Integrated sensing and communication (ISAC) enables reliable transmission by sensing targets in scattering environment. However, a single base station (BS) struggles to accurately detect three-dimension (3D) moving targets. Meanwhile, asynchronicity caused by spatially separated transceivers introduces timing offsets (TOs) and carrier frequency offsets (CFOs), which impair the accuracy of sensing parameter. To address this, this paper studies an orthogonal frequency division multiplexing (OFDM) networked ISAC system, where multiple base stations (BSs) cooperatively sense multiple unmanned aerial vehicles (UAV) through a joint active and passive sensing framework. The sensing signals received from the BSs are transmitted through a backhaul-limited link to a fusion center (FC) for sensing parameter estimation. In this context, we propose a novel cooperative sensing scheme for UAV parameter estimation based on quantized signals. The joint active and passive sensing problem is formulated as a problem with imperfect parameters (such as TO and CFO). An efficient algorithm is proposed to solve the resulting problem. Simulation results indicate that the proposed cooperative sensing design achieves higher estimation accuracy than other baselines. The results also confirm the performance advantage of multi-BS cooperative sensing over conventional single-BS sensing.

This paper designs a novel pilot scheme for extralarge massive MIMO (XL-MIMO) systems, by leveraging the spatial non-stationarity introduced by the large aperture of the extremely large aperture arrays (ELAA). The spatial nonstationarity results in distinct visibility for different users at ELAA, particularly those located far apart, which reduces inter-user interference. This property motivates our novel pilot scheme to group users with distinct visibility regions to share the same frequency subcarriers for channel estimation, hence reducing pilot overhead while accommodating numerous users. Specifically, the proposed pilot scheme employs frequencydivision multiplexing for inter-group channel estimation, while intra-group users - benefiting from strong spatial orthogonality from the distinct visibility region - are distinguished by shifted cyclic codes, similar to code-division multiplexing. Additionally, we propose a low-complexity channel estimation algorithm within a turbo Bayesian inference framework, where the channel support of each user at the ELAA features clustered sparsity in the antenna-delay domain and is modeled by a 2 -dimensional (2-D) Markov random field. Simulations show that the proposed pilot scheme and algorithm allow the XL-MIMO system to support more users, and deliver superior channel estimation performance.

Frame aggregation (FA) significantly enhances throughput by frame header reduction and payload compression. However, FA affects the performance of latency and deadline adherence due to aggregation delays. In this paper, we investigate time-sensitive FA scheduling to ensure low latency and low delay violation probability while maintaining high throughput. We formulate the complex FA scheduling problem by constraint programming (CP), which considers variable FA sizes and queue availability in FA scheduling and yields near-optimal solutions. To address the NP-hard problem imposed by CP, we propose a composite dispatching cost - genetic algorithm (CDC-GA) for FA scheduling. According to our theoretical analysis, the proposed CDC encourages more aggressive aggregation when frame header overhead is high, thereby optimizing for both throughput and time-centric performance. The GA then refines the initial schedule using a proposed ternary chromosome encoding, which comprehensively captures all necessary scheduling decisions. The proposed CDC-GA approach accommodates various FA structures (e.g., fixed/variable length, with/without data compression), and outperforms the existing approaches, both conventional and learning-based, by adeptly handling variable FA sizes and integrating the impact of FA on time-sensitive performance. Simulation results show that the proposed CDC-GA significantly outperforms the existing approaches, improving throughput by 77% and reducing average latency by 36%.

Massive heterogeneous IoT networks encounter significant random access (RA) challenges due to diverse Quality of Service (QoS) requirements and resource constraints. To address these issues, we first propose a fuzzy logic-assisted multi-criterion access priority ranking (FL-MCAPR) scheme to prioritize RA for IoT devices, integrating delay, channel interference, and energy factors. The resulting suitability values enable adaptive and fine-grained backoff adjustments in large-scale IoT deployments. Next, hybrid RA control schemes with a deployability-descending double-queue (D3Q) structure and access priority-backoff window model optimize preamble and backoff allocation. In addition, preamble parallelization and early-stage collision detection enhance RA throughput by expanding resources and reducing collisions. Using D3Q, the analytical RA throughput is derived, informing an optimization problem to determine Access Class Barring (ACB) factors balancing low delay and energy efficiency, considering all RA resources. Building on these results, the hybrid fuzzy logic-guided RA management (HFL-RAM) scheme is developed for comprehensive RA management, systematically evaluated in terms of delay and throughput. Finally, a lightweight pseudo-Bayesian estimation method is applied, which relies solely on two observable quantities to estimate the contending MTCD traffic. Simulation results demonstrate that the proposed HFL-RAM scheme consistently outperforms conventional approaches, effectively managing traffic heterogeneity across a wide range of traffic loads.

With the growing number of users in multi-user multiple-input multiple-output (MU-MIMO) systems, demodulation reference signals (DMRS) are efficiently multiplexed in the code domain via orthogonal cover codes (OCC) to ensure orthogonality and minimize pilot interference. In this paper, we investigate uplink DMRS-based channel estimation for MU-MIMO systems with Type II OCC pattern standardized in third generation partnership project (3GPP) Release 18, leveraging location-specific statistical channel state information (SCSI) to enhance performance. Specifically, we propose a SCSI-assisted Bayesian channel estimator (SA-BCE) based on the minimum mean square error criterion to suppress the pilot interference and noise, albeit at the cost of cubic computational complexity due to matrix inversions. To reduce this complexity while maintaining performance, we extend the scheme to a windowed version (SA-WBCE), which incorporates antenna-frequency domain windowing and beam-delay domain processing to exploit asymptotic sparsity and mitigate energy leakage in practical systems. To avoid the frequent real-time SCSI acquisition, we construct a grid-based location-specific SCSI database based on the principle of spatial consistency, and subsequently leverage the uplink received signals within each grid to extract the SCSI. Facilitated by the multilinear structure of wireless channels, we formulate the SCSI acquisition problem within each grid as a tensor decomposition problem, where the factor matrices are parameterized by the multi-path powers, delays, and angles. The computational complexity of SCSI acquisition can be significantly reduced by exploiting the Vandermonde structure of the factor matrices. Simulation results demonstrate that the proposed location-specific SCSI database construction method achieves high accuracy, while the SA-BCE and SA-WBCE significantly outperform state-of-the-art benchmarks in MU-MIMO systems.

Federated learning (FL) is a promising distributed machine learning approach with enhanced data privacy protection. However, wireless communication remains a key bottleneck, directly affecting the efficiency and performance of FL. In this paper, we introduce a device-centric cell-free network to mitigate the negative effects of random fading and limited radio resources on FL. The convergence gap, representing the difference between the FL model’s performance and that of the optimal model, is analyzed to evaluate the impact of communication and computation factors, including inter-device interference, on FL performance. Then, access point (AP)-device association, transmission power, and computation frequency are jointly optimized to minimize the convergence gap. Lyapunov techniques are employed to decouple the long-term optimization into a series of online solvable problems. A deep reinforcement learning-based scheme is proposed to optimize the AP association and transmission power for devices, reducing the computational complexity from a prohibitive level to a real-time feasible quadratic level. Additionally, a closed-form solution for the optimal device computation frequency is derived. Simulation results show that the proposed scheme significantly outperforms the traditional cell-free FL and cellular FL schemes in both model training performance and energy efficiency.

Pixel-based reconfigurable intelligent surfaces (RISs) employ a novel design to achieve high reflection gain at a lower hardware cost by eliminating the phase shifters used in traditional RIS. However, this design presents challenges for channel estimation and passive beamforming due to its non-separable state response, rendering existing solutions ineffective. To address this, we first approximate the non-separable RIS response functions using a kernel-based method and a deep neural network, achieving high accuracy while reducing computational and memory complexity. Next, we propose a simplified cascaded channel model that focuses on dominated scattering paths with limited unknown parameters, along with customized algorithms to estimate short-term and long-term parameters separately. Finally, we introduce a low-complexity passive beamforming algorithm to configure the discrete RIS state vector, maximizing the achievable rate. Our simulation results demonstrate that the proposed solution significantly outperforms various baselines across a wide SNR range.

With the advancement of wireless communications toward higher user densities and increasingly complex environments, near-field communication has emerged as a critical research focus. Supporting multi-user access in this regime with manageable complexity remains a key challenge, particularly due to the coupling between the spherical wavefront effect and the spatial non-stationarity (SnS) property, which complicates the exploitation of spatial correlation among user channels. To address this, we propose a unified multi-user channel model that incorporates joint support to capture the structured sparsity shared across users. Additionally, we introduce a two-dimensional (2D) Markov prior to model both local sparsity pattern continuity across adjacent grid points and joint burst sparsity in the shared support structure. Based on this, we develop a spatial information-sharing-aided framework that alternately estimates model parameters. Specifically, an inverse-free variational Bayesian inference (IF-VBI) algorithm is employed in the channel estimation module to avoid high-dimensional matrix inversion while enabling information exchange among users via joint support. In the common grid update module, joint updates across users are performed to achieve a full spatial-domain optimum, whereas in the joint visible region (VR) matrix detection module, the user-sharing structure is exploited to decouple and efficiently solve the VR matrix. Simulation results validate the effectiveness of the proposed approach, demonstrating improved estimation accuracy and computational efficiency in multi-user near-field extremely large-scale multiple-input-multiple-output (XL-MIMO) systems.

Massive multi-input multi-output (MIMO) combined with orthogonal time frequency space (OTFS) modulation has emerged as a promising technique for high-mobility scenarios. However, its performance could be severely degraded due to channel aging caused by user mobility and high processing latency. In this paper, an integrated scheme of uplink (UL) channel estimation and downlink (DL) channel prediction is proposed to alleviate channel aging in time division duplex (TDD) massive MIMO-OTFS systems. Specifically, first, an iterative basis expansion model (BEM) based UL channel estimation scheme is proposed to accurately estimate UL channels with the aid of carefully designed OTFS frame pattern. Then a set of Slepian sequences are used to model the estimated UL channels, and the dynamic Slepian coefficients are fitted by a set of orthogonal polynomials. A channel predictor is derived to predict DL channels by iteratively extrapolating the Slepian coefficients. Simulation results verify that the proposed UL channel estimation and DL channel prediction schemes outperform the existing schemes in terms of normalized mean square error of channel estimation/prediction and DL spectral efficiency, with less pilot overhead.

Integrated sensing and communication (ISAC) has drawn great attention for its capacity to simultaneously support wireless communication and environmental sensing. However, in dynamic ISAC systems, environmental mobility poses critical challenges in both dynamic channel estimation and continuous sensing parameter tracking across multiple time slots under severe Doppler effect. To address these challenges with a unified framework, we propose a novel base station-user cooperative sensing approach for joint dynamic channel estimation and sensing parameter tracking, including target/scatterer locations and actual velocities, which is formulated as a maximum a posterior (MAP) problem. In cases where some moving radar targets also serve as communication scatterers, the spatial overlap induces an underlying partially common sparsity between the location-domain sensing and communication channels. Based on this, we develop a two-dimensional Markov Model (2D-MM) based on dynamic location grids to capture the spatio-temporal correlations and partially common sparsity, thereby enhancing both sensing and communication performance. To alleviate the high-complexity matrix inverse in the E-step of sparse Bayesian inference, we propose a dynamic subspace-constrained variational Bayesian inference (D-SCVBI) algorithm with the aid of prior information obtained from the two-dimensional discrete Fourier transform (2D-DFT) localization and state evolution model. Finally, simulations show that the proposed D-SCVBI algorithm attains remarkable performance gains over various baselines.

In this paper, we propose a Bayesian Reinforcement Learning (BRL)-based CSI feedback, beamforming, and IRS control scheme for IRS-assisted massive MIMO-OFDM systems. Firstly, the proposed approach utilizes the equivalent CSI for optimization, aligning with current channel estimation protocols without necessitating extensive modifications. Secondly, it employs a practical IRS control model that optimizes the effective capacitance of IRS control circuits rather than IRS reflection coefficients, accurately reflecting the IRS's frequencyresponsive behavior to enhance system performance. Additionally, we advocate bypassing the reconstruction of the CSI at the BS to eliminate information irrelevant to beamforming and IRS control, thereby boosting feedback efficiency. Simulation results demonstrate that the proposed IRS-CSI-BRL scheme significantly outperforms start-of-the-art solutions in feedback overhead reduction and system data rate enhancement.

Sparse vector coding (SVC) is a promising short-packet transmission method for ultra reliable low latency communication (URLLC) in next-generation communication systems. In this paper, a block SVC (BSVC) based short-packet transmission scheme is proposed to further enhance the transmission performance of SVC. The core idea behind the proposed scheme is to transmit short-packet data information after block sparse transformation. At the transmitter, the information bits are divided into two parts: one part is mapped into the non-zero indexes of block sparse vectors, and the other part is mapped into non-zero values through phase or amplitude modulation. After pseudo-random spreading, the block sparse vectors are mapped to time-frequency resources for transmission. At the receiver, the decoding problem is transformed into a block sparse signal recovery problem. A cyclic block orthogonal matching pursuit (CBOMP) algorithm is proposed for decoding by leveraging block-structured sparse prior information. The upper bound of block error rate (BLER) performance over Rayleigh channels is derived to verify the decoding performance of the proposed CBOMP algorithm. Extensive simulation results verify that the proposed BSVC scheme outperforms the existing SVC schemes in terms of BLER, transmission latency and spectral efficiency over fading channels.

In Intelligent Reflecting Surface (IRS)-assisted massive Multiple-Input Multiple-Output (MIMO) systems, the downlink channel state information (CSI) needs to be fed back to the base station (BS) and utilized to perform the beamforming and IRS control for high spectral efficiency performance. However, the intricate nature of these systems, characterized by a vast number of antennas, subcarriers, and IRS elements, exacerbates the CSI feedback overhead and complicates the optimization of beamforming and IRS parameters, potentially compromising spectral efficiency. Addressing these challenges, this paper introduces a Bayesian Reinforcement Learning (BRL)-based approach, named IRS-CSI-BRL, for efficient CSI feedback, beamforming, and IRS control. Firstly, the IRS-CSI-BRL approach utilizes the equivalent CSI for optimization, aligning with current channel estimation protocols without necessitating extensive modifications. Secondly, it employs a practical IRS control model that optimizes the effective capacitance of IRS control circuits rather than IRS reflection coefficients, accurately reflecting the IRS’s frequency-responsive behavior to enhance system performance. Additionally, we advocate bypassing the reconstruction of the CSI at the BS to eliminate information irrelevant to beamforming and IRS control, thereby boosting feedback efficiency. Another distinctive feature of the proposed scheme is that its output format is probability distributions, which enables the incorporation of model-assisted knowledge about the latent space and boosts the algorithm’s robustness. Simulation results demonstrate that the proposed IRS-CSI-BRL scheme significantly outperforms start-of-the-art solutions in feedback overhead reduction and system data rate enhancement while maintaining exceptional robustness. Furthermore, this approach maintains flexibility, allowing for the incorporation of an additional training loss function for full CSI reconstruction if needed.

The integration of Internet-of-Things (IoT) and the fifth-generation (5G) networks presents challenges due to low access efficiency caused by massive random access (RA) requests. To this end, both preamble parallelization (PP) and colliding preambles reuse (CPR) modes are proposed as critical RA control methods to enhance access performance. In this paper, we aim to maximize the random access efficiency (RAE) in a smart city scenario to determine the optimal control mode selection between the PP and CPR over the device heterogeneity with limited RA resources. We establish an access order-backoff window (AOBW) mapping model, where RA requirements are mapped onto the backoff time. It offers greater flexibility of backoff window size than previous work to guarantee diverse application and service requirements. Thanks to the derived closed-form expressions of the actual RAE, an RAE maximization algorithm is developed, which optimizes performance across both PP and CPR modes, achieving optimal performance in access delay and access throughput.

This paper investigates a fully distributed federated learning (FL) problem, in which each device is restricted to only utilize its local dataset and the information received from its adjacent devices that are defined in a communication graph to update the local model weights for minimizing the global loss function. To incorporate the communication graph constraint into the joint posterior distribution, we exploit the fact that the model weights on each device is a function of its local likelihood and local prior and then, the connectivity between adjacent devices is modeled by a Dirichlet distribution. In this way, the joint distribution can be factorized naturally by a factor graph. Based on the Dirichlet-based factor graph, we propose a novel distributed approximate Bayesian inference algorithm that combines loopy belief propagation (LBP) and variational Bayesian inference (VBI) for distributed FL. Specifically, VBI is used to approximate the non-Gaussian marginal posterior as a Gaussian distribution in local training process and then, the global training process resembles Gaussian LBP where only the mean and variance are passed among adjacent devices. Furthermore, we propose a new damping factor design according to the communication graph topology to mitigate the potential divergence and achieve consensus convergence. Simulation results verify that the proposed solution achieves faster convergence speed with better performance than baselines.

Sparse vector coding (SVC) is a promising short-packet transmission method for ultra reliable low latency communication (URLLC) in next-generation communication networks. In this paper, a block SVC (BSVC) based short-packet transmission scheme is proposed to further enhance the transmission performance of SVC. The core idea behind the proposed scheme is to transmit short-packet data information after block sparse transformation. At the transmitter, the transmitted information bits are divided into two parts: one part is mapped into the non-zero indexes of block sparse vectors, and the other part is mapped into non-zero values through quadrature amplitude modulation. After pseudo-random spreading, the block sparse vectors are mapped to time-frequency resources for transmission. At the receiver, the decoding problem is transformed into a block sparse signal recovery problem. A cyclic block matching pursuit algorithm is proposed for accurate decoding by leveraging block-structured sparse prior information. Simulation results verify that the proposed BSVC scheme outperforms the existing SVC schemes in terms of packet error rate and spectral efficiency.

In this paper, we propose a closed form (CF) based channel estimation approach for direct current biased optical-orthogonal frequency division multiplexing (DCO-OFDM) visible light communication (VLC) systems. This is the first work to utilize light emitting diode (LED)'s limited bandwidth to reduce the number of pilots while maintaining good performance. A comb-type pilot pattern is employed, requiring a small number of pilots on few DCO-OFDM subcarriers and blocks. It is high spectral efficiency, as a large number of pilots on all subcarriers and some blocks are not required as in a block-type pilot pattern. The proposed CF approach performs the line-of-sight (LoS) channel estimation and LED's limited bandwidth estimation in a CF via two formulated functions on two subcarriers. Simulation results show that the proposed CF approach provides bit error ratio (BER) and mean square error (MSE) performances better than the block-type pilot based methods in the literature, requiring less pilots.

In this paper, we investigate multi-resolution model compression for deep neural networks (DNNs) from a Bayesian perspective. By considering the DNN models with channel masks and proposing a resolution likelihood as well as a two-layer sparse prior for the channel masks, we formulate the multi-resolution model compression as a Bayesian inference problem. To solve this problem, we propose a partial update block variational Bayesian inference (PUB- VBI) algorithm which can infer an approximate posterior for the intractable true posterior. The variational posterior and the updating rules are carefully designed such that the proposed algorithm has a low complexity. Simulation results demonstrate that our proposed method can outperform the baselines on various neural network models and datasets.

We investigate straggler-relay association and ener-gy consumption minimization for cooperative relay assisted fed-erated learning (FL) over fading channels to tackle the straggler effect and limited device energy. To the best of our knowledge, this is the first work to explore joint computation-communication optimization for cooperative relay assisted FL over fading chan-nels, where some devices act as relays for stragglers. A closed-form expression for the computation frequency is derived to facilitate low-complexity straggler identification. A bandwidth sharing decode-and-forward relay scheme is proposed, which benefits both straggler and relay. The closed-form expressions for the transmission power, which minimizes the computation and communication energy of devices under global time constraints, are derived. A low-complexity joint straggler-relay association and multi-domain resources optimization (JSAMRO) algorithm is proposed. Simulation results show that the proposed JSAMRO algorithm achieves a significant performance gain in terms of device's energy consumption and availability rate over the comparison schemes.

We propose an adaptive low-complexity orthogonal matching pursuit (ALOMP) channel estimation approach for direct current biased optical-orthogonal frequency division multi-plexing (DCO-OFDM) systems in optical wireless communication (OWC), requiring a single DCO-OFDM block. This is the first investigation to utilize light-emitting diode (LED)'s limited bandwidth to reduce the high complexity of the traditional OMP method in OWC, while maintaining good channel estimation performance. A number of termination factors are designed to reduce the number of iterations, by exploring correlations between channel impulse responses (CIRs) in time domain caused by LED's limited bandwidth. The proposed ALOMP approach provides complexity close to the minimum mean square error (MMSE) method, and requires just two iterations, significantly less than the traditional OMP method with a large number of iterations. We employ OMP to estimate two CIRs in time domain, and formulate two equations. The line-of-sight (LoS) channel and LED's limited bandwidth are estimated separately via the formulated equations rather than jointly in all CIRs in time domain or on all sub carriers in frequency domain in previous works. Also, we derive the lower bound of the proposed ALOMP approach, and the theoretical bit error rate (BER) including channel estimation errors. Simulation results verify the proposed ALOMP approach.

Federated learning, as a well-known framework for collaborative training among distributed local sensors and devices, has been widely used in practical learning applications. To reduce communication resource consumption and training delay, acceleration training algorithms, especially momentum-based methods, are further developed for the training process. However, it is observed that under the influence of transmission noise, existing momentum methods exhibit poor training performance due to the noise accumulation along with the momentum term. This motivates us to propose a novel acceleration algorithm to achieve an efficient trade-off between the training acceleration and noise smoothing. Specifically, to obtain clearer insights into the model update dynamics, we utilize a stochastic differential equation model to mimic the discrete-time training trajectory. Through high-order drift approximation analysis on a general momentum-based SDE model, we propose a dynamic momentum weight and gradient stepsize design for the update rule, which is adaptive to both the training state and gradient quality. Such adaptation ensures that the training algorithm can seize good update opportunities and avoid noise explosion. The corresponding discrete-time training algorithm is then derived via discretization of the proposed SDE model, which shows a superior training performance compared to state-of-the-art baselines.

The swift evolution of Internet of Things (IoT) technologies has facilitated the rapid deployment of heterogeneous devices in industrial systems. In this work, we focus on the identification and temperature tracking control for a furnace system with a single slab and a single heater over a wirelessly connected IoT controller. Specifically, we characterize the IoT-based furnace temperature control system using an integrated-control-and-communication framework that involves thermal modeling of convection and conduction in the furnace plant, as well as wireless modeling of the communication network between the furnace plant and the remote estimator-controller center. Based on the framework, we first propose a novel stochastic-approximation-based online algorithm to learn the optimal temperature tracking control solution for the furnace system with knowledge of the furnace dynamics. After that, we extend the temperature tracking control approach to deal with the furnace system with unknown furnace dynamics and propose a novel normalized-stochastic-gradient-descent (NSGD)-based algorithm to simultaneously identify and control the furnace system at the remote controller in an online manner. Using the Lyapunov stability analysis and ordinary differential equation (ODE) method, we theoretically demonstrate the asymptotic convergence of the proposed learning algorithms. Numerical analysis is conducted for our proposed temperature tracking control scheme and several state-of-the-art schemes. Our results demonstrate that our proposed scheme outperforms the baseline schemes in terms of temperature tracking accuracy and fuel efficiency, in the presence of the wireless interface.

Although deep neural networks (DNNs) have been remarkably successful in numerous areas, the performance of DNN is compromised in federated learning (FL) scenarios because of the large model size. A large model can induce huge communication overhead during the federated training, and also induce infeasible storage and computation burden at the clients during the inference. To address these issues, we investigate structured model compression in FL to construct sparse models with regular structure such that they require significantly less communication, storage and computation resources. We do this by proposing a three-layer hierarchical prior, which can promote a common regular sparse structure in the local models. We design a decentralized Turbo variational Bayesian inference (D-Turbo-VBI) algorithm to solve the resulting federated training problem. With the common regular sparse structure, both upstream and downstream communication overhead can be reduced, and the final model also has a regular sparse structure, which requires significantly less local storage and computation resources. Simulation results demonstrate that our proposed algorithm can efficiently reduce the communication overhead during federated training and the resulting model can achieve a significantly lower sparsity rate and inference time compared to the baselines while maintaining a competitive accuracy.

Due to the influence of stochastic gradients, the existing algorithms suffer from slow convergence, noise explosion, and even failure to converge in practice, which motivates us to propose an accelerated algorithm to tackle these issues. Recognizing the potential of gradient, momentum, and conjugate gradient as promising search directions, we propose a 3-D acceleration algorithm, which uses a weighted combination of these three basis. Specifically, in order to analyze the dynamics of the discrete-time algorithm during the update process, we provide a general framework for approximating the discrete-time algorithm in the weak sense by a continuous-time stochastic differential equation. We exploit the continuous-time formulation together with Lyapunov drift optimization to derive novel adaptive step sizes, which effectively improve the performance of the algorithm in stabilizing noise and accelerating convergence. Extensive numerical experiments demonstrate the proposed algorithm’s superiority in convergence rate, computation complexity, and noise robustness compared to state-of-the-art baselines.

Short frame structure and its optimization plays an important role in ultra reliable and low latency communication (URLLC). We investigate and compare the latency and throughput performances of the independent encoding (IE) and joint encoding (JE) frame structures for heterogeneous multi-device URLLC in the finite block length regime. There is a counter-intuitive finding that, despite a longer frame, IE enables a much lower average latency and higher reliability than JE, thanks to lower queuing latency, while JE achieves higher throughput with lower traffic heterogeneity, thanks to less channel dispersion. It is also shown that traffic heterogeneity has less adverse effects on the performance of the IE frame structure, and can even help reduce its average latency with the shortest block length first (SBF) scheduling rule proposed. We also provide an intensive analysis of the trade-off between pilot power and pilot overhead, with near-optimal pilot power and block length derived in closed form. Low-complexity joint pilot power, pilot length and block length optimization algorithms are proposed for IE and JE frame structures. Numerical results verify the effectiveness of the proposed algorithms, and also show that pilot power optimization plays a significant role in enhancing throughput at low to medium SNR.

Bayesian modelling of the channel distribution is a crucial step before channel recovery specially in the underdetermined scenario in multiple input multiple output (MIMO) antenna setups. In complicated dynamic propagation environments such as the ones encountered in Unmanned Aerial Vehicles (UAVs) Air to Ground (A2G) channels, Bayesian modelling might not be feasible or the model may not be able to approximate the different aspects of the true distribution well enough. Thus, estimation performance will be affected irrespective of the efficiency of recovery algorithm. To exploit the temporal correlations and imperfections in the real channels in such a scenario, we design a temporally correlated adversarial regulariser using Variational recurrent neural networks (VRNN) and train the framework on simulated channel dataset. The framework can be trained directly with channel samples, thus, allowing channel modelling and estimation without explicit tractable Bayesian models in highly dynamic systems. We then propose a temporally correlated deep compressed sensing algorithm which does not depend on the expressibility of the networks and provide theoretical results for existence and recovery. Numerical experiments demonstrate its effectiveness for channel estimation in A2G channels and show superior channel recovery and improved modelling even for out-of-distribution channels.

Channel estimation is crucial to modern wireless systems and becomes increasingly challenging when the ultra-sized antenna is configured in sub-6GHz wireless communication systems. In an ultra-massive multiple-input multiple-output (U-MIMO) orthogonal frequency division multiplex (OFDM) system, the channel demonstrates spatial non-stationarity. Additionally, the limited pilot location in the OFDM system further complicates the channel estimation process. In this paper, we propose a model-data dual-driven (MDD) scheme to jointly perform the model-driven non-stationary channel denoising and the data-driven channel interpolation in an end-to-end way, which is followed by a low-complexity channel refinement module to improve the robustness of the proposed scheme. Specifically, image contour extraction (ICE) is utilized to effectively eliminate the non-stationary noises in the channel matrices before being sent to the downstream interpolation network. An enhanced convolutional neural network (CNN)-based residual network (eCNN-RN) is developed to perform non-linear interpolations for recovering the U-MIMO-OFDM channels. Based on ICE, the proposed online refinement module can improve the generalizability of the learned model to a practical environment. Numerical experiments demonstrate the efficiency and the effectiveness of the cross-fertilization of the model-driven and data-driven approaches.

The optimal implementation of federated learning (FL) in practical edge computing systems has been an outstanding problem. In this paper, we propose an optimization-based quantized FL algorithm, which can appropriately fit a general edge computing system with uniform or nonuniform computing and communication resources at the workers. Specifically, we first present a new random quantization scheme and analyze its properties. Then, we propose a general quantized FL algorithm, namely GQFedWAvg. Specifically, GQFedWAvg applies the proposed quantization scheme to quantize wisely chosen model update-related vectors and adopts a generalized mini-batch stochastic gradient descent (SGD) method with the weighted average local model updates in global model aggregation. Besides, GQFedWAvg has several adjustable algorithm parameters to flexibly adapt to the computing and communication resources at the server and workers. We also analyze the convergence of GQFedWAvg. Next, we optimize the algorithm parameters of GQFedWAvg to minimize the convergence error under the time and energy constraints. We successfully tackle the challenging non-convex problem using general inner approximation (GIA) and multiple delicate tricks. Finally, we interpret GQFedWAvg’s function principle and show its considerable gains over existing FL algorithms using numerical results.

Most existing downlink channel estimation methods rely on channel sparsity in the angular domain to reduce pilot overhead for massive multiple-input multiple-output (MIMO) systems. Compared with channel estimation methods based on predefined basis or offline dictionary learning, in this paper, we design an online two-timescale joint uplink/downlink dictionary learning and channel estimation (TTS-JDLCE) algorithm for dynamic scenarios, where the channel sparsity and angle reciprocity between uplink and downlink transmissions are both exploited to reduce the pilot overhead. The downlink channel estimation is constructed as a TTS stochastic optimization problem with a constraint coupled by the long-term dictionary and short-term sparse channel representations. Treating the dictionary as a learnable parameter, the proposed algorithm can capture dynamic spatial information for enhancing performance. By introducing a relaxed TTS primal-dual decomposition (PDD) framework, the original problem is decomposed into a long-term online dictionary learning subproblem and a family of short-term sparse channel estimation subproblems. Besides, the deep unfolding technique is employed to extract gradient information from short-term problems, which circumvents the non-closed form and non-convexity of long-term subproblem by constructing a convex surrogate problem. Finally, simulations show that the proposed method remarkably reduces the pilot overhead and achieves significant performance gains over various baselines.

