of the smart relaying technique to the important Nakagami and Hoyt generalised fading channels. Performance analysis proves that, at high signal-to-noise ratio, the maximal diversity order achieved by the smart relaying system under the Nakagami channel is mSD+min{mSR, mRD}, where mSR, mRD and mSD are the fading figures of the source–relay (S–R), relay–destination (R–D) and source–destination (S–D) links. Under the Hoyt fading channel, the diversity order is 2. The obtained results on the diversity order are shown to be insensitive to the quality of the R–D feedback channel.

A design method is introduced for linear dispersion (LD) space-time codes in wireless relay networks under Rayleigh fading channels. The codes are designed with a stochastic quasi-gradient algorithm to minimize the upper bound of the average pairwise error probability. Simulation results show that the optimized codes achieve a coding gain of about 2 dB over codes that are randomly generated based on the isotropic distribution.

This letter generalizes the application of signal space diversity (SSD) over multiplicative fading channels, where fading is represented by the product ofKstatistically independent Nakagami-mrandom variables. The pairwise error probability (PEP) for the systems is first obtained in a closed-form using generalized hypergeometric functions. Based on the obtained PEP expression, it is shown that the error performance over multiplicative fading channels can be significantly improved by using high diversity constellations. Furthermore, by employing SSD with a sufficiently large dimension, it is observed that the adverse effects of multiplicative fading can be practically eliminated without any power nor bandwidth expansion. Simulation results for both uncoded and coded systems are provided to show the agreement with the analysis.

For underdetermined linear systems, original sphere decoding (SD) algorithms fail due to zero diagonal elements in the upper-triangular matrix of the QR or Cholesky factorization of the underdetermined matrix. To solve this problem, this paper presents a low-complexity generalized sphere decoding (GSD) approach by transforming the original underdetermined problem into the full-column-rank one so that standard SD can be directly applied on the transformed problem. Since the introduced transformation maintains the dimension of the original problem for all M-QAM's, the proposed GSD approach provides significant reduction in complexity as compared to other GSD schemes, especially for M-QAM with large signaling constellation. Both performance and expected complexity are analyzed to provide the comprehensive relationships between the performance and complexity of the proposed GSD and its parameters. Illustrative simulation and analytical results are in good agreement in terms of both the performance and complexity and indicate that with the properly selected design parameters, the proposed GSD scheme can approach the optimum maximumlikelihood decoding (MLD) performance with low complexity for underdetermined linear communication systems including underdetermined MIMO systems, and the proposed expected complexity analysis can be used as reliable complexity estimation for practical implementation of the proposed algorithm and serve as reference for other GSD algorithms.

This paper generalizes the application bit-interleaved coded modulation with iterative decoding (BICM-ID) using signal space diversity (SSD) overkeyholeNakagami-mfading channels. The tight union bound on the asymptotic error performance is first analytically derived. The near-optimal rotation matrix with respect to both the asymptotic performance and the convergence behavior is then determined. In particular, it is demonstrated that the suitable rotation matrix is the one that has 1) all entries equal in magnitude, 2) a high diversity order, and 3) a large minimum product of the ratios between squared distances to the powermand log-squared distances to the powermof the rotated constellation scaled by factors of signal-to-noise ratio (SNR) and the parameterm. Various analytical and simulation results show that by employing SSD with a sufficiently large dimension, the error performance can closely approach that over an additive white Gaussian noise (AWGN) channel, even in the worst case of keyhole fading.

In this paper, using diagonal signal repetition with Alamouti code employed as building blocks, we propose a high-rate groupwise space-time block code (GSTBC) which can be effectively decoded by a low-complexity successive interference cancellation (SIC) based receiver. The proposed GSTBC and SIC based receiver are jointly designed such that the diversity repetition in a GSTBC can induce the dimension expansion to suppress interfering signals as well as to obtain diversity gain. Our proposed scheme can be easily applied to the case of large number of antennas while keeping a reasonably low complexity at the receiver. It is found that the required minimum number of receive antennas is only two for the SIC based receiver to avoid the error floor in performance. The simulation results show that the proposed GSTBC with SIC based receiver obtains a near maximum likelihood (ML) performance while having a significant performance gain over other codes equipped with linear decoders.

In this paper, we consider an amplify-and-forward wireless relay system where multiple source nodes communicate with their corresponding destination nodes with the help of relay nodes. Conventionally, each relay equally distributes the available resources to its relayed sources. This approach is clearly sub-optimal since each user experiences dissimilar channel conditions, and thus, demands different amount of allocated resources to meet its quality-of-service (QoS) request. Therefore, this paper presents novel power allocation schemes to i) maximize the minimum signal-to-noise ratio among all users; ii) minimize the maximum transmit power over all sources; iii) maximize the network throughput. Moreover, due to limited power, it may be impossible to satisfy the QoS requirement for every user. Consequently, an admission control algorithm should first be carried out to maximize the number of users possibly served. Then, optimal power allocation is performed. Although the joint optimal admission control and power allocation problem is combinatorially hard, we develop an effective heuristic algorithm with significantly reduced complexity. Even though theoretically sub-optimal, it performs remarkably well. The proposed power allocation problems are formulated using geometric programming (GP), a well-studied class of nonlinear and nonconvex optimization. Since a GP problem is readily transformed into an equivalent convex optimization problem, optimal solution can be obtained efficiently. Numerical results demonstrate the effectiveness of our proposed approach.

This paper studies the problem of adaptive resource allocation for downlink transmission at the base station (BS) of future multi-service IP-based networks. A delay margin based scheduling (DMS) approach is proposed to determine the queuing priority of an incoming packet by taking into consideration the distance between its currently experienced delay and its maximum allowed limit since such an instantaneous delay margin can timely and faithfully quantify its urgency. A number of queue architectures that aim to increase the bandwidth efficiency by integrating DMS with opportunistic user channel based scheduling (UCS) are also suggested. Wireless channel bandwidth efficiency and delay outage probability of the proposed approaches are extensively investigated by simulations.

In this paper, we consider an amplify-and-forward (AF) wireless relay system where multiple source nodes communicate with their corresponding destination nodes with the help of relay nodes. While each user is assisted by one relay, one relay can assist many users. Conventionally, each relay node is assumed to equally distribute the available bandwidth and power resources to all sources for which it helps to relay information. Realizing the sub-optimality of this approach, in this paper, we present efficient power allocation schemes to i) maximize the minimum end-to-end signal-to-noise ratio among all users; ii) minimize the total transmit power over all sources; iii) maximize the system throughput. Our approach is based on geometric programming (GP), a well-studied class of nonlinear and nonconvex optimization. Since a GP problem is readily transformed into an equivalent convex optimization problem, optimal power allocation can be obtained efficiently. Numerical results demonstrate the effectiveness of our proposed approach.

For a decode-and-forward (DF) relaying system with maximal-ratio combining (MRC) at the destination, spatial diversity is lost except when the source-relay link is reliable. This paper considers and analyzes a smart relaying scheme, in which the relay scales its transmitted power adaptively to the channel conditions without exceeding the total power used in the conventional relaying system. Performance analysis proves that a diversity order of two is always achieved with binary- phase-shift-keying (BPSK) and quadrature-phase-shift-keying (QPSK) constellations. The order-2 diversity is also observed from numerical results for rectangular quadrature amplitude modulation (QAM).

This paper addresses the problem of design of practical communication strategies for multi-antenna amplify-and-forward and decode-and-forward relay systems. We show that simple linear techniques at source and destination in conjunction with maximum ratio combining can provide an optimal transmission strategy in terms of received SNR without imposing a huge computational load over the relay node(s). Besides, the structure of precoding matrices are very similar at source and relay nodes, which reduces the complexity as all nodes can play the role of source and relay nodes without changing their transmission structure. Numerical results show that the proposed transmission and reception techniques can improve the received SNR, and hence enhance the ergodic capacity.

The effects of carrier frequency offset (CFO), sampling frequency offset (SFO), and channel responses on the received samples are analyzed and explored to develop the turbo joint channel estimation, synchronization and decoding scheme for coded MIMO-OFDM systems. For burst transmission, with initial estimates derived from the pre-amble, the proposed scheme can operate without the need of pilot tones during the data segment. Simulation results also show that the proposed scheme offers fast convergence and low mean-squared-error (MSE) over quasi-static Rayleigh multi-path fading channels, and can be used in a coded MIMO-OFDM transceiver in presence of multipath fading, CFO, and SFO to achieve a bit-error-rate (BER) performance comparable to that in an ideal case of perfect synchronization and channel estimation after three iterations.

The symbol error rate (SER) and the bit error rate (BER) of orthogonal space-time block codes (OSTBCs) with antenna selection over keyhole fading channels are examined. Considered are receive antenna selection, transmit antenna selection, and joint antenna selection at both the transmitter and the receiver. The exact SER of OSTBCs for M-PSK and square M-QAM constellations is obtained using the technique of moment generating function. By applying the Bonferroni-type bounds, tight lower and upper bounds for both the SER and BER are provided in closed-form expressions with finite-range single integrals. The bounds can be applied to arbitrary constellations and mappings. Numerical results show that the bounds can be used to provide practically the exact SER and BER over a wide range of the signal-to-noise ratio.

This paper applies the sigma mapping of M-QAM in linear diversity-embedded space-time block codes (LDE-STBC) to achieve different diversity levels and various coding gains for unequal error protection (UEP). By controlling the basis vectors of sigma mapping, the bit error rate (BER) performance of each bit in a layer can be made to satisfy its quality-of-service (QoS) requirement. Moreover, with the low decoding complexity offered by sigma mapping, highly-flexible codes can be designed.

It has been shown that relaying systems can offer spatial and multipath diversity in amplify-and-forward (AF) relay-assisted transmission over frequency-selective fading channels when trellis coded modulation (TCM) is applied as an outer code. However, the price for such advantages is the high decoding complexity. To overcome this disadvantage, the technique of subcarrier grouping is applied for orthogonal frequency division multiplexing (OFDM)-based relaying systems. If there exists a strong line-of-sight path in the relay-to-destination link, it is shown that the system can achieve both maximal spatial and multipath diversity gains. When all the underlying channels are frequency-selective, simulation results indicate that the full spatial and multipath diversity gains can also be realized.

An efficient multi-user detection scheme for overloaded MC-CDMA systems is presented. The proposed detection scheme for overloaded systems achieves the optimal performance for constant-modulus modulation schemes, for example MPSK, and provides near-optimal performance for non-constant-modulus modulation, for example M-QAM. Moreover, it has overall lower complexity than the other optimal detection algorithms, especially suitable for group-orthogonal MC-CDMA systems, where group-based technique keeps multi-user receiver size relatively small.

Bit-interleaved coded modulation with iterative decoding (BICM-ID) using signal space diversity (SSD) is considered for cascaded Rayleigh fading channels. A tight bound on the asymptotic error probability is derived to determine the optimal rotation matrix for SSD design and to identify the key parameters that influence the system performance. It is shown that, for small modulation constellation, a cascaded Rayleigh fading causes a much more severe performance degradation than a conventional Rayleigh fading. However, BICM-ID employing SSD with a sufficiently large constellation can close the performance gap between the conventional and cascaded Rayleigh fading channels, and their performance can closely approach that over an AWGN channel. Illustrative simulation results for various scenarios are in a good agreement with analytical derivations.

The symbol error rate (SER) and the bit error rate (BER) of orthogonal space-time block codes (OSTBCs) with antenna selection over keyhole fading channels are examined. Considered are receive antenna selection, transmit antenna selection, and joint antenna selection at both the transmitter and the receiver. The exact SERs of OSTBCs for M-PSK and square MQAM constellations are obtained using the technique of moment generating function (MGF). By applying the Bonferroni-type bounds, tight lower and upper bounds for both the SER and BER are provided in closed-form expressions with finite-range single integrals. The bounds can be applied to arbitrary constellations and mappings. Numerical results show that the bounds can be used to provide practically the exact SER and BER over a wide range of the signal-to-noise ratio.

This paper addresses the problem of optimal number of relays and relay selection and proposes a cooperative transmission scheme with an appealing multiplexing-diversity trade-off. We show that a distributed BLAST transmission in conjunction with successive nulling and cancellation at destination can achieve the optimal trade-off given that the relays are selected according to a specific criterion. Illustrative results indicate that the proposed method can increase the strength of relay networks in providing reliable communications.

This paper proposes a pilot-aided algorithm for the joint estimation of the channel impulse response (CIR), carrier frequency offset (CFO) and sampling frequency offset (SFO) in burst-mode multiple-input multiple-output (MIMO) - orthogonal frequency division multiplexing (OFDM) systems. We employ the vector recursive least-squares (RLS) algorithm that can function as an adaptive filter with multiple outputs for the joint estimation problem in MIMO scenarios. Specifically, based on the received signal samples that contain pilot tones in the frequency domain, we formulate a cost function that includes the multi-antenna channel impulse responses and frequency offsets for the vector RLS-based joint estimation of CIR CFO and SFO. Analytical and simulated results show that, over large ranges of CFO and SFO values, the proposed estimation and tracking approach offers fast convergence, high stability, and provides a near optimum receiver performance that is remarkably close to the ideal one in the case of perfect channel estimation and synchronization over Rayleigh multi-path fading channels.

Zero-forcing (ZF) precoding can asymptotically achieve the sum-rate capacity offered by the dirty paper coding (DPC) in a multiple-input multiple-out (MIMO) broadcast (BC) channel in the limit of the large number of users K. However, its performance is degraded for relatively small K, e.g., Kles100, partly due to the excessive transmit power penalty when the channel matrix of selected user subset is poorly conditioned. To avoid this power penalty, we propose to use channel inversion regularization (CIR) in the precoder in MIMO BC channels. Unlike the interference-free ZF, maximizing sum-rate capacity using CIR precoder becomes a nonlinear, nonconvex optimization problem, which cannot be solved by simple water-filling strategy. Hence, we propose an efficient optimal power allocation strategy for the selected users based on gradient projection (GP) method. Simulation results show that the proposed precoding and power allocation scheme achieves better sum-rate performance than ZF for a wide range of K.

This paper presents MIMO precoder designs for frequency-selective channels based on the transmit, receive and channel path correlation matrices. Optimal linear precoder structures are derived based on ergodic capacity criterion for three different scenarios: uncorrelated channel paths but equal spatial correlation matrices, uncorrelated channel paths but unequal spatial correlation matrices, and correlated channel paths. For channels with uncorrelated paths, the preceding structure is composed of a number of parallel precoders designed for frequency-flat fading channels. Simulation results show that the proposed precoders outperform others based only on spatial correlation in various propagation scenarios. Their achievable capacity is better in highly correlated (either spatial or path correlation) environments.

This paper proposes a pilot-aided joint channel estimation and synchronization scheme for burst-mode orthogonal frequency division multiplexing (OFDM) systems. The scheme eliminates the need of an IFFT block while keeping the low number of parameters to be estimated for low complexity without sacrificing the performance and convergence speed. For fast convergence and high performance, we develop a linearized cost function of the carrier frequency offset (CFO), sampling clock frequency offset (SFO) and channel impulse response (CIR) coefficients based on received signal samples and pilot tones in frequency domain and the corresponding recursive least square (RLS) estimation and tracking algorithm. For channel responses, CIR coefficients are estimated to benefit their low number and then transformed to the channel transfer function in order to keep low complexity. The ICI introduced by rotation due to CFO and SFO is analyzed and modeled, and a simple maximum-likelihood (ML) scheme based on the preamble is developed for coarse estimation of initial CFO and SFO values to be used in suppression of dominant ICI effects and in fine RLS estimation and tracking. Simulation results demonstrate that, in large practical ranges of CFO and SFO values, the proposed pilot-aided joint channel estimation and synchronization scheme provides a receiver performance remarkably close to the ideal case of perfect channel estimation and synchronization.

Exploiting signal space diversity (SSD) to improve the error performance of communications systems over fading channels has been shown to be a very effective technique. The application of SSD in bit-interleaved coded modulation with iterative decoding (BICM-ID) is considered for cascaded Rayleigh fading channels, which are suitable for mobile-to-mobile communications. A tight bound on the asymptotic error performance is first derived. The bound is then used to find the optimal rotation matrix. It is shown that employing SSD in a sufficiently large constellation can close the performance gap between a conventional Rayleigh fading channel and a cascaded Rayleigh fading channel. In fact, similar to the case of conventional Rayleigh fading, it is demonstrated that the error performance of BICM-ID with SSD over a cascaded Rayleigh fading channel can also closely approach the performance of BICM-ID over an AWGN channel. Various analytical and simulation results are provided to confirm the analysis.

This paper presents an analytical approach to evaluate the expected complexity of a generalized sphere decoding (GSD) algorithm, lambda - GSD, for underdetermined integer least- squares (ILS) problems. The analytical approach is used to derive the closed-form formulas to approximate the expected complexity of the lambda - GSD algorithm, by utilizing special statistical properties in the transformed channel matrix. Analytical and simulation results in various scenarios are in good agreement, indicating that the proposed complexity analysis can be used as reliable complexity estimation for practical implementation of lambda - GSD and can serve as reference for other GSD algorithms.

This paper proposes an iterative joint source-channel decoding (IJSCD) scheme for the transmission of H.264 compressed video over a noisy channel. It uses channel coding along with H.264 semantic verification. The structure, selection of design parameters, and performance of the proposed IJSCD based on a rate-1/2 recursive systematic convolutional (RSC) code over an AWGN channel are described and discussed as an illustrative example. In the example, for the same PSNR, the proposed IJSCD scheme offers a significant saving of 2.1dB in required channel SNR as compared to a system using the same RSC code alone. Furthermore, the performance can be improved by iterative decoding at the cost of increased delay. Hence, a tradeoff can be made between performance improvement and delay.

This paper generalizes the application of bit interleaved coded modulation with iterative decoding (BICM-ID) using signal space diversity (SSD) over keyhole Nakagami-m fading channels. The tight union bound on the asymptotic error performance is first analytically derived. The optimal rotation matrix with respect to both the asymptotic performance and the convergence behavior is then determined. In particular, it is demonstrated that the most suitable rotation matrix is the one that has i) all entries equal in magnitude, ii) high diversity order, and iii) large minimum product of the ratios between squared distances and log-squared-distances of the rotated constellation scaled by factors of SNR and the parameter m. Various analytical and simulation results show that by employing SSD with a sufficiently large dimension, the error performance can closely approach that over an AWGN channel, even in the worst case of keyhole fading.