In this paper, we investigate multi-criteria client selection and energy consumption minimization for hierarchical federated learning (HFL) to deal with clients' heterogeneity and limited energy. To the best of our knowledge, this is the first work to investigate multi-criteria client selection for HFL. A fuzzy logic assisted client selection (FLACS) scheme is proposed, where multiple criteria are taken into account, including the distance, clients' battery capacity and computational resource. The FLACS scheme enables a significant performance gain in terms of the clients' average normalized suitability over the previous schemes. A joint communication and learning factors optimization (JCLFO) algorithm is proposed to minimize the system energy consumption. Thanks to the derived closed-form expressions for the optimal aggregation intervals, computation frequency and transmission power, the JCLFO algorithm can achieve the optimal performance in terms of the system energy consumption and converge within only 3 iterations, with a significant complexity reduction over exhaustive search.

This paper investigates optimal implementations of federated learning (FL) in practical edge computing systems with possibly distinct computing and communication resources at the server and workers. First, we present a new random quantization scheme and analyze its properties. Then, we propose a general quantized FL algorithm, namely HQFedWAvg, and analyze its convergence. HQFedWAvg adopts the proposed quantization scheme and a generalized mini-batch stochastic gradient descent (SGD) method and has several adjustable algorithm parameters to maximally adapt to the computing and communication resources at the server and workers. Next, we optimize the algorithm parameters of HQFedWAvg. The resulting challenging non-convex optimization problem is successfully tackled using several optimization techniques. Numerical results demonstrate HQFedWAvg's considerable performance gains over existing FL algorithms and interpret its function principle.

In massive machine-type communications, data transmission is usually considered sporadic, and thus inherently has a sparse structure. This paper focuses on the joint activity detection (AD) and channel estimation (CE) problems in massive-connected communication systems with low-resolution analog-to-digital converters. To further exploit the sparse structure in transmission, we propose a maximum posterior probability (MAP) estimation problem based on both sporadic activity and sparse channels for joint AD and CE. Moreover, a majorization-minimization-based method is proposed for solving the MAP problem. Finally, various numerical experiments verify that the proposed scheme outperforms state-of-the-art methods.

In this paper, we propose a Variational Bayesian Autoencoder (VBA)-based channel state information (CSI) compression and feedback scheme for massive multiple-input multiple-output (MIMO) systems. The proposed scheme incorporates the model-assisted knowledge of low-dimensional feedback features and the sparsity of channel to achieve enhanced compression efficiency. We also design a CsiVBA architecture that outputs distributions of the feedback features and the channel at the encoder and decoder, respectively, which facilitates a Bayesian training formulation exploiting the underlying channel sparsity. In addition, we also propose a low-complexity training scheme for new networks of different bit rates, significantly reducing the retraining cost for new compression requirements. Simulation results show that the proposed scheme achieves better rate-distortion trade-offs than the state-of-the-art solutions.

Supporting wireless transmission of large-scale control systems is a challenging task due to the scarcity of wireless resources in the industrial internet of things (IIOT). To reduce wireless resource consumption while maintaining control stability, this paper investigates the wireless networked predictive control system, where only part of the control devices is permitted to transmit their state information to the centralized controller in each control cycle. For the rest unscheduled control devices, the centralized controller predicts their state information via the Gaussian process regression method. To evaluate the control performance and the wireless resources consumption, we formulate a joint optimization problem of control device scheduling, power allocation, and bandwidth allocation. The joint predictive control and communication optimization (JPCCO) scheduling algorithm is proposed to minimize both the control cost and communication cost. As for control device scheduling, we proposed a fuzzy logic based scheduling ranking (FL-SR) method, where control devices are ranked in descending order according to the fuzzy output. Numerical results show that the proposed JPCCO scheduling method with FL-SR outperforms the previous scheduling methods without predictive control, enabling a more stable wireless networked control system with less wireless resources.

Pilot contamination incurred by strong inter-cell interference seriously degrades the performance of channel estimation in massive multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. We propose an independent component analysis (ICA) and sparse recovery algorithm based semi-blind channel estimation scheme, referred to as sparse ICA (SICA), for multi-cell massive MIMO-OFDM systems, which does not require any prior information of intercell interference and therefore is more practical. The proposed SICA scheme enables accurate channel estimation by exploiting both the high-order statistics of the received signal and channel sparsity in angle domain. The SICA scheme performs in a semi-blind manner as it is much more robust against pilot overhead than the previous approaches, and requires only one OFDM symbol as pilot to achieve a superior normalized mean square error of channel estimation. Furthermore, the complexity required by SICA is much lower than that required by the previous work, thanks to the negligible complexity of interference sources number estimation based on sparse recovery algorithm.

This article investigates the scheduling framework of the federated multitask learning (FMTL) problem with a hard-cooperation structure over wireless networks, in which the scheduling becomes more challenging due to the different convergence behaviors of different tasks. Based on the special model structure, we propose a dynamic user and task scheduling scheme with a block-wise incremental gradient aggregation algorithm, in which the neural network model is decomposed into a common feature-extraction module and$M$task-specific modules. Different block gradients with respect to different modules can be scheduled separately. We further propose a Lyapunov-drift-based scheduling scheme that minimizes the overall communication latency by utilizing both the instantaneous data importance and the channel state information. We prove that the proposed scheme can converge almost surely to a KKT solution of the training problem such that the data-distortion issue is resolved. Simulation results illustrate that the proposed scheme significantly reduces the communication latency compared to the state-of-the-art baseline schemes.

The performance capabilities of models trained in a federated learning (FL) setting over wireless networks can be significantly affected by the underlying properties of the transmission channel. Even for shallow models, there can be an acute degradation in performance which necessitates the development of algorithms which are robust to transmission channel effects, such as noise and fading. In this work, we present a two-pronged approach to overcome the limitations of existing wireless machine learning (ML)-based algorithms. First, to tackle the effect of channel noise, we incorporate a novel tracking-based stochastic approximation scheme in the standard federated averaging pipeline which averages out the effect of the channel noise. In contrast to previous works on FL with a noisy channel, we provide exact convergence guarantees for our algorithm without the need to increase the transmission power gain. Second, to combat channel fading and further optimize the power consumption at the client level, we propose an adaptive transmission policy obtained by solving an optimization problem with long-term constraints. The solution is obtained in an online manner via a dual decomposition method. The superior empirical performance of the proposed scheme compared to state-of-the-art works is demonstrated on standard ML tasks.

This article studies a sequential task offloading problem for a multiuser mobile-edge computing (MEC) system. While most of the existing works consider static one-shot offloading optimization with fixed wireless channel conditions and fixed computational tasks, we consider a dynamic optimization approach, which embraces wireless channel fluctuations and random deep neural network (DNN) task arrivals over an infinite horizon. Specifically, we introduce a local CPU workload queue (WD-QSI) and a MEC server workload queue (MEC-QSI) to model the dynamic workload of DNN tasks at each wireless device (WD) and the MEC server, respectively. The transmit power and the partitioning of the local DNN task at each WD are dynamically determined based on the instantaneous channel conditions (to capture the transmission opportunities) and the instantaneous WD-QSI and MEC-QSI (to capture the dynamic urgency of the tasks) to minimize the average latency of the DNN tasks. The joint optimization can be formulated as an ergodic Markov decision process (MDP), in which the optimality condition is characterized by a centralized Bellman equation. However, the brute force solution of the MDP is not viable due to the curse of dimensionality as well as the requirement for knowledge of the global state information. To overcome these issues, we first decompose the MDP into multiple lower dimensional sub-MDPs, each of which can be associated with a WD or the MEC server. Next, we further develop a parametric online$Q$-learning algorithm, so that each sub-MDP is solved locally at its associated WD or the MEC server. The proposed solution is completely decentralized in the sense that the transmit power for sequential offloading and the DNN task partitioning can be determined based on the local channel state information (CSI) and the local WD-QSI at the WD only. Additionally, no prior knowledge of the distribution of the DNN task arrivals or the channel statistics will be needed for the MEC server. The proposed solution can achieve the superb performance over various state-of-the-art baselines.

Deep learning (DL)-based channel estimation has achieved remarkable success. However, most existing works focus on the white Gaussian noise which are inapplicable for cell-edge users under inter-cell interference (ICI). In this paper, we address this issue by proposing a novel DL-based channel estimation solution with a cascaded model-based and model-free deep neural network (DNN) structure. Specifically, the model-based module is designed by the variational Bayesian inference (VBI) technique to suppress the time-varying ICI, and the model-free module is designed by the Denoising Sparse Autoencoder (DSAE) structure to further refine the channel estimation. The proposed DNN is firstly pre-trained by offline supervised training, and various channel statistics are encapsulated in the DNN weights with the assist of a hyper-prior net modelling different sparse priors for different training samples. Then, an online Bayesian learning algorithm is proposed to train the model-based VBI module based on real-time pilot samples to track the online channel statistics. Simulation results verify that the proposed solution outperforms various state-of-the-art baseline schemes in a large SINR range with comparable performance to the estimator with genie-aided channel statistics.

Multiplexing or transmission diversity via dual links enables the reduction in age of information (AoI). We address the open issue of selection between the two transmission modes for AoI-oriented short-packet system over fading channels, with a comprehensive analysis. Closed-form expressions for the average AoI and peak AoI (PAoI) are derived based on the discrete-time Markov-chain process. Then, to obtain the explicit region of preference (RoP) and quantitative PAoI gains of multiplexing/diversity over the single-queue case, we derive the signal-to-noise ratio (SNR) threshold for transmission mode selection, which is shown to be a decreasing function of the arrival rate and saturates at high arrival rate. Also, the monotonicity of the PAoI gains by multiplexing and diversity, and their achievable gains, are analyzed in a comprehensive manner. It is shown that diversity is able to achieve a PAoI gain of more than 3 dB over the single-queue case at low SNR, while multiplexing has a larger RoP than diversity and is selected at high SNR and high arrival rate. Furthermore, both throughput and PAoI violation probability are considered alongside the average PAoI for a wide range of tradeoff in system design.

In this paper, we investigate an intelligent reflecting surface (IRS)-assisted integrated sensing and communication (ISAC) system design in a clutter environment. Assisted by an IRS equipped with a uniform planar array (UPA), a multi-antenna base station (BS) is targeted for simultaneously sensing multiple targets in the non-light-of-sight (NLoS) region and communicating with multiple communication users (CUs). We consider the joint IRS-assisted ISAC design in the case with Type-I or Type-II CUs, where each Type-I CU and Type-II CU can and cannot cancel the interference from sensing signals, respectively. Under the perfect communication/sensing channel state information assumption, we aim to maximize the minimum sensing beampattern gain among multiple targets, where the sensing beampattern gain qualifies the achieved illumination signal power at the given location of the target of interest. We jointly optimize the BS’s communication-sensing beamformers and the IRS’s phase shifting matrix, subject to the signal-to-interference-plus-noise ratio (SINR) constraint for each Type-I/Type-II CU, the interference power constraint per clutter, the transmission power constraint at the BS, and the cross-correlation pattern constraint. Due to the design variable coupling, the joint IRS-assisted ISAC design problem is shown to be non-convex in the case with Type-I or Type-II CUs. To circumvent the non-convexity dilemma, we propose semidefinite relaxation (SDR) based alternating optimization algorithms in both cases, for which the computational complexity and convergency behavior are analyzed. In the case with Type-I CUs, we show that the dedicated sensing signal at the BS can help enhance the sensing performance gain. By contrast, the dedicated sensing signal at the BS is not required for the IRS-assisted ISAC designs in the case with Type-II CUs. Numerical results are provided to show that the proposed IRS-assisted ISAC design schemes achieve a significant gain over the existing benchmark schemes.

We consider a multiple-input single-output (MISO) optical wireless communications (OWC) system with highly correlated channels, causing bit error rate (BER) performance degradation. Because of intensity modulation and direct detection (IM/DD), the transmitted signals are real-valued and non-negative, which limits the utilization of precoding in phase domain. We employ direct current biased optical orthogonal frequency division multiplexing (DCO-OFDM) modulation to design a group of phase rotation (PR) factors in frequency domain for OWC systems, robust against high channel correlations. The proposed PR-based precoding has a number of advantages over the power factor-based design in the literature: i) no change of transmission power on each LED; ii) no signal-to-noise ratio (SNR) and no BER degradation on transmitted signals of all LEDs. We formulate an optimization problem to obtain the optimal PR factors by maximizing the minimum pairwise Euclidean distances between all candidate signals. The optimization problem is non-convex, requiring multi-dimensional exhaustive searches. In order to reduce the complexity, we propose three low-complexity PR-based precoding approaches which provide BER performances better than the power factor-based designs in the literature, and are close to their own analytical results derived, respectively. The proposed approaches are validated via a built testbed, producing comparable performance between measured and simulated BERs.

To alleviate the local computation demands from the ever-increasing computation-intensive mobile applications, Mobile Edge Computing (MEC) has proved promising. Especially, by opportunistically offloading these computation tasks to the MEC server, the delay of computing could be significantly improved through communication. In this paper, we develop an analytical framework for joint communication and computation resources allocation for multi-user MEC systems. Specifically, to retrieve the combined effect of communication and computation capabilities, we establish a dual queue system, including a data queue sub-system and a computation queue sub-system. To address the associated stochastic resource optimization problem, we propose a low-complexity resource allocation algorithm by Lyapunov optimization to stabilize all the sub-queue systems. As the practical buffers are finite, the conventional delay analysis of Lyapunov optimization becomes inaccurate. Alternatively, we model the stochastic queue lengthes as discrete time controlled random walk processes, which are transformed to continuous time Stochastic Differential Equations (SDEs) with reflections by strong approximation. According to the steady state analysis on the SDEs, we derive closed-form steady state distributions of the queue lengths, and then obtain the average delay performance with finite buffers. Finally, the accuracy of the proposed delay analysis is verified through simulation.

Despite the many advantages of using deep neural networks over shallow networks in various machine learning tasks, their effectiveness is compromised in a federated learning setting due to large storage sizes and high computational resource requirements for training. A large model size can potentially require infeasible amounts of data to be transmitted between the server and clients for training. To address these issues, we investigate the traditional and novel compression techniques to construct sparse models from dense networks whose storage and bandwidth requirements are significantly lower. We do this by separately considering compression techniques for the server model to address downstream communication and the client models to address upstream communication. Both of these play a crucial role in developing and maintaining sparsity across communication cycles. We empirically demonstrate the efficacy of the proposed schemes by testing their performance on standard datasets and verify that they outperform various state-of-the-art baseline schemes in terms of accuracy and communication volume.

Dictionary learning is a widely used unsupervised learning method in signal processing and machine learning. Most existing works on dictionary learning adopt an off-line approach, and there are two main off-line ways of conducting it. One is to alternately optimize both the dictionary and the sparse code, while the other is to optimize the dictionary by restricting it over the orthogonal group. The latter, called orthogonal dictionary learning (ODL), has a lower implementation complexity and, hence, is more favorable for low-cost devices. However, existing schemes for ODL only work with batch data and cannot be implemented online, making them inapplicable for real-time applications. This article, thus, proposes a novel online orthogonal dictionary scheme to dynamically learn the dictionary from streaming data, without storing the historical data. The proposed scheme includes a novel problem formulation and an efficient online algorithm design with convergence analysis. In the problem formulation, we relax the orthogonal constraint to enable an efficient online algorithm. We then propose the design of a new Frank–Wolfe-based online algorithm with a convergence rate of$\mathcal {O}(\ln t/t^{1/4})$. The convergence rate in terms of key system parameters is also derived. Experiments with synthetic data and real-world Internet of things (IoT) sensor readings demonstrate the effectiveness and efficiency of the proposed online ODL scheme.

The optimal design of federated learning (FL) algorithms for solving general machine learning (ML) problems in practical edge computing systems with quantized message passing remains an open problem. This paper considers an edge computing system where the server and workers have possibly different computing and communication capabilities and employ quantization before transmitting messages. To explore the full potential of FL in such an edge computing system, we first present a general FL algorithm, namely GenQSGD, parameterized by the numbers of global and local iterations, mini-batch size, and step size sequence. Then, we analyze its convergence for an arbitrary step size sequence and specify the convergence results under three commonly adopted step size rules, namely the constant, exponential, and diminishing step size rules. Next, we optimize the algorithm parameters to minimize the energy cost under the time constraint and convergence error constraint, with the focus on the overall implementing process of FL. Specifically, for any given step size sequence under each considered step size rule, we optimize the numbers of global and local iterations and mini-batch size to optimally implement FL for applications with preset step size sequences. We also optimize the step size sequence along with these algorithm parameters to explore the full potential of FL. The resulting optimization problems are challenging non-convex problems with non-differentiable constraint functions. We propose iterative algorithms to obtain KKT points using general inner approximation (GIA) and tricks for solving complementary geometric programming (CGP). Finally, we numerically demonstrate the remarkable gains of GenQSGD with optimized algorithm parameters over existing FL algorithms and reveal the significance of optimally designing general FL algorithms.

In this paper, we design a two-timescale approach for joint uplink/downlink (UL/DL) dictionary learning and channel estimation (TTS-JDLCE) for frequency division multiplexing (FDD) massive multiple-input multiple-output (MIMO) systems in dynamic scenarios. With channel sparsity and angle reciprocity between UL and DL transmissions, the joint UL/DL dictionary is regarded as a learnable parameter to capture dynamic spatial information at a slower timescale than instantaneous downlink channel estimation. By introducing the primal-dual decomposition (PDD) framework, the original non-convex two-timescale stochastic optimization problem is decomposed into a long-term online dictionary learning subproblem and a family of short-term sparse channel estimation subproblems, and then solved in a divide-and-conquer manner, which can converge to the stationary solutions of the original problem over time. Finally, simulations show that the proposed method remarkably reduces the pilot overhead and achieves significant performance gain over various baselines.

In this paper, we consider a reinforcement learning (RL) based multi-user downlink communication system. An actor-critic based deep channel prediction (CP) algorithm is proposed at the base station (BS) where the actor network directly outputs the predicted CSI without channel reciprocity. Different from the existing methods which either require the perfect channel state information (CSI), or estimate outdated CSI and set strict constraints on pilot sequences, the proposed algorithm has no such premised knowledge requirements or constraints. Deep-Q learning and policy gradient methods are adopted to update the parameters of the proposed prediction network, with the objective of maximizing the overall transmission sum rate. Numerical simulation results and the complexity analysis verify that the proposed CP algorithm could beat the existing traditional and learning based methods in terms of sum rate over different channel models and different numbers of users and antennas.

The rapidly developing energy harvesting (EH) technology is a promising solution to the durability issue in the battery-powered Internet of Things (IoT) systems. In this article, underlaid device-to-device (D2D) transmission powered by radio signals harvested from cellular systems is studied. By considering the dilemmas among EH, D2D transmission opportunity, and interference management, we propose two transmission policies: 1) Policy 1 requires that the available power in the battery should be no less than the D2D transmission power and 2) Policy 2 not only sets the constraint on available power but also introduces theguard zonerule to protect D2D transmissions from severe interference. The employment of aguard zonein this situation is technically challenging since the original distribution of energy arrival will thereby be changed. We derive expressions in closed or semiclosed forms for the considered D2D transmission performance metrics with the stochastic geometry framework and Poisson hole process. With numerical simulation results, the influences of varying network parameters on D2D performances are illustrated. The results show that by introducing a guard zone, the D2D successful transmission rate can be increased by 41.2%. All the developed D2D frameworks and the summarized useful remarks are used to provide meaningful design insights and guidelines for the deployment strategies of EH-based D2D wireless networks.

Motivated by increasing powerful edge devices with data-intensive computing and limited storage size, we study a MapReduce-based wireless distributed computing framework by allocating a portion of files in the remote data center to the network edge and utilizing computation and memory resources at the edge. Our framework is composed of three step phases: 1)Map; 2)Shuffle; and 3)Reduce. However, in the data shuffling stage, shuffling many data accounts for a large amount of the total running time over wireless interference networks will degrade its performance. Moreover, data shuffling between pervasive edge devices with limited spectrum bandwidth is very challenging. Today, many devices focus on computing functions rather than collecting all the individual wireless data centers. Therefore, we can use over-the-air computation (AirComp) technology to reliably compute multiple target functions by harnessing interference in the multiple-access channel with a higher computation efficiency than the traditional orthogonal multiaccess scheme that combats interference. We study a mixed-timescale optimization of the transmitting–receiving (Tx-Rx) policy and file allocation to minimize the averaged computation mean-squared error (MSE) under the power constraint of each device. File allocation control is adaptive to the long-term statistical channel state information (CSI), while the Tx-Rx policy is adaptive to the CSI and file allocation strategy. We decompose the problem into a short-term Tx-Rx policy and a long-term file allocation control problem to tackle the joint nonconvex optimization. Simulation results indicate the effectiveness of our proposed two-timescale algorithm and the advantages of our computation framework over the state-of-the-art baselines.

We propose a model-driven Bayesian deep learning framework for multiple access uplink systems in Multiuser MIMO systems. Utilizing tools from Streaming Variational Inference, we combine graphical models with neural networks to enable fast online machine learning. The proposed distributed inference framework is shown to be robust and suitable for the online scenario. Our simulations demonstrate the robustness of the proposed solution in online propagation environments and its ability to capture uncertainty.

Over-the-air gradient aggregation and data-aware scheduling have recently drawn great attention due to the outstanding performance in improving communication efficiency for federated edge learning applications. However, in this case, the estimated gradient suffers from the channel and data distortion induced by channel fading and data-aware scheduling, which introduces significant bias and harms the training performance. To solve these problems, we propose a dynamic data and channel adaptive sensor scheduling and power control algorithm combining a residual feedback mechanism. Instead of discarding the gradients not transmitted to the central server, each sensor keeps track of a local residual to store these gradients. Furthermore, by connecting the model update iterations to a dynamic evolution process, we utilize the Lyapunov drift optimization method to analyze the relationship between the training gain and resource allocation. The derived decentralized optimal solution is adaptive to both the channel state information and data importance to seize good transmission opportunity and important gradients. Theoretical analysis is provided on the convergence of the proposed algorithm in practical training scenarios. Simulation results further illustrate that under the same power cost, the proposed scheme has a much faster convergence rate and lower training loss compared to existing baselines.

Considering the wide application of multiple types of sensors with diversified data sensing and collection capabilities, we focus on the resulting hybrid data partitioning among the local data set distributed at the edge sensors, especially the practical training implementation of federated learning (FL) under such a setting, where the neural network (NN) is trained collaboratively without requiring the sensors to share their data. Different from the conventional FL schemes, since each local sensor now only has partial data samples with type-specific features, the traditional stochastic gradient descent (SGD)-based training method cannot be directly utilized due to the intertype and intratype data coupling. To address this issue, we first transform the training problem into the primal–dual domain utilizing the corresponding Lagrangian and propose a stochastic primal-descent dual-ascent training method with a two-side residual feedback mechanism. Such a method can be implemented in a scalable way and compensate for the data distortion and loss caused by the practical transmission noise. Furthermore, a decentralized joint scheduling, bandwidth allocation, and dynamic quantization policy is proposed by analyzing the performance at each training iteration and the consumed transmission resources. The proposed method is adaptive to not only the channel state information (CSI) but also the instantaneous gradient importance and dynamic gradient statistics. The closed-form convergence analysis is provided, and the simulation experiments illustrate the superior performance of the proposed scheme.

With the rapid development of mobile computing, mission-critical Internet of Things (IoT) systems have become popular. Typical mission-critical IoT systems may contain complicated unknown and unstable elements and it is of particular importance to identify and stabilize them as unstable systems may experience catastrophic consequences. We consider the identification and optimal control for a mission-critical IoT system over multiple-input–multiple-output (MIMO) fading channels. First, we focus on the optimal control of the mission-critical IoT system, assuming that the system dynamics are known, and propose a novel stochastic-approximation-based algorithm to learn the optimal control solution for the IoT controller in an online manner. Second, we extend the optimal control framework to deal with the unknown mission-critical IoT system and propose a novel normalized-stochastic-gradient-descent-based algorithm to simultaneously identify and control the system in an online manner. Using the Lyapunov stability analysis, we theoretically show the asymptotic optimality of the proposed learning algorithms. Numerical results are analyzed for our proposed scheme and for several state-of-the-art learning schemes in terms of the computational complexity, convergence, and stability performance. Specifically, the proposed scheme can be implemented more than 50% faster than the state-of-the-art learning schemes. Moreover, the system identification performance of the proposed scheme can achieve a normalized system identification mean square error (MSE) of around 0.01 in 100 iterations. This is a substantial improvement compared to the baseline algorithms, where the normalized system identification MSE diverges.

Mobile-edge computing (MEC) integrated with multiple radio access technologies (multi-RATs) is a promising technique for satisfying the growing low-latency computation demand of intelligent Internet of Things (IoTs) applications. Under the wireless MapReduce framework for executing nomographic functions, this article investigates the joint RAT selection and transceiver design for over-the-air (OTA) aggregation of intermediate values (IVAs) in multi-RAT MEC systems, while taking into account the energy budget constraint for the local computing and IVA transmission per wireless device (WD), so as to adapt to the instantaneous communication opportunities in multiple RATs and the dynamic computational task loads. To provide a complete Pareto optimal solution, we minimize the weighted sum of the computational mean squared error (MSE) of the aggregated IVA at the RAT receivers, the IVA transmission cost of the WDs, and the associated transmission time delay. The joint RAT selection and transceiver design problem for OTA aggregation of the IVAs is a nonconvex mixed-integer problem, which is NP hard. We develop a low-complexity algorithm to solve the challenge by continuous relaxation and alternating optimization. Specifically, the optimal receive beamforming vectors at the gNB/access points (APs) are shown to be the minimum MSE (MMSE) filters. Exploiting the hidden convexity of the remaining subproblem, we obtain an efficient iterative algorithm by alternating between the RAT selection and the transmit coefficient variables for OTA aggregation of IVAs. Extensive numerical results verify the effectiveness of our proposed design as compared to other existing schemes.

Federated learning (FL) is a machine learning framework, where multiple distributed edge Internet of Things (IoT) devices collaboratively train a model under the orchestration of a central server while keeping the training data distributed on the IoT devices. FL can mitigate the privacy risks and costs from data collection in traditional centralized machine learning. However, the deployment of standard FL is hindered by the expense of the communication of the gradients from the devices to the server. Hence, many gradient compression methods have been proposed to reduce the communication cost. However, the existing methods ignore the structural correlations of the gradients and, therefore, lead to a large compression loss which will decelerate the training convergence. Moreover, many of the existing compression schemes do not enable over-the-air aggregation and, hence, require huge communication resources. In this work, we propose a gradient compression scheme, named FedOComp, which leverages the correlations of the stochastic gradients in FL systems for efficient compression of the high-dimension gradients with over-the-air aggregation. The proposed design can achieve a smaller deceleration of the training convergence compared to other gradient compression methods since the compression kernel exploits the structural correlations of the gradients. It also directly enables over-the-air aggregation to save communication resources. The derived convergence analysis and simulation results further illustrate that under the same power cost, the proposed scheme has a much faster convergence rate and higher test accuracy compared to existing baselines.

In this article, we propose an online training framework for deep neural network (DNN)-based mmWave massive multiple-input multiple-output (MIMO) channel estimation (CE) in Internet-of-Things (IoT) systems with nonlinear amplifier distortions. The DNN-based channel estimator is trained online in the IoT device based on real-time received pilot measurements from the base station (BS) without knowledge of the true channels, and can simultaneously generate CE in real time. To realize this, we first propose three axioms for a legitimate online loss function under known nonlinearity, based on which we develop a channel model-free online training algorithm with convergence analysis. For unknown nonlinearity, we propose a two-stage DNN structure with nonlinear modules, for which the DNN-based CE and nonlinear functions can be jointly trained online based on real-time received pilots. Simulation results show that the proposed solution achieves better CE accuracy than traditional compressive sensing (CS) algorithms while enjoying a much faster computational efficiency. In addition, the proposed method is robust to various nonlinear channel model mismatches and is able to track the change of the nonlinear channel model.

In this article, we consider the remote state estimation for nonlinear dynamic systems with known linear dynamics and unknown nonlinear perturbations. The nonlinear dynamic plant is monitored by multiple distributed sensors over a random access wireless network with shared common radio channel. We focus on the communication strategy and remote state estimation algorithm design so as to achieve a remote state estimation stability subject to unknown nonlinearities in plant and various wireless impairments, such as multisensor interference, wireless fading, and additive channel noise. By exploiting the additive properties of the physical wireless channels, we propose a novel information fusion over-the-air mechanism to address the signal collision and interference among the sensors. Utilizing the partial knowledge on the linear dynamics of the plant, we also propose a novel recurrent neural network (RNN)-based remote state estimator aided by a virtual state estimation mean-square-error (MSE) process. We further propose a novel online training algorithm such that the RNN at the remote estimator can effectively learn the unknown plant nonlinearities. Using the Lyapunov drift analysis approach, we establish closed-form sufficient requirements on the communication resources needed to achieve almost sure stability of both state estimation and RNN online training in high signal-to-noise ratio (SNR) regime. As a result, our proposed scheme is asymptomatic optimal for large SNR in the sense that both the plant state and the unknown plant nonlinearities can be perfectly recovered at the remote estimator. The proposed scheme is also compared with various baselines and we show that significant performance gains can be achieved.

In this paper, reinforcement learning (RL) based end-to-end channel prediction (CP) and beamforming (BF) algorithms are proposed for multi-user downlink system. Different from the previous methods which either require perfect channel state information (CSI), or estimate outdated CSI and set constraints on pilot sequences, the proposed algorithms have no such premised assumptions or constraints. Firstly, RL is considered in channel prediction and the actor-critic aided CP algorithm is proposed at the base station (BS). With the received pilot signals and partial feedback information, the actor network at BS directly outputs the predicted downlink CSI without channel reciprocity. After obtaining the CSI, BS generates the beamforming matrix using zero-forcing (ZF). Secondly, we further develop a deep RL based two-layer architecture for joint CP and BF design. The first layer predicts the downlink CSI with the similar actor network as in the CP algorithm. Then, by importing the outputs of the first layer as inputs, the second layer is the actor-critic based beamforming layer, which can autonomously learn the beamforming policy with the objective of maximizing the transmission sum rate. Since the learning state and action spaces in the considered CP and BF problems are continuous, we employ the actor-critic method to deal with the continuous outputs. Empirical numerical simulations and the complexity analysis verify that the proposed end-to-end algorithms could always converge to stable states under different channel statistics and scenarios, and can beat the existing traditional and learning based benchmarks, in terms of transmission sum rate.