This paper presents a zero-forcing algorithm based on the partial channel knowledge to select the best users for transmission without causing interference to others in a broadcast MIMO system. The proposed scheme preserves the optimal achievable ergodic sum capacity of zero-forcing techniques in limit of large number of active users with significant reduction in complexity and feedback load for channel information updates. Both analytical and simulation results on its advantages in feedback load, complexity and achievable sum rate are presented in different scenarios.

This paper presents a turbo coded OFDM receiver structure using joint synchronization, channel estimation and decoding. In this proposed scheme, the estimator uses soft-decision information from the previous soft-input soft-output (SISO) decoder to produce better estimates of the unknown parameters, which will in turn help the decoder to make more reliable decision. The whole process will be performed in an iterative manner and good system performance can be achieved with only a few iterations for moderate initial synchronization errors.

Multi-dimensional mapping over groups of subcarriers, called subcarrier mapping, is proposed and investigated for bit-interleaved coded in OFDM with iterative decoding (BI-COFDM-ID) over correlated frequency-selective Rayleigh fading channels. A tight bound on the asymptotic error performance is first presented and used to establish the design criterion. It is then shown that one could choose an optimal subcarrier grouping scheme and an optimal multi-dimensional mapping independently to achieve the best overall error performance. Analytical and simulation results show that it is not necessary to implement linear constellation precoding (LCP) in the proposed system while still achieving the full diversity offered by frequency-selective fading channels, and at the same time providing significant coding gains compared to the previously studied BI-COFDM-ID. Such coding gains are obtained without any power nor bandwidth expansion and with the same receiver complexity.

A complete queueing model for radio link layer performance analysis is developed assuming adaptive modulation and coding (AMC) at the physical layer and truncated automatic repeat request (ARQ)-based error control at the link layer. From the model, queue length distribution and average queueing delay can be calculated. The average queueing delay is then used to estimate transmission control protocol (TCP) throughput performance using a fixed-point approach. Using the model, we are able to choose signal-to-noise ratio thresholds of different transmission modes for AMC at the physical layer for different persistence levels of ARQ at the link layer so that TCP throughput is maximized. We observe that channel correlation negatively impacts the TCP throughput performance. Also, throughput enhancement of TCP NewReno over TCP Reno is observed to be non-negligible only if no ARQ-based error recovery is employed at the link layer.

The application of Bonferroni-type bounds is investigated for orthogonal space-time block codes over a keyhole Nakagami-m fading channel, which includes a cascaded Rayleigh fading channel as a special case. In particular, upper and lower bounds on the symbol error rate and bit error rate are derived and shown to be very tight at any signal-to-noise ratio. The developed bounds are applicable for arbitrary signal constellations and mappings and can be accurately computed with single finite-range integrals.

This paper studies the bit error probability of coded unitary space-time modulation with iterative decoding where neither the transmitter nor the receiver knows the channel fading coefficients. The tight error bound with respect to the asymptotic performance is first analytically derived for any given unitary constellation and mapping rule. Design criteria regarding the choice of unitary constellation and mapping are then established. Furthermore, using the unitary constellation obtained from orthogonal design with quadrature phase-shift keying (QPSK or 4-PSK) and 8-PSK, two different mapping rules are proposed. The first mapping rule gives the most suitable mapping for systems that do not implement iterative processing, which is similar to a Gray mapping in coherent channels. The second mapping rule yields the best mapping for systems with iterative decoding. In particular, analytical and simulation results show that with the proposed mappings of the unitary constellations obtained from orthogonal designs, the asymptotic error performance of the iterative systems can closely approach a lower bound which is applicable to any unitary constellation and mapping

This paper presents a performance analysis of bit-interleaved coded-modulation with iterative decoding (BICM-ID) and complex N-dimensional signal space diversity in fading channels to investigate its performance limitation, the choice of the rotation matrix and the design of a low-complexity receiver. The tight error bound is first analytically derived. Based on the design criterion obtained from the error bound, the optimality of the rotation matrix is then established. It is shown that using the class of the optimal rotation matrices, the performance of BICM-ID systems over a Rayleigh fading channel approaches that of the BICM-ID systems over an AWGN channel when the dimension of the signal constellation increases. Furthermore, by exploiting the sigma mapping for any M-ary QAM constellation, a very simple sub-optimal, but yet effective iterative receiver structure suitable for signal constellations with large dimensions is proposed. Simulation results in various cases and conditions indicate that the proposed receiver can achieve the analytical performance bounds with low complexity

Abstract In this paper, adaptive baseband polynomial predistortion techniques are introduced to counter‐balance the AM/AM and AM/PM non‐linear effects of the transmit power amplifier. The proposed polynomial predistortion scheme is based on polar coordinate representation. Both LMS and RLS concepts are used to derive the adaptive algorithms. An enhanced LMS‐based algorithm with fast convergence and low complexity is proposed. For very fast convergence, a cascaded RLS‐based adaptive polynomial predistorter structure is introduced. The performance of the proposed schemes in terms of intermodulation distortion, spectral regrowth, and convergence rate are examined. The obtained results show that the polynomial predistortion schemes can be used in M‐QAM transmitters with power amplifiers operating near saturation to achieve a highest power efficiency. Copyright © 2006 John Wiley & Sons, Ltd.

Abstract This paper presents two group‐based tone diversity schemes, namely, group‐orthogonal tone‐combining‐diversity (GO‐TCD) and group‐optimal tone‐selection‐diversity (GO‐TSD), for orthogonal frequency division multiple access (OFDMA) system in broadband wireless communications. In both schemes, the entire frequency band ofNsub‐carriers is divided intoNG = N/Lfsub‐carrier groups with each havingLfsub‐carriers. When the channel knowledge is not available at the transmitter, GO‐TCD, which has a split‐and‐group structure to reduce the peak‐to‐average ratio (PAR) and employs the multi‐user detection for maximum‐likelihood (ML) estimation to increase the number of active users, is proposed and shown to provide a lower PAR and similar performance as compared with the group‐orthogonal multi‐carrier CDMA, and outperforms the random‐hopping (RH)‐OFDMA and matched‐filter based MC‐CDMA. When the channel knowledge is available at the transmitter, GO‐TSD can be used to select the best sub‐carrier in a given sub‐carrier group for any active user and shown to offer a throughput comparable to the optimal tone selection but with much less complexity. Copyright © 2006 John Wiley & Sons, Ltd.

This paper presents a MIMO preceding design for frequency-selective fading channels using the transmit and channel path correlation matrices. The effect of spatial and path correlation on the MIMO system capacity are investigated to develop the optimal linear precoder structures based on ergodic capacity criterion for uncorrelated and correlated channel paths. It is shown that in the uncorrelated case the precoder structure consists of a number of parallel precoders designed for frequency-flat fading channel paths. Performance evaluation by simulation in various MIMO channels shows that the proposed preceding scheme provides a good capacity improvement as compared to existing preceding schemes using only spatial correlation, and its performance is better in an environment with higher (spatial or path) correlation.

This paper proposes a multi-scale queuing (MSQ) analysis for long-range dependent traffic inputs and both fixed and variable service rates. Based on the observation that the queue length actually shapes both the traffic and service distributions at each time scale, the proposed queue-constrained (QC)-MSQ framework includes this inter-scale coupling effect in the queuing analytical models in order to enhance the estimation accuracy. Simulations are used to verify the accuracy of the analysis. The results show that the proposed analytical technique provides a more accurate estimation of the queue length and delay distributions, and that this accuracy is less affected by the traffic loading, correlation level, and variation.

This paper presents a performance analysis of bit-interleaved coded-modulation with iterative decoding (BICM-ID) and complex N-dimensional signal space diversity in fading channels to investigate its performance limitation, the choice of the rotation matrix and the design of a low-complexity receiver. The tight error bound is first analytically derived. Based on the design criterion obtained from the error bound, the optimality of the rotation matrix is then established. It is shown that using the class of the optimal rotation matrices, the performance of BICM-ID systems over a Rayleigh fading channel approaches that of the BICM-ID systems over an AWGN channel when the dimension of the signal constellation increases. Furthermore, by exploiting the sigma mappings for M-QAM constellations, a very simple suboptimal, but yet effective iterative receiver structure suitable for signal constellations with large dimensions is proposed. Simulation results in various cases and conditions indicate that the proposed receiver can achieve the analytical performance bounds with low complexity.

For underdetermined MIMO systems, sphere decoding (SD) fails due to zero diagonal elements in the upper-triangular matrix of the QR or Cholesky factorization of the underdetermined channel matrix. This paper presents a low-complexity generalized sphere decoding (GSD) approach by transforming the original underdetermined problem into the full-column-rank one so that standard SD can be directly applied on the transformed problem. As the introduced transformation maintains the original problem dimension, the proposed GSD algorithm provides significant reduction in complexity as compared to other GSD schemes, especially for M-QAM with large signaling constellation. Performance analysis shows that the proposed GSD algorithm can achieve or approach the optimum maximum-likelihood decoding (MLD) performance by proper selection of design parameters.

Optimal Spectrum Management (OSM) computes the optimal PSD for all modems in Digital Subscriber Lines (DSL) systems to achieve the maximum possible data rates. Unfortunately, its complexity grows exponentially in the number of users N and becomes computationally intractable for large N. This paper shows that the non-convex optimization problem in OSM can be reformulated as an equivalent global concave minimization problem by representing its objective function as a difference of two convex functions (d.c.). A prismatic branch and bound algorithm is applied to find the global optimum that only requires solving a sequence of linear programming sub-problems with a substantial reduction in complexity, especially for large N.

The application of bit-interleaved coded modulation and iterative decoding (BICM-ID) in OFDM systems with modulation diversity over frequency selective Rayleigh fading channels is considered. A tight bound on the asymptotic error performance is first derived for general preceding over all N subcarriers and used to establish the best achievable performance with modulation diversity. It is then shown that preceding over subgroups of at least L subcarriers per group, where L is the number of channel taps, is sufficient to achieve this best performance while keeping the receiver complexity at minimum. The jointly optimum subcarrier grouping and rotation matrix are derived by solving the Vandermonde linear system

Outage probability has been commonly used as the primal metric to investigate system capacity of cellular networks. Outage probability is a system-level metric that lacks key information with regards to outage performance within the lifetime of a particular session. Analysis based on the satisfied-user criteria has been recently recommended by Universal Mobile Telecommunication Systems (UMTS) where the outage percentage for each individual session is computed for a more genuine estimate of system capacity. In this paper, the session outage performance is modeled as an alternating renewal process with exponential holding times (ARP/E). Estimation of satisfied-user probability resolves to derivation of the total downtime (bad-time) percentage of an ARP/E. An exact formula for the satisfied-user probability is derived for speech services. The ARP/E analysis developed for speech is also employed as an approximation for the satisfied-user probability of WWW browsing users

A complete queueing model for radio link layer performance analysis is developed assuming adaptive modulation and coding (AMC) at the physical layer and truncated automatic repeat request (ARQ)-based error control at the link layer. From the analysis the queue length distribution and the average queueing delay can be calculated. The average queueing delay is then used to estimate TCP (Transmission Control Protocol) throughput performance using a fixed point approach. The analytical model enables us to choose signal-to-noise ratio (SNR) thresholds of the different transmission modes for AMC at the physical layer for different persistence levels of ARQ at the link layer so that the TCP throughput is maximized. We observe that channel correlation negatively impacts the TCP throughput performance. Also, throughput enhancement of TCP NewReno over TCP Reno is non-negligible only if no ARQ-based error recovery is employed at the link layer of the protocol stack.

A cooperative coding scheme using M-ary modulation for high spectral efficiency and repetitive information transmission for diversity improvement is proposed. The encoder is a parallel concatenation of two bit-interleaved coded M-ary modulators (BICM) with scalable repetition of information symbols. As a result, the fraction of repeated information and the spectral efficiency of M-ary modulation can be used for bandwidth-performance tradeoff. Simulation results for different M-PSK and M-QAM systems in various block fading scenarios show that for a given bandwidth efficiency, a proper selection of the fraction of repeated information and the spectral efficiency of M-ary modulation can provide a large performance improvement.

This paper discusses an adaptive modulation/coding (AMC) strategy based on frequency-domain channel statistical information for OFDM systems in a frequency-selective fading environment. The proposed approach is based on the fact that the statistical properties of a time-varying channel vary very slowly with time and thus it is possible to obtain reliable knowledge at the transmitter. A programmable Reed-Solomon RS (n,k) code used with an adaptive multi-level modulation scheme is proposed as an illustrative example. Derivations of the frequency-domain channel statistical properties and performance of the proposed technique are presented along with illustrative results.

This paper examines the bit error probability (BEP) of bit-interleaved coded modulation and iterative decoding (BICM-ID) in OFDM systems with modulation diversity (MD) over correlated frequency selective Rayleigh fading channels. A tight bound on the asymptotic error performance is derived for the general preceding over all the N subcarriers, which includes preceding over subcarrier groups as a special case. A design parameter that characterizes the effects of signal constellation, mapping and modulation diversity on the asymptotic performance is then established. Various analytical and simulation results are provided to confirm the tightness of the derived error bound.

This paper studies the bit error probability (BEP) of coded unitary space-time modulation with iterative decoding where neither the transmitter nor the receiver knows the channel fading coefficients. The tight error bound on the asymptotic performance is first analytically derived for given unitary constellation and mapping rule. Design criterion regarding the choice of unitary constellation and mapping is then established to achieve the best asymptotic performance. Furthermore, using the unitary constellation obtained from orthogonal design and 4-PSK, two mapping rules are proposed. In particular, one mapping is the best mapping for systems with iterative decoding, whereas the other mapping is most suitable for systems that do not implement iteration process. The latter mapping is similar to Gray mapping considered for coherent channels. Analytical and simulation results show that with the proposed mapping of the unitary constellation obtained from orthogonal design, the error performance of the iterative systems can approach very near the performance of the ideal unitary constellation and mapping

This paper presents an analytical model suitable to evaluate wideband code-division multiple-access (WCDMA) system capacity in terms of supported multimedia services achieving the required percentage of satisfied users. The satisfied-user criteria proposed by the Universal Mobile Telecommunications System introduce session-level performance measures, providing a more comprehensive notion of users' quality-of-service (QoS), but requiring knowledge of both outage probability and outage correlation behavior. By using a two-state (good-bad) Gilbert channel to represent the outage behavior, the model parameters and satisfied-user probability for both speech and data sessions are derived for different uplink and downlink scenarios. The proposed analytical model can be used for WCDMA system dimensioning to assess the throughput and session interruption probabilities of data services and their impact on the satisfied-user probability of speech services.

This paper presents efficient multiuser margin optimization algorithms suitable for multicarrier digital subscriber line (DSL) systems using Dynamic Spectrum Management (DSM). The favorable monotonicity and fairness properties of multiuser margin are employed to formulate a box-constrained nonlinear least squares (NLSQ) problem for multiuser margin maximization, which is efficiently solved by using a scaled-gradient trust-region approach with Broyden Jacobian update. Based on this NLSQ formulation, a multiuser harmonized margin (MHM) optimization algorithm for resource allocation is developed. A Newton-Raphson method is also developed for fast margin estimation and used within the MHM. The MHM algorithm converges efficiently to a solution for the best common equal margin to all users, while explicitly guaranteeing their target rate requirements. (This is the reason for the term harmonized.) Furthermore, its predominantly distributed structure can be implemented in DSL/DSM scenarios with only Level 1 coordination. Simulation results of various cases verify the convergence to the unique optimal solution within 5-10 iterations.

This paper presents a general framework for precoder designs for MIMO systems using partial channel knowledge on the transmit and receive correlation matrices at the transmitter. It is shown that the optimal linear precoder for any uncoded and coded MIMO system based on the MMSE or ergodic capacity criterion, or for an orthogonal ST coded MIMO system based on the minimum PEP criterion, is an eigen-beamformer that transmits the signal along eigenvectors of the transmit correlation matrix. Based on the eigen-values of both the transmit and receive correlation matrices, power loading across the eigen-beams is determined by water-pouring policy. Individual effects of the transmit and receive correlation matrices on the system performance are investigated. Simulation results show noticeable performance improvement over MIMO systems without precoder, particularly when the transmit correlation matrix has low rank

This paper presents an adaptive downlink resource allocation strategy for multi-user orthogonal frequency division multiplexing (OFDM) cellular systems, which minimizes first the transmission bandwidth and then the transmission power. By analysis, it is shown that by keeping the minimum numbers of allocated sub-carriers to satisfy user rate requirements, the probability of interference occurrence in the sub-carriers is reduced and improves the expected link outage probability in the presence of flat fading and shadowing. The proposed bandwidth-constrained power minimization (BCPM) problem is formulated as a linear programming problem and used to derive three-step BCPM schemes. Simulation results in a cellular system to support voice and data services in presence of both frequency selective fading and shadowing show that the proposed BCPM schemes outperform the power minimization (PM) in terms of system outage and packet error rate.

Two interference-based sub-carrier group assignment strategies in dynamic resource allocation are proposed for MC-CDMA wireless systems to achieve high throughput in a multi-cell environment. Least interfered group assignment (LIGA) selects for each session the sub-carrier group on which the user receives the minimum interference, while best channel ratio group assignment (BCRGA) chooses the sub-carrier group with the largest channel response-to-interference ratio. Both analytical framework and simulation model are developed for evaluation of throughput distribution of the proposed LIGA and BCRGA schemes. An iterative approach is devised to handle the complex interdependency between multi-cell interference profiles in the throughput analysis. Illustrative results show significant throughput improvement offered by the proposed interference-based assignment schemes for MC-CDMA multi-cell wireless systems. In particular, under low loading conditions, LIGA renders the best performance. However, as the load increases BCRGA tends to offer superior performance.

This paper presents QoS-based routing algorithms using the end-to-end delay satisfaction balancing concept. The algorithms are suitable for networks that do not use per-path reservation. A nonlinear optimization problem and a gradient-based solution are formulated for off-line computation of the optimal route configuration. An approximation of the optimization problem is developed for on-line distributed processing. Using the approximation, vector routing tables can be used to set-up paths for arriving calls. Performance of the proposed schemes is evaluated and compared with that of minimum-delay, minimum-hop, and min-interference routing algorithms. Results show that using an objective function based on delay satisfaction balancing enables the network to accommodate more users of varying end-to-end delay requirements.