The deployment of federated learning in a wireless network, calledfederated edge learning(FEEL), exploits low-latency access to distributed mobile data to efficiently train an AI model while preserving data privacy. In this work, we study the spatial (i.e., spatially averaged) learning performance of FEEL deployed in a large-scale cellular network with spatially random distributed devices. Both the schemes of digital and analog transmission are considered, providing support of error-free uploading and over-the-air aggregation of local model updates by devices. The derived spatial convergence rate for digital transmission is found to be constrained by a limited number of active devices regardless of device density and converges to the ground-true rate exponentially fast as the number grows. The population of active devices depends on network parameters such as processing gain and signal-to-interference threshold for decoding. On the other hand, the limit does not exist for uncoded analog transmission. In this case, the spatial convergence rate is slowed down due to the direct exposure of signals to the perturbation of inter-cell interference. Nevertheless, the effect diminishes when devices are dense as interference is averaged out by aggressive over-the-air aggregation. In terms of learning latency (in second), analog transmission is preferred to the digital scheme as the former dramatically reduces multi-access latency by enabling simultaneous access.

In massive multiple-input multiple-output (MIMO) systems employing hybrid analog-digital precoder, there are two commonly used antenna structures that have their own pros and cons, namely, the fully-connected antenna structure (FCAS) and the partially-connected antenna structure (PCAS). The FCAS achieves better spectrum efficiency (SE) even with reduced radio frequency (RF) chains, but the hardware cost and power consumption are still grievous. Contrarily, the PCAS has less hardware cost and power consumption, but suffers from severe performance loss. In this paper, by combining the advantages of both structures, we first propose a sparse adaptive antenna structure (SAAS) for the implementation of the hybrid precoder, which can jointly control the on/off state of all phase shifters (PS) and RF chains through a switch network. Then, a sparse hybrid precoding (SHP) optimization problem based on the proposed SAAS is established aiming at minimizing the total power consumption under individual average data rate requirements. To tackle the challenging non-smooth non-convex stochastic optimization (NSO) emerged with the SHP design and reduce the power consumption of PSs and RF chains, we propose a sparse smooth approximation based an online algorithm to find a stationary point of the NSO problem and establish its convergence. Simulations verify that the proposed antenna structure and algorithm achieve a better balance between power consumption and system throughput than the existing schemes.

In this paper, we consider a wireless multihop device-to-device (D2D) based mobile edge computing (MEC) system, where the destination wireless device (WD) is scheduled to compute nomographic functions. Under the MapReduce framework and motivated by reducing communication resource overhead, we propose a new multi-level over-the-air (OTA) aggregation scheme for the destination WD to collect the individual partially aggregated intermediate values (IVAs) for reduction from multiple source WDs in the data shuffling phase. For OTA aggregation per level, the source WDs employ a truncated channel-inverse structure multiplied by their individual transmit coefficients in transmission over the same time-frequency resource blocks, and the destination WD finally uses a receive filtering factor to construct the aggregated IVA. Under this setup, we develop a unified transceiver design framework that minimizes the mean squared error (MSE) of the aggregated IVA at the destination WD subject to the source WDs’ individual power constraints, by jointly optimizing the individual transmit coefficients of the source WDs and the receive filtering factor of the destination WD. The formulated power-constrained MSE minimization problem is non-convex. First, based on the primal decomposition method, we derive the closed-form solution under the special case of a common transmit coefficient. This shows that the common transmit coefficient of the source WDs is determined by the minimal transmit power budget among them. Next, for the general case, we transform the original problem into a quadratic fractional programming problem, and then develop a low-complexity algorithm to obtain the (near-) optimal solution by leveraging Dinkelbach’s algorithm along with the Gaussian randomization method. Numerical results are provided to demonstrate the significant performance gains achieved by the proposed multi-level OTA aggregation scheme over various existing schemes.

Over-the-air computation (AirComp) has emerged as a promising technique in future intelligent wireless networks, which enables swift functional computation among distributed wireless devices (WDs) by exploiting the superposition property of wireless channels. This paper studies a newhierarchicalAirComp network over a large area, in which a set of intermediate relays are exploited to facilitate the massive data aggregation from a large number of WDs. Under this setup, we present a two-phase amplify-and-forward (AF) relaying protocol. In the first phase, the WDs simultaneously send their data to the relays, while in the second phase, the relays amplify the respectively received signals and concurrently forward them to the fusion center (FC) for aggregation. Our objective is to minimize the computational mean squared error (MSE) at the FC, by jointly optimizing the transmit coefficients of the WDs, the AF coefficients of the relays, and the de-noising factor of the FC, subject to their individual transmit power constraints. First, we consider the centralized design with global channel state information (CSI), in which the inter-relay signals can be exploited beneficially for data aggregation. In this case, we develop an alternating-optimization-based algorithm to obtain a high-quality solution to the computational MSE minimization problem. The obtained solution shows that the phase of the transmit coefficient at each WD is opposite to that of the WD-relay-FC channel to ensure the signal phase alignment at the FC, and the transmit power of each WD/relay follows a regularized composite-channel-inversion structure to strike a balance between minimizing the signal-magnitude-misalignment-induced error and the noise-induced error. Next, to reduce the signaling overhead caused by the centralized design, we consider an alternative decentralized design with partial CSI, in which the relays and the FC make their own decisions by only requiring the channel power gain information across different relays. In this case, the relays and FC need to treat the inter-relay signals as harmful interference or noise. Accordingly, we optimize the transmit coefficients of the WDs associated with each relay, and the relay AF coefficients (together with the FC de-noising factor) in an iterative manner, which can be implemented efficiently in a decentralized way. Finally, numerical results show the fast convergence of the proposed centralized and decentralized designs. It is also shown that both designs achieve significant MSE performance gains over benchmark schemes without the joint optimization.

The enormous data distributed at the network edge and ubiquitous connectivity have led to the emergence of the new paradigm of distributed machine learning and large-scale data analytics. Distributed principal component analysis (PCA) concerns finding a low-dimensional subspace that contains the most important information of high-dimensional data distributed over the network edge. The subspace is useful for distributed data compression and feature extraction. This work advocates the application of over-the-air federated learning to efficient implementation of distributed PCA in a wireless network under a data-privacy constraint, termed AirPCA. The design features the exploitation of the waveform-superposition property of a multi-access channel to realize over-the-air aggregation of local subspace updates computed and simultaneously transmitted by devices to a server, thereby reducing the multi-access latency. The original drawback of this class of techniques, namely channel-noise perturbation to uncoded analog modulated signals, is turned into a mechanism for escaping from saddle points during stochastic gradient descent (SGD) in the AirPCA algorithm. As a result, the convergence of the AirPCA algorithm is accelerated. To materialize the idea, descent speeds in different types of descent regions are analyzed mathematically using martingale theory by accounting for wireless propagation and techniques including broadband transmission, over-the-air aggregation, channel fading and noise. The results reveal the accelerating effect of noise in saddle regions and the opposite effect in other types of regions. The insight and results are applied to designing an online scheme for adapting receive signal power to the type of current descent region. Specifically, the scheme amplifies the noise effect in saddle regions by reducing signal power and applies the power savings to suppressing the effect in other regions. From experiments using real datasets, such power control is found to accelerate convergence while achieving the same convergence accuracy as in the ideal case of centralized PCA.

Optimal algorithm design for federated learning (FL) remains an open problem. This paper explores the full potential of FL in practical edge computing systems where workers may have different computation and communication capabilities, and quantized intermediate model updates are sent between the server and workers. First, we present a general quantized parallel mini-batch stochastic gradient descent (SGD) algorithm for FL, namely GenQSGD, which is parameterized by the number of global iterations, the numbers of local iterations at all workers, and the mini-batch size. We also analyze its convergence error for any choice of the algorithm parameters. Then, we optimize the algorithm parameters to minimize the energy cost under the time constraint and convergence error constraint. The optimization problem is a challenging non-convex problem with non-differentiable constraint functions. We propose an iterative algorithm to obtain a KKT point using advanced optimization techniques. Numerical results demonstrate the significant gains of GenQSGD over existing FL algorithms and reveal the importance of optimally designing FL algorithms.

In this paper, we propose a two-stage deep neural network (DNN)-based channel estimator for massive multiple-input multiple-output (MIMO) systems with nonlinear amplifier distortions. The proposed two-stage structure is able to jointly learn a DNN-based channel estimator and the nonlinear transfer functions online based on real-time received pilot measurements, while generating channel estimation (CE) simultaneously. This is realized by a careful design of an online loss function that does not depend on ground truth channels while taking into account the unknown nonlinear distortions. Simulation shows that the proposed scheme outperforms the traditional compressive-sensing (CS) algorithms in terms of CE accuracy, and enjoys a much faster computational time during channel inferencing. The proposed scheme is also robust to various model mismatches and can adapt to the change of the underlying channel model.

In this paper, we propose a novel joint trajectory and communication scheduling scheme for multiple unmanned aerial vehicles (UAVs) enabled wireless caching networks. To exploit the favorable propagation of air-to-ground channels and spatial multiplexing gains, we consider an ultra dense UAVs enabled content-centric wireless transmission network, where massive UAVs are deployed to transmit cached contents to a group of random distributed ground users. We formulate this problem as a infinite horizon ergodic stochastic differential game (SDG) for optimizing the users’ quality-of-experience (QoE) on the concept of request queues for cached contents. By means of mean-field game (MFG) analysis, we derive a reduced-complexity control solution. In addition, we further propose a novel model-specific deep neural network (DNN) to learn the PDEs numerically by exploiting a homotopy perturbation method (HPM).

Federated learning (FL) has been widely utilized to leverage the distributed dataset and processing capability of local sensors while preserving data privacy. Considering the utilization of multiple groups of sensors with diverse sensing functions in IoT wireless networks, we focus on a new hybrid data partitioning scenario, where each sensor can only obtain partial data samples on type-specific feature space. This results in the combined sample parallelism among same-type sensors and feature parallelism among different types of sensors, which brings challenges to designing scalable and communication-efficient training algorithms. Different from the conventional FL settings, we transform the training problem to the primal-dual domain and propose a novel hierarchical FL framework where both intra-type and inter-type over-the-air collaboration between local sensors are utilized to exploit the sample and feature diversity. Simulation results illustrate the importance of such collaborative training and the efficiency of the proposed transmission scheme.

In this paper, we propose an online deep learning (DL)-based channel estimation (CE) for massive multiple-input multiple-output (MIMO) systems with limited pilots, where the training stage can be implemented online based on real-time received pilot signals. This is realized by introducing a sparsifying loss function that is model-free and does not need ground truth labeled channel data. Simulation results show that the proposed online DL-based scheme achieves comparable channel estimation performance to traditional model-based compressive sensing (CS) algorithms while enjoying a much faster computation during the channel inferencing stage. In addition, the proposed scheme is also robust to various model mismatches and is able to track the change of the underlying propagation environment.

We consider the recurrent neural network (RNN)-based remote state estimation for nonlinear dynamic systems with unknown state dynamics. The nonlinear dynamic plant is monitored by multiple distributed IIoT sensors over a random access wireless network with shared common spectrum. We focus on the remote state estimation algorithm design so as to achieve remote state estimation stability subject to noninvertible nonlinear sensor state observations, imperfect channel state information (CSI) at the remote estimator, and various wireless impairments, such as multisensor interference, wireless fading, and additive channel noise. Utilizing a state diffeomorphism, the original system is transformed into a canonical form with a linear rank deficient observation matrix. We propose a novel RNN remote state estimator based on the pole placement design associated with the transformed rank deficient state measurement matrices. We further propose a novel online training algorithm such that the RNN at the remote estimator can not only address the divergence issue over wireless networks but also effectively learn the unknown nonlinear plant dynamics despite rank deficiency and imperfect CSI. Using the Lyapunov drift analysis approach, we establish closed-form sufficient requirements on the communication resources needed to achieve almost sure stability of both state estimation and RNN online training in the high signal-to-noise ratio (SNR) regime. As a result, our proposed scheme is asymptomatic optimal for large SNR in the sense that both the plant state and the unknown plant nonlinearity can be perfectly recovered at the remote estimator. The proposed scheme is also compared with various baselines and we show that significant performance gains can be achieved.

This article investigates the analog gradient aggregation (AGA) solution to overcome the communication bottleneck for wireless federated learning applications by exploiting the idea of analog over-the-air transmission. Despite the various advantages, this special transmission solution also brings new challenges to both transceiver design and learning algorithm design due to the nonstationary local gradients and the time-varying wireless channels in different communication rounds. To address these issues, we propose a novel design of both the transceiver and learning algorithm for the AGA solution. In particular, the parameters in the transceiver are optimized with the consideration of the nonstationarity in the local gradients based on a simple feedback variable. Moreover, a novel learning rate design is proposed for the stochastic gradient descent algorithm, which is adaptive to the quality of the gradient estimation. Theoretical analyses are provided on the convergence rate of the proposed AGA solution. Finally, the effectiveness of the proposed solution is confirmed by two separate experiments based on linear regression and the shallow neural network. The simulation results verify that the proposed solution outperforms various state-of-the-art baseline schemes with a much faster convergence speed.

Emerging hardware technology enables the utilization of a large number of multitype sensors with diverse sensing capabilities for data collection and the training of AI models. Each sensor collects partial data samples on a type-specific feature space, which results in hybrid data partitioning across the local datasets and brings challenges to developing a novel communication-efficient and scalable training algorithm. We propose a hierarchical federated learning framework for such hybrid data partitioning with a multitier-partitioned neural network architecture. Specifically, we adopt a primal-dual transform to decompose the training problem on both the sample and feature space. Then, a stochastic coordinate gradient descent ascent algorithm is implemented with intratype and intertype over-the-air aggregation for the update of the primal variables and dual variables, respectively. The incorporation of over-the-air aggregation for signal transmission naturally harnesses the channel perturbations and interference for lower communication complexity and preserved privacy. Despite the influence of transmission noise and channel distortion, convergence analysis is provided for general objective functions, which illustrates the robust training performance of the proposed algorithm with a theoretical guarantee.

We consider remote state estimation for an industrial Internet-of-Things (IoT) system, where the plant dynamics are monitored by a number of distributed industrial IoT sensors. We propose an “estimation friendly” remote state estimation framework, which not only maintains low computational complexity but also provides better estimation stability performance. Specifically, we propose a novel over-the-air-aggregation-based multiple access, which enhances the observability performance of the state estimation system and hence, provides better estimation stability. Additionally, exploiting the sparsity in the observation matrix induced by the over-the-air-aggregation-based multiple access, we propose a low-complexity 2-D message passing state estimation algorithm, where the cyclic loops in the 2-D factor graphs are removed based on the quasi-diagonal transformation of the aggregated channel matrix of the IoT sensors. As a result, the proposed state estimation scheme is of low complexity and can achieve exact maximum a posterior estimation. Using the Lyapunov drift analysis, we derive the closed-form necessary and sufficient conditions for stability of the mission-critical remote state estimation system. The numerical results demonstrate that the proposed scheme has a low computational complexity. Furthermore, it is scalable with the number of sensors and has a low power consumption.

In this paper, we propose a novel joint trajectory and communication scheduling scheme for multiple unmanned aerial vehicles (UAVs) enabled wireless caching networks. To exploit the favorable propagation of air-to-ground channels, we consider an ultra dense UAVs enabled content-centric wireless transmission network, where massive UAVs are deployed to transmit cached contents to a group of random distributed ground users. We formulate the problem as an infinite horizon ergodic stochastic differential game (SDG) for optimizing the users' quality-of-experience (QoE). In particular, stochastic dynamics of channel states, UAVs' mobility, energy queues and content request queues are modeled in this game. To deal with the state coupling between the UAVs, we consider a limiting problem for large number of UAV based on mean field analysis. A reduced-complexity decentralized solution can be obtained through mean-field equilibrium analysis. To further reduce the solution complexity on each UAV, we propose a model-specific deep neural network (DNN) to learn the optimal control solution in an online manner. The DNN is not arbitrarily generated but tailored to the structural properties of the value function and stationary distribution based on the homotopy perturbation method analysis. Finally, simulation results are provided to show that the proposed solution can achieve significant gain over the existing baselines.

We propose a Bayesian deep learning framework for model driven online sparse channel estimation task in Multi-user MIMO systems. Tools from Bayesian neural network and stochastic variational Bayesian Inference are utilized to capture aleatoric and epistemic uncertainty estimates. We treat the network prediction as an auxiliary variable to allow inference performance to be unaffected by the stage of training of the network. In addition to providing uncertainty estimates, being Bayesian, the framework enables us the possibility to marginalize over penalty parameters and is well suited for online scenario with changing environments. Our simulations show that the framework is robust to model mismatch, and efficiently captures uncertainty in the predictions.

In this paper, we consider a mission-critical remote state estimation system with asynchronous massive access of the IoT sensors. We focus on remote state estimation stability of the system in the presence of asynchronous access of the sensors. Exploiting the sparsity in the observation matrix induced by the asynchronous access, we propose a low complexity 2-D message passing state estimation algorithm, where the cyclic loops in the 2-D factor graphs are removed based on the Gaussian-elimination-based quasi-diagonalization of the oversampled aggregated channel matrix of the IoT sensors. As a result, the proposed state estimation scheme is of low complexity and can achieve exact MAP estimation. Using Lyapunov drift analysis, we derive closed-form necessary and sufficient conditions for stability of the mission-critical remote state estimation system. We show that our proposed scheme can achieve significant performance gain over various state-of-the-art baselines for the large-scale system under asynchronous massive access.

Channel estimation is crucial to modern wireless systems and becomes more and more challenging with the growth of user throughput in sub-6 GHz multiple input multiple output configuration. Plenty of literature spends great efforts in improving the estimation accuracy, while the interpolation schemes are overlooked. To deal with this challenge, we exploit the super-resolution image recovery scheme to model the non-linear interpolation mechanisms. Moreover, in order to extend the estimation scheme into the non-stationary environment which is especially attractive in the coming 6G, we utilize the recurrent network structure to approximate the non-linear channel statistic correlation to model the non-stationary behavior which is difficult to accomplish in the theoretical way. To make it more practical, we offline generate numerical channel coefficients according to the statistical channel models to train the neural networks and directly apply them in different environments. As shown in this paper, the proposed unified super-resolution based channel estimation scheme can outperform the conventional approaches in both stationary and non-stationary scenarios, which we believe can significantly change the current channel estimation method in the near future.

In this paper, we aim to give a unified performance limit analysis of the visible light-based positioning (VLP) for a vehicular user equipment (UE), which will help to understand the essence of time-domain localization cooperation and gain insights into how to improve the performance limit of the vehicular VLP system. This is challenging due to the complex system models and the complex dependency between UE location performance and orientation performance. To achieve the above goal, we will first characterize the closed-form error bounds of the UE location and orientation at each time slot, respectively, in terms of Fisher information. Generally, the VLP error will propagate over time as the vehicular UE moves, and hence the VLP error at the current time slot is affected by the VLP performance at the previous time slot, the UE mobility and the channel quality. Based on the obtained VLP error bounds, we then reveal the impact of prior UE location knowledge, UE mobility and signal-to-noise-ratio on the VLP performance. Furthermore, the time-domain evolution of the VLP error is studied, where the convergence of the time-domain VLP error evolution is established and its closed-form stable state is quantified, which will shed light on the long-term performance of the vehicular VLP system.

In massive MIMO systems, the effective channel state information (CSI) is an essential prerequisite for the beamforming (BF) design. While there is a handful of previously proposed compressive sensing (CS)-based channel estimation algorithms in the literature, the large BF gain provided by the massive MIMO array has not been fully exploited in the channel estimation stage. In order to obtain higher BF gain and better estimation performance with less pilot overhead, we study the joint design of the transmitting and receiving hybrid pilot BF as well as the associated channel tracking scheme. Specifically, a Markov prior is used to model the temporal correlation in massive MIMO channels over different time slots. Then, the hybrid pilot BF is optimized by maximizing the mutual information between the channel measurements and the corresponding downlink sparse channels with the Markov prior. Following, we derive an efficient channel tracking algorithm called Turbo Bayesian Inference (Turbo-BI) to solve the resulting CS problem and generate the channel prior information required to calculate the mutual information for the optimization of hybrid pilot BF in the next time slot. The proposed Turbo-BI can exploit both the sparsity and the temporal correlation of massive MIMO channels to enhance the estimation performance. Finally, simulations show that our proposed algorithm can achieve significant gain over the existing state-of-the-art baselines.

Massive MIMO has been regarded as a key enabling technique for 5G and beyond networks. Nevertheless, its performance is limited by the large overhead needed to obtain the high-dimensional channel information. To reduce the huge training overhead associated with conventional pilot-aided designs, we propose a novel blind data detection method by leveraging the channel sparsity and data concentration properties. Specifically, we propose a novel$\ell _{3}$-norm-based formulation to recover the data without channel estimation. We prove that the global optimal solution to the proposed formulation can be made arbitrarily close to the transmitted data up to a phase-permutation ambiguity. We then propose an efficient parameter-free algorithm to solve the$\ell _{3}$-norm problem and resolve the phase-permutation ambiguity. We also derive the convergence rate in terms of key system parameters such as the number of transmitters and receivers, the channel noise power, and the channel sparsity level. Numerical experiments will show that the proposed scheme has superior performance with low computational complexity.

Compressive channel estimation (CCE) schemes have been proposed in millimeter-wave (mmWave) systems. The design of the analog combining matrix plays an important role to provide high channel recovery quality. In this paper, we propose a fixed-loading-based flexible hybrid beamforming (HBF) architecture (FL-FHA), which enables an arbitrary number of phase shifters (PSs) and a flexible connection strategy for the analog network. With the proposed flexible HBF architecture, we develop a variational-Bayesian-inference (VBI)-based algorithm to estimate the mmWave channel exploiting the channel support side information (CSSI) at the base station. Furthermore, we propose a parallel configuration optimization (CO) algorithm to optimize the FL-FHA according to CSSI, aiming at improving the overall CE performance. Simulation results show that the proposed CSSI-assisted joint CO and CCE outperforms the baselines with a small number of phase shifters.

In this paper, we consider a MIMO cyber physical system (CPS) in which a sensor amplifies and forwards the observed MIMO plant state to a remote controller via a MIMO fading channel. We focus on the MIMO amplify-and-forward (AF) precoding design at the sensor to minimize a weighted average state estimation error at the remote controller subject to an average communication power gain constraint of the sensor. The MIMO AF precoding design is formulated as an infinite horizon average cost Markov decision process (MDP). To deal with the curse of dimensionality associated with the MDP, we propose a novel continuous-time perturbation approach and derive an asymptotically optimal closed-form priority function for the MDP. Based on this, we derive a closed-form first-order optimal dynamic MIMO AF precoding solution, and the solution has an event-driven control structure. Specifically, the sensor activates the strongest eigenchannel to deliver a dynamically weighted combination of the plant states to the controller when the accumulated state estimation error exceeds a dynamic threshold. We further establish technical conditions for ensuring the stability of the MIMO CPS.

In this article, we are interested in leak identification (LI) for water supply pipelines using transient-wave (pressure) measurement data. This is challenging since water pipeline system conditions are usually uncertain in practice. For instance, the pipeline diameter, the friction factor, and the pipeline shape will vary. The conventional signal propagation model-based LI methods rely on a deterministic system model with perfectly known and fixed-value parameters, which limits their application in general cases. To address this challenge, we design a novel deep neural network (DNN)-based machine learning approach to solve the LI problem. First, we propose a novel fusion-enhanced stochastic optimization algorithm for the DNN training, which can greatly improve the DNN training performance and hence the LI accuracy, without increasing the computational cost. Second, we design a novel convolutional-based pooling network to extract the stable texture feature of transient-wave samples, thus achieving a reliable LI solution against the pipeline system dynamics. It is shown in experiments that, thanks to the above system design, the proposed DNN-based LI method can achieve a failure rate lower than $6\times 10^{-4}$ when the signal-to-noise ratio is 0 dB, which outperforms the conventional LI methods.

In this paper, we consider a mission-critical control system, where an unstable dynamic plant is monitored by multiple distributed IoT sensors over a wireless communication network with shared common spectrum. To reduce the complexity of Kalman filtering, we consider a constant gain filter at the remote controller. We propose a decentralized dynamic scheduling and information fusion of the IoT sensors to stabilize the unstable dynamic plant. The proposed scheme has a state-driven multiple access structure, where a large state estimation MSE (high transmission urgency) and good wireless channel conditions (good transmission opportunities) promote the active mode of the sensors. Using the Lyapunov techniques, we provide the closed-form sufficient condition for stability and closed-form characterizations on the trade-off between the state estimation MSE and average power consumption of the sensors. We also propose a design guideline for the constant filter gain via minimizing the state estimation MSE. The proposed scheme is also compared with various representative literature and we show that significant performance gains can be achieved.

In many applications in wireless communications, we need to recover a structured sparse signal from a linear measurement model with uncertain sensing matrix. There are two challenges of designing an algorithm framework for this problem. How to choose a flexible yet tractable sparse prior to capture different structured sparsities in specific applications? How to handle a sensing matrix with uncertain parameters and possibly correlated entries? As will be explained in the introduction, existing common methods in compressive sensing (CS), such as approximate message passing (AMP) and variational Bayesian inference (VBI), may not work well. To better address this problem, we propose a novel Turbo-VBI algorithm framework, in which a three-layer hierarchical structured (3LHS) sparse prior model is proposed to capture various structured sparsities that may occur in practice. By combining the message passing and VBI approaches via the turbo framework, the proposed Turbo-VBI algorithm is able to fully exploit the structured sparsity (as captured by the 3LHS sparse prior) for robust recovery of structured sparse signals under an uncertain sensing matrix. Finally, we apply the Turbo-VBI framework to solve two application problems in wireless communications and demonstrate its significant gain over the state-of-art CS algorithms.

In this paper, visible light-based positioning (VLP) is studied. VLP is greatly challenging because (i) it is essentially a non-convex optimization problem since the visible-light received signal strength (RSS) is nonlinear with the user equipment (UE) position; and (ii) in addition to the UE location, the visible light RSS also depends on the UE orientation and small-scale channel gains, which are unknown in practice. This complicates the VLP problem due to the enlarged searching space. To address these challenges, we propose a location-domain grid sampling scheme. Specifically, the location-domain grid sampling can potentially partition the location space into small cells, and hence the non-convexity challenge of RSS-based VLP is mitigated. In addition, using the location-domain grid sampling, we transform VLP into a sparse recovery problem. A novel group sparse learning (GSL) algorithm with self-adaptive location-domain grids is proposed to achieve an efficient RSS-based VLP solution, via exploring the inherent sparse structure. The convergence of our GSL algorithm is established. Thanks to the adaptivity of dynamic location-domain grids, the required number of location-domain grids can be significantly reduced, compared with conventional fixed-grid-based GSL solutions. Moreover, the proposed GSL-based VLP method jointly learns the UE location, orientation and channel gain, thus achieving a robust RSS-based VLP solution against parameter uncertainties. Finally, our simulation result verifies the large performance gain of the proposed RSS-based VLP solution over state-of-the-art VLP baselines, thanks to our self-adaptive grid sampling and problem-specific group sparse learning.

In this paper, joint estimation for channel and Inphase/Quadrature imbalance (IQI) is investigated in the downlink Frequency Division Duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. First, exploiting the sparsity of massive MIMO channels and the timescale separation of channels and IQI, we derive a two-timescale sparse maximum a posterior (MAP) formulation for the joint estimation, where the IQI parameter is the long- term variable and the sparse channel is the short- term variable. Then we propose a two-timescale online joint sparse estimation (TOJSE) algorithm to solve the problem, which can converge to the stationary solutions of the original two-timescale non-convex stochastic optimization problem over time. Finally, simulations show that our proposed TOJSE algorithm can achieve significant gain over various baselines.

Channel estimation is crucial for modern WiFi system and becomes more and more challenging with the growth of user throughput in multiple input multiple output configuration. Plenty of literature spends great efforts in improving the estimation accuracy, while the interpolation schemes are overlooked. To deal with this challenge, we exploit the super-resolution image recovery scheme to model the non-linear interpolation mechanisms without pre-assumed channel characteristics in this paper. To make it more practical, we offline generate numerical channel coefficients according to the statistical channel models to train the neural networks, and directly apply them in some practical WiFi prototype systems. As shown in this paper, the proposed super-resolution based channel estimation scheme can outperform the conventional approaches in both LOS and NLOS scenarios, which we believe can significantly change the current channel estimation method in the near future.

Indoor localization becomes a raising demand in our daily lives. Due to the massive deployment in the indoor environment nowadays, WiFi systems have been applied to high accurate localization recently. Although the traditional model based localization scheme can achieve sub-meter level accuracy by fusing multiple channel state information (CSI) observations, the corresponding computational overhead is significant. To address this issue, the model-free localization approach using deep learning framework has been proposed and the classification based technique is applied. In this paper, instead of using classification based mechanism, we propose to use a logistic regression based scheme under the deep learning framework, which is able to achieve sub-meter level accuracy (97.2cm medium distance error) in the standard laboratory environment and maintain reasonable online prediction overhead under the single WiFi AP settings. We hope the proposed logistic regression based scheme can shed some light on the model-free localization technique and pave the way for the practical deployment of deep learning based WiFi localization systems.

Millimeter wave (mmWave) systems have potential advantages for high accuracy localization due to their large bandwidth and highly directional and sparse channel. The existing schemes on mmWave system localization only explore the channel's angular sparsity and restrict the representation of angular parameters to fixed grids. In this paper, we propose a joint sparse channel model with adjustable grids of angles and delays of mmWave paths, which not only enables exploiting the joint sparsity in angular and delay domain, but also mitigates the quantization error due to fixed grids. We formulate the estimation of channel, angles and delays as a sparse Bayesian learning problem and propose to solve it using an in-exact block minorization-maximization algorithm and then determine the user location. We verify the performance of the algorithm by simulations.

Many important application scenarios in the future fifth generation (5G) systems, such as indoor navigation and autonomous driving, rely on accurate localization of users. In this paper, we propose a sparse-Bayesian-inference (SBI) based direct location algorithm for massive MIMO systems, which can exploit the sparse and high-resolution nature of angle of arrival (AoA) and any available statistical location information (SLI), to significantly improve the user localization accuracy. The existing common methods in SBI, such as approximate message passing (AMP) an variational Bayesian inference (VBI), may not work well for the massive MIMO localization problem due to their respective drawbacks. To overcome these drawbacks, we first propose a novel three-layer hierarchical structured (3LHS) sparse prior model to incorporate both the structured sparsity of the massive MIMO channel and the SLI into the SBI-based localization formulation. Then we propose a structured VBI algorithm called 3LHS-VBI to solve the resulting SBI-based localization problem. Finally, simulations verify the superior performance of the proposed location algorithm.