This paper presents a performance analysis of WWW browsing services in a mixed voice and WWW traffic environment and proposes a mechanism that guarantees minimum throughput for active browsing sessions. Outage probability and expected throughputs are derived. Simulation results on the WWW browsing download time performance are also presented. Illustrative results indicate that the proposed guaranteed throughput (GT) power allocation scheme significantly improves the WWW browsing page download performance and satisfied-user probability.

Multiuser margin maximization algorithms are developed for multi-carrier digital subscriber loops (DSL) employing dynamic spectrum management (DSM). Margin maximization is desirable for constant bit rate applications and provides protection against various non-stationary and bursty noise sources. Most single-user margin maximization algorithms rely on a fixed crosstalk assumption, which does not hold in DSM. Thus with direct extension of single-user algorithms in DSM scenarios, one user's margin maximization can lead to the failure of other users in meeting their target rates. In this paper, we explore the favorable monotonicity and fairness properties in multiuser margin and use them to formulate a box-constrained non-linear least squares (NLSQ) problem that can be solved by using a scaled gradient trust region approach with Broyden Jacobian update. This algorithm efficiently converges to a solution providing the best common equal margin to all users while explicitly guaranteeing that each user's target rate requirement is satisfied. The algorithm can be implemented in practical DSL-DSM scenarios with only Level-1 coordination.

This paper presents a two-dimensional optical code-division multi-access (2D-OCDMA) system using multi-wavelength pulse modulation (MWPM), double optical hard-limiters (DHL) and modified carrier-hopping prime sequences (MCHP). Design criteria to reduce multi-access interference (MAI) are established and indicate that suitable signature sequences for 2D-OCDMA/MWPM must have good cross-correlation property in terms of both time-shift and wavelength-shift. Performance analysis of 2D-OCDMA/MWPM/DHL systems in presence of MAI and photo-detector shot noise is developed. Simulation and analytical results are in very good agreement and indicate that the proposed 2D-OCDMA/MWPM/DHL systems using MCHP sequences can offer a much larger capacity than others, suitable for applications in broadband multi-access optical networks

This paper presents a precoder design based only on the correlation matrices to avoid the need of perfect channel knowledge at the transmitter and receiver in practical MIMO systems. The structures for optimal linear precoders are derived for three criteria: minimum pairwise error probability, minimum mean squared-error, and maximum ergodic (mean) channel capacity. The minimum pairwise error probability and minimum mean squared-error criteria lead to the same optimum precoder structure. It is shown that, in general, the optimal linear transformations for the three criteria are the eigen-beamformers that transmit the signal along the eigenvectors of the transmit correlation matrix. Power loading across the eigen-beams is determined by considering the eigenvalues of both transmit and receive correlation matrices and can be viewed as a water-pouring policy. Simulation results show noticeable performance improvement over conventional systems, particularly when their transmit correlation matrix has low rank.

The paper develops a framework for relating system performance to a user scheduling mechanism in downlink OFDM mobile cellular systems for delay-tolerant traffic. The performance of dynamic resource allocation techniques using best user (BU) and round robin (RR) strategies for user scheduling, and best sub-carrier assignment with power constraint and interference learning (BSA-PC-IL), is evaluated in terms of the fraction of satisfied users and system spectral efficiency (in kbps/MHz/cell). Simulation results indicate that in a low-mobility, single-cell environment, RR performs better than BU. However, in a high-mobility environment, BU significantly outperforms RR for both single-cell and multi-cell mobile systems. In a slow-mobility, multi-cell environment, BU has a slightly better performance than RR. In general, the BU scheme has a much slower degradation rate in the fraction of satisfied users at increased system loads than the RR scheme.

This paper proposes the log shifted gamma (LSG) approximation to model the sum of M lognormal distributed random variables. The closed-form probability density function (PDF) of the resulting LSG random variable (RV) is presented and its parameters are derived from those of the M individual lognormal RV by using an iterative moment matching technique. Simulation results on the cumulative distribution function (CDF) of sum of M lognormal random variables in different conditions are used as reference curves to compare various approximation techniques. LSG approximation is found to provide better accuracy over a wide CDF range, especially for large M and/or standard deviation.

In this paper, we present a fine granularity loading scheme using joint optimization of modulation and coding for discrete multitone (DMT) modulation towards achieving maximum information rate conveyed. While most existing algorithms strived for the optimal energy distribution to maximize rate, the bits loaded were always constrained to integers. It was initially believed that most (not all) of the granularity losses could be recovered through 'bit-rounding' and 'energy re-scaling' after an optimal 'water-filling' approach. But this was observed only for the total power constrained case. With the advent of peak power constraint, we show that the room for optimization in the energy domain severely constrained and granularity losses constitute a significant percentage of the achievable data rate. To recover these losses, we propose a loading scheme that integrates the coding scheme with the bit-loading algorithm. For achieving near-continuous rate adaptation, the family of Reed-Solomon (RS) codes has been used for their low redundancy, high flexibility in correction capability and highly programmable architecture. Simulation results with very high bit rate digital subscriber line (VDSL)-DMT system show more than 20% improvement in most cases.

This paper develops quality of service (QoS) based routing and priority class assignment algorithms using an end-to-end delay satisfaction balancing approach. Unlike the single-class delay satisfaction balancing optimization problem, the nonlinear multi-priority formulation is only convex within specific regions, and is infeasible otherwise. At first, a centralized off-line computation technique is proposed to calculate both the route configuration and end-to-end priority assignment. A gradient-based solution in the convex region and a heuristic to overcome the discontinuity are derived. An on-line distributed approximation is then presented. Performance evaluation shows that combining route and priority class assignment enables the network to accommodate more users of varying end-to-end delay requirements.

This paper presents efficient dynamic resource allocation schemes with interference mitigation techniques for multimedia services in downlink OFDM mobile cellular systems. The performance of the proposed algorithms is evaluated in terms of user quality of service (QoS) and system spectral efficiency. It is shown that the best sub-carrier allocation (BSA) scheme with interference mitigation and macro-diversity techniques gives significant performance gains in terms of system spectral efficiency. Furthermore, sharing the system bandwidth amongst real-time stream-type voice and bursty data services can support much larger system loads than having a hard division.

This paper presents a precoder design based on the minimum mean squared error (MMSE) criterion and using the knowledge of only the transmit and receive correlation matrices of the underlying MIMO channel. The optimal transformations are shown to be eigen-beamformers, which transmit the signal along eigenvectors of the transmit correlation matrix. Power loading across the eigenbeams are determined based on eigenvalues of both transmit and receive correlation matrices and can be viewed as a waterpouring policy. Performance of the proposed precoders using partial channel information in various MIMO channels is evaluated and compared to that of precoders based on full channel knowledge at transmitter. It was shown that as the number of the transmit and receive antennas increases, the MMSE-based MIMO precoders using partial channel knowledge can achieve the performance of precoders using full channel knowledge. Furthermore, the channel that gives the best performance is the one with only one strong eigen mode for cases with a large number of receive antenna or a small number of transmit antenna. For small number of receive antennas or large number of transmit antennas, the full rank channel with equal gain eigen modes will gives the best performance.

This paper addresses the radio resource management (RRIM) structure and efficient dynamic capacity allocation techniques for hybrid TDMA/CDMA mobile cellular networks to support multimedia services with different quality-of-service (QoS) requirements in an interference-limited environment. Based on the received interference information, the minimum instantaneous power (MIP) algorithm aims to assign timeslot-codes that minimize the required transmitted power to maintain a target SINR. However, the MIP, being distributed, does not make any attempt to avoid generation of interference elsewhere across the network. Aiming to reduce both potentially received and generated interference in timeslot-code assignment, the proposed minimum sum of path-loss ratio (MS-PLR) algorithm incorporates the mutual large-scale path-loss ratios between different active mobiles in the cost function of a given timeslot during assignment. Simulation results for various traffic scenarios are used for performance evaluation and comparison. It is shown that MS-PLR can offer an additional capacity increase of 24% over MIP. The impacts of bursty WWW traffic on speech performance are also examined.

This paper presents an analysis of an efficient dynamic downlink multi-carrier resource allocation algorithm based on channel information suitable for packet-based voice and multimedia services in mobile cellular systems in a frequency-selective fading environment. The proposed algorithm sorts the time-varying sub-carrier responses according to their expected received signal strength, and allocates the best subcarrier(s) to each active user to satisfy its traffic requirements while maintaining the lowest possible transmitted power and smallest possible number of allocated sub-carriers. The performance of the proposed algorithm is evaluated in terms of user outage probability and throughput in relation to the system load. Analytical and simulation results are compared and used to examine the effects of using highly bandwidth-efficient adaptive modulation/coding schemes, performance degradation due to practical implementation, and shadowing on the system performance.

Packet loss performance in telecommunication networks has usually been represented by its expected value. Such characterization fails to set a quantitative assessment for the impact of packet loss on the session or connection quality and, hence, customer satisfaction. With packet traffic dominating future wireless networks, UMTS recommendations have defined a robust packet loss metric that evaluates the call performance. A user is satisfied if the percentage of packets lost from within his call does not exceed a defined threshold and the loss period does not persist longer than a given interval. We derive the satisfied-user probability for N-multiplexed voice calls in a 3G network assuming the first criterion. We compare the analytical results with simulations to demonstrate the accuracy of the technique proposed.