In this paper, we consider the stabilization of multiagent dynamic systems. We propose a novel cloud-assisted information sharing solution for the mission-critical Internet of Things control applications. In the proposed design, the sensors and controllers communicate with the cloud network using modulation-free transmissions. Via exploiting the additive properties of the physical wireless channels, signal collision, and interference among the agents is utilized for multiagent stabilization by means of information fusion over the air (AirFuse). Utilizing the proposed AirFuse mechanism, the cloud provides observation side-information and control side-information efficiently to the sensor and the controller in each agent, respectively, which substantially enhance the stabilization performance of the multiagent dynamic system. Using the Lyapunov drift analysis approach, we further establish closed-form sufficient requirements on the communication resources needed to achieve stabilization of the multiagent dynamic system. Compared with the conventional multiagent dynamic systems without cloud assistance, we explicitly quantify the benefits of the cloud assistance in terms of stability performance and show that the proposed cloud-assisted solution enjoys superior scalability performance with ultra low access latency.

The wireless backhaul network provides an attractive solution for the urban deployment of fifth generation (5G) wireless networks that enables future ultra dense small cell networks to meet the ever-increasing user demands. Optimal deployment and management of 5G wireless backhaul networks is an interesting and challenging issue. In this paper, we propose the optimal gateways deployment and wireless backhaul route schemes to maximize the cost efficiency of 5G wireless backhaul networks. In generally, the changes of gateways deployment and wireless backhaul route are presented in different time scales. Specifically, the number and locations of gateways are optimized in the long time scale of 5G wireless backhaul networks. The wireless backhaul routings are optimized in the short time scale of 5G wireless backhaul networks considering the time-variant over wireless channels. Numerical results show the gateways and wireless backhaul route optimization significantly increases the cost efficiency of 5G wireless backhaul networks. Moreover, the cost efficiency of proposed optimization algorithm is better than that of conventional and most widely used shortest path (SP) and Bellman-Ford (BF) algorithms in 5G wireless backhaul networks.

The spectral efficiency of small-cell wireless networks is limited by the backhaul capacity of the base stations (BSs) as well as the severe interference from the neighboring BSs. One promising approach to improve the spectral efficiency of small-cell wireless networks is cache-aided cooperative transmission, where caching at the BSs can alleviate the high-speed backhaul capacity requirement and cooperative transmission can enhance the signal-to-interference-plus-noise ratio. A key issue is that the cached content may not be located at the nearest BS, which means that to access such content, a user needs to overcome strong interference from the nearby BSs. We propose an interference-aware dual-mode caching and user-centric open-loop cooperative transmission scheme that embraces spatial caching diversity and user-centric open-loop cooperative transmission, and alleviate the interference issue in the system. Based on the proposed scheme, we derive a tractable expression of the average successful transmission probability (STP) in terms of key system parameters. We then design a low-complexity algorithm to optimize the average STP with respect to the bandwidth and the cache storage capacity allocation. Simulations show that the proposed scheme achieves a higher average STP than the existing caching schemes.

Recently, unmanned aerial vehicles (UAVs) have been utilized to extend the coverage and capacity of terrestrial wireless networks. In such a UAV-aided wireless network, we can exploit the more favorable line-of-sight (LOS) propagation in the UAV-user wireless link to achieve better coverage and higher capacity. Furthermore, the flexible deployment of UAVs makes them ideal for catering to ad hoc demand. Due to the lack of fixed-line backhaul, UAVs act like relays with wireless backhaul links in UAV-aided wireless networks. Unlike the conventional relays in terrestrial wireless networks, the positions of UAV-Rs can be optimized to maximize the system performance. Although the optimal UAV positioning problem has been studied under simple LOS/probabilistic channel models, the joint optimization of UAV positions, user association, and wireless backhaul capacity allocation remains unsolved under realistic channel models, which will be addressed in this paper. The joint optimization problem is a challenging mixed non-convex and combinatorial problem. Combining the block coordinate descent and successive convex approximation (SCA) methods, we propose a sparse parallel block SCA algorithm, which can find a near-optimal solution by exploiting the structures of the propagation model. The simulations verify the significant gain of the proposed solution over existing solutions.

Recently, physical layer (PHY) caching has been proposed to exploit the dynamic side information induced by caches at base stations (BSs) to support coordinated multi-point (CoMP) and achieve high degrees of freedom (DoF) gains. Due to the limited cache storage capacity, the performance of PHY caching depends heavily on the cache content placement algorithm. In the existing algorithms, the cache content placement is adaptive to the long-term popularity distribution in an offline manner. We propose an online PHY caching framework, which adapts the cache content placement to microscopic spatial and temporary popularity variations to fully exploit the benefits of PHY caching. Specifically, the joint optimization of online cache content placement and content delivery is formulated as a mixed-timescale drift minimization problem to increase the CoMP opportunity and reduce the cache content placement cost. We propose a low-complexity algorithm to obtain a throughput-optimal solution. Moreover, we provide a closed-form characterization of the maximum sum DoF in the stability region and study the impact of key system parameters on the stability region. The simulations results show that the proposed online PHY caching framework achieves large gain over existing solutions.

Hybrid beamforming (BF) is a promising solution for massive MIMO with limited RF chains. To reduce the amount of pilot overhead for channel estimation, compressed sensing techniques that exploit channel sparsity have been proposed. One key issue is how to design the RF (analog) training vectors to achieve higher BF gain with fewer pilots. Specifically, narrow-beam RF training requires large pilot overhead for finding strongest paths, and random RF training suffers from low BF gain. We propose to use a mixture of narrow-beam and random RF training vectors, and optimize the fraction of the two sets of RF training vectors based on channel support side information (CSSI) at the BS. We show that this optimized fraction exhibits a phase transition: when the CSSI accuracy exceeds a certain threshold, the maximum number of narrow-beam RF training vectors should be used to focus beams in all directions indicated by the CSSI. Otherwise, only random RF training vectors should be used to explore the unknown channel support. Moreover, we derive closed-form bounds on the channel estimation error. Both the analysis and simulations show that the proposed method can achieve substantial gains over various baseline methods.

Mobile edge computing (MEC) has recently emerged as a promising technology to release the tension between computation-intensive applications and resource-limited mobile terminals (MTs). In this paper, we study the delay-optimal computation offloading in computation-constrained MEC systems. We consider the computation task queue at the MEC server due to its constrained computation capability. In this case, the task queue at the MT and that at the MEC server are strongly coupled in a cascade manner, which creates complex interdependences and brings new technical challenges. We model the computation offloading problem as an infinite horizon average cost Markov decision process (MDP) and approximate it to a virtual continuous time system (VCTS) with reflections. Different from most of the existing works, we develop the dynamic instantaneous rate estimation for deriving the closed-form approximate priority functions in different scenarios. Based on the approximate priority functions, we propose a closed-form multi-level water-filling computation offloading solution to characterize the influence of not only the local queue state information (LQSI) but also the remote queue state information (RQSI). Furthermore, we discuss the extension of our proposed scheme to multi-MT multi-server scenarios. Finally, the simulation results show that the proposed scheme outperforms the conventional schemes.

Although there has been a wide investigation on channel estimation in frequency-division duplex (FDD) massive multiple-input-multiple-output (MIMO) systems, the effect of imperfect radio frequency (RF) chains have been largely ignored. In this paper, we consider a downlink massive MIMO system with in-phase/quadrature imbalance (IQI) at the base station (BS). Focusing on the joint estimation for channel and IQI, we model the joint estimation problem as a two-timescale non-convex optimization based on maximum a posteriori (MAP) estimate, where the IQI parameter is treated as the long-term variable and the sparse channel vector is short-term. We propose a batch algorithm and a two-timescale online joint sparse estimation (TOJSE) algorithm to solve the problem. The proposed batch algorithm utilizes all the previously received signals to update the current long-term variable, which can achieve better performance but with increasing computational complexity over time. In contrast, the TOJSE algorithm solves the short-term problem related to the current system state and constructs a recursive convex approximation to update the long-term variable in each iteration. Thus, the memory requirements and computational complexity of the TOJSE are remarkably reduced. Moreover, for the low mobility regime, a dynamic TOJSE algorithm is further presented to exploit the temporal correlation of channel support. Finally, the simulations show that our proposed algorithms can achieve significant gain over various baselines.

In this paper, we are interested in the joint estimate of user equipment (UE) position and orientation for visible light communication (VLC) with uncertain emission power. This joint estimation is a non-convex problem with a huge search space. To address this challenge, a novel VLC localization algorithm is proposed, which converges to the stationary solution of the joint estimation problem at an asymptotic quadratic convergence rate due to our problem-specific surrogate function design. A closed-form update rule is obtained via exploiting the hidden convex structure of the non-convex optimization problem. Hence, our algorithm has low complexity compared with the particle swarm-based optimization methods. In addition, the closed-form Cramer-Rao lower bounds (CRLBs) on the estimation errors of the respective UE location, orientation and LED emitting power are derived. Moreover, the effect of critical parameters such as signal-to-noise ratio (SNR), the number of LED transmitters, transmission distance and non-line-of-sight propagation on the VLC localization performance are revealed. The simulation result verifies that the proposed VLC localization algorithm under unknown VLC emitting power can achieve a huge performance gain over the state-of-the-art localization baselines.

In this paper, we aim at providing a unified performance analysis framework for visible light-based positioning (VLP) using received signal strength (RSS), which can be used to gain insights into improving the performance of RSS-based VLP systems. Specifically, we first obtain a closed-form Cramer-Rao lower bound (CRLB) for the user equipment (UE) location and orientation, respectively. Then, we reveal the impact of the signal-to-noise ratio (SNR), transmission distance, prior knowledge and the number of LED sources on the RSS-based VLP performance. Moreover, the impact of the non-line-of-sight (NLOS) propagation on the RSS-based VLP performance is studied. It is shown that the RSS-based VLP performance will hit an error floor caused by the unknown NLOS links. These NLOS-caused UE location and orientation error floors in the high SNR region are analyzed. Finally, the information contribution of each LED source is studied to give an intuitive understanding on the impact of the LED array geometry on the RSS-based VLP performance. The obtained CRLB and the associated VLP performance analysis form a theoretical basis for the design of efficient VLP algorithms and VLP performance optimization strategies (e.g., resource allocation and smart LED source selection).

Mobile edge computing (MEC) has recently emerged as a promising technology to release the tension between computation-intensive applications and resource-limited mobile terminals (MTs). In this paper, we construct a Markov decision process (MDP) framework to optimize the delay performance in computation-constrained mobile edge networks. Different to most of the existing works, we consider the computation task queue at the MEC server due to its constrained computation capability. In this case, the task queue at the MT and that at the MEC server are mutually strongly coupled in a cascade manner, which creates complex interdependence and brings new technical challenges. To address the challenge, the infinite horizon average cost MDP is reformulated to a virtual continuous time system (VCTS) with reflections. We derive the closed-form approximate priority functions for the MDP in different system scenarios with dynamic rate estimation. Based on the approximate priority function, we propose a multi-level water filling solution to characterize the influence of not only the local queue state information (LQSI) but also the remote queue state information (RQSI) on the computation offloading policy in a closed form. Finally, the simulation results show that the proposed computation offloading scheme outperforms the conventional schemes.

Recently, a two-timescale hybrid precoding (THP) scheme has been proposed to reduce the implementation cost of massive MIMO systems. In THP, the MIMO precoder consists of a high- dimensional RF precoder adaptive to the channel statistics and a low-dimensional baseband precoder adaptive to the instantaneous effective channel state information (CSI). Since the channel statistics changes at a slow timescale and is approximately the same for different subbands, only a single RF precoder is required to cover all subbands over a long term, which helps to reduce the hardware cost and implementation complexity of RF precoder. Moreover, the CSI signaling overhead can also be reduced. In this paper, we consider power minimization for massive MIMO systems with THP and individual average rate constraints. Due to the two-timescale design and individual average rate constraints, the problem is a challenging non-convex stochastic optimization problem. We propose an online constrained stochastic successive convex approximation (CSSCA) algorithm to find a stationary point of the power minimization problem. Simulations show that the proposed algorithm achieves significant gains over various baseline algorithms.

Due to the demand for higher throughput, there is need for aggregating more carriers to serve one user equipment (UE). Massive carrier aggregation (MCA) may help as it aggregate a large number of potentially non-contiguous carriers spanning a wide bandwidth. However, implementing MCA brings challenges to the design of the receiver and corresponding data recovery algorithms. For example, if we assign a separate receiver chain for each carrier, the number of receiver chains will be large, which imposes a huge cost. If we use a single receiver chain for all non-contiguous carriers, an expensive high rate analog-to-digital converter (ADC) is required to sample the entire span of the carriers. To reduce the cost, we propose a receiver architecture that employs only one receiver chain with a non-uniform ADC whose sampling rate is much smaller than the Nyquist rate, and a low cost power amplifier with small dynamic range. Under such architecture, the received signal suffers from non-linear distortion and interference, and the resulting data recovery is a challenging non-linear compressive sensing problem. We propose an algorithm to jointly mitigate the interference and recover the data, which is proved to have theoretical performance guarantees and verified advantageous over baselines in simulations.

In massive MIMO aided cloud radio access network (C-RAN), plenty of remote radio heads (RRHs), each equipped with a massive MIMO array, are distributed within a specific geographical area and are connected to a centralized baseband unit (BBU) pool through fronthaul links. One major performance bottleneck in the uplink of massive MIMO aided C-RAN is that, the RRHs need to transport a huge amount of data to the BBU for baseband processings. Existing fronthaul compression methods that rely on fully-digital processing are not suitable for the massive MIMO regime due to their high implementation cost. To overcome this challenge, we propose a two-timescale hybrid analog-and-digital spatial compression scheme at RRHs to reduce the fronthaul data, where the analog filter is updated at a slow timescale according to the channel statistics to achieve massive MIMO array gain, and the digital filter is updated at a fast timescale according to the instantaneous effective channel state information (CSI) to achieve spatial multiplexing gain. Such a design can alleviate the performance bottleneck of limited fronthaul with reduced hardware cost and power consumption, and is more robust to the CSI delay. We propose an online algorithm for the two-timescale non-convex optimization of analog and digital filters. Simulations verify the advantages of the proposed scheme over state-of-the-art baseline schemes.

In this paper, the identification of multiple leaks in pipes based on transient waves is studied, which is, however, quite challenging due to its nonconvex nature with lots of local optima. Existing approaches need the number of leaks and suffer from a huge computational complexity that is increased exponentially with the number of leaks. To provide a scalable solution, we propose a compressive sensing (CS) framework to solve the multileaks identification. We first exploit the sparseness nature of leak locations through spatial sampling. Then, we formulate the multileak identification as a CS problem, where the spatial sample-dependent components form a basis matrix and the leak sizes are viewed as a sparse signal. We establish the convergence of spatial sampling mismatch and the two-restricted isometry property of basis matrix to justify the proposed CS framework. The proposed CS framework renders a superior-performance solution to multileak identification and its computational complexity is linear with the number of leaks, which is a significant technical improvement over existing approaches. In addition, a closed-form Cramer-Rao lower bound (CRLB) on the leak localization errors is derived. A geometric insight of CRLB evolution is presented to give us an intuitive understanding of the contribution of new measurements to leak localization performance.

Considering the expensive radio frequency (RF) chain, huge training overhead, and feedback burden issues in massive MIMO, in this letter, we propose a mixed-timescale per-group hybrid precoding scheme under an adaptive partially connected antenna structure, where the RF precoder is implemented using an adaptive connection network (ACN) and M analog phase shifters (APSs), where M is the number of antennas at the base station. Exploiting the mixed time stage channel state information (CSI) structure, the joint-design of ACN, and APSs is formulated as a statistical signal-to-leakage-and-noise ratio maximization problem, and a heuristic group RF precoding algorithm is proposed to provide a near-optimal solution. Simulation results show that the proposed design advances at better energy efficiency and lower hardware cost, CSI signaling overhead and computational complexity than the conventional hybrid precoding schemes.

We consider a wireless device-to-device caching network where n nodes are placed on a regular grid of area A (n). Each node caches LCF (coded) bits from a library of size LF bits, where L is the number of files and F is the size of each file. Each node requests a file from the library independently according to a popularity distribution. Under a commonly used “physical model” and Zipf popularity distribution, we characterize the optimal per-node capacity scaling law for extended networks (i.e., A (n) = n). Moreover, we propose a cache-induced hierarchical cooperation scheme and associated cache content placement optimization algorithm to achieve the optimal per-node capacity scaling law. When the path loss exponent α <; 3, the optimal per-node capacity scaling law achieved by the cache-induced hierarchical cooperation can be significantly better than that achieved by the existing state-of-the-art schemes. To the best of our knowledge, this is the first work that completely characterizes the per-node capacity scaling law for wireless caching networks under the physical model and Zipf distribution with an arbitrary skewness parameter τ. While scaling law analysis yields clean results, it may not accurately reflect the throughput performance of a large network with a finite number of nodes. Therefore, we also analyze the throughput of the proposed cache-induced hierarchical cooperation for networks of practical size. The analysis and simulations verify that cache-induced hierarchical cooperation can also achieve a large throughput gain over the cache-assisted multihop scheme for networks of practical size.

Caching at base stations (BSs) is a promising approach for supporting the tremendous traffic growth of content delivery over future small-cell wireless networks with limited backhaul. This paper considers exploiting spatial caching diversity (i.e., caching different subsets of popular content files at neighboring BSs) that can greatly improve the cache hit probability, thereby leading to better overall system performance. A key issue in exploiting spatial caching diversity is that the cached content may not be located at the nearest BS, which means that to access such content, a user needs to overcome strong interference from the nearby BSs; this significantly limits the gain of spatial caching diversity. In this paper, we consider a joint design of frequency reuse and caching, such that the benefit of an improved cache hit probability induced by spatial caching diversity and the benefit of interference coordination induced by frequency reuse can be achieved simultaneously. We obtain a closed-form characterization of the approximate successful transmission probability for the proposed scheme and analyze the impact of key operating parameters on the performance. We design a low-complexity algorithm to optimize the frequency reuse factor and the cache storage allocation. Simulations show that the proposed scheme achieves a higher successful transmission probability than existing caching schemes.

Providing backhaul connectivity for macro and pico base stations (BSs) constitutes a significant share of infrastructure costs in future heterogeneous networks (HetNets). To address this issue, the emerging idea of flexible backhaul is proposed. Under this architecture, not all the pico BSs are connected to the backhaul, resulting in a significant reduction in the infrastructure costs. In this regard, pico BSs without backhaul connectivity need to communicate with their nearby BSs in order to have indirect accessibility to the backhaul. This makes the radio resource management (RRM) in such networks more complex and challenging. In this paper, we address the problem of cross-layer RRM in HetNets with flexible backhaul. We formulate this problem as a two-timescale non-convex stochastic optimization, which jointly optimizes flow control, routing, interference mitigation, and link scheduling in order to maximize a generic network utility. By exploiting a hidden convexity of this non-convex problem, we propose an iterative algorithm which converges to the global optimal solution. The proposed algorithm benefits from low complexity and low signaling, which makes it scalable. Moreover, due to the proposed two-timescale design, it is robust to the backhaul signaling latency as well. Simulation results demonstrate the significant performance gain of the proposed solution over various baselines.

Spectrum aggregation (SA) across heterogeneous channels, including both dedicated and shared channels, provides the potential for improving spectrum utilization and fulfilling the requirement of broadband services. Heterogeneous SA brings new technical challenges on multisystem coexistence on shared channels and the resource allocation over heterogeneous channels. In this paper, we develop an analytical framework for heterogeneous SA from a queue stability perspective. To make all systems on the shared channels stable, we design a resource allocation algorithm for the coexistence of multiple systems. Specifically, we derive the closed-form modified water-filling power control for the single-pair case by Lyapunov optimization and prove that it achieves the queue stability for all systems. Based on the results, we propose a low-complexity suboptimal resource allocation algorithm for multipair SA, which is a NP-hard problem. We partition user pairs into groups by using graph coloring and allocate the shared channels to pair groups according to the maximal weight bipartite matching model. The simulation results verify the queue stability and show that the proposed schemes outperform the conventional schemes.

Due to the demand for higher throughput, there is need for aggregating more carriers to serve one user equipment. Massive carrier aggregation (MCA) may help as it aggregates a large number of potentially non-contiguous carriers spanning a wide bandwidth. However, implementing MCA brings challenges to the design of the receiver and corresponding data recovery algorithms. For example, if we assign a separate receiver chain for each carrier, the number of receiver chains will be large, which imposes a huge cost. If we use a single receiver chain for all non-contiguous carriers, an expensive high rate analog-to-digital converter (ADC) is required to sample the entire span of the carriers. To reduce the cost, we propose a receiver architecture that employs only one receiver chain with a non-uniform ADC, whose sampling rate is much smaller than the Nyquist rate, and a low cost power amplifier with small dynamic range. Under such architecture, the received signal suffers from non-linear distortion and interference, and the resulting data recovery is a challenging non-linear compressive sensing problem. We propose an algorithm to jointly mitigate the interference and recover the data, which is proved to have theoretical performance guarantees and verified advantageous over baselines in simulations.

Channel estimation (CE) in massive MIMO system with limited RF chains is known to be a challenging problem. In many cases, base station (BS) can obtain certain channel support side information (CSSI), which can be exploited to enhance the CE performance and reduce the pilot overhead. Due to imperfectness of prior information, it's very important to utilize the CSSI in an optimal way to improve the CE performance. We propose an optimal-tuned weighted LASSO algorithm which can fully exploit the imperfect CSSI to optimize the CE performance in massive MIMO system with limited RF chains. In the proposed algorithm, the weighted l_{1} norm is used as the regularization function and the weights on the known support part (as indicated by the CSSI) and the rest are different. Based on the accuracy of CSSI, we obtain closed-form solution for the optimal LASSO weights which minimize the asymptotic normalized squared error (aNSE). Moreover, we derive closed-form expression of the minimum aNSE and characterize the minimum number of required pilots to achieve stable channel recovery. The theoretical analysis and simulation both show the performance advantages of our proposed solution over various baselines.

In content-centric wireless networks, physical layer (PHY) caching has been proposed to exploit the dynamic side information induced by base station (BS) cache to support Coordinated Multi-Point (CoMP) and achieve huge capacity gain. The performance of PHY caching depends heavily on the cache content placement algorithm. In existing algorithms, the cache content placement is adaptive to the long-term popularity distribution in an offline manner. We propose an online PHY caching framework based on the concept of virtual interest packet (VIP) in a virtual network. The VIP captures microscopic spatial and temporary popularity variations, and thus the VIP-based online PHY caching can adapt the cached content to the microscopic popularity variations to fully exploit the benefits of PHY caching. The joint optimization of online caching and content delivery is formulated as a mixed timescale drift minimization problem and a low complexity algorithm is proposed to find the optimal solution. Simulations show that the proposed solution achieves large gain over existing solutions.

Existing multicasting schemes for massive content delivery do not fully utilize multicasting opportunities in elastic content-oriented services. In this paper, we propose a novel temporal-spatial request aggregation-based multicasting scheme in a large-scale cache-enabled wireless network, which can effectively exploit multicasting opportunities in asynchronous file requests for elastic services to improve spectral efficiency. Utilizing tools from stochastic geometry, we derive tractable expressions for the successful transmission probability. The analytical results show that the successful transmission probability increases and the energy consumption decreases, at the cost of delay increase, in the large and small user density regions. Based on the analytical results, we further optimize the successful transmission probability with respect to the caching probability and BS on/off period. We obtain closed-form solutions in the two asymptotic regions. Finally, we characterize the temporal and spatial request aggregation gains in the two asymptotic regions, and reveal the impacts of the popularity profile on them.

The integration of two complementary technologies, millimeter-wave (mmWave) communications and massive multiple-input multiple-output (MIMO), will play a key role in enabling gigabit access in 5G systems. However, implementing mmWave massive MIMO using the traditional fully digital architecture will lead to prohibitive hardware complexity as it requires a massive number of RF chains matching antennas in number. To address this issue, the hybrid beamforming architecture has been recently proposed for efficient implementation of mmWave massive MIMO. Specifically, large-scale MIMO beamforming is implemented in the analog domain, called analog beamforming, that exploits the sparsity in mmWave channels for dramatic dimension reduction for digital MIMO signal processing. The typical phase-array implementation of analog beamforming introduces the uni-modulus constraints on the beamforming coefficients and renders the classic MIMO techniques unsuitable. This motivates the novel design framework, called Kronecker analog beamforming, proposed in this paper for multi-cell multiuser massive MIMO systems over mmWave channels characterized by sparse propagation paths. The framework relies on the decomposition of analog beamforming vectors and path observation vectors into Kronecker products of factor vectors with uni-modulus elements. Exploiting the properties of Kronecker product, different factors of the analog beamformer are designed for either nulling interference paths or coherently combining data paths. Thereby, Kronecker analog beamforming achieves interference nulling and signal enhancement both in the analog domain as well as dimension reduction for digital beamforming.

Recently, compressive channel estimation (CE) has been proposed to reduce the pilot overhead for massive MIMO with limited RF chains. One key issue is how to design the RF (analog) training vectors to achieve higher beamforming (BF) gain with fewer pilots. Specifically, narrow-beam RF training requires large pilot overhead for finding strongest paths, and random RF training suffers from low BF gain. We propose to use a mixture of narrow-beam and random RF training vectors, and exploit the channel support side information (CSSI) at the BS to do joint RF training and compressive CE. The narrow-beam RF training vectors are used to achieve a high BF gain, and the random RF training vectors are used to explore the unknown channel support to reduce the pilot overhead. Moreover, we derive closed-form bounds on the CE error. Both the analysis and simulations show that the proposed method can achieve substantial gains over various baseline methods.

In this paper, we study the delay-aware access control of massive random access for machine-type communications (MTC). We model this stochastic optimization problem as an infinite horizon average cost Markov decision process. To deal with the distributive requirement and the exponential computational complexity, we first exploit the property of successful access probability to transform the coupling to the constraint on the number of MTC devices attempting to access. As a result, we decompose the Bellman equation into multiple fixed point equations for each MTC device by primal-dual decomposition. Based on the equivalent per-MTC fixed point equations, we propose the online hierarchical stochastic learning algorithm to estimate the local Q-factors and determine the access decision at the MTC devices separately with the assistance of the base station which broadcasts common control information only. Finally, the simulation result shows that the proposed hierarchical stochastic learning algorithm has significant performance gain over the baseline algorithm.

We study the capacity scaling law of a device-to-device (D2D) caching network where n nodes are placed on a regular grid of area n. Each node caches some (coded) bits from a content library and requests a file from the library independently according to the Zipf popularity distribution. We propose a cache-induced hierarchical cooperation scheme which achieves the optimal capacity scaling law under a commonly used “physical model”. When the path loss exponent α <; 3, the capacity scaling law can be significantly better than the throughput scaling laws achieved by the existing state-of-the-art schemes. To the best of our knowledge, this is the first work that completely characterizes the capacity scaling law for wireless caching networks under the physical model.

Resource management in the power and time-frequency domains is an important issue in distributed network localization. Since highly accurate ranging requires a large amount of time-frequency resources, cooperation among nodes without proper link selection may not be feasible. To address this issue, two resource management games are formulated, and Stackelberg equilibrium and link bargaining equilibrium are proposed as the solution concepts for efficient link selection and power allocation. Distributed algorithms are derived and analyzed using game theoretical approaches. It is demonstrated that the proposed strategies can achieve a lower mean squared error of position estimation with fewer ranging measurements.

This paper proposes a new design of non-orthogonal multiple access (NOMA) under secrecy considerations. We focus on a NOMA system, where a transmitter sends confidential messages to multiple users in the presence of an external eavesdropper. The optimal designs of decoding order, transmission rates, and power allocated to each user are investigated. Considering the practical passive eavesdropping scenario where the instantaneous channel state of the eavesdropper is unknown, we adopt the secrecy outage probability as the secrecy metric. We first consider the problem of minimizing the transmit power subject to the secrecy outage and quality of service constraints, and derive the closed-form solution to this problem. We then explore the problem of maximizing the minimum confidential information rate among users subject to the secrecy outage and transmit power constraints, and provide an iterative algorithm to solve this problem. We find that the secrecy outage constraint in the studied problems does not change the optimal decoding order for NOMA, and one should increase the power allocated to the user whose channel is relatively bad when the secrecy constraint becomes more stringent. Finally, we show the advantage of NOMA over orthogonal multiple access in the studied problems both analytically and numerically.

Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) seamlessly integrates two wireless technologies, mmWave communications and massive MIMO, which provides spectrums with tens of GHz of total bandwidth and supports aggressive space division multiple access using large-scale arrays. Though it is a promising solution for next-generation systems, the realization of mmWave massive MIMO faces several practical challenges. In particular, implementing massive MIMO in the digital domain requires hundreds to thousands of radio frequency chains and analog-to-digital converters matching the number of antennas. Furthermore, designing these components to operate at the mmWave frequencies is challenging and costly. These motivated the recent development of the hybrid-beamforming architecture, where MIMO signal processing is divided for separate implementation in the analog and digital domains, called the analog and digital beamforming, respectively. Analog beamforming using a phase array introduces uni-modulus constraints on the beamforming coefficients. They render the conventional MIMO techniques unsuitable and call for new designs. In this paper, we present a systematic design framework for hybrid beamforming for multi-cell multiuser massive MIMO systems over mmWave channels characterized by sparse propagation paths. The framework relies on the decomposition of analog beamforming vectors and path observation vectors into Kronecker products of factors being uni-modulus vectors. Exploiting properties of Kronecker mixed products, different factors of the analog beamformer are designed for either nulling interference paths or coherently combining data paths. Furthermore, a channel estimation scheme is designed for enabling the proposed hybrid beamforming. The scheme estimates the angles-of-arrival (AoA) of data and interference paths by analog beam scanning and data-path gains by analog beam steering. The performance of the channel estimation scheme is analyzed. In particular, the AoA spectrum resulting from beam scanning, which displays the magnitude distribution of paths over the AoA range, is derived in closed form. It is shown that the inter-cell interference level diminishes inversely with the array size, the square root of pilot sequence length, and the spatial separation between paths, suggesting different ways of tackling pilot contamination.

Dense wireless networks are a promising solution to meet the huge capacity demand in 5G wireless systems. However, there are two implementation issues, namely, the interference and backhaul issues. To resolve these issues, we propose a novel network architecture called the backhaul-limited cached dense wireless network (C-DWN), where a physical layer (PHY) caching scheme is employed at the base stations (BSs), but only a fraction of the BSs have wired payload backhauls. The PHY caching can replace the role of wired backhauls to achieve both the cache-induced multiple-input-multiple-output (MIMO) cooperation gain and cache-assisted multihopping gain. Two fundamental questions are addressed. Can we exploit the PHY caching to achieve linear capacity scaling with limited payload backhauls? If so, how much cache is needed? We show that the capacity of the backhaul-limited C-DWN indeed scales linearly with the number of BSs if the BS cache size is larger than a threshold that depends on the content popularity. We also quantify the throughput gain due to cache-induced MIMO cooperation over conventional caching schemes (which exploit purely the cached-assisted multihopping). Interestingly, the minimum BS cache size needed to achieve a significant cache-induced MIMO cooperation gain is the same as that needed to achieve the linear capacity scaling.