This paper presents adaptive downlink multi-carrier resource allocation strategies based on channel information suitable for real-time multimedia services in mobile cellular systems. Both user quality-of-service (QoS) and system performance in terms of system spectral efficiency are considered in the evaluation and comparison of the proposed schemes. Simulation results show that by employing the time-varying channel responses to arrange and allocate the sub-carriers according their expected signal-to-noise ratio (SNR) quality, the proposed scheme offers a substantial increase in system spectral efficiency in supporting real-time multimedia services of different QoS requirements. Interference avoidance strategies are also introduced to further enhance the performance of voice services in cellular systems.

This paper discusses the structure and performance of an Internet protocol (IP)-based satellite communications system to provide multimedia services. Uplink scheduling and switching to support IP differentiated services (DiffServ) traffic in a multibeam environment are addressed. End-to-end performance of a multibeam satellite communications system using an on-board switch is evaluated using simulation. Aggregate real-time and non-real-time traffic using different DiffServ classes is considered and the effects of their burstiness and long-range dependent behavior on the queueing performance are examined. Multiple-frequency time-division multiple-access is used on the uplink in conjunction with a dynamic capacity allocation scheme. Higher priority is given to voice and video real-time traffic to avoid delay variation. On-board downlink queue for non-real-time traffic is provided to achieve high statistical multiplexing gain.

This letter proposes a strategy using a generalized Gaussian distribution (GGD) to characterize the extrinsic information generated from the constituent maximum a posteriori (MAP) decoders in order to improve the performance of an iterative turbo decoder for finite block lengths. A matching technique based on the measured moments and distance criterion is introduced to dynamically select the appropriate parameters of the GGD model for the extrinsic information in each iteration. The simulation results indicate that the proposed strategy can offer performance gain in medium block lengths over both additive white Gaussian noise and Rayleigh-fading channels.

Abstract This paper considers direct‐sequence code‐division multiple‐access with zero‐correlation zone sequences (ZCZ‐CDMA) and orthogonal frequency‐division multiple‐access (OFDMA) schemes using M‐ary QAM signaling for broadband wireless communications. Their system structures, complexities and performances in both AWGN and multipath frequency‐selective fading channels are evaluated and compared. For ZCZ‐CDMA, joint suppression of the multipath fading interference and multiple‐access interference can be achieved with a reduced family‐size of the spreading sequences. For OFDMA, analytical and simulation results indicate that it has the same performance as ZCZ‐CDMA in fast time‐varying multipath fading channels. In time‐invariant or slowly time‐varying channels, where the channel information can be made available to transmitters, OFDMA outperforms ZCZ‐CDMA, offers a higher capacity and is more flexible for system reconfiguration with a comparable computational complexity. Copyright © 2004 John Wiley & Sons, Ltd.

Abstract A channel‐estimate‐based frequency‐domain equalization (CE‐FDE) scheme for wireless broadband single‐carrier communications over time‐varying frequency‐selective fading channels is proposed. Adaptive updating of the FDE coefficients are based on the timely estimate of channel impulse response (CIR) to avoid error propagation that is a major source of performance degradation in adaptive equalizers using least mean square (LMS) or recursive least square (RLS) algorithms. Various time‐domain and frequency‐domain techniques for initial channel estimation and adaptive updating are discussed and evaluated in terms of performance and complexity. Performance of uncoded and coded systems using the proposed CE‐FDE with diversity combining in different time‐varying, multi‐path fading channels is evaluated. Analytical and simulation results show the good performance of the proposed scheme suitable for broadband wireless communications. For channels with high‐Doppler frequency, diversity combining substantially improves the system performance. For channels with sparse multi‐path propagation, a tap‐selection strategy used with the CE‐FDE systems can significantly reduce the complexity without sacrificing the performance. Copyright © 2004 John Wiley & Sons, Ltd.

By using multiscale wavelet models (MWM), the multi-scale queuing (MSQ) technique is extended to evaluate the queue length distribution for two DiffServ classes fed with long-range dependent (LRD) traffic. The higher-priority class is first served by using an MWM/D/1 queue to estimate the unused capacity that is subsequently characterized by a MWM model in the analysis of the queue length distribution of the lower-priority MWM/MWM/1 queue. Comparison with simulation results shows that the proposed analytical technique can provide a tight bound on the queue length distribution.

In this paper, fast adaptive polar polynomial predistorters using the recursive-least-square (RLS) concept are presented. Based on the polar predistortion polynomial representation, the recursive relations are derived to formulate the suitable RLS procedure. Two other RLS-based structures are also proposed: the simplified RLS for low complexity and the cascaded RLS for faster convergence. Their complexity and performance in terms of convergence rate and out-of-band power emission are discussed.

Performance-analysis of a switching system using priority-based dynamic capacity allocation is presented. The system provides connectivity between a number of point-to-multipoint communication access clusters. The scheduling of traffic transmission at each access cluster is based on a priority scheme with priority given to real-time (rt) traffic over nonreal-time (nrt) traffic. An analytical model is developed to evaluate the covariance functions of both rt and nrt traffic arriving at the output ports. Aggregate traffic arrival streams are approximated to 2-state Markov-modulated Poisson processes (MMPPs) by matching their statistical characteristics. Analytical and simulation results on performance of an example 4 /spl times/ 4 switch for different traffic loads are discussed.

An analytical method is developed to evaluate the symbol error probability of multilevel pulse amplitude modulation (M-PAM) system using Tomlinson-Harashima(1971, 1972) precoding (THP) over frequency-selective fading channels. Illustrative results obtained by using the developed analysis and simulation indicate an excellent agreement.

Turbo product codes (TPC) are investigated for use in frequency-hopping spread-spectrum (FH-SS) communications in partial-band interference. Binary orthogonal FSK is employed with noncoherent envelope detection. The Fossorier-Lin (1995) algorithm of soft-decision decoding based on ordered statistics is employed for soft-in/soft-out decoder instead of Chase (1972) algorithm to reduce the required E/sub b//N/sub J/ for a given packet failure probability. Performance of TPC for FH-SS with and without memory is evaluated by simulation. A numerical method to calculate the upper bound on performance is also given. The results show that the low-complexity TPC has a similar performance to the high-complexity convolutional turbo codes (CTC) for FH-SS without memory. For FH-SS with memory, full interleaving is used for TPC to achieve a good performance at low duty factors of partial-band interference.

In this paper, an adaptive polynomial predistortion technique is introduced to counterbalance the AM/AM and AM/PM nonlinear effects of the transmit power amplifier. Based on a polar coordinate representation, the proposed adaptation method has low complexity and guarantees a fast rate of convergence. Simulation results on spectra, bit error rates, and intermodulation distortion show that with the polynomial predistorter, M-QAM can be used with a transmit power amplifier operating near saturation to achieve the highest power efficiency, while its transmitted spectrum and performance are kept close to those in a linear channel. The effects of the quantization are also presented.

This paper presents an iterative soft-input/soft-output (SISO) decoder for product code using optimality test and amplitude clipping. A modified expression for computing the soft-output of a SISO decoder is proposed. The correlation discrepancy is employed to provide an optimality test performed in row and column decoding for evaluation of the reliability of row and column decision codewords. Based on the optimality test, a variable reliability factor is introduced for fast convergence. The optimality test is also used to derive a stopping criterion. Furthermore, amplitude clipping is employed to improve the performance of turbo product code. Simulation results on the performance of the introduced SISO decoder are presented.

The performance of a priority-based dynamic capacity allocation suitable for wireless ATM systems is presented. The scheduling of ATM cell transmission in each uplink TDMA frame is based on a priority scheme with priority given to real-time traffic over nonreal-time traffic. Real-time traffic exceeding the uplink capacity is lost while nonreal-time traffic that cannot be served is stored in a first-in first-out (FIFO) queue. An analytical model is developed to evaluate the cell loss ratio (CLR) of both real-time and nonreal-time traffic. Aggregate voice, video, and data traffic is modeled by three two-state Markov-modulated Poisson processes (MMPPs). Analytical results for different system capacities and various traffic loads and scenarios are discussed. Simulation results with on-off sources and approximating MMPP sources are also presented.

The performance of multistage interference cancellation (MIC) and three combining techniques, i.e., multipath decorrelating (MIC-DECO), optimum combining (MIC-OPTM), and RAKE combining (MIC-RAKE) for asynchronous quadrature phase-shift keying/direct-sequence code-division multiple access over frequency-selective multipath Rayleigh fading channels is studied. The analytical bit-error probabilities of the MIC-DECO and MIC-OPTM are derived and shown to be in a good agreement with simulation results. Both analytical and simulation results show that the MIC-DECO, MIC-OPTM, and MIC-RAKE in a multiuser environment provide a good performance close to the ideal performance in a single-user system even in the presence of channel estimation error.

Performance analysis of a wireless ATM switching system using priority-based dynamic capacity allocation is presented. The system provides connectivity between a number of clusters of wireless ATM remote terminals. The scheduling of ATM cell transmission at each lint with TDMA frame is based on a priority scheme with priority given to real-time traffic over non-real-time traffic. Both types of traffic arriving at the downstream links are approximated by a 2-state Markov-modulated Poisson process (MMPP) in order to obtain analytical results. Illustrative results for two different traffic loads and at the output of 4/spl times/4 switch are discussed.