With the drastic increase of mobile devices, there are more and more mobile traffic and repeated requests for content. In 5G networks, small cell base stations (SBSs) caching and caching in wireless device-to-device network can effectively decrease the mobile traffic during peak hours. Currently, most of the related work is focused on how to cache content on SBSs and on mobile devices, and it is assumed that the user can download the entire requested content through the connected SBSs and mobile devices. However, few works have taken user mobility and the randomness of contact duration into consideration. How to improve the caching strategy by exploiting user mobility is still a challenging problem. Thus, in this paper, we first investigate the problem of how to conduct caching placement on SBS and on mobile devices leveraging user mobility, aiming to maximize the cache hit ratio. Specifically, the caching placement on SBSs and on mobile devices is formulated as an integer programming problem, and submodular optimization is adopted to solve the formulated problem. Then, we give the optimal transmission power of SBSs and mobile devices to deliver the caching content in order to reduce the energy cost. Simulation results prove that our caching strategy is more efficient than other existing caching strategies in terms of both cache hit ratio and energy efficiency.

In this paper, we study the average sum DoF of 2 cached interference networks over random user requests. First, 3 we derive a closed-form average sum DoF upper bound of 4 the cached interference channels over all possible caching and 5 transmission strategies. Then, we propose an online cache content 6 placement algorithm to maximize the achievable average sum 7 DoF without explicit knowledge of the popularity of the content 8 files. We show that the gap between the DoF upper bound and 9 an achievable DoF is small for some typical scenarios. Moreover, 10 we quantify the DoF gain due to caching with respect to some important system parameters. We show that large DoF gain over 11 12 interference network without cache is possible even if the number 13 of files L → ∞. Both simulation and analysis indicate that the proposed scheme has significant gains over various baselines.

In cloud radio access networks (C-RANs), the baseband units and radio units of base stations are separated, which requires high-capacity fronthaul links connecting both parts. In this paper, we consider the delay-aware fronthaul allocation problem for C-RANs. The stochastic optimization problem is formulated as an infinite horizon average cost Markov decision process. To deal with the curse of dimensionality, we derive a closed-form approximate priority function and the associated error bound using perturbation analysis. Based on the closed-form approximate priority function, we propose a low-complexity delay-aware fronthaul allocation algorithm solving the per-stage optimization problem. The proposed solution is further shown to be asymptotically optimal for sufficiently small residual interference. Finally, the proposed fronthaul allocation algorithm is compared with various baselines through simulations, and it is shown that significant performance gain can be achieved.

Recently, physical layer (PHY) caching at the base station (BS) has been proposed to induce MIMO cooperation among peer BSs to reduce interference and improve the spectrum efficiency in the radio access network (RAN). Compared with caching at the core network (CN) gateway (CN-caching), which covers a large number of users, it may appear that PHY-caching covers less users and results in lower cache hit rate. In this paper, we analyze the tradeoff between spectrum efficiency gain induced by PHY-caching and CN-backhaul offloading gain induced by CN-caching. Specifically, we derive the Pareto optimal tradeoff between the spectrum efficiency gain and CN-backhaul offloading gain. Surprisingly, we show that even though PHY-caching covers less users than CN-caching (single cell versus multiple cells), it is still worthwhile to do PHY-caching at the BS.

The knowledge of CSI at the BS (CSIT) is required to achieve the high spectrum efficiency promised by massive MIMO. In Frequency-Division Duplex (FDD) Massive MIMO systems, the CSIT is obtained via downlink channel estimation and uplink channel feedback, However, the acquisition of CSIT is a very challenging problem in practical FDD massive MIMO systems with a large number of antennas. Recently, compressive sensing has been applied to reduce pilot and CSIT feedback overheads in massive MIMO systems by exploiting the underlying channel sparsity. However, standard sparse recovery algorithms have stringent requirement on the channel sparsity level for robust channel recovery and this severely limits the operating regime of the solution. To overcome this issue, we propose a joint burst LASSO algorithm to exploit additional joint burst-sparse structure in multi-user (MU) massive MIMO channels. Simulations show that the joint burst LASSO algorithm can alleviate the stringent requirement on the sparsity level for robust channel recovery and substantially enhance the channel estimation performance over existing solutions.

Acquisition of accurate channel state information (CSI) at the base station (CSIT) is a major challenge of deploying frequency-division duplexing (FDD) massive MIMO systems. Although compressive sensing (CS) based CSIT estimation approaches have been proposed to reduce the pilot training overhead for massive MIMO systems, the existing schemes cannot properly dimension the minimum required pilot symbols to estimate the CSIT of all users at the required CSIT quality, because of the loose bounds on the required number of measurements for successful CS recovery and the unknown sparsity levels of user channels. In this paper, we propose a robust closed-loop compressive CSIT feedback and estimation framework which not only exploits the joint sparsity structure of the multi-user (MU) massive MIMO channels to improve the CSIT estimation performance, but also has the learning capability to adapt to the minimum pilot and feedback resources needed under unknown and time-varying channel sparsity levels. We establish the convergence of the proposed closed-loop adaptation algorithm. Simulations show that the proposed framework has substantial performance gain over conventional open-loop algorithms and is robust to dynamic sparsity and model mismatch.

Recently, hybrid beamforming, which consists of an analog RF precoder and a digital baseband precoder, has been proposed for massive MIMO systems to reduce the number of RF chains and power consumption at the base station (BS). This paper studies the impact of channel state information (CSI) on the sum capacity of massive MIMO systems with quantized hybrid beamforming where the RF precoder is selected from a finite size codebook. Two types of CSI at the BS (CSIT) are assumed: full instantaneous CSIT (full channel matrix between the BS and users) and hybrid CSIT (channel statistics plus the low dimensional effective channel matrix after RF precoding). We derive asymptotic sum capacity expressions under these two types of CSIT. We find that, in most cases, exploiting the full instantaneous CSIT can only achieve a marginal SNR gain and hybrid CSIT is sufficient to achieve the first order gain provided by massive MIMO.

In this paper, we consider the problem of cross-layer radio resource management in heterogeneous networks (HetNets) with flexible backhaul, which aims at minimizing the total transmit power of base stations (BSs) while guaranteeing the average end-to-end data rate requirement of each data flow. We formulate the problem as a two-time scale stochastic optimisation, where the long-timescale control variables are flow control, routing control and interference coordination, while the short-timescale control variable is instantaneous beamforming within each cell. Using a stochastic cutting plane (SCP) method, we propose a cross-layer queue-state information (QSI) aware radio resource management (RRM) solution in which the long-term controls are updated centrally at radio resource management server (RRMS) without needing to know the global statistical information of the network, and the short-term control variables are updated locally at each BS with only the local instantaneous QSI and channel-state information (CSI) available at each cell. Simulation results show the significant performance gain of our proposed algorithm compared to various baselines.

We propose a physical layer (PHY) caching scheme for wireless ad hoc networks. The PHY caching exploits cache-assisted multihop gain and cache-induced dual-layer CoMP gain, which substantially improves the throughput of wireless ad hoc networks. In particular, the PHY caching scheme contains a novel PHY transmission mode called the cache-induced dual-layer CoMP, which can support homogeneous opportunistic CoMP in the wireless ad hoc network. Compared with traditional per-node throughput scaling results of Θ ( 1/√N ), we can achieve O(1) per node throughput for a cached wireless ad hoc network with N nodes. Moreover, we analyze the throughput of the PHY caching scheme for regular wireless ad hoc networks and study the impact of various system parameters on the PHY caching gain.

How to obtain accurate channel state information at the base station (CSIT) is a key implementation challenge behind frequency-division duplex massive MIMO systems. Recently, compressive sensing (CS) has been applied to reduce pilot and CSIT feedback overheads in massive MIMO systems by exploiting the underlying channel sparsity. However, brute-force applications of standard CS may not lead to good performance in massive MIMO systems, because standard sparse recovery algorithms have quite a stringent requirement on the sparsity level for robust recovery and this severely limits the operating regime of the solution. Moreover, since the channel support is usually correlated across time, it is possible to obtain partial channel support information (P-CSPI) from previously estimated channel support. Motivated by the above observations, we propose a P-CSPI aided burst Least Absolute Shrinkage and Selection Operator (LASSO) algorithm to exploit both the P-CSPI and additional structured properties of the sparsity, namely, the burst sparsity in massive MIMO channels. We also accurately characterize the asymptotic channel estimation error of the P-CSPI aided burst LASSO algorithm. Both the analysis and simulations show that the P-CSPI aided burst LASSO algorithm can alleviate the stringent requirement on the sparsity level for robust channel recovery and substantially enhance the channel estimation performance over existing solutions.

This paper investigates the ergodic achievable secrecy rate (EASR) of multiple-antenna amplify-and-forward relay systems, where one eavesdropper can wiretap the relay. To reveal the capability of the multiple-antenna relay in improving the secrecy performance, we derive new tight closed-form expressions of the EASR for three secure transmission schemes: artificial noise aided precoding (ANP), destination based jamming (DBJ) and eigen-beamforming (EB). We also derive the lower bounds of the EASR for ANP and DBJ with a large antenna array at the relay, and investigate their corresponding asymptotic performance in the high SNR and low SNR regimes to show valuable intrinsic insights as well. Based on the asymptotic analysis, we optimally allocate the power to the information signal and the artificial noise. Both the analysis and simulation results indicate that, in the moderate-to-high SNR regime, ANP achieves considerable performance gain over DBJ and EB, while in the low SNR regime, EB outperforms the other two schemes with equal power allocation. As SNR grows large, the EASR of EB approaches a constant independent of the first hop channel. Moreover, in the high SNR regime, it is optimal to allocate around half of total power to artificial noise for ANP and most of the power to artificial noise for DBJ.

Interference is a key issue that limits the Degrees of Freedom (DoF) in conventional wireless networks. In this paper, we propose a novel physical layer (PHY) caching scheme to achieve DoF gains in interference networks. Specifically, by properly caching a portion of the content files at each transmitters, the PHY topology can be opportunistically transformed from the unfavorable interference channel topology into a more favorable MIMO broadcast channel topology and enjoy a large DoF gain. We first propose a novel caching scheme to significantly improve the MIMO cooperation opportunity induced by PHY caching. Then we quantify the DoF gain w.r.t. some important system parameters. Both analysis and simulation show that the proposed scheme has significant gains over various baselines.

The cloud radio access network (C-RAN) is an emerging network architecture that holds the promise of coping with the explosive growth of mobile wireless data. In this paper, by considering the stochastic traffic arrivals and time-varying channel conditions, we address the stochastic optimization of joint remote radio head (RRH) activation and linear beamforming to maintain delay performance and minimize average network power consumption in downlink slotted C-RAN. We first formulate the joint optimization as a group sparse beamforming problem. Based on Lyapunov optimization technique, the stochastic optimization problem is then transformed into a queue-aware joint RRH activation and beamforming problem, which can be greedily solved at each slot. Finally, a low-complexity stationary algorithm with guaranteed convergence and closed-form expressions is proposed. The proposed algorithm can be implemented in a parallel manner, thus it is highly scalable to a large-sized C-RAN. Extensive numerical simulations validate the effectiveness of the proposed algorithm.

A mixed timescale cross-layer resource optimization scheme is proposed for a multi- antenna device-to-device (D2D) network. We consider the two timescale joint optimization of beamforming, routing and flow control, where the short-term beamforming control at each transmit device is adaptive to the local real-time CSI, while the long-term routing and flow control is adaptive to the global topology and channel statistics of the D2D network. We propose a stochastic cutting plane algorithm to find the global optimal solution of the joint optimization problem. The proposed solution has low signaling overhead and does not require explicit knowledge of the channel statistics. Simulations show that the proposed solution achieves significant performance gain over several baselines.

In this paper, we focus on the problem of hierarchical cross-layer dynamic resource allocation for heterogeneous networks with flexible backhaul. We formulate the radio resource management problem as a two timescale non-convex stochastic optimisation problem which jointly optimizes flow control, routing control, interference mitigation and link scheduling in order to maximize a generic network utility. We propose an iterative hierarchical control structure where the long-term controls are adaptive to large scale fading and the short-term control is adaptive to the local CSI within a pico or macro BS, to find the optimal solution. The proposed solution benefits from low complexity and requires low signalling and message passing among different nodes, which makes it scalable. Moreover, due to the proposed two-timescale hierarchical design, it is robust to the backhaul signalling latency as well. Simulation results demonstrate the significant performance gain of the proposed solution over various baselines.

This paper investigates the ergodic secrecy capacity (ESC) of multiple-antenna amplify-and-forward relay systems, where one eavesdropper can wiretap the relay. To reveal the capability of the multiple- antenna relay in improving the secrecy performance, we derive new tight closed-form lower bounds of the ESC for two secure transmission schemes: artificial noise aided precoding (ANP) and eigen-beamforming (EB). We also derive the lower bound of the ESC for ANP with a large antenna array at the relay, and investigate their corresponding asymptotic performance in the high and low SNR regimes. Based on the asymptotic analysis, we optimally allocate the power to the information signal and the artificial noise. Both the analysis and simulation results indicate that, in the moderate-to-high SNR regime, ANP achieves considerable performance gain over EB, while in the low SNR regime, EB outperforms ANP with equal power allocation. As SNR grows large, the ESC of EB approaches a constant only related to the number of relay antennas. Moreover, in the high SNR regime, it is optimal to allocate around half of total power to artificial noise for ANP.

High-precision positioning techniques have a promising future in various applications. Recent work shows that inter-node cooperation can increase the positioning accuracy. In general, such cooperation consumes additional power, and current power application techniques are limited in synchronous networks. To study the power allocation in asynchronous networks, this paper proposes a power management game for cooperative localization, where each user allocates power that minimizes its square error positioning bound (SPEB) penalized by the power cost. Such game-theoretic power allocation policy not only considers how to allocate power over different cooperative links, but also manages the total power consumed for a better performance and power trade-off. We show that, in two-user networks, the power management game admits a unique cooperating Nash Equilibrium (NE) in high SNR region. In addition, as the power budget goes to infinity, the proposed game-theoretic power allocation policy can achieve SPEB arbitrarily close to that under exhaustive power allocation, but requires much less power.

The inefficiency of the cellular-network architecture has prevented the promising theoretic gains of communication technologies such as network MIMO, massive MIMO and distributed antennas from fully materializing in practice. The revolutionary cell-less cloud radio access networks (C-RANs) are under active development to overcome the drawbacks of cellular networks. In C-RANs, centralized cloud signal processing and minimum onsite hardware make it possible to deploy ubiquitous distributed antennas and coordinate them to form a gigantic array, called the ubiquitous array (UA). This paper focuses on designing techniques for UA communications and characterizing their performance. To this end, the UA is modeled as a continuous circular array enclosing target mobiles and free-space propagation is assumed, which allows the use of mathematical tools including Fourier series and Bessel functions in the analysis. First, exploiting the UA's large circular structure, a novel scheme for multiuser channel estimation is proposed to support noiseless channel estimation using only single pilot symbols. Channel estimation errors due to interference are proved to be Bessel functions of inter-user distances normalized by the wavelength. Besides increasing the distances, it is shown that the errors can be also suppressed by using pilot sequences and eliminated if the sequence length is longer than the number of mobiles. Next, for data communication, we first consider channel conjugate transmission that compensates the phase shift in propagation and thereby allows receive coherent combining. The multiuser interference powers are derived as Bessel functions of normalized inter-user distances. Last, we propose the design of multiuser precoders in the form of Fourier series whose coefficients excite different phase modes of the UA. Under the zero-forcing constraints, the precoder coefficients are proved to lie in the null space of a derived matrix with elements being Bessel functions of normalized inter-user distances.

In this paper, we consider the delay-optimal fronthaul allocation problem for cloud radio access networks (CRANs). The stochastic optimization problem is formulated as an infinite horizon average cost Markov decision process. To deal with the curse of dimensionality, we derive a closed-form approximate priority function and the associated error bound using perturbation analysis. Based on the closed-form approximate priority function, we propose a low-complexity delay-optimal fronthaul allocation algorithm solving the per-stage optimization problem. The proposed solution is further shown to be asymptotically optimal for sufficiently small cross link path gains. Finally, the proposed fronthaul allocation algorithm is compared with various baselines through simulations, and it is shown that significant performance gain can be achieved.

Cloud Radio Access Network (CRAN) is proposed as a promising network architecture for future mobile communications. In this paper, we consider the topic of active user detection (AUD) and channel estimation (CE) in uplink CRAN systems with sparse active users. Different from conventional AUD and CE approaches which require the length of uplink pilots to scale with the number of users times the number of antennas per user, a novel algorithm will be proposed to substantially reduce the uplink training overhead by leveraging the technique of compressive sensing (CS). To achieve this goal, we first transform the problem of AUD and CE into standard CS problems. We then propose a modified Bayesian compressive sensing (BCS) algorithm to conduct AUD and CE in CRAN, which exploits not only the active user sparsity, but also the innate heterogeneous path loss effects and the joint sparsity structures in multi-antenna uplink CRAN systems.

Consider a multicarrier communication scenario where both the information (data) and channel have sparse structure. To exploit such sparse structure, we propose to use a receiver working at sampling rate much lower than Nyquist rate (sub-Nyquist sampling) to acquire the data corrupted by noise as well as multipath channel. With the sub-Nyquist rate samples, the joint channel estimation and data recovery is formulated as a sparse maximum likelihood estimation (MLE) problem which maximizes the associated non-concave likelihood function under the sparsity constraints on channel and data. This Sparse MLE framework is proved to provide solutions with bounded error w.r.t. the true value of channel and data under certain restricted isometry property (RIP) conditions. We propose an alternating sparse matching pursuit (ASMP) algorithm to solve the non-convex Sparse MLE problem. We also establish the sufficient conditions for ASMP to converge to solution with bounded error under certain restricted isometry property (RIP) conditions. Simulations show that when sparse structure is exploited, a low rate sub-Nyquist receiver using ASMP performs nearly as well as a Nyquist rate receiver.

In this paper, we propose a physical layer (PHY) caching scheme to exploit cache-assisted dynamic multihopping gain and cache-induced opportunistic CoMP gain in wireless ad hoc networks. In particular, the PHY caching scheme contains a novel PHY transmission mode called the cache-induced dual-layer CoMP which can support homogeneous opportunistic CoMP in wireless adhoc networks and substantially improves the throughput. Compared with traditional per-node throughput scaling results of Θ (1/√N), we can achieve O(1) per node throughput for a cached wireless ad hoc network with N nodes. Moreover, we study the impact of various system parameters on the PHY caching gain. Simulations also verified that the proposed PHY caching achieves significant gains over existing wireless caching schemes.

One of the main advantages of utilizing flexible backhaul in future HetNets is that it can provide better user experience through flexible resource allocation. For this purpose, it is important to provide the required quality of service (QoS) in such networks. In this paper, we consider the problem of cross-layer radio resource management in HetNets with flexible backhaul which guarantees minimum total transmit power of BSs as well as the average end-to-end data rate requirement of each data flow. The problem is formulated as a two-timescale stochastic optimisation, where the long-timescale control variables are flow control, routing control and interference mitigation, while the short-timescale control variable is instantaneous beamforming within each cell. Using stochastic cutting plane, we propose an online cross-layer hierarchical algorithm in which the long-term controls are updated centrally at radio resource management server (RRMS) without needing to know the global statistical information of the network, and the short-term control variables are updated locally at each BS with only the local instantaneous CSI available at each cell. The proposed algorithm has low complexity and signalling overhead. Moreover, simulation results show the significant gains of our proposed algorithm over various baselines.

In this paper, we study the grid power-delay tradeoff in a point-to-point energy harvesting wireless communication system with finite energy storage capacity serving delay-sensitive applications. This communication system is powered by both grid and renewable power sources. First, we consider the average grid power consumption minimization subject to the data queue stability and renewable energy availability constraints. By exploring the optimality property and using the theory of random walks, we transform the grid power minimization problem to an asymptotically equivalent problem. Using the Lyapunov drift approach, we obtain an online dynamic power control policy to solve the asymptotically equivalent problem. Then, we introduce a novel analysis framework to study the grid power-delay tradeoff relationship of the online power control in the small delay regime. Specifically, using continuous-time approximation, dynamic programming and sample-path approach, we obtain bounds on the average delay and grid power consumption, which are asymptotically tight in the small delay regime. Based upon the derived closed-form expressions, we quantify the impacts of energy storage capacity and some other system parameters on the grid power-delay tradeoff.

Energy-aware routing is a promising technique for reducing energy consumption in future networks. Under this scheme, traffic loads are aggregated over a subset of the network links, allowing other links to be turned off to save energy. Since the capacity of links is the main limiting constraint in this problem, to further improve energy saving, the idea of using redundancy elimination (RE) in energy-aware routing has been proposed. As performing RE in routers consumes some energy, it should be specified, which routers should perform RE and which links should be deactivated so that the total energy consumption of the network is minimized. As a result, the problem of energy-aware routing with redundancy elimination, which is known to be NP-hard, arises. In this paper, we first model the problem as a mixed integer linear program (MILP). Since this problem is NP-hard, we propose an efficient heuristic solution. For this purpose, we apply Lagrangian relaxation to the problem and then prove that the obtained formulation is totally unimodular. Under this property, we can relax the integer variables to efficiently determine the solution in polynomial time. Simulation results show the advantages of our proposed heuristic solution over previous ones in terms of approximately twice the energy saving, as well as a lower number of active RE-routers. Furthermore, we show that our method can be applied to the generic energy-aware routing problem (i.e., without RE) as well.

In this paper, we consider the dynamic power control for delay-aware D2D communications. The stochastic optimization problem is formulated as an infinite horizon average cost Markov decision process. To deal with the curse of dimensionality, we utilize the interference filtering property of the CSMA-like MAC protocol and derive a closed-form approximate priority function and the associated error bound using perturbation analysis. Based on the closed-form approximate priority function, we propose a low-complexity power control algorithm solving the per-stage optimization problem. The proposed solution is further shown to be asymptotically optimal for a sufficiently large carrier sensing distance. Finally, the proposed power control scheme is compared with various baselines through simulations, and it is shown that significant performance gain can be achieved.

Delay optimal control of multi-hop networks remains a challenging problem even in the simplest scenarios. In this paper, we consider delay optimal control of a two-hop half-duplex network with independent identically distributed ON-OFF fading. Both the source node and the relay node are equipped with infinite buffers and have exogenous bit arrivals. We focus on delay optimal link selection to minimize the average sum queue length over a finite horizon subject to a half-duplex constraint. To solve the problem, we introduce a new approach, whereby an actual discrete time system (ADTS) is approximated using a virtual continuous time system (VCTS). We obtain an asymptotically delay optimal policy in the VCTS. Using the relationship between the VCTS and the ADTS, we obtain an asymptotically delay optimal policy in the ADTS. The obtained policy has both a priority feature and a safety stock feature. It offers good design insights for wireless relay networks. In addition, the obtained policy has a closed-form expression, does not require knowledge of arrival statistics, and can be implemented online. Finally, using renewal theory and the theory of random walks, we analyze the average delay resulting from the asymptotically delay optimal policy.

In this paper, we consider queue-aware beamforming control for video streaming applications in multiantenna interference network. Using heavy traffic approach, we derive the diffusion limit for the discrete time queuing system and obtain an ergodic excess beamforming control problem for the diffusion limit system. The ergodic control problem is to minimize the average power costs of the access points subject to the constraints on the playback interruption costs and buffer overflow costs of the mobile users. To deal with the queue coupling challenge, we utilize the weak interference coupling property in the network. Using the theories of calculus and perturbation techniques, we derive a closed-form approximate priority function of the optimality equation and the associated error bound. Based on this approximation, we propose a low complexity queue-aware beamforming control algorithm, which is asymptotically optimal for sufficiently small cross-channel path gain. Finally, the proposed scheme is compared with various baselines through simulations and it is shown that significant performance gain can be achieved.

It is well-known that interference alignment (IA) can achieve substantial theoretical gains in multiple-input multiple-output (MIMO) networks. However, the conventional works usually assume the full channel state information (CSI) is available at the transmitter side, which would create a overwhelming CSI feedback overhead for practical frequency-division duplexing (FDD) wireless systems. To implement IA in practice, it is highly desirable to reduce the amount of required CSI feedback overhead. In this paper, we consider IA in MIMO interference multicast networks under partial CSI feedback, and we attempt to minimize the CSI feedback cost subject to IA feasibility constraints with a given degree of freedom (DoF) requirements. First, we propose a CSI feedback profile to embrace two important CSI feedback reduction strategies and we use the metric of CSI feedback dimension to quantify the associated CSI feedback cost. We then formulate the IA conditions under partial CSI feedback in MIMO interference multicast networks and we derive new IA feasibility conditions under the proposed partial CSI feedback framework. Based on these results, we consider the CSI feedback dimension minimization subject to the IA feasibility constraints with a given DoF requirements in MIMO interference multicast networks, which is formulated as a combinatorial optimization problem. Based on the specific problem structure, we derive an asymptotically optimal solution for a category of network topologies and we further obtain closed-form tradeoff results between DoFs and the CSI feedback cost for MIMO interference multicast networks.

We propose a subspace constrained precoding scheme to fully unleash the gain provided by massive antenna array with reduced channel state information (CSI) signaling overhead. The MIMO precoder at each base station (BS) is partitioned into an inner precoder and a Transmit (Tx) subspace control matrix. The inner precoder is adaptive to the local CSI at each BS for spatial multiplexing gain. The Tx subspace control is adaptive to the channel statistics for inter-cell interference mitigation. Specifically, the Tx subspace control is formulated as a QoS optimization problem which involves an SINR chance constraint where the probability of each user's SINR not satisfying a service requirement must not exceed a given outage probability. Such chance constraint cannot be handled by the existing methods due to the two-stage precoding structure. To tackle this, we propose a bi-convex approximation approach, which consists of three key ingredients: random matrix theory, chance constrained optimization and semidefinite relaxation. Then we propose an efficient algorithm to find the optimal solution of the resulting bi-convex approximation problem. Simulations show that the proposed design has significant gain over various baselines.

In this paper, we investigate the random access for an energy harvesting secondary user (SU) in a cognitive radio system, in which the SU harvests energy from radio frequency (RF) radiation of the primary user (PU). With multipacket reception channel model, the SU can increase its throughput through not only utilizing the idle periods of the PU, but also opportunistically sharing the PU spectrum with some probability when the PU is active. By choosing the appropriate random access probability, we maximize the throughput of the SU under the constraint of the primary queue stability. We also define the energy-limited region and spectrum-limited region to specify the tradeoff between the SU performance and the PU activity. Furthermore, we investigate the effect of the primary queueing delay constraint on the throughput performance of the SU.

Singular value decomposition (SVD) plays an important role in signal processing for multi-input multi-output (MIMO) communication systems. Under massive MIMO scenarios, as the channel matrix is very large, implementing SVD at every frame is highly inefficient. Existing literature on iterative SVD algorithms are mostly heuristic based, and the associated tracking performance under time-varying channels is not clear. The difficulties of deriving and analyzing SVD algorithms are due to the non-convexity of the associated optimization problem and the time-varying nature of the MIMO channel. In this paper, we formulate the problem on Grassmann manifolds and derive a multi-stream iterative SVD algorithm using optimization techniques. To enhance the tracking performance under time-varying channels, we propose a compensation algorithm to offset the motion of the time-varying target eigenspace. We analyze the convergence behavior of the proposed algorithm, where we show that under some mild conditions, the proposed iterative SVD algorithm with compensations has zero tracking error, despite the underlying problem being non-convex and the channel being time-varying. The complexity of the algorithm is only O(n2p) for estimating p singular vectors, compared with O(n3) for the SVD of a n × n channel matrix.

Consider the scenario where a receiver acquires information (data) corrupted by interference and noise. Both the information and interference have a sparse structure. To fully exploit theindividual sparse structureof the information and interference, the joint interference mitigation and data recovery is formulated as a sparse maximum likelihood estimation (MLE) problem which maximizes the associated likelihood function underindividual sparsity levels(ISLs) constraints. We propose analternating optimization(AO) recovery algorithm to solve thenon-convexsparse MLE problem. Under certain restricted isometry property (RIP) conditions, we show that the proposed AO algorithm converges to the optimal solution of the sparse MLE problem. We also derive an upper bound of the corresponding estimation error for the information. Simulations show that the proposed solution achieves significant gain over various baselines.

Interference alignment (IA) can achieve the optimal capacity scaling with respect to SNR but most existing IA designs require full channel state information (CSI) at the transmitters. In this paper, we consider IA processing with partial CSI feedback in MIMO cellular networks and we use the feedback dimension to quantify the first order CSI feedback cost. Conventional IA cannot be used because only partial CSI knowledge can be used to design the IA pre-coders. Therefore, we establish a new set of feasibility conditions for IA under the proposed partial CSI feedback scheme. Based on these results, we formulate the problem of CSI feedback dimension minimization subject to the constraints of IA feasibility. We further propose an asymptotically optimal solution and derive closed-form trade-off results between the CSI feedback cost and IA performance in MIMO cellular networks.

To fully utilize the spatial multiplexing gains or array gains of massive MIMO, the channel state information must be obtained at the transmitter side (CSIT). However, conventional CSIT estimation approaches are not suitable for FDD massive MIMO systems because of the overwhelming training and feedback overhead. In this paper, we consider multi-user massive MIMO systems and deploy the compressive sensing (CS) technique to reduce the training as well as the feedback overhead in the CSIT estimation. We propose a distributed compressive CSIT estimation and feedback scheme to exploit the hidden joint sparsity structure in the user channel matrices and we obtain simple insights into how the joint channel sparsity can be exploited to improve the CSIT recovery performance.

Wireless recharging can be realized by microwave power transfer (MPT) that delivers energy wirelessly from stations called power beacons (PBs) to mobile devices by microwave radiation. To implement mobile charging by MPT, this paper proposes a new network architecture that overlays an uplink cellular network with randomly deployed PBs for powering mobiles, called a hybrid network. We investigate the deployment of the hybrid network under an outage constraint on data links by developing a stochastic-geometry network model where single-antenna base stations (BSs) and PBs form independent homogeneous Poisson point processes with densities λband λp, respectively, and single-antenna passive mobiles are uniformly distributed in Voronoi cells generated by BSs. In this model, the transmission powers of mobiles and PBs are fixed to be constants p and q, respectively. Moreover, a PB either radiates isotropically, called isotropic MPT, or directs energy towards target mobiles by beamforming, called directed MPT. The model is used to derive the tradeoffs between the network parameters {p, λb, q, λp) under the outage constraint and assuming infinite energy storage at mobiles. It is shown that for isotropic MPT, the product qλpλbα/2has to be above a given threshold so that PBs are sufficiently dense; for directed MPT, zmqλpλbα/2with zmdenoting the array gain should exceed a different threshold to ensure short distances between PBs and their target mobiles. In addition, similar results are derived for the case of mobiles having small energy storage.

We focus on a full-duplex multi-user MIMO system where the base station (BS) serves multiple uplink and downlink users simultaneously. Both the BS and the mobile stations (MSs) are equipped with multiple antennas. The performance of the system is limited by the self-interference at the base station and the interference caused by the uplink users on the downlink users. To address this issue, we propose to jointly design the beamformers of the BS and MSs. An optimization problem is formulated to maximize the weighted sum data rate subject to maximum power constraints. Although the problem is non-convex, it can be solved via iterative minimization of weighted sum mean square error (MSE), and a stationary point of the problem can be obtained. Strategies to choose uplink and downlink users and to determine initial point for the optimization problem are also proposed. Simulation results show that the weighted sum data rate achieved by full-duplex system is substantially higher than that achieved by baseline half-duplex systems.

We propose a multi-timescale interference mitigation scheme for massive MIMO cellular networks. The MIMO precoder at each base station (BS) is partitioned into an inner precoder and an outer precoder. The inner precoder controls the intra-cell interference and is adaptive to local channel state information (CSI) at each BS (CSIT). The outer precoder controls the inter-cell interference and is adaptive to channel statistics. Such hierarchical precoding structure reduces the number of pilot symbols required for CSI estimation in massive MIMO downlink and is robust to the backhaul latency. We study non-convex joint optimization of the outer precoders, the user selection, and the power allocation to maximize a general concave utility. Using the hidden convexity of the non-convex problem, we propose an iterative algorithm to find the optimal solution. We also obtain a low complexity algorithm with provable convergence. Simulations show that the proposed design has significant gain over various state-of-the-art baselines.

In this paper, we consider an application-driven beamforming control for video streaming in multi-antenna interference networks. The optimization objectives for video streaming are the application level performance metrics, namely the playback interruption cost and buffer overflow cost. Using heavy traffic approximation technique, we first derive the diffusion limit for the discrete time queueing system. Based on the diffusion limit, we then formulate an infinite horizon ergodic control problem to minimize the average power of the base stations subject to the constraints on the playback interruption costs and buffer overflow costs of the mobile users. To deal with the queue coupling challenge, we utilize the weak interference coupling property and derive a closed-form approximate value function of the associated optimality equation using perturbation analysis. Based on the closed- form approximate value function, we propose a low complexity queue-aware beamforming control algorithm, which is asymptotically optimal for sufficiently small cross-channel path gain. Finally, the proposed scheme is compared with various baselines through simulations and it is shown that significant performance gain can be achieved.

Advanced interference mitigation techniques such as cooperative MIMO (CoMP) have been proposed to improve the spectral efficiency of future 5G wireless networks. However, conventional CoMP is quite costly because it requires high capacity backhaul for payload exchange between the BSs. In this paper, we propose a novel solution framework of cache-induced opportunistic CoMP to achieve the CoMP gain for multimedia streaming applications without expensive backhaul. Specifically, by caching a portion of the media files, the base stations (BS) opportunistically employ CoMP to transform the cross-link interference into spatial multiplexing gain. We study a mixed-timescale optimization of (short timescale) MIMO precoding and (long timescale) cache control to minimize the transmit power under the QoS constraint. By exploiting the timescale separations of the optimization variables, we derive low complexity multi-timescale solution for the MIMO precoding and cache control. The solution has significant gains over various baselines.

Massive MIMO is a promising technology in future wireless communication networks. However, it raises a lot of implementation challenges, for example, the huge pilot symbols and feedback overhead, requirement of real-time global CSI, large number of RF chains needed, and high computational complexity. We consider a two-tier precoding strategy for multi-cell massive MIMO interference networks, with an outer precoder for inter-cell/inter-cluster interference cancellation, and an inner precoder for intra-cell multiplexing. In particular, to combat with the computational complexity issue for the outer precoding, we propose a low complexity online iterative algorithm to track the outer precoder under time-varying channels. We follow an optimization technique and formulate the problem on the Grassmann manifold. We develop a low complexity iterative algorithm, which converges to the global optimal solution under static channels. In time-varying channels, we propose a compensation technique to offset the variation of the time-varying optimal solution. We show with our theoretical result that, under some mild conditions, perfect tracking of the target outer precoder using the proposed algorithm is possible. Numerical results demonstrate that the two-tier precoding with the proposed iterative compensation algorithm can achieve a good performance with a significant complexity reduction compared with the conventional two-tier precoding techniques in the literature.

Microwave power transfer (MPT) delivers energy wirelessly from stations called power beacons (PBs) to mobile devices by microwave radiation. This provides mobiles practically infinite battery lives and eliminates the need of power cords and chargers. To enable MPT for mobile recharging, this paper proposes a new network architecture that overlays an uplink cellular network with randomly deployed PBs for powering mobiles, called a hybrid network. The deployment of the hybrid network under an outage constraint on data links is investigated based on a stochastic-geometry model where single-antenna base stations (BSs) and PBs form independent homogeneous Poisson point processes (PPPs) with densities λband λp, respectively, and single-antenna mobiles are uniformly distributed in Voronoi cells generated by BSs. In this model, mobiles and PBs fix their transmission power at p and q, respectively; a PB either radiates isotropically, called isotropic MPT, or directs energy towards target mobiles by beamforming, called directed MPT. The model is used to derive the tradeoffs between the network parameters (p, λb, q, λp) under the outage constraint. First, consider the deployment of the cellular network. It is proved that the outage constraint is satisfied so long as the product pλbα/2is above a given threshold where α is the path-loss exponent. Next, consider the deployment of the hybrid network assuming infinite energy storage at mobiles. It is shown that for isotropic MPT, the product qλpλbα/2has to be above a given threshold so that PBs are sufficiently dense; for directed MPT, zmqλpλbα/2with zmdenoting the array gain should exceed a different threshold to ensure short distances between PBs and their target mobiles. Furthermore, similar results are derived for the case of mobiles having small energy storage.

Recent results have shown that interference alignment (IA) achieves the optimal throughput scaling law w.r.t. SNR. However, except for a few special cases, finding transceivers to achieve IA in MIMO interference networks is still an open problem. This problem is challenging due to the non-convex nature of the interference optimization problem. Inspired by recent success in using tools from algebraic geometry to analyze the feasibility conditions of IA, in this work, we adopt algebraic geometry analysis to guide IA algorithm design. Specifically, we first explore the relation between algebraic independence and feasibility of polynomial equation sets, and transform the IA problem into an equivalent polynomial form, whose policy space is convex. Then we reformulate the transformed IA problem into an interference optimization problem. By exploiting the connection between algebraic independence and full rankness of Jacobian matrix, we prove that in the interference optimization problem, there is no performance gap between local and global optimums when IA is feasible. This property enables us to easily design IA algorithms by adopting existing local search algorithms. Combining the propositions obtained in this work and those obtained in the authors' prior work on IA feasibility, we have established a unified algebraic framework for both IA feasibility analysis and algorithm design.

Multi-user multi-input-multi-output (MU-MIMO) systems usually require users to feedback the channel state information (CSI) for scheduling. Most of the existing literature on the reduced feedback user scheduling focused on the throughput performance and the queueing delay was usually ignored. As the delay is important for real-time applications, it is desirable to have a low feedback queue-aware user scheduling algorithm for MU-MIMO systems. This paper proposes a two timescale queue-aware user scheduling algorithm, which consists of a queue-aware mobile-driven feedback filtering stage and a SINR-based user scheduling stage. The feedback policy is obtained by solving a queue-weighted optimization problem. In addition, we evaluate the associated queueing delay performance by using the large deviation analysis. The large deviation decay rate for the proposed algorithm is shown to be much larger than the CSI-only scheduling algorithm. Numerical results demonstrate the large performance gain of the proposed algorithm over the CSI-only algorithm, while the proposed one requires only a small amount of feedback.

Interference is a major performance bottleneck in Heterogeneous Network (HetNet). The LTE-A working groups are actively studying enhanced inter-cell interference coordination (eICIC) techniques such as almost blank subframes (ABS). We propose a two timescale hierarchical radio resource management (RRM) scheme for HetNet with dynamic ABS control. The long term controls, such as dynamic ABS, are adaptive to the large scale fading at a RRM server for interference control. The short term control (user scheduling) is adaptive to the local channel state information (CSI) within each BS to exploit the multi-user diversity. Such hierarchical RRM has low signaling overhead, good scalability, and robustness w.r.t. latency of the backhaul signaling. The two timescale optimization problem is challenging due to the exponentially large solution space. We exploit the sparsity in the HetNet interference graph and derive structural properties for the optimal ABS control. Based on that, we propose a two timescale alternative optimization solution which has low complexity and is asymptotically optimal at high SNR.

In this paper, a low-complexity delay-aware cross-layer scheduling algorithm for two-hop relay communication systems is proposed. The complex interactions of the queues at the source node and the$M$relay nodes (RSs) are modeled as an infinite horizon average reward Markov decision process (MDP), whose state space involves the joint queue state information (QSI) of the queues at the source node and the$M$RSs as well as the joint channel state information (CSI) of all S-R and R-D links. To address the curse of dimensionality, an equivalent MDP formulation is first proposed, where the system state depends only on global QSI. Furthermore, using approximate MDP and stochastic learning, an auction-based distributed online learning algorithm is derived, where each node iteratively estimates a per-node value function based on real-time observations of the local CSI and local QSI as well as signaling between relays. The combined distributed learning converges almost surely to a global optimal solution for large arrivals. Finally, it is showed by simulations that the proposed scheme achieves significant gain compared with various baselines such as the conventional CSIT-only control and the throughput optimal control (in stability sense).

Considering backhaul consumption, it may not be the best choice to engage all the time in full cooperative MIMO for interference mitigation. In this paper, we propose a novel downlink partial cooperative MIMO (Pco-MIMO) physical layer (PHY) scheme, which allows flexible tradeoff between the partial data cooperation level and the backhaul consumption. Based on this Pco-MIMO scheme, we consider dynamic transmit power and rate allocation according to the imperfect channel state information at transmitters (CSIT) and the queue state information (QSI) to minimize the average delay cost subject to average backhaul consumption constraints and average power constraints. The delay-optimal control problem is formulated as an infinite horizon average cost constrained partially observed Markov decision process (CPOMDP). By exploiting the special structure in our problem, we derive an equivalent Bellman Equation to solve the CPOMDP. To reduce computational complexity and facilitate distributed implementation, we propose a distributed online learning algorithm to estimate the per-flow potential functions and Lagrange multipliers (LMs) and a distributed online stochastic partial gradient algorithm to obtain the power and rate control policy. We prove the convergence and asymptotic optimality of the proposed solution.

It is often impractical to obtain global channel knowledge and conduct centralized optimization for wireless networks. We study distributed weighted sum-rate maximization (WSRM) in general MIMO interference networks, named B-MAC interference networks. It is desirable to exploit the structure of the problem to design distributed algorithms with high performance and low signaling overhead. We recently unveiled a polite water-filling (PWF) structure satisfied by all Pareto optimal inputs of important achievable regions of the B-MAC interference networks. The PWF offers an elegant method to decompose a network into multiple equivalent single user channels and thus, facilitates the design of distributed algorithms. Based on the PWF, we design efficient distributed algorithms which only need local channel knowledge and converge to a stationary point of the WSRM problem. For TDD networks, the duality inherited in PWF and the channel reciprocity are further exploited to reduce the signaling overhead. The proposed algorithms are shown by simulations to outperform the state-of-the-art.

Saddle point problems arise from many wireless applications, and primal-dual iterative algorithms are widely applied to find the saddle points. In the existing literature, the convergence results of such algorithms are established assuming the problem specific parameters remain unchanged during the iterations. However, this assumption is unrealistic in time varying wireless systems, as explicit message passing is usually involved in the iterations and the channel state information (CSI) may change in a time scale comparable to the algorithm update period. This paper investigates the convergence behavior and the tracking error of primal-dual iterative algorithms under time varying CSI. The convergence results are established by studying the stability of an equivalent virtual dynamic system derived in the paper, and the Lyapunov theory is applied for the stability analysis. We show that the average tracking error is proportional to the time variation rate of the CSI. We also derive an adaptive primal-dual algorithm by introducing a compensation term to reduce the tracking error under the time varying CSI.

In this paper, we consider linear MIMO transceiver design for a cellular two-way amplify-and-forward relaying system consisting of a single multi-antenna base station, a single multi-antenna relay station, and multiple multi-antenna mobile stations (MSs). Due to the two-way transmission, the MSs could suffer from tremendous multi-user interference. We apply an interference management model exploiting signal space alignment and propose a transceiver design algorithm, which allows for alleviating the loss in spectral efficiency due to half-duplex operation and providing flexible performance optimization accounting for each user's quality of service priorities. Numerical comparisons to conventional two-way relaying schemes based on bidirectional channel inversion show that the proposed scheme achieves superior error rate and average data rate performance.

In this paper, we analyze the fundamental power-delay tradeoff in point-to-point wireless communication systems with renewable energy source. We consider the max queue-weighted (MWQ) algorithm, where the transmitter determines the rate and power control actions based on the instantaneous channel state information (CSI), the data queue state information (DQSI) and renewable energy storage information (RESI). We exploit a general fluid dynamics of the data queue and renewable energy storage buffer using continuous time dynamic equations. Using the sample-path approach and renewal theory, we decompose the average delay in terms of multiple unfinished works along a sample path, and obtain bounds for the average delay and AC power consumption under the MWQ algorithm, which are asymptotically tight at small delay regime. We show that despite the randomness of the renewable energy arrival and energy buffer size, the average AC power (P) of the MWQ policy is given by P = O(D exp(1/D)) at small delay D regime. We also quantify the impacts of the renewable energy storage size in the scaling coefficient.

Delay-optimal control of multi-hop networks remains a challenging problem even in the simplest scenarios. In this paper, we consider delay-optimal control of a two-hop half-duplex network with i.i.d. on-off fading. Both the source node and the relay node are equipped with infinite buffers and have exogenous bit arrivals. We focus on delay-optimal link selection to minimize the average bit delay subject to a half-duplex constraint. To solve the problem, we introduce a new approach whereby an actual discrete time system (ADTS) is approximated using a virtual continuous time system (VCTS). Using dynamic programming, we recursively solve the delay minimization problem in the VCTS in terms of a simpler prototype problem, which can be addressed using continuous-time optimal control techniques. We show that the obtained solution in the VCTS is asymptotically optimal in the ADTS. Our solution has a closed-form expression and does not require knowledge of the arrival statistics. Finally, using renewal theory and the theory of random walks, we analyze the average delay resulting from the asymptotically optimal solution.

Attributed by its breakthrough performance in interference networks, interference alignment (IA) has attracted great attention in the last few years. However, despite the tremendous works dedicated to IA, the feasibility conditions of IA processing remains unclear for most network typologies. The IA feasibility analysis is challenging as the IA constraints are sets of high-degree polynomials, for which no systematic tool to analyze the solvability conditions exists. In this work, by developing a new mathematical framework that maps the solvability of sets of polynomial equations to the linear independence of their first order terms, we propose a sufficient condition that applies to K-pairs MIMO interference networks with general typologies. We have further proved that the sufficient condition aligns with the necessary conditions under some special configurations.

Interference alignment (IA) is a joint-transmission technique that achieves the capacity of the interference channel for high signal-to-noise ratios (SNRs). However, most prior works on IA are based on the impractical assumption that perfect and global channel-state information(CSI) is available at all transmitters, resulting in overwhelming feedback overhead. To substantially suppress the feedback overhead, this paper proposes an efficient design of the feedback framework for IA in the K-user multiple-input multiple-output (MIMO) interference channel. The proposed feedback topology supports sequential CSI exchange (feedback and feedforward) between transmitters and receivers and reduces the feedback overhead from a cubic function of K to a linear one, compared to conventional feedback approaches. Given the proposed feedback topology, we consider the limited feedback channel from the receivers to corresponding interferers and analyze the effect of quantization error which generates the residual interference. Also, an efficient feedback-bit allocation algorithm that minimizes the upper-bound of sum residual interference is proposed.

In this paper, we analysis the capacity of the clustered network multiple-input multiple-output (MIMO) systems with overlap. We firstly propose a strategy for clustering base stations (BSs) for the analysis tractability. With an assumption of a large number of transmit and receive antennas, an analytical expression of the ergodic capacity using random matrix theorems is derived. In particular, we develop an asymptotic capacity in low signal to noise ratio (SNR) regime. Based on that, we suggest that the number of overlapping degree of freedom (ODOF) is larger than ⌈B/6⌉ for the sake of the capacity increment, where B refers to the total number of BSs in the multicell network. We show through numerical examples that the analytical results match the simulation results even when the number of antennas is small.

In this paper, a comprehensive survey is given on several major systematic approaches in dealing with delay-aware control problems, namely the equivalentrate constraint approach, the Lyapunov stability drift approach, and the approximate Markov decision process approach using stochastic learning. These approaches essentially embrace most of the existing literature regarding delay-aware resource control in wireless systems. They have their relative pros and cons in terms of performance, complexity, and implementation issues. For each of the approaches, the problem setup, the general solution, and the design methodology are discussed. Applications of these approaches to delay-aware resource allocation are illustrated with examples in single-hop wireless networks. Furthermore, recent results regarding delay-aware multihop routing designs in general multihop networks are elaborated. Finally, the delay performances of various approaches are compared through simulations using an example of the uplink OFDMA systems.

This paper addresses the stability and queueing delay of space-division multiple access (SDMA) systems with bursty traffic, where zero-forcing beamforming enables simultaneous transmissions to multiple mobiles. Computing beamforming vectors relies on quantized channel state information (CSI) feedback (limited feedback) from mobiles. Define the stability region for SDMA as the set of multiuser packet-arrival rates for which the steady-state queue lengths are finite. Given perfect feedback of channel-direction information (CDI) and equal power allocation over scheduled queues, the stability region is proved to be a convex polytope having the derived vertices. A similar result is obtained for the case with perfect feedback of CDI and channel-quality information (CQI), where CQI allows scheduling and power control for enlarging the stability region. For any set of arrival rates in the stability region, multiuser queues are shown to be stabilized by the joint queue-and-beamforming control policy that maximizes the departure-rate-weighted sum of queue lengths. The stability region for limited feedback is found to be the perfect-CSI region multiplied by one minus a small factor. The required number of feedback bits per mobile is proved to scale logarithmically with the inverse of the above factor as well as linearly with the number of transmit antennas minus one. The effect of limited feedback on queueing delay is also quantified. CDI quantization errors are shown to multiply average queueing delay by a factorM>; 1. For givenM→ 1, the number of feedback bits per mobile is proved to beO(-log2(1-1/M)) .

Cooperative communication is an important technology in next generation wireless networks. Aside from conventional amplify-and-forward (AF) and decode-and-forward (DF) protocols, the partial decode-and-forward (PDF) protocol is an alternative relaying scheme that is especially promising for scenarios in which the relay node cannot reliably decode the complete source message. However, there are several important issues to be addressed regarding the application of PDF protocols. In this paper, we propose a PDF protocol and MIMO precoder designs at the source and relay nodes. The precoder designs are adapted based on statistical channel state information for correlated MIMO channels, and matched to practical minimum mean-square-error successive interference cancelation (MMSE-SIC) receivers at the relay and destination nodes. We show that under similar settings, the proposed MIMO precoder design with PDF protocol and MMSE-SIC receivers achieves substantial performance enhancement compared with conventional baselines.

In this paper, we propose an iterative interference alignment (IA) algorithm for quasi-static MIMO cellular networks. Such networks impose a key technical challenge in the IA algorithm design, namely the overlapping between the direct and interfering links due to the cellular topology. We shall address this challenge and propose a three stage IA algorithm. As illustration, we analyze the achievable degree of freedom (DoF) of the proposed algorithm for a symmetric MIMO cellular network. The derived DoF bound is also backward compatible with that achieved on K-pair MIMO interference channels.

We consider delay-optimal link selection for a two-hop three-node cooperative network with bursty packet arrivals, where both the source node and the half-duplex cooperative node have exogenous arrivals. We consider the problem of minimizing the random sum queue length process subject to link selection constraints under a general bursty bit flow model and obtain a simple closed-form delay-optimal link selection policy, requiring only 1 bit of state information for each queue. Furthermore, using the structure of the delay-optimal link selection policy, we obtain the closed-form average bit delay performance for deterministic and Poisson packet arrival processes, Finally, we derive a new lower bound for the delay penalty incurred by the (throughput-optimal) dynamic backpressure (DBP) link selection algorithm, as compared with the delay-optimal link selection policy.

In this paper, we analyze the fundamental power-delay tradeoff in point-to-point OFDM systems under imperfect channel state information quality and non-ideal circuit power. We consider a family of generalized dynamic backpressure (GDBP) algorithms parameterized by α which determines the relative importance of the system delay. The transmitter determines the rate and power control actions based on the instantaneous channel state information (CSIT) and the queue state information (QSI). We exploit a general fluid queue dynamics using a continuous time dynamic equation. Using the sample-path approach and renewal theory, we decompose the average delay in terms of multiple unfinished works along a sample path, and derive an upper bound on the average delay under the GDBP power control, which is asymptotically accurate at small delay regime. We show that despite imperfect CSIT quality and non-ideal circuit power, the average power (P) of the GDBP policy scales with delay (D) as P = O(Dexp(1/D)) at small delay regime. The impact of CSIT quality and circuit power appears as the coefficients of the scaling law.

The algorithms in this paper exploit optimal input structure in interference networks and is a major advance from the state-of-the-art. Optimization under multiple linear constraints is important for interference networks with individual power constraints, per-antenna power constraints, and/or interference constraints as in cognitive radios. While for single-user MIMO channel transmitter optimization, no one uses general purpose optimization algorithms such as steepest ascent because water-filling is optimal and much simpler, this is not true for MIMO multiaccess channels (MAC), broadcast channels (BC), and the non-convex optimization of interference networks because the traditional water-filling is far from optimal for networks. We recently found the right form of water-filling, polite water-filling, for some capacity/achievable regions of the general MIMO interference networks, named B-MAC networks, which include BC, MAC, interference channels, X networks, and most practical wireless networks as special cases. In this paper, we use weighted sum-rate maximization under multiple linear constraints in interference tree networks, a natural extension of MAC and BC, as an example to show how to design highly efficiency and low complexity algorithms. Several times faster convergence speed and orders of magnitude higher accuracy than the state-of-the-art are demonstrated by numerical examples.

In this paper, we propose an opportunistic buffered decode-wait-and-forward (OBDWF) protocol to exploit both relay buffering and relay mobility to enhance the system throughput and the end-to-end packet delay under bursty arrivals. We consider a point-to-point communication link assisted by K mobile relays. We illustrate that the OBDWF protocol could achieve a better throughput and delay performance compared with existing baseline systems such as the conventional dynamic decode-and-forward (DDF) and amplified-and-forward (AF) protocol. In addition to simulation performance, we also derived closed-form asymptotic throughput and delay expressions of the OBDWF protocol. Specifically, the proposed OBDWF protocol achieves an asymptotic throughput ΘL(\log_2 K) with ΘL(1) total transmit power in the relay network. This is a significant gain compared with the best known performance in conventional protocols (ΘL(\log_2 K) throughput with ΘL(K) total transmit power). With bursty arrivals, we show that both the stability region and average delay of the proposed OBDWF protocol can achieve order-wise performance gain ΘL(K) compared with conventional DDF protocol.

In this paper, we consider an open-loop network MIMO system with K BSs serving K private MSs and Mccommon MS based on a novel partial cooperation overlaying scheme. Exploiting the heterogeneous path gains between the private MSs and the common MSs, each of the K BSs serves a private MS non-cooperatively and the K BSs also serve the Mccommon MSs cooperatively. The proposed scheme does not require closed loop instantaneous channel state information feedback, which is highly desirable for high mobility users. Furthermore, we formulate the long-term distributive power allocation problem between the private MSs and the common MSs at each of the K BSs using a partial cooperative game. We show that the long-term power allocation game has a unique Nash Equilibrium (NE) but standard best response update may not always converge to the NE. As a result, we propose a low-complexity distributive long-term power allocation algorithm which only relies on the local long-term channel statistics and has provable convergence property.

In this paper, we consider the problem of multi-relay selection for multi-stream cooperative MIMO systems with M relay nodes. Traditionally, relay selection approaches are primarily focused on selecting one relay node to improve the transmission reliability given a single-antenna destination node. As such, in the cooperative phase whereby both the source and the selected relay nodes transmit to the destination node, it is only feasible to exploit cooperative spatial diversity (for example by means of distributed space time coding). For wireless systems with a multi-antenna destination node, in the cooperative phase it is possible to opportunistically transmit multiple data streams to the destination node by utilizing multiple relay nodes. Therefore, we propose a low overhead multi-relay selection protocol to support multi-stream cooperative communications. In addition, we derive the asymptotic performance results at high SNR for the proposed scheme and discuss the diversity-multiplexing tradeoff as well as the throughput-reliability tradeoff. From these results, we show that the proposed multi-stream cooperative communication scheme achieves lower outage probability compared to existing baseline schemes.

In this paper, we investigate the convergence behavior of the primal-dual scaled gradient algorithm (PDSGA) for solving distributed network utility maximization problems under time-varying fading channels. Our analysis shows that the proposed PDSGA converges to a limit region rather than a point under FSMC channels. We also show that the asymptotic tracking errors are given by O(T̅/N̅), where T̅ and N̅ are the update interval and the average sojourn time of the FSMC, respectively. Based on these analysis, we derive a distributive solution for determining the scaling matrices based on local CSI at each node. The numerical results show the superior performance of the proposed PDSGA over several baseline schemes.

In this paper, we propose a distributive queue-aware intra-cell user scheduling and inter-cell interference (ICI) management control design for a delay-optimal cellular downlink system with M base stations(BSs). Each BS has K downlink queues for K users respectively with heterogeneous arrivals and delay requirements. The ICI management control is adaptive to joint queue state information (QSI) over a slow time scale, while the user scheduling control is adaptive to both the joint QSI and the joint channel state information (CSI) over a faster time scale. We show that the problem can be modeled as an infinite horizon average cost Partially Observed Markov Decision Problem (POMDP), which is NP-hard in general. By exploiting special problem structure, we shall derive an equivalent Bellman equation to solve the POMDP problem. To address the distributive requirement and the issue of dimensionality and computation complexity, we derive a distributive online stochastic learning algorithm, which only requires local QSI and local CSI at each of the M BSs. We show that the proposed learning algorithm converges almost-surely and has significant gain compared with various baselines. The proposed solution only has linear complexity order O(MK).

In this paper, we focus on a rank constrained optimization problem with general Schur-convex/concave objective function and multiple trace/log-determinant constraints. We first derive a structural result on the optimal solution of the rank constrained problem without relaxation using majorization theory. Based on the solution structure, we transform the rank constrained problem into an equivalent problem with a unitary constraint. After that, we derive an iterative projected steepest descent algorithm which converges to a local optimal solution. Furthermore, we shall show that under some special cases, we could even derive closed form global optimal solution. The numerical results show the superior performance of the our proposed technique over the baseline schemes, the rank relaxation based randomization technique.

Transmit beamforming increases throughput and transmission range of a wireless communication system. However, the required feedback of channel state information (CSI) consumes radio resources that otherwise can be used for data transmission. This makes "Feedback or no feedback?" a relevant question to ask. This paper answers this question by proposing intelligent feedback control using a Markov decision process. The feedback controller turns feedback on/off according to the channel state and the criterion of maximum net throughput, namely throughput minus average feedback cost. Assuming channel isotropicity and Markovity, the state of the feedback controller reduces to two channel parameters. This allows the optimal control policy to be efficiently computed using dynamic programming. The optimal control policy is proved to be of the threshold type. Under this policy, feedback is performed whenever a channel parameter indicating the accuracy of transmit CSI is below a threshold, which varies with channel power. The above result holds regardless of whether the controller's state space is discretized or continuous. Simulation shows that feedback control increases net throughput by up to 0.5 bit/s/Hz without requiring additional bandwidth or antennas.

In this paper, we consider a robust approximate lattice alignment design for K-pairs quasi-static MIMO interference channels. The traditional interference alignment on the signal space is infeasible for K > 3 and perfect channel state information (CSI) is required for all the alignment schemes in the literature, which is impractical in practice. Furthermore, the structured lattice, widely used in the practical communications, can create a structured interference space compared with random Gaussian symbols. However, the existing alignment schemes on the lattice codes either require infinite SNR or require symmetric interference channel coefficients. Furthermore, they all assume perfect CSI knowledge. In this paper, we propose an approximate lattice alignment scheme for general complex interference channels in the general SNR regimes as well as a two-stage decoding algorithm to decode the desired signal from the approximated structured interference space. We derive the achievable data rate based on the approximate lattice alignment scheme and formulated the precoder, equalizer as well as the interference quantization design as a mixed integer and continuous optimization problem. By exploiting specific problem structures and incorporating CSI errors in the design, we derive low complexity robust solutions. We show that the proposed scheme has significant gain compared with the existing baselines.

In this paper, we propose a distributive delay-optimal power and relay selection algorithm for two-hop cooperative OFDM systems. The complex interactions of the queues at the source node and the M relays (RSs) are modeled as an infinite horizon average reward Markov Decision Process (MDP), whose state space involves the joint queue state (QSI) of the queue at the source node and the queues at the M RSs as well as the joint channel state (CSI) of all S-R links and R-D links. As a first step to address the curse of dimensionality, we propose a reduced state MDP formulation. From the associated Bellman's equation, we show that the delay-optimal power control (and link selection algorithm), which are functions of both the CSI and QSI, has a multi-level water-filling structure. Furthermore, using stochastic learning, we derive a distributive online learning algorithm in which each node recursively estimates a per-node potential function based on real-time observations of the local CSI and local QSI only. We show that the combined distributive learning converges almost surely to a global optimal solution for large arrivals. Finally, we show by simulation that the delay performance of the proposed scheme is significantly better than various baselines such as the conventional CSIT-only control and the throughput optimal control (in stability sense).