M-QAM has been considered to achieve high bandwidth efficiency for broadband wireless communications. However, due to its envelope fluctuation, it exhibits large spectral re-growth and performance degradation when the transmit power amplifier operates in a nonlinear region close to saturation. In this paper, an adaptive predistortion technique suitable for DSP implementation at the baseband signals is introduced to counter-balance the AM/AM and AM/PM nonlinear effects of the transmit power amplifier. Based on nonlinear adaptive Volterra filtering, the proposed pre-distortion technique shows that M-QAM can be used with a transmit power amplifier operating near saturation to a highest power efficiency, while its transmitted spectrum and performance are kept close to those in a linear channel. The convergence behavior of the adaptive predistortion technique is analyzed. The spectral re-growth and performance of a 16 QAM system using a predistorter/SSPA are evaluated using simulation. The adaptive predistortion technique has a low complexity and fast convergence.

We combined the multistage adaptive parallel interference canceller with an antenna array. Two combination schemes are proposed. In the first approach, interference cancellation is performed at each element of the antenna followed by beamforming. The second method reduces the complexity by carrying out beamforming first. The output of the beamformer is used by the interference canceller. The proposed method provides a better performance than the array decorrelating detector at a considerably low complexity.

In this paper, a multilevel-quantized soft-limiting (SL-MQ) detector for frequency hopping spread spectrum multiple access (FH-SSMA) systems is proposed and analyzed. Numerical and simulation results in frequency selective Rayleigh fading channels show that compared to the hard-limiting (HL) detector, the new SL-MQ with M=4 can improve the system capacity by almost 10% at the bit error rate level of 10/sup -3/. Furthermore, the performance of the SL-MQ has low sensitivity to the optimum value of the amplitude threshold so that it can tolerate an inaccurate estimate of the optimum in practice.

A multistage interference cancellation (MIC) technique with RAKE diversity (MIC-RAKE) for QPSK asynchronous direct sequence code division multiple access (DS/CDMA) system over frequency selective multipath Rayleigh fading channels is presented. Unlike the conventional MIC, which tries to subtract the lump sum of the multiple access interference (MAI) and the self-interference (SI), the MIC-RAKE attempts to cancel the MAI and the partial SI, and to treat the residual SI as useful signal for symbol decision. The RAKE combining is employed to collect signal replicas over multiple fading paths. The upper and lower bounds on the bit error probability are derived by using a Gaussian approximation. Furthermore, the effect of the channel estimation error is studied. Analysis and simulation show that the MIC-RAKE can provide a performance close to the ideal performance of single-user system, and outperforms the conventional MIC even in the presence of channel estimation error.

An adaptive multistage parallel interference cancellation technique based on the partial interference cancellation (IC) approach of Divsalar and Simon (see Tech. Rep. 95-21, JPL Publication, 1995) was proposed by Xue, Weng, Le-Ngoc and Tahar (see Proc. of VTC'SS, Vancouver, Canada, 1999) for multipath fading channels. In this paper, the proposed technique is applied to develop a receiver structure in an AWGN environment. Unlike the scheme of Divsalar et al., the weighting factors in this proposed scheme are derived by minimizing the mean-square error between the received signal and its estimate through an LMS algorithm. Neither training sequence nor pilot signal is needed. The complexity of the proposed adaptive multistage PIC structure is much lower than that of linear multiuser detectors. Simulation results show the superior performance of the proposed receiver structure over an AWGN channel and in various conditions.

This paper introduces a multistage interference cancellation (MIC) technique with diversity reception for quadrature phase shift keying (QPSK) asynchronous direct-sequence code division multiple access (DS/CDMA) systems over frequency-selective multipath Rayleigh fading channels. Unlike the previous MIC, which tries to remove the lump sum of the multiple-access interference (MAI) and self-interference (SI), this introduced MIC attempts to cancel only the MAI and part of the SI due to the intersymbol interference, while treating the remaining SI created by the current symbol as useful information for symbol decision. In this technique, the RAKE combining is used to collect signal replicas over multiple fading paths. Upper and lower bounds on the bit error probability are derived using a Gaussian approximation and the characteristic function method. Furthermore, effects of channel estimation error on the performance are studied. Analytical and simulation results show that the introduced MIC can provide a performance extremely close to that in an ideal single-user environment and outperforms the previous MIC even in the presence of channel estimation error.

Although the multistage interference cancellation detector is simple in structure, its performance degrades when the number of active users becomes large. In some cases, the performance is even worse than that without cancellation, due to the lack of the exact knowledge of the interfering signal in cancellation. Partial interference cancellation suggested by Divsalar and Simon (see IEEE Trans. Commun., vol.46, p.258-68, 1998) tries to remedy this weakness by reducing the cost of a wrong interference estimation through a weight in each stage. This paper presents an adaptive multistage structure based on the partial interference cancellation approach. In this structure, the weights are obtained by minimizing the mean-square error between the received signal and its estimate through a least mean square (LMS) algorithm. The resulting weights contain reliability information for the hard decisions made in the previous stage. Neither a training sequence nor a pilot signal is needed in the proposed scheme, and its complexity is much lower than that of linear multiuser detectors. Simulation results show that the proposed scheme can outperform some of the existing interference cancellation methods in both the additive white Gaussian noise (AWGN) and the multipath fading channels.

We consider a priority based service for traffic on the up and down links of satellite networks employing on-board processing. Real-time and jitter tolerant traffic are modulated onto a frame based flow structure with priority given to the real-time traffic. The real-time cell arrivals exceeding the frame capacity are lost while excess jitter tolerant traffic is stored. We develop an analytical technique to find the cell loss probability of the real-time traffic and the buffer overflow probability of the jitter tolerant traffic. The two sources of real-time traffic, voice and video, are each modeled as two-state Markov modulated Poisson processes (MMPP). The MMPP is also used to model jitter tolerant traffic. These results are applied to the up link of a satellite system.

This paper presents an analytical model to evaluate the performance of the multistage multicarrier demultiplexer/demodulator (M-MCDD) in the presence of quantization noise as well as additive white Gaussian noise (AWGN) and adjacent channel interference (ACI). The analysis has been limited to halfband filter coefficient quantization. A procedure for numerical computation of the probability of bit error based on the Gram-Charlier series technique is introduced.

Combined free/demand-assignment multiple-access (CFDAMA) schemes are suitable for broad-band packet satellite communications systems serving a finite number of bursty data sources. The performance analysis of the CFDAMA using piggy-backed (PB) reservation is presented. The probability generating function (PGF) of the packet delay is developed. The performance is evaluated in terms of three performance measures: average packet delay, variance of packet delay, and cumulative probability distribution of packet delay. Performance comparison with other pertinent schemes shows CFDAMA-PB to be superior for a wide range of user population sizes.

In this paper, we propose a multiple access scheme based on a hybrid combination of TDMA and CDMA, referred to as multi-code TDMA (MC-TDMA). The underlying TDMA frame structure allows for the transmission of variable bit rate (VBR) information, while the CDMA provides inherent statistical multiplexing. The system is studied for a multimedia satellite environment with long-range dependent data traffic, and VBR real-time voice and video traffic. Simulation results show that with MC-TDMA, the data packet delay and the probability of real-time packet loss can be maintained low. The above advantages are achieved at the expense of soft blocking, which occurs when many packets are transmitted simultaneously, but on different spreading codes. Similar conclusions are drawn for simulations where the channel propagation conditions, adjacent beam interference, and imperfect power control are considered.

A fuzzy system is proposed to estimate the level of burstiness from the number of voice, video and data calls demanding call set-up. Estimated level of burstiness, further, is used for admitting demands based on the criteria that the utility of resources is maximized and loss probability in the destination queue is minimized.

This paper presents an analytical model to evaluate the performance of the multistage multicarrier demultiplexer/demodulator (M-MCDD) for satellite communications in the presence of additive white Gaussian noise (AWGN) and adjacent channel interference (ACI). The probability of bit error is derived. A procedure for numerical computation of the probability of bit error based on the Gauss quadrature rule (GQR) is introduced. Effects of imperfect filtering and channel separation on the performance are discussed.

A new cellular structure for a versatile Reed-Solomon (RS) decoder is introduced based on time domain decoding algorithm. The time domain decoding algorithm is restructured to be suitable for introducing the cellular structure. The main advantages of this structure are its versatility and very simple cellular structure. By versatile decoder we mean a decoder that can be programmed to decode any (n, k) RS code defined in Galois field 2/sup m/ with a fixed block length n and a fixed symbol size m. This decoder can correct both errors and erasures for any message length k. The introduced decoder is cellular and has a very simple structure and hence it is suitable for VLSI designs.

By embedding a decision-feedback equalizer (DFE) into the structure of a maximum-likelihood sequence estimator (MLSE), an adaptive combined DFE/MLSE scheme is proposed. In this combined DFE/MLSE, the embedded DFE has three functions: (i) prefiltering the received signals and truncating the equivalent channel response into the desired one, (ii) compensating for channel distortions, and (iii) providing the MLSE detector with predicted values of input signals. Since the embedded MLSE detector operates on the predicted signals the detected symbols at the output of the DFE/MLSE do not suffer any delay and can be directly fed back into the embedded DFE so that the error propagation, which usually takes place in a conventional DFE, can be greatly reduced. Analytical and simulation results indicate that the performance is significantly improved by the DFE/MLSE compared to the conventional DFE while its computation complexity is much less than that of the conventional MLSE receiver. The combined DFE/MLSE can use different adaptive structures (block-updating, sliding window updating or symbol-by-symbol updating) to meet different performance objectives. Moreover, the proposed DFE/MLSE provides a trade-off between performance and complexity with a parameter m representing the MLSE detection depth as well as the number of predicting steps of the embedded DFE. For some particular values of m, this scheme is capable of emulating the conventional DFE, MLSE-VA, adaptive LE-MLSE equalizer, adaptive DDFSE, and adaptive BDFE without detection delay.