In network MIMO systems, channel state information is required at the transmitter side to multiplex users in the spatial domain. Since perfect channel knowledge is difficult to obtain in practice, limited feedback is a widely accepted solution. The dynamic number of cooperating BSs and heterogeneous path loss effects of network MIMO systems pose new challenges on limited feedback design. In this paper, we propose a scalable limited feedback framework using per-cell codebooks, along with a low-complexity feedback indices selection algorithm. We show that the proposed per-cell codebook limited feedback design can asymptotically achieve the same performance as the joint-cell codebook approach. We also derive an asymptotic per-user throughput loss due to limited feedback with per-cell codebooks. Based on that, we show that when the number of per-user feedback-bits Bkis O(NnTnRlog2(ρgksum)), the system operates in the noise-limited regime in which the per-user throughput is O (uRlog2(nRρgksum/NnT)). On the other hand, when the number of per-user feedback-bits does not scale with the system SNR ρ, the system operates in the interference-limited regime where the per-user throughput is O(nRBk/(NnT)2). Numerical results show that the proposed design is very flexible to accommodate dynamic number of cooperating BSs and achieves much better performance compared with other baselines (such as the Givens rotation approach).

In this paper, we analyze the uplink goodput (bits/sec/Hz successfully decoded) and per-user packet outage in a cellular network using multi-user detection with successive interference cancellation (MUD-SIC). We are interested to study the role of macro-diversity (MDiv) between multiple base stations on the MUD-SIC performance where the effect of potential error-propagation during the SIC processing is taken into account. While the jointly optimal power and decoding order in the MUD-SIC are NP hard problem, we derive a simple on/off power control and asymptotically optimal decoding order with respect to the transmit power. Based on the information theoretical framework, we derive the closed-form expressions on the total system goodput as well as the per-user packet outage probability.

In this paper, we quantify the benefits of employing relay station in large-coverage cognitive radio systems which opportunistically access the licensed spectrum of some small-coverage primary systems scattered inside. Through analytical study, we show that even a simple decode-and-forward (SDF) relay, which can hold only one packet, offers significant pathloss gain in terms of the spatial transmission opportunities and link reliability. However, such scheme fails to capture the spatial-temporal burstiness of the primary activities, that is, when either the source-relay (SR) link or relay-destination (RD) link is blocked by the primary activities, the cognitive spectrum access has to stop. To overcome this obstacle, we further propose buffered decode-and-forward (BDF) protocol. By exploiting the infinitely long buffer at the relay, the blockage time on either SR or RD link is saved for cognitive spectrum access. The buffer gain is shown analytically to improve the stability region and average end-to-end delay performance of the cognitive relay system.

In network MIMO systems, channel state information is required at the transmitter side to multiplex users in the spatial domain. Since perfect channel knowledge is difficult to obtain in practice, limited feedback is a widely accepted solution. The dynamic number of cooperating BSs and heterogeneous path loss effects of network MIMO systems pose new challenges on limited feedback design. In this paper, we propose a scalable limited feedback design for network MIMO systems with multiple base stations, multiple users and multiple data streams for each user. We propose a limited feedback framework using per-cell product codebooks, along with a low-complexity feedback indices selection algorithm. We show that the proposed per-cell product codebook limited feedback design can asymptotically achieve the same performance as the joint-cell codebook approach. We also derive an asymptotic per-user throughput loss due to limited feedback with per-cell product codebooks. Based on that, we show that when the number of per-user feedback-bits Bkis {O}( NnTnRlog2(ρgksum)), the system operates in the noise-limited regime in which the per-user throughput is {O} ( nRlog2(nRρgksum/NnT)). On the other hand, when the number of per-user feedback-bits Bkdoes not scale with the system SNR ρ, the system operates in the interference-limited regime where the per-user throughput is {O}(nRBk/(NnT)2). Numerical results show that the proposed design is very flexible to accommodate dynamic number of cooperating BSs and achieves much better performance compared with other baselines (such as the Givens rotation approach).

In this paper, we propose a robust transceiver design for the K-pair quasi-static MIMO interference channel. Each transmitter is equipped with M antennas, each receiver is equipped with N antennas, and the kthtransmitter sends Lkindependent data streams to the desired receiver. In the literature, there exist a variety of theoretically promising transceiver designs for the interference channel such as interference alignment-based schemes, which have feasibility and practical limitations. In order to address practical system issues and requirements, we consider a transceiver design that enforces robustness against imperfect channel state information (CSI) as well as fair performance among the users in the interference channel. Specifically, we formulate the transceiver design as an optimization problem to maximize the worst-case signal-to-interference-plus-noise ratio among all users. We devise a low complexity iterative algorithm based on alternative optimization and semi-definite relaxation techniques. Numerical results verify the advantages of incorporating into transceiver design for the interference channel important practical issues such as CSI uncertainty and fairness performance.

In this paper, we propose a distributive queue-aware intra-cell user scheduling and inter-cell interference (ICI) management control design for a delay-optimal celluar downlink system with M base stations (BSs), and K users in each cell. Each BS has K downlink queues for K users respectively with heterogeneous arrivals and delay requirements. The ICI management control is adaptive to joint queue state information (QSI) over a slow time scale, while the user scheduling control is adaptive to both the joint QSI and the joint channel state information (CSI) over a faster time scale. We show that the problem can be modeled as an infinite horizon average cost Partially Observed Markov Decision Problem (POMDP), which is NP-hard in general. By exploiting the special structure of the problem, we shall derive an equivalent Bellman equation to solve the POMDP problem. To address the distributive requirement and the issue of dimensionality and computation complexity, we derive a distributive online stochastic learning algorithm, which only requires local QSI and local CSI at each of the M BSs. We show that the proposed learning algorithm converges almost-surely (with probability 1) and has significant gain compared with various baselines. The proposed solution only has linear complexity order O(MK).

In this paper, we consider delay-optimal power and subcarrier allocation design for OFDMA systems with NFsubcarriers, K mobiles and one base station. There are K queues at the base station for the downlink traffic to the K mobiles with heterogeneous packet arrivals and delay requirements. We shall model the problem as a K-dimensional infinite horizon average reward Markov Decision Problem (MDP) where the control actions are assumed to be a function of the instantaneous Channel State Information (CSI) as well as the joint Queue State Information (QSI). We propose an online stochastic value iteration solution using stochastic approximation. The proposed power control algorithm, which is a function of both the CSI and the QSI, takes the form of multi-level water-filling. We prove that under two mild conditions in Theorem 1, the proposed solution converges to the optimal solution almost surely (with probability 1) and the proposed framework offers a possible solution to the general stochastic NUM problem. By exploiting the birth-death structure of the queue dynamics, we obtain a reduced complexity decomposed solution with linear O(KNF) complexity and O(K) memory requirement.

In this paper, we consider a cognitive multi-hop relay secondary user (SU) system sharing the spectrum with some primary users (PU). The transmit power as well as the hop selection of the cognitive relays can be dynamically adapted according to the local (and causal) knowledge of the instantaneous channel state information (CSI) in the multi-hop SU system. We shall determine a low complexity, decentralized algorithm to maximize the average end-to-end throughput of the SU system with dynamic spatial reuse. The problem is challenging due to the decentralized requirement as well as the causality constraint on the knowledge of CSI. Furthermore, the problem belongs to the class of stochastic Network Utility Maximization (NUM) problems which is quite challenging. We exploit the time-scale difference between the PU activity and the CSI fluctuations and decompose the problem into a master problem and subproblems. We derive an asymptotically optimal low complexity solution using divide-and-conquer and illustrate that significant performance gain can be obtained through dynamic hop selection and power control. The worst case complexity and memory requirement of the proposed algorithm is O(M2) and O(M3) respectively, where M is the number of SUs.

In this paper, we consider spatial-division multipleaccess (SDMA) systems with one base station with multiple antennae and a number of single antenna mobiles under noisy limited CSIT feedback. We propose a robust noisy limited feedback design for SDMA systems. Furthermore, we show that despite the noisy feedback, the average system goodput scales as O(nT)(1-ϵ)nT-1(Cfb-log2(Nn)))and O(nT.log2P) in the interference limited regime (CfbnT- 1) log2P + log2Nn) and noise-limited regime respectively.

In this paper, we consider delay-optimal power and subcarrier allocation design for OFDMA systems with K mobiles and one base station. There are K queues at the base station for the downlink traffic to the K mobiles with heterogeneous packet arrivals and delay requirements. We shall model the problem as a K-dimensional infinite horizon average reward Markov decision problem (MDP) where the control actions are assumed to be a function of the instantaneous channel state information (CSI) as well as the joint queue state information (QSI). This problem is challenging because it corresponds to a stochastic network utility maximization (NUM) problem where general solution is still unknown. We propose an online stochastic value iteration solution using stochastic approximation. The proposed power control algorithm, which is a function of both the CSI and the QSI, takes the form of multi-level water-filling. We prove that under some mild conditions, the proposed solutions converge to the optimal solution almost surely and the proposed framework offers a possible solution to the general stochastic NUM problem. By exploiting the birth-death structure of the queue dynamics in the Poisson arrivals, we obtain a reduced complexity decomposed solution with linear O(KNF) complexity and memory requirement.

Cooperative communication is an important technology in next generation wireless networks. Partial decode-and-forward provides an alternative solution for the conventional amplify-and-forward as well as decode-and-forward relay protocols. However, there are several important issues to be addressed regarding the application of partial decode-and-forward protocol. In this paper, we address the practical issues by proposing a joint MIMO precoder design for the multi-antenna cooperative system with correlated MIMO fading and partial decode-and-forward relay protocol. In addition, the MIMO precoders at both the source and the relay are matched to the MMSE-SIC receivers at the destination. We find that under similar system settings, joint MIMO precoder design with partial decode-and-forward relay protocol and MMSE-SIC receivers achieves substantial performance enhancement and has important practical significance.

In this paper, we consider a relay-assisted cognitive cellular downlink system dynamically accessing a spectrum licensed to a primary network, thereby improving the efficiency of spectrum usage. A cluster-based relay-assisted architecture is proposed, where relay stations are employed for minimizing the interference to users in the primary network and achieving fairness for cell-edge users. Based on this architecture, an optimal solution is derived for jointly controlling data rate, transmission power, and subband allocation to optimize the weighted sum goodput where proportional fair scheduling (PFS) is included as a special case. As shown by simulations, the proposed solution achieves significant throughput gains and better user fairness compared with existing designs.

This paper considers the delay-optimal power and transmission threshold control design in S-ALOHA network with FSMC fading channels. The random access system consists of an access point with K competing users, each has access to the local channel state information (CSI) and queue state information (QSI) as well as the common feedback (ACK/NAK/Collision) from the access point. We seek to derive the delay-optimal control policy (composed of threshold and power control) in symmetric network. The optimization problem belongs to the special memoryless policy case of K-agent infinite horizon decentralized Markov decision process (DEC-MDP), and finding the optimal memoryless policy is shown to be computationally challenging. To obtain a feasible and low complexity solution, we recast the optimization problem into two subproblems, namely the power control problem and the threshold control problem. For a given threshold control policy, the power control problem is decomposed into a reduced state MDP for single user so that the overall complexity is O(NJ), where N and J are the buffer size and the cardinality of the CSI states. For the threshold control problem, we exploit some special structure of the collision channel and common feedback information to derive a low complexity solution. The delay performance of the proposed design is shown to have substantial gain relative to conventional throughput optimal approaches for S-ALOHA.

In previous study on multi-user delay optimal problem, the exponentially increasing state space become one of the main obstacle. Moreover, packet drop due to finite buffer size is not taken into account. In this paper, we exploit the birth-death dynamics of the buffered SDMA systems and proposed a new approach, namely stochastic decomposition, to derive the delay optimal power adaptation scheme in a SDMA system. Unlike the conventional CSI-only power control solution, the delay-optimal power control has the multi-level water-filling structure in which the QSI determines the water-level and the CSI determines the power allocation across the SDMA users. This new approach overcomes the complexity issue mentioned above and allow us to obtain closed-form performance expressions so as to obtain the following first-order insights: 1) The water-filling levels { 1/αk, q, N̅{itk} } under different QSIs q ∈ {1, 2, …L} is an increasing geometric series. 2) The optimal average delay U̅k* achieved by the multilevel water filling algorithm is τ/O(log P̅k+ O(log log P̅k) − λkwhile that achieved by traditional CSI-only scheme is τ/O(log P̅k) − λk. 3) Minimum average power required to satisfy a packet drop rate constraint ∈d(due to finite buffer) is given by: log log(P̅k,min) ∝ − log ∈d/L + log (λk) + log (N̅k).

In this paper, utilizing the relay buffers, we propose an opportunistic decode-wait-and-forward relay scheme for a point-to-point communication system with a half-duplexing mobile relay network. The proposed scheme achieves the maximum throughput of Θ(log K) at a cost of O(1) total transmission power and O(K=q) average end-to-end packet delay, where 0 ≪ q ≤ ½ measures the speed of relays' mobility. It can be proved that this system throughput is unattainable for the existing designs with low relay mobility. Therefore, the proposed relay scheme can exploit the time diversity and relays' mobility more efficiently.

In a frequency-division-duplex (FDD) system, uplink and downlink are allocated equal bandwidths. Due to the relative sparseness of uplink Internet traffic, the uplink spectrum is usually under-utilized. In this paper, we propose dynamic spectrum access algorithms allowing relay-assisted downlink to opportunistically transmit over the uplink spectrum, thereby maximizing its usage efficiency. Specifically, uplink sub-carrier allocation and scheduling based on spectrum sensing are jointly designed for minimizing received interference and thereby enhancing throughput under the constraint of interference. The proposed algorithms support both centralized and distributed implementation. Simulation results demonstrate that the proposed cognitive algorithms contribute significant capacity gains compared with existing designs.

The provision for spectrally efficient multicast broadcast services (MBS) is one of the key functional requirements for next generation wireless communication systems. The challenge inherent to MBS is to ensure that all MBS receivers can be served, and one effective solution to this problem is to employ MIMO multicast transmit precoding. In previous works on MIMO multicast transmit precoding design, the authors proposed algorithms given 1) perfect transmitter-side channel state information (CSIT) or 2) specific channel conditions that facilitate precoder optimization with imperfect CSIT. In this paper, we focus on transmit precoding design for MBS where the CSIT is obtained via codebook based limited feedback. In addition, we derive the asymptotic closed-form expressions for the average minimum receive signal-noise-ratio (SNR) among the MBS receivers and study the order of growth with respect to the number of MBS receivers, the number of feedback bits and the number of transmit antennas.

In this paper, we consider delay-optimal MIMO precoder and power allocation design for a MIMO Link in wireless fading channels. There are L data streams spatially multiplexed onto the MIMO link with heterogeneous packet arrivals and delay requirements. The transmitter is assumed to have knowledge of outdated channel state information (CSIT) as well as the joint queue state information (QSI) of the L buffers. Using static sorting of the L eigenchannels, we decompose the L-dimensional MDP into L independent 1-dimensional MDP and derived low complexity precoding and power control policies (with linear complexity) to minimize average delays of the L application streams.

In this paper, we shall analysis the uplink system goodput (bits/sec/Hz successfully decoded) and per-user packet outage in a cellular network using multi-user detection with successive interference cancellation (MUD-SIC). Slow fading channels and interference cancellation error occurs due to packet outage in each decoding stage are considered. We are interested to study the roles of macro-diversity, decoding order and power allocation on the MUD-SIC performance where the effect of potential error-propagation during the SIC detection is taken into account. Based on the information theoretical framework, we derive the closed-form expressions on the total system goodput as well as the per-user packet outage probability. We show that the system goodput does not scale with SNR due to mutual interference in the SIC process and macro-diversity (MDiv) could alleviate the problem and benefit to the system goodput.

In most of the existing works on cognitive radio (CR) systems, the spectrum sensing and the cross-layer scheduling are designed separately, where the cross-layer scheduling is performed based on the hard sensing information (HSI) generated from the sensing modular. In this paper, we shall propose a joint cross-layer and sensing design and study its performance advantages over the aforementioned traditional decoupled approaches. We shall also propose a joint design framework to optimize a system utility by adapting the power allocation and the subcarrier assignment across the secondary users (under a average interference constraint to the primary users) based on both the channel state information (CSI) and the raw sensing information (RSI). Simulation results reveals its substantial performance gain over the conventional CR systems.

Cognitive and cooperative technologies are two core components in the design of next generation wireless networks. One key issue associated with cognitive transmission is the inefficient spectrum sharing of the secondary system, especially for secondary communications separated by long distance. To boost the spectrum sharing efficiency, cognitive multi-relay system appears to be an attractive solution for the cognitive transmission systems. In this paper, we consider a cognitive multi-relay (CMR) system and propose a novel CMR buffered decode-and-forward protocol that exploit the buffers in the source and each relay node. Moreover, we derive the closed-form average end-to-end delay and the stability region by exploiting the birth-death nature of the queue dynamics and the methods of state aggregation and queue dominance. Comparing with the baseline protocols through analytical and numerical results, the proposed CMR-BDF scheme can dynamically adjust the cognitive transmission to exploit the spatial PU burstiness while simultaneously benefits from the advantage of double-sided selection diversity in both the source-relay and relay-destination interfaces.

Spectrum sharing between wireless networks improves the usage efficiency of radio spectrums. This paper addresses spectrum sharing between a cellular uplink and a mobile ad hoc networks. These networks use either all uplink frequency subchannels or their disjoint subsets, called spectrum underlay and spectrum overlay, respectively. Given these methods, the capacity tradeoff between the coexisting networks is analyzed in terms of transmission capacity. For a network with Poisson distributed transmitters, this metric is defined as the maximum density of transmitters subject to an outage constraint for a given signal-to-interference ratio (SIR). Using stochastic geometry, the transmission-capacity tradeoff between the coexisting networks is derived, where both spectrum overlay and underlay as well as successive interference cancellation (SIC) are considered. In particular, for small target outage probability, the transmission capacities of the coexisting networks are proved to satisfy a linear equation. Its coefficients depend on the spectrum sharing method and whether SIC is applied. This linear equation shows that spectrum overlay is more efficient than spectrum underlay.

Spectrum sharing between wireless networks improves the efficiency of spectrum usage, and thereby alleviates spectrum scarcity due to growing demands for wireless broadband access. To improve the usual underutilization of the cellular uplink spectrum, this paper addresses spectrum sharing between a cellular uplink and a mobile ad hoc networks. These networks access either all frequency subchannels or their disjoint subsets, called spectrum underlay and spectrum overlay, respectively. Given these spectrum sharing methods, the capacity trade-off between the coexisting networks is analyzed based on the transmission capacity of a network with Poisson distributed transmitters. This metric is defined as the maximum density of transmitters subject to an outage constraint for a given signal-to-interference ratio (SIR). Using tools from stochastic geometry, the transmissioncapacity trade-off between the coexisting networks is analyzed, where both spectrum overlay and underlay as well as successive interference cancelation (SIC) are considered. In particular, for small target outage probability, the transmission capacities of the coexisting networks are proved to satisfy a linear equation, whose coefficients depend on the spectrum sharing method and whether SIC is applied. This linear equation shows that spectrum overlay is more efficient than spectrum underlay. Furthermore, this result also provides insight into the effects of network parameters on transmission capacities, including link diversity gains, transmission distances, and the base station density. In particular, SIC is shown to increase the transmission capacities of both coexisting networks by a linear factor, which depends on the interference-power threshold for qualifying canceled interferers.

In this paper, we focus on the asymptotic cross layer analysis of multi-antenna systems with transmit MMSE (Tx- MMSE) beamforming, near orthogonal scheduling and outdated CSIT. To capture the effect of the potential packet outage, we introduce the average system goodput, which measures the average b/s/Hz delivered to the mobiles successfully, as the system performance objective. We derive closed-form expressions for the optimal power and rate allocations as well as a low complexity near orthogonal user scheduling (NEOUS) algorithm to solve the cross-layer optimization problem.We derive the asymptotic order of growth in system goodput for general CSIT error variance σ2and found that for sufficiently large nT (number of antennas at the base station) and K (number of users) where K = g-1(nT) for some strictly increasing function g(x) = o(x), the the system goodput grows in the order of nT log[(1-σ2) logK] when σ2< 1. This is the same order of growth as the optimal order of growth in broadcast channels with perfect CSIT and hence, the NEOUS is order-optimal. On the other hand, we need exponentially larger K to compensate for the penalty in multiuser diversity gain due to CSIT errors.

It is well known that multiple-input multiple-output (MIMO) systems have high spectral efficiency, especially when channel state information at the transmitter (CSIT) is available. In many practical systems, it is reasonable to assume that the CSIT is obtained by a limited (i.e., finite rate) feedback and is therefore imperfect. We consider the design problem of how to use the limited feedback resource to maximize the achievable information rate. In particular, we develop a low complexity power on/off strategy with beamforming (or Grassmann precoding), and analytically characterize its performance. Given the eigenvalue decomposition of the covariance matrix of the transmitted signal, refer to the eigenvectors as beams, and to the corresponding eigenvalues as the beam's power. A power on/off strategy means that a beam is either turned on with a constant power, or turned off. We will first assume that the beams match the channel perfectly and show that the ratio between the optimal number of beams turned on and the number of antennas converges to a constant when the numbers of transmit and receive antennas approach infinity proportionally. This motivates our power on/off strategy where the number of beams turned on is independent of channel realizations but is a function of the signal-to-noise ratio (SNR). When the feedback rate is finite, beamforming cannot be perfect, and we characterize the effect of imperfect beamforming by quantization bounds on the Grassmann manifold. By combining the results for power on/off and beamforming, a good approximation to the achievable information rate is derived. Simulations show that the proposed strategy is near optimal and the performance approximation is accurate for all experimented SNRs.

It is well known that cross-layer scheduling which adapts power, rate and user allocation can achieve significant gain on system capacity. However, conventional cross-layer designs all require channel state information at the base station (CSIT) which is difficult to obtain in practice. In this paper, we focus on cross-layer resource optimization based on ACK/NAK feedback flows in orthogonal frequency-division multiplexing (OFDM) systems without explicit CSIT. While the problem can be modeled as Markov decision process (MDP), brute-force approach by policy iteration or value iteration cannot lead to any viable solution. Thus, we derive a simple closed-form solution for the MDP cross-layer problem, which is asymptotically optimal for sufficiently small target packet error rate (PER). The proposed solution also has low complexity and is suitable for real-time implementation. It is also shown to achieve significant performance gain compared with systems that do not utilize the ACK/NAK feedbacks for cross-layer designs or cross-layer systems that utilize very unreliable CSIT for adaptation with mismatch in CSIT error statistics. Asymptotic analysis is also provided to obtain useful design insights.

In this paper, we propose two robust limited feedback designs for multiple-input multiple-output (MIMO) adaptation. The first scheme, namely, thecombined designjointly optimizes the adaptation, CSIT (channel state information at the transmitter) feedback as well as index assignment strategies. The second scheme, namely, thedecoupled design, focuses on the index assignment problem given an error-free limited feedback design. Simulation results show that the proposed framework has significant capacity gain compared to the naive design (designed assuming there is no feedback error). Furthermore, for large number of feedback bits$C_{\rm fb}$, we show that undertwo-nearest constellationfeedback channel assumption, the MIMO capacity loss (due to noisy feedback) of the proposed robust design scales like${\cal O}(P_e2^{-{{C_{\rm fb}}\over{t+1}}})$for some positive integer$t$. Hence, the penalty due to noisy limited feedback in the proposed robust design approaches zero as$C_{\rm fb}$increases.

In this paper, we consider distributive subband, power and rate allocation for a two-hop downlink transmission in an orthogonal frequency-division multiple-access (OFDMA) cellular system with fixed relays which operate in decode-andforward strategy. We take into account of the penalty of packet errors due to imperfect CSIT and system fairness by considering weighted sum goodput as our optimization objective. Based on the cluster-based architecture, we obtain a fast-converging distributive solution with only local imperfect CSIT by using decomposition of the optimization problem. To further reduce the signaling overhead and computational complexity, we propose a reduced feedback distributive solution, which can achieve asymptotically optimal performance for large number of users with arbitrarily small feedback overhead per user.We also derive asymptotic average system throughput so as to obtain useful design insights.

In this paper, we consider the delay-sensitive power and transmission threshold control design in S-ALOHA network with FSMC fading channels. The random access system consists of an access point with K competing users, each has access to the local channel state information (CSI) and queue state information (QSI) as well as the common feedback (ACK/NAK/Collision) from the access point. We seek to derive the delay-optimal control policy (composed of threshold and power control). The optimization problem belongs to the memoryless policy K-agent infinite horizon decentralized Markov decision process (DEC-MDP), and finding the optimal policy is shown to be computationally intractable. To obtain a feasible and low complexity solution, we recast the optimization problem into two subproblems, namely the power control and the threshold control problem. For a given threshold control policy, the power control problem is decomposed into a reduced state MDP for single user so that the overall complexity is O(NJ), where N and J are the buffer size and the cardinality of the CSI states. For the threshold control problem, we exploit some special structure of the collision channel and common feedback information to derive a low complexity solution. The delay performance of the proposed design is shown to have substantial gain relative to conventional throughput optimal approaches for S-ALOHA.

It is well known that cross-layer scheduling can boost the spectral efficiency of multi-user OFDMA systems through multi-user selection diversity but existing designs usually have two important assumptions - users are delay-insensitive and channel state information at the transmitter (CSIT) is perfect. In practice, users have heterogeneous delay requirements and CSIT usually becomes outdated in time varying channel, which in turns leads to systematic packet errors and hence results in significant degradation on the throughput and delay performance in the OFDMA systems. In this paper, a novel cross-layer design problem is formulated as a convex optimization problem, on which a delay-sensitive jointly optimal power, rate and subcarrier allocation scheme is proposed so as to maintain heterogeneous users' delay requirement as well as achieving a target packet outage probability through combining queueing theory and information theory. Furthermore, we obtain closed-form asymptotic performance of the proposed delay-sensitive scheduler. Unlike the well-known SNR gain of Θ(log K)in conventional crosslayer scheduler with perfect CSIT, we demonstrate a cross-layer SNR gain of Θ((1 - σΔH2)log K)can still be achieved under heterogeneous delay constraints and outdated CSIT with error variance σ2ΔH. Finally, simulation results show that our proposed delay-sensitive CSIT error considerate schemes provide robust system performance enhancement over conventional CSIT error inconsiderate opportunistic scheduler and naive queue length based MAX-Weight scheduler while satisfying heterogeneous delay requirements even at moderate to high CSIT errors.

Abstract-Delay-sensitive cross-layer design has recently attracted increasing interests in providing delay performance guarantee and exploiting multi-user diversity throughput advantage for real-time traffic. Most of the existing solutions are centralized which are undesirable from the complexity scalability, signaling overhead scalability as well as the flexibility w.r.t. dynamic active sessions. In this work, we focus on delay-sensitive decentralized cross-layer design of OFDMA systems. Due to the stochastic system state, combinatorial nature of subcarrier allocation as well as the consideration for efficient feedback, conventional brute force decomposition technique failed to work. By using a novel two-level dual decomposition technique with comparison argument, we shall derive a feedback-efficient decentralized OFDMA cross-layer solution, with consideration of the stochastic system state, heterogeneous delay requirements, outdated CSIT as well as the combinatorial nature of subcarrier allocation. The proposed distributive design achieves optimal performance as the centralized solution scheduling with scalable complexity, signaling overhead as well as flexibility w.r.t. dynamic active sessions in the systems. In addition, using asymptotic analysis, we have derived the minimum feedback cost for ϵ-optimal system performance, wherein the average feedback cost per user is also shown to quickly approaches zero as systems scales.

In this paper, we propose an integrated cross-layer optimization framework for resource allocation over a multi-user MIMO system using transmit group MMSE (Tx-GMMSE) at the base station. We adopt the full-rate full-diversity orthogonal STBC in the multi-user Tx-GMMSE physical layer allowing a flexible tradeoff of spatial multiplexing (for spectral efficiency) and full rate spatial diversity (for protection against packet outage) with various number of antenna groupings. The proposed cross-layer framework, which determines the user selection, rate selection, power selection as well as mode selection (spatial diversity or spatial multiplexing) for each user, can introduce robustness to the potential packet transmission errors even at moderate to large CSIT errors.

In this paper, we consider delay-optimal MIMO precoder and power allocation design for a MIMO link in wireless fading channels. There are L data streams spatially multiplexed onto the MIMO link with heterogeneous packet arrivals and delay requirements. The transmitter is assumed to have knowledge of the channel state information (CSI) as well as the joint queue state information (QSI) of the L buffers. Using L-dimensional Markov decision problem (MDP), we obtain optimal precoding and power allocation policies for general delay regime, which consists of an online solution and an offline solution. The online solution has negligible complexity but the offline solution has worst case complexity part((N + 1)L) where N is the buffer size. Using static sorting of the L eigenchannels, we decompose the MDP into L independent 1-dimensional subproblems and obtained low complexity offline solution with linear complexity order part(NL) and close-to-optimal performance.

Power control plays a critical role in cognitive radio (CR) system to achieve a balance between efficient sharing of licensed spectrum and interferences to primary system (PU). Most of the existing works considered power control under perfect knowledge of system state (such as channel state information (CSIT) and sensing information (SIT)). In this paper, we shall focus on the design and analysis of power control scheme under imperfect knowledge of system state for SISO and OFDM systems. We obtained asymptotically optimal power control policy, taking into account of imperfect system state knowledge and packet errors due to channel outage. In addition, we found that system parameters affect asymptotic average goodput by O(-ln(rhopRp)e-sigmae2/1-sigmae2) and O(e-rhopRpe-sigmae2/1-sigmae2) under low and high PU activities respectively where rhopRp, sigmae2represents PU activity level and CSIT quality respectively.

In this paper, we propose a novel closed-loop approach for robust downlink multi-antenna cross-layer design with outdated channel state information at the transmitter (CSIT). Based on the ACK/NAK feedbacks from the mobiles, the proposed cross-layer design does not require the knowledge of CSIT error statistics. We formulate the cross-layer design as a mixed combinatorial search and Markov decision process (MDP). While it is well-known that general solutions for MDP are very complex, one important contribution of this paper is that we obtain simple closed-form power, rate and user allocation policies which are asymptotically optimal for small target frame error rate (FER). Simulation results illustrate that the performance of the proposed closed-loop cross-layer design is very robust with respect to the outdated CSIT, CSIT error model mismatch as well as channel variations due to Doppler.