Error-trapping decoding techniques are attractive due to their simple structure. Since 1962 several improved error-trapping methods have been devised in an effort to extend the capability and effectiveness in decoding multiple-error-correcting cyclic codes. Prange (1962) and MacWilliams (1964) introduced a (T, U) permutation group applied to this error-trapping decoding strategy by making use of a set of code-preserving permutation to obtain k error-free positions from which the rest of the code word could be reconstructed. Recently, exact lower bounds on the code length n for (n, k, 2t+1) cyclic codes have been found by using 5-step and 3-step (T, U) permutation groups. The present paper presents a study on the relationship between the code parameters n, k, t and the number of permutation steps s, with t being odd. Some examples on the capability of (T, U) permutation decodable (PD) cyclic codes are illustrated.>

The idea of combining RS (Reed-Solomon) codes with multiphase signaling schemes on fading channels is introduced. The performance of these schemes over a Rayleigh fading channel is evaluated for different decoding strategies, i.e., errors-only, errors-and-erasures, and soft-decision decoding techniques. Both analytical and simulation results show that substantial coding gains are obtained compared to the uncoded reference system.>

A technique for reducing the number of inversions in the time-domain decoding algorithm based on an algebraic decoder (Blahut's decoder) is introduced. It is proved that the modified algorithm is equivalent to the original one. The modified algorithm can be used in the universal Reed-Solomon decoder to decrease complexity.>

This paper presents a concatenated coded modulation scheme for error control as applied to a spread spectrum multiple access system. A system model is proposed that allows one to apply modulation code (block) and a pseudo-noise (PN) spreading sequence to the data symbols to be transmitted. Gold sequences are employed for the purpose of PN spreading. Some good inner codes were chosen from Kasami (1990). Performance of those codes in an additive Gaussian noise channel is investigated with 5-30 users transmitting simultaneously. The performance of the system for coded 8-PSK were compared with uncoded QPSK under similar environment. The coded modulation scheme showed significant performance improvement with little or no bandwidth increase. The short modulation code is used as the inner code and a relatively powerful Reed-Solomon (RS) code is used as the outer code. Soft decision Viterbi decoding can be used for inner code. The proposed scheme is attractive for high speed satellite communications for file transfer applications where great reliability is desired.>

A performance analysis to compute the packet loss, call blocking, and packet delays of a typical user in an integrated voice-data-video satellite internetworking environment is discussed. The uplink technique used is a hybrid packet/circuit switched approach of the demand assignment type, while the downlink is a time-division-multiplexing (TDM) technique. Onboard the satellite, a baseband nonblocking switch is used to route the packets from input to output ports. Various amounts of input and output buffering as well as priority rules and blocking resolution algorithms are used. The authors conduct a performance analysis for the problems at hand and identify the best ranges for the different parameters involved.>

The authors prove by analysis the possibility of gaining a minimum of 2 dB in signal-to-noise ratio by just, splitting the users to three or more groups and identifying each one by an orthogonal waveform (on top of his short Gold code) in a PSK/DS spread-spectrum network. User signals may arrive in a code-asynchronous fashion at the receiver, however, it is shown that by using the scheme the average code cross-correlation is minimal compared to the classic code division multiple access (CDMA) system. Both the chip-synchronous and asynchronous cases are investigated and the uniform and optimal cases of dividing the users into orthogonal groups are analyzed. The superior bit error and network data throughput results in the different fading and forward error correction environments make the system a strong candidate for competitive domestic applications.>

A maximum-likelihood sequence estimation (MLSE) receiver structure for constant-envelope quadrature pulse-overlapping modulated (QPOM) signals in fading channels is presented. QPOM is first decomposed into a liner encoder followed by a memoryless modulator. The trellis diagram representing this inherent nonredundant coding structure is then used to construct its MLSE receiver. The upper bounds on the average bit error probability in both AWGN and Rayleigh fading channels are derived. Computer simulations are also used to verify the analytical results. In fast-fading shadowed mobile satellite channels the scheme is shown to outperform conventional QPSK techniques. It maintains the low complexity of 4PSK, but its performance is comparable to that of four-state 8PSK TCM schemes. The constant envelope, compact spectrum, superior performance, and low complexity enable QPOM to meet the requirements of low cost, small size, and high power and bandwidth efficiencies for portable/mobile satellite systems.>

The exact lower bounds on codelength n for three-step (T, U) permutation decodable binary cyclic codes of even-valued error number t (t>or=4) are presented. Since the derivation of these results involves only the error position, the results are applicable to cyclic codes over GF(2/sup m/).>

A versatile Reed-Solomon (RS) decoder structure based on the time-domain decoding algorithm (transform decoding without transforms) is developed. The algorithm is restructured, and a method is given to decode any RS code generated by any generator polynomial. The main advantage of the decoder structure is its versatility, that is, it can be programmed to decode any Reed-Solomon code defined in Galois field (GF) 2/sup m/ with a fixed symbol size m. This decoder can correct errors and erasures for any RS code, including shortened and singly extended codes. It is shown that the decoder has a very simple structure and can be used to design high-speed single-chip VLSI decoders. As an example, a gate-array-based programmable RS decoder is implemented on a single chip. This decoder chip can decode any RS code defined in GF (2/sup 5/) with any code word length and any number of information symbols. The decoder chip is fabricated using low-power 1.5- mu , two-layer-metal, HCMOS technology.>

The probability of bit error is evaluated for a communication system using frequency hopping for jamming rejection and quadrature partial-response signaling for baseband modulation. The environment in which the system is operating is modeled as severe partial-band tone jamming under frequency-selective fading affecting both the signal and the jammer. Error-correction coding is used to improve the performance of the system.>

The performance of intersymbol-interference and jitterfree offset-QPSK (IJF-OQPSK) and partial-response (PR) IJFOQPSK modems in a nonlinearly amplified multichannel environment is studied and compared to that of conventional QPSK and OQPSK modems. Power efficiency is maximized by operating the high-power amplifier (HPA) in full saturation. The effect of adjacent-channel interference and flat fade on the performance of illustrative nonlinear satellite systems is investigated. Simulation and measurement results are reported. It is shown that both IJF-OQPSK and PR-IJF-OQPSK modems exhibit a significant performance improvement compared to QPSK and OQPSK modems, in illustrative SCPC and on-board regenerative satellite systems, they provide an improvement of more than 4 dB compared to QPSK.

The error probability (Pe) performance of intersymbolinterference and jitter-free offset-QPSK (IJF-OQPSK) modems in a cochannel and adjacent-channel interference environment is evaluated using a computer simulation technique. Hardware design and experiments have been completed to verify the simulation results. The results indicate that a spectral efficiency of 1.5 bits/s/Hz can be obtained with hard-limited IJF-OQPSK channels. This is a significant improvement compared to hard-limited QPSK, OQPSK, and MSK systems. It is expected that the IJF-OQPSK scheme will have applications in low-cost power and bandwidth efficient earth-stations and terrestrial radio systems with transmit high-power amplifiers operating in saturation.

The probability of error (Pe) performance of an intersymbol-interference and jitter-free-offset QPSK (IJF-OQPSK) modem in conventional and regenerative satellite channels is studied and compared to that of a QPSK modem using computer simulation techniques. The results show that the IJF-OQPSK modem performs better than QPSK in both cascaded nonlinear channels and regenerative satellite links, especially with saturated earth station HPA and transponder TWTA.

New power and bandwidth efficient modulation techniques, named intersymbol interference and jitter-free (IJF)-QPSK and IJF-OQPSK, are presented. The properties of the IJF-QPSK and IJF-OQPSK signals in linear and nonlinear earth-station-satellite systems are studied. A finite-state Markov chain model is used to calculate power spectra of hard-limited IJF-QPSK and IJF-OQPSK. The model provides insights into the spectral spreading action of the ideal hard-limiter on the IJF-QPSK and IJF-OQPSK signals, Experimental, simulation, and theoretical results are in good agreement. The results indicate that the IJF-OQPSK modulated signal exhibits much less spectrum spreading than QPSK, OQPSK, and MSK. The error probability (Pe) performance of IJF-QPSK and IJFOQPSK in an additive white Gaussian noise and adjacent-channel interference environment is evaluated. The results show that the Peperformance of the IJF-OQPSK is superior to that of QPSK, OQPSK, and MSK in narrow-band nonlinear channels.

An analysis of a symbol timing recovery (STR) technique using digital processing is presented. The ratio of the discrete spectral power at the symbol rate frequency to the nearby continuous spectral power is used as a criterion of STR performance. It is shown that this power ratio equals the quality factor of the narrow bandpass filter and that it does not depend on the value of the delay element. The performance of the STR subsystem is consequently determined by the quality factor of the bandpass filter rather than by the specific delay. In addition to this, some experimental evidence that the additive channel noise has little effect on the power ratio is given. A modified phase-locked loop with anLCprefiiter is proposed to extract the symbol timing clock. This prefilter improves the acquisition and synchronization performance of the PLL. The STR technique presented here has the advantages of lower cost and simpler hardware implementation over other serial STR techniques.