In most of the existing works on cognitive radio (CR) systems, the spectrum sensing and the cross-layer scheduling are designed separately. Specifically, the sensing module first determines whether or not a channel resource is available for the CR system based on the sensing information. The scheduling module then schedules the data transmission of different users on the available channels based on the hard-decision sensing information (HSI). In this paper, we shall propose a joint crosslayer and sensing design and study its performance advantages over the aforementioned traditional decoupled approaches. We shall consider the downlink transmission of an OFDMA-based secondary system sharing the spectrum with primary users using cognitive radio technology. We shall rely on the joint design framework to optimize a system utility, which adapts the power allocation and the subcarrier assignment across the secondary users (under a average interference constraint to the primary users) based on both the channel state information (CSI) and the raw sensing information (RSI). In addition, we shall also propose a distributed implementation for the cross-layer sensing and scheduling design using primal-dual decomposition approach. Simulation results reveals the substantial performance gain of the proposed joint design over the conventional CR systems.

In this paper, we shall focus on the design and asymptotic performance analysis of joint source channel coding (JSCC) for MIMO channels with limited Channel State Information (CSI) feedback. We consider rate adaptation, spatial power adaptation as well as precoder adaptation. We find that the optimal distortion exponent (achieved with perfect CSIT) can be realized using only spatial power adaptation and precoder adaptation with sufficiently large feedback rate. We also compare the average distortion of the limited feedback JSCC system using fixed bandwidth expansion scheme and dynamic bandwidth expansion scheme. We show that dynamic bandwidth expansion is more effective to reduce the limited feedback JSCC performance especially at large average bandwidth expansion.

To significantly enhance the spectral efficiency of the MBS service, UEP and MIMO are important core technologies to realize such challenging goal. Taking into account of the implementation constraints, 4 times 2 is widely accepted as a reasonable MIMO setup and is a mandatory configuration of the next generation wireless systems. Within the 4 times 2 MIMO configuration, D-STTD is an important transmission scheme which strikes a balance between spatial diversity and spatial multiplexing and it can achieve better performance than VBLAST. Traditional approaches focus on different ways to introduce UEP in single antenna systems. However, it is difficult to apply the conventional schemes in the D-STTD systems since the decoding error is mainly contributed by the spatial interference between different streams. In this paper, we shall propose a novel UEP scheme for MBS services using D-STTD in 4times2 MIMO systems and formulate a general design framework to determine various parameters of the proposed UEP scheme. For illustration, we give two examples on the determination of the UEP parameters for the uncoded UEP design and the coded UEP design. From the results, we found that the proposed UEP scheme can achieve significant MBS system performance gain compared to those baseline systems.

Cognitive radio and dynamic spectrum access represent a new paradigm shift in more effective use of limited radio spectrum. One core component behind dynamic spectrum access is the sensing of primary user activity in the shared spectrum. Conventional distributed sensing and centralized decision framework involving multiple sensor nodes is proposed to enhance the sensing performance. However, it is difficult to apply the conventional schemes in reality since the overhead in sensing measurement and sensing reporting as well as in sensing report combining limit the number of sensor nodes that can participate in distributive sensing. In this paper, we shall propose a novel, low overhead and low complexity energy detection based cooperative sensing framework for the cognitive radio systems which addresses the above two issues. The energy detection based cooperative sensing scheme greatly reduces the quiet period overhead (for sensing measurement) as well as sensing reporting overhead of the secondary systems and the power scheduling algorithm dynamically allocate the transmission power of the cooperative sensor nodes based on the channel statistics of the links to the BS as well as the quality of the sensing measurement. In order to obtain design insights, we also derive the asymptotic sensing performance of the proposed cooperative sensing framework based on the mobility model. We show that the false alarm and mis-detection performance of the proposed cooperative sensing framework improve as we increase the number of cooperative sensor nodes.

A new MIMO channel model based on angular-domain, double-directional channel model is proposed. Angular domain is a more appropriate coordinate to describe the MIMO channel, since the incoming rays and outgoing rays appear in clusters in the real world. This model is developed based on linear space and sampling theory and is applicable to any practical antenna geometry and includes the actual radiation pattern of the antenna array. It is compared with other existing models, including i.i.d., Kronecker, virtual channel representation and eigenbeam model, using channel estimation based model validation. Both simulation and experimental results show that it outperforms other angular-domain based models in terms of channel estimation error. A more accurate channel model not only facilitates better performance analysis, but also channel estimation in practical communication system.

Delay-sensitive cross-layer design has recently attracted increasing interests in providing delay performance guarantee and exploiting multi-user diversity throughput advantage for real-time traffic. However, existing cross-layer designs usually require centralized scheduling and information gathering, including full channel state information (CSI) and delay requirements, to exploit multiuser diversity gain as well as QoS (delay) provisioning. In this work, we provide a novel distributive solution to the delay-sensitive cross-layer OFDMA scheduling problem as well as introducing significant saving in the average feedback cost per user compared to classical cross- layer design. Simulation results illustrate that our proposed distributive design achieves the optimal performance as the centralized scheduling with much lower implementation complexity while maintaining heterogeneous users' delay requirements. Effects of CSIT outdateness are also considered in the proposed distributive cross-layer design framework.

This paper considers the relay selection problem for the cooperative spatial multiplexing scheme in wireless systems with M relay nodes. We consider half-duplex relays in which the relay nodes cannot transmit and receive simultaneously on the same frequency. To improve the transmission reliability, traditional approaches employ cooperative spatial diversity (such as distributed space-time coding) during the cooperative phase when relay and source both transmit to the destination. Most of the relay selection schemes considered in the literature deal with selecting one relay only. However, when we consider wireless systems with multi-antenna destination, we may have opportunities to transmit extra data through cooperative spatial multiplexing by selecting multiple relays to participate in the cooperative phase. In this paper, we shall propose a low complexity multi- relay selection algorithm for the cooperative spatial multiplexing using decode-and-forward (DF) protocol. We have also analyzed the asymptotic performance as well as the diversity-multiplexing tradeoff (DMT) of the proposed system. We found that the proposed cooperative spatial multiplexing scheme with multi- relay selection achieves a much better DMT tradeoff than the traditional cooperative diversity schemes.

The performance degradation in convolutionally coded V-BLAST system is analyzed in this paper. Compared with coded linear filtering approach, it is shown that the coded V-BLAST suffers from serious degradation due to the effect of error propagation. Considering the constraint of the system, a performance improvement method is proposed. It adjusts the weighting of soft bits, where the weighting for one stream is found based on the equivalent error rate of that stream. Additional computation needed by the proposed method is neglectable. Both theoretical analysis and simulation verify the effectiveness of the proposed method.

Recent research has demonstrated that by utilizing channel state information at the transmitter, the physical layer can be optimized to provide higher link capacity and throughput, more efficiently share the channel with multiple users, increase range by exploiting diversity due to spatial and frequency selectivity, and simplify multi-user receivers through known interference cancellation. Unfortunately, acquiring channel state information at the transmitter is difficult. In most systems, the only opportunity for the transmitter to learn about the channel is through a feedback control channel. Because feedback information is control overhead, the rate of the feedback channel is limited. This motivates the study of limited feedback techniques where only partial or quantized information from the receiver is conveyed back to the transmitter.

It is now well known that employing channel adaptive signaling in wireless communication systems can yield large improvements in almost any performance metric. Unfortunately, many kinds of channel adaptive techniques have been deemed impractical in the past because of the problem of obtaining channel knowledge at the transmitter. The transmitter in many systems (such as those using frequency division duplexing) can not leverage techniques such as training to obtain channel state information. Over the last few years, research has repeatedly shown that allowing the receiver to send a small number of information bits about the channel conditions to the transmitter can allow near optimal channel adaptation. These practical systems, which are commonly referred to as limited or finite-rate feedback systems, supply benefits nearly identical to unrealizable perfect transmitter channel knowledge systems when they are judiciously designed. In this tutorial, we provide a broad look at the field of limited feedback wireless communications. We review work in systems using various combinations of single antenna, multiple antenna, narrowband, broadband, single-user, and multiuser technology. We also provide a synopsis of the role of limited feedback in the standardization of next generation wireless systems.

Cross-layer designs for OFDMA systems have been shown to offer significant gains of spectral efficiency by exploiting the multiuser diversity over the temporal and frequency domains. In this paper, we shall propose a robust optimal cross-layer design for downlink TDD-OFDMA systems with imperfect channel state information at the base station (CSIT) and unknown interference in slow fading channels. Exploiting the ACK/NAK (1-bit) feedbacks from the mobiles, the proposed cross-layer design does not require knowledge of the CSIT error statistics or interference statistics. To take into account of the potential packet error due to the imperfect CSIT and unknown interference, we define average system goodput (which measures the average b/s/Hz successfully delivered to the mobile) as our optimization objective. We formulate the cross-layer design as a state-space control problem. The optimal power, optimal rate and optimal user allocations are determined as the output equations from the system states based on dynamic programming approach. Simulation results illustrate that the performance of the proposed closed-loop cross-layer design is very robust with respect to imperfect CSIT, unknown interference, model mismatch as well as channel variations due to Doppler.

We propose a joint set of linear precoder designs for single cell uplink multiuser space-time block coded multiple- input multiple-output systems with multi-packet reception by exploiting outdated channel state information. By deriving the pairwise error probability with respect to both minimum and average codeword distance design metrics, we formulate the design as an optimization problem subject to transmit power constraint for each user. Due to the non-convex nature of the optimization problem, we devise an iterative algorithm to solve for linear precoding structure for general space-time block code. For orthogonal space-time block code, we also propose a simplified distributed algorithm to solve for a closed-form solution. Asymptotic analysis on the effect of quality of the outdated channel state information (CSI) on the precoder structure is also presented. Simulation results are provided to demonstrate the effectiveness of the proposed designs for different space-time block codes at various CSI qualities.

We address the problem of designing a linear precoder for space-time coded multiple-input multiple-output (MIMO) system with imperfect knowledge of channel state information at the transmitter (CSIT), subject to a total transmit power constraint. Assuming an uncorrelated flat-fading channel and using a maximum likelihood decoder at the receiver, we analytically reveal that the derived precoder is a function of the noise variance, the eigenvalues of the estimated channel matrix and the eigenvalues of the codeword distance matrix. Furthermore, the power allocation on the eigenvalues of the precoder is shown to follow the water-pouring policy and hence the proposed precoder allocates more power to the stronger channels.

In this paper, we derive analytically the per-user packet outage probability and the total system goodput for multiaccess systems using multiuser detector with adaptive successive interference cancellation (MUD-SIC). We consider a multiuser wireless system with n mobile users and a base station. We assume slow fading channels where packet transmission error (outage) is the primary concern even if strong channel coding is applied. To capture the effect of potential packet error, we consider the average packet error probability and the total system goodput, which measures the average b/s/Hz successfully delivered to the base station, of the n users. Unlike previous works, our analysis focus on the error-propagation effects in MUD-SIC detector where the packet outage event for the i-th decoded user is coupled with that in the i - 1,.., 1-th decoding attempts. We shall derive the optimal SIC decoding order (to maximize system goodput) and evaluate the closed-form per-user packet outage probabilities for the n users for MUD-SIC. Simulation results are used to verify the analytical expressions.

There are two important aspects of cross-layer gains in multiuser OFDMA systems with slow fading channels. They are the system goodput gain as well as the packet diversity gain. The former aspect of cross-layer designs has been well- studied under perfect CSIT conditions and is known as the multi-user diversity gain (MuDiv). In cross-layer OFDMA systems with perfect CSIT, it is well known that the system throughput (ergodic capacity) scales in the order of O(log log Kldquo) due to the MuDiv gain, where K is the number users. However, in slow fading channels with delayed CSIT, there will always be potential packet errors (due to channel outage if the scheduled data rate exceeds the instantaneous mutual information) even if very strong channel coding is applied at the base station. In this case, the cross-layer packet outage diversity is important to protect the packet errors due to channel outage and there is a natural tradeoff between the goodput gain and packet diversity. In this paper, we shall focus on the asymptotic tradeoff analysis between the system goodput gain and the packet outage diversity gain in cross-layer OFDMA systems with delayed CSIT.

In this paper, we analyze the role of CSIT on the fundamental performance tradeoff for a MISO/SIMO link. Defining CSIT quality order as alpha = - log sigma2Deltah/ logSNR, we showed that using rate adaptation, one can achieve an average diversity order of dmacr(alpha, r macr) = (1 + alpha - r macr)nwherenis the number of transmit or receive antennas, r macr is the average multiplexing gain and alpha is the CSIT quality. We also showed that this diversity order is optimal for r macr isin [0.1 - alpha] and alpha < 1. The relationship suggests that imperfect CSIT can also provide additional diversity order and interpret the CSIT quality order as the maximum achievable spatial multiplexing gain with n diversity order.

Cross-layer scheduling and hybrid ARQ (HARQ) are effective means to improve the spectral efficiency of wireless systems. However, most of the existing works handle HARQ and cross-layer scheduling in a decoupled manner based on heuristic approaches. In this paper, we propose an integrated design framework for cross-layer scheduling and HARQ design in multiuser systems with slow fading channel and outdated knowledge of channel state information at the base station (CSIT). We consider both the chase combining and incremental redundancy in HARQ. We define the average system goodput (which measures the average bits successfully delivered to the receiver) as the measure of system performance. Based on information theoretical approach, we derive the asymptotically optimal cross-layer policy (power allocation, rate allocation and user selection policies) to optimize the average system goodput with HARQ. In addition, we derive analytically the closed-form expression of the average system goodput, from which we can obtain useful design insights such as the role of HARQ in the overall cross-layer gain, the tradeoff between the system goodput and the HARQ delay as well as the sensitivity of the average system goodput with respect to the CSIT quality.

In this paper, we shall focus on a robust joint rate, power and precoder design for MIMO slow fading channels with noisy limited feedback. To take into consideration of the potential packet errors, we optimize the system goodput (b/s/Hz successfully delivered to the receiver) with respect to a general model of limited feedback error. We show that the codebook design that maximizes the system goodput can be converted to an equivalent "maximin" problem. Numerical results show that the proposed framework can achieve significant goodput gain comparing to various naive designs (designed for error- free limited feedback). Simulation results demonstrated the importance of taking the potential CSIT errors into the limited feedback design for robust performance.

This paper considers the resource allocation problem in the wireless OFDMA system with a relay node. We consider orthogonal relay in which the relay node cannot transmit and receive simultaneously on the same frequency. As a result, the use of relay node may enhance or degrade the achievable transmission rate depending on the instantaneous channel states between the source and the relay, the relay and the destination as well as the source and the destination. Hence, it is very important to dynamically adjust the resources (subbands) allocated to the relay node so that the relay is used only at the right time according to the instantaneous channel states. Conventional approaches dynamically schedule the usage of the relay node in a centralized manner in which full knowledge of the channel states between any two nodes in the network is required. However, perfect knowledge of the channel states at various nodes is very difficult to obtain. In this paper, we shall propose a resource allocation algorithm, which iteratively allocates resource to the source and relay, and converge to the close-to-optimal allocation. Asymptotic achievable rate of the proposed algorithm is derived. We show that the system achieves significant improvement of the achievable rate compared to the point-to-point baseline system without relay as well as the baseline system with random subband allocation.

Cross-layer scheduling and hybrid ARQ (HARQ) are effective means to improve the spectral efficiency of wireless systems and have been widely adopted in next generation wireless systems. However, most of the existing works handle HARQ and cross-layer scheduling in a decoupled manner based on heuristic approach. In this paper, we propose an integrated design framework for cross-layer scheduling and HARQ design in TDD multiuser systems with outdated knowledge of channel state information at the base station (CSIT). We focus on incremental redundancy in HARQ and derive the asymptotically optimal cross-layer policy (power allocation, rate allocation and user selection policies) to optimize the system goodput. In addition, we derive analytically closed-form expressions for the average system goodput where we can obtain useful design insights. For instance, the average system goodput scales in the order of O(In L) where L is the maximum transmission number of a packet, illustrating how the HARQ can enhance the cross-layer system goodput gain.

Channel state information at transmitter (CSIT) is important for achieving high spectral efficiency in multi-input multi-output(MIMO) wireless systems especially for slow fading channels. In practice, only a limited number of bits can be allocated to carry the CSIT feedback, namely the limited feedback. Moreover, the limited CSIT feedback may suffer from noise on feedback channel. With the uncertainty on the CSIT, the packet transmitted may be corrupted (packet outage) if the transmitted rate exceeds the instantaneous mutual information. In this paper, we shall focus on a robust joint rate, power and precoder design for MIMO slow fading channels with noisy limited feedback. We optimize the system goodput (b/s/Hz successfully delivered to the receiver) with respect to a general model of limited feedback error. We show that the codebook design that maximizes the system goodput can be converted to an equivalent "maximin" problem. Numerical results show that the proposed framework can achieve significant goodput gain comparing to various naive designs (designed for error-free limited feedback). Simulation results demonstrated the importance of taking the potential CSIT errors into the limited feedback design for robust performance.

We investigate the impact of mobility on downlink scheduling in multi-user multiple antenna systems. We formulate several different designs of the cross-layer scheduler and evaluate how their throughput and fairness performance change as user mobility varies. Our model captures both the physical layer and system-level parameters affected by mobility. The results have shown how sensitive the different scheduler designs are with regard to user mobility and provide new insights into how the scheduler should be designed according to mobility conditions.

This article describes a low power design approach for a UHF passive RFID tag baseband system. It proposes a new RFID tag baseband architecture which is compatible with the EPC C1G2 UHF RFID protocol. Advanced low power design approaches are adopted, including separating driving clocks, applying an improved Tausworthe sequence generator, moving window PIE decoding algorithm, idle scheme and parallel operating scheme. The tag supports three commands, which are read, write and query. It consists of a 136 bits one-time programmable memory, rectifier, charge pump, clock divider, analog frontend and baseband system. SimuLink co-verification approach is applied for system functional test. The chip was designed and fabricated successfully by using 0.18 mum 6 layers CMOS technology

Motivated by the fact that combining space-time block codes with channel precoding can make the system robust against channel fading while achieving both coding and diversity gains, we propose a joint set of linear precoder designs for uplink multiuser space-time block coded multiple-input multiple-output systems with multipacket reception by exploiting outdated channel state information. In particular, we formulate the design as an optimization problem of minimizing pairwise error probability subject to transmit power constraint for each user. Due to non-convex nature of the optimization problem, we devise an iterative algorithm to solve for linear precoding structure for general space-time block code. For orthogonal space-time block code, we also propose a simplified distributed algorithm to solve for a closed-form solution. Asymptotic analysis of the effect of channel quality on the precoder structure is also presented. Simulation results are provided to demonstrate the effectiveness of the proposed designs for orthogonal and quasi-orthogonal space-time block codes over the conventional beamforming system.

In this paper, we consider on the asymptotic tradeoff analysis between the system goodput gain and the packet outage diversity gain in cross-layer OFDMA systems with slow frequency selective fading and delayed CSIT. The OFDMA cross-layer design with delayed CSIT is modeled as an optimization problem where the rate adaptation, power adaptation and subcarrier allocation policies are designed to optimize the system goodput (b/s/Hz successfully received by the mobiles). We derived simple closed-form expressions for the power and rate allocations as well as the asymptotic order of growth in system goodput for general CSIT error sigmae2. We found that the system goodput scales in the order of O(log(1-sigmae2/ Nd(log K + Ndlog log K))) for large K where Nd is the packet outage diversity and K is the number of user in the cross-layer OFDMA system. Hence, double exponentially larger K is needed to compensate for the penalty in system goodput gain due to CSIT errors sigmae2or packet outage diversity Ndfor large Nd.

This paper considers the resource allocation problem in the wireless OFDMA systems with a relay node. We consider half-duplex relay in which the relay node cannot transmit and receive simultaneously on the same frequency. As a result, the use of relay node may enhance or degrade the system throughput depending on the instantaneous channel states between the source and relay, the relay and the destination as well as the source and destination. Hence, it is very important to dynamically adjust the resource (subcarrier) allocated to the relay node so that the relay is used only at the right time according to the instantaneous channel states. Conventional approaches dynamically schedule the usage of the relay node in a centralized manner in which full knowledge of the channel states between any two nodes in the network is required. However, perfect knowledge of the channel states at various nodes are very difficult to obtain. In this paper, we shall propose a distributed resource allocation algorithm on the OFDMA system with half-duplex relay by employing rateless code. Based on the ACK/NAK exchanges between the source, destination and relay, the proposed algorithm iteratively allocates resource to the source and relay, and converge to the close-to-optimal allocation within finite steps. The resource allocation algorithm has low complexity and provable convergence property. Asymptotic throughput performance of the proposed algorithm is derived. We show that the system achieves significant throughput gain compared to the point-to-point baseline system without relay as well as the baseline system with random subcarrier allocation.

We consider transmission of a continuous amplitude i.i.d. source over a M parallel quasi-static Rayleigh fading channels. Due to the non-ergodic nature of the channel, Shannon's source-channel separation theorem does not hold and the optimal performance requires a joint optimization of the source and channel variables. Our goal in this paper is to characterize the expected end-to-end source distortion in the high SNR regime. Defining source distortion exponent as Delta = - limSNRrarrinfinlog ED/ SNR, we derive the optimal distortion exponent for any bandwidth expansion ratio b. Our optimality is with respect to system with perfect channel state information at the transmitter (CSIT) and thus refers to the best achievable performance. In particular, we prove the achievability of the source distortion exponent upper bound, derived by considering ergodic channel, with both rate adaptive channel coding diversity and source coding diversity scheme where only imperfect CSIT is available. Although we consider a memoryless Gaussian source in this paper, the derived optimal distortion exponent holds for a general class of source distribution.

A critical factor in the deployment of spatial multiplexing multiple-input multiple-output (MIMO) systems is the cost of multiple analog transmit/receive chains at the mobile terminal side. To mitigate this problem, antenna subset selection at the transmitter/receive has been proposed. With antenna selection, a small number of analog chains are multiplexed between a larger number of transmit/receive antenna elements. In this paper, we derive simple and robust criteria to antenna selection for V-BLAST error rate performance maximization. Both cases, where channel state information (CSI) at the transmitter is available and unavailable, are studied. Simulation results show that the proposed algorithm provides a very close performance to the optimum exhaustive search performance.

Adapting user selection, power allocation and rate allocation, cross layer centralized scheduling can achieve a tremendous gain due to multiuser selection diversity in wireless networks. Yet, conventional cross layer designs all require channel state information at transmitters (CSIT), which is difficult to obtain in practice. In this paper, a closed loop cross layer design with no CSIT in a block fading frequency selective environment is proposed as an extension of flat fading case in (Ka Ming Ho, 2006). The solution to optimal power, rate allocation and user selection involves stochastic programming and thus has high complexity, especially when the channel is highly frequency selective. Instead, we would like to focus on two suboptimal algorithms, namely CP-TX and OP-TX, which are based on ACK/NAK feedbacks from selected mobiles. In practical situations, OP-TX can achieve more than 67 % of the goodput of the optimal algorithm.

This paper considers a joint design of linear precoders and equalizers for uplink multiuser multiple-input multiple-output communications systems over uncorrelated flat-fading channels with imperfect channel state information at both the transmitters and the receivers. By minimizing the mean square error (MSE) of all users, or equivalently the sum-MSE, under a sum-power constraint, we model the linear transceiver design as an optimization problem. Instead of relying on complex iterative algorithms, the authors propose a low-complexity, non-iterative but close-to-optimal precoder design solution by relaxing the problem into a simpler convex optimization problem. In this way, a closed-form linear transceiver structure can be derived by using the Karush-Kuhn-Tucker (KKT) optimality conditions. Based on the closed-form solution and random matrix theory, the authors obtain a simple asymptotic expression on the average sum-MSE, which can provide useful design insights.

Cross-layer scheduling has been proposed to effectively boost the spectral efficiency of multi-user orthogonal frequency division multiple access (OFDMA) systems through multi-user selection diversity under two important assumptions - users are delay insensitive and channel state information at the transmitter (CSIT) is perfect. However, in practice, users may be delay sensitive with heterogeneous delay requirements, and CSIT usually becomes outdated in time varying channel. When CSIT is outdated, there will always be systematic packet errors even if powerful channel coding is applied, resulting in significant degradation on the delay performance of heterogeneous users. In this paper, we propose a novel delay-sensitive cross-layer design framework for multi-user OFDMA systems with heterogeneous delay requirements in slow fading channels, by utilizing both queueing theory and information theory to model the system dynamics as well as accounting impacts upon outdated CSIT. Based on the framework, the optimal power and subcarrier allocation solution is shown to maintain heterogeneous users' delay constraints and guarantee a fixed target outage probability, and its asymptotic multi-user diversity gain over fixed allocation scheme is also obtained analytically. Simulation results further show that our proposed delay-sensitive CSIT error considerate schemes provide robust system performance enhancement over naive scheduler while satisfying heterogeneous delay requirements even at moderate to high CSIT errors.

The knowledge of channel state information at the transmitter (CSIT) is very important to enhance the spectral efficiency of MIMO links through precoder and power adaptation. In practice, only limited CSIT knowledge is usually available. In most existing works, the focus was on the precoder and power adaptation design for i.i.d. MIMO channels to optimize the ergodic capacity or SNR. The issues of rate adaptation and spatial correlation between antennas, which plays a key role in the overall system performance in slow fading channels, has been ignored in the existing works. In this paper, we shall propose an integrated framework for the rate and precoder adaptation under limited feedback for slow and spatially correlated MIMO fading channels. To capture the effects of potential packet errors, we consider average system goodput, which measures the b/s/Hz successfully delivered to the receiver, as our performance measure. The average system goodput is maximized subject to a total transmit power constraint and a target frame error rate (FER) constraint. Simulation results show our integrated design outperforms the Grassmannian precoder design in i.i.d. and spatially correlated MIMO channels. Furthermore, the limited feedback efficiency increases as the correlation coefficient increases.

Cognitive radio technology facilitates spectrum reuse and alleviates spectrum crunch. One fundamental problem in cognitive radio is to avoid the interference caused by other communication systems sharing the same frequency band. However, spectrum sensing cannot guarantee accurate detection of the interference in many practical situations. Hence, it is crucial to design robust receivers to combat the in-band interference. In this paper, we first present a simple pilot aided interference detection method. To combat the residual interference that cannot be detected by the interference detector, we further propose a robust joint interference detection and decoding scheme. By exploiting the code structure in interference detection, the proposed scheme can successfully detect most of the interfered symbols without requiring the knowledge of the interference distribution. Our simulation results show that, even without any prior knowledge of the interference distribution, the proposed joint interference detection and decoding scheme is able to achieve a performance close to that of the maximum likelihood decoder with the full knowledge of the interference distribution

Link adaptation allows the transmitter to adapt to changing channel conditions. Critical to the design of link adaptation is the accuracy of channel state information at the transmitter. In this paper, we investigate the effect of imperfect channel state information and feedback delay on the performance of a multiple-input multiple-output zero-forcing receiver with adaptive power allocation. A closed-form approximate upper bound on bit error rate is derived for M-ary phase shift keying and M-ary quadrature amplitude modulation and it is valid for arbitrary numbers of transmit and. receive antennas. Comparison with Monte Carlo simulations is also provided, showing that the results derived from this analytical bound are useful for system design.

Orthogonal frequency division multiple access (OFDMA) and multicarrier code division multiple access (MC-CDMA) have recently drawn much attention for being potential candidates of future generation cellular systems. In single-cell scenario, while MC-CDMA is good at achieving frequency diversity when there is no channel state information available at the transmit side (CSIT), OFDMA achieves higher capacity than MC-CDMA due to its finer resolution in exploiting multiuser diversity with CSIT. Whether multiple-input-multiple-output (MIMO) MC-CDMA or OFDMA is a better option in multi-cell system remains unjustified in the literature. In this paper, we study the ergodic capacity and the goodput of MIMO-MC-CDMA and MIMO-OFDMA downlink systems with CSIT in multi-cell scenario assuming that the base station has the knowledge of the average inter-cell interference level only. Several types of users modeling different interference patterns are considered: (I) high data rate delay-insensitive users, (II) high data rate delay-sensitive .users, and (III) voice users (low data rate and bursty). Optimal resource allocation algorithms are used to compute the capacities of the systems, while a simple heuristic is used to obtain the achievable goodputs for both systems. The effects of path loss, number of antennas and different user types are studied and insightful results are obtained. We find that OFDMA has a higher system goodput for both Type I and Type II high data rate users, while MC-CDMA has a higher goodput for Type III users. Compared to MC-CDMA, the goodput of an OFDMA system is more sensitive to the activity factor of the voice users and suffers from noticeable loss. This demonstrates the superiority of the two systems in different practical situations.

This paper proposes a cross-layer scheduling scheme for OFDMA wireless systems with heterogeneous delay requirements. We shall focus on the cross-layer design which takes into account both queueing theory and information theory in modeling the system dynamics. We propose a delay-sensitive cross-layer design, which determines the optimal subcarrier allocation and power allocation policies to maximize the total system throughput, subject to the individual user's delay constraint and total base station transmit power constraint. The delay-sensitive power allocation was found to be multilevel water-filling in which urgent users have higher water-filling levels. The delay-sensitive subcarrier allocation strategy has linear complexity with respect to number of users and number of subcarriers. Simulation results show that substantial throughput gain is obtained while satisfying the delay constraints when the delay-sensitive jointly optimal power and subcarrier allocation policy is adopted.

In this paper, the downlink rate quantization and cross-layer scheduling design are investigated in multiuser multiple-input single-output (MISO) systems with imperfect channel state information at transmitter (CSIT). We shall propose a systematic analytical design framework based on information theoretical approach. To capture the effect of the potential packet outage, we introduce the average system goodput, which measures the average b/s/Hz delivered to the mobiles successfully, as the system performance objective. Numerical results demonstrate that, by considering the statistics of CSIT errors into the design, the proposed scheduling scheme provides significant performance enhancement.

It is well-known that MIMO links in slow fading channels can benefit significantly through rate and power adaptations when there is knowledge of channel state information at the transmitter (CSIT). However, practical MIMO transmitter can only support finite combinations of the channel encoding rates and modulation constellation levels due to the finite number of bits allocated in the control overhead (embedded in the packet transmission). In this paper, we shall address various practical MIMO adaptation design issues associated with discrete transmission modes. We propose a systematic design framework to address the MIMO adaptation design based on information theoretical framework. The design problem is cast into an optimization problem similar to the classical vector quantization problem with a modified distortion measure. As an illustration, we apply the design framework to a 2 times 2 MIMO link and compare the goodput performance with the optimal performance predicted by the theory. We found that the goodput performance of the MIMO link predicted by the theoretical framework matches the actual performance closely

In this paper, we propose a systematic design framework to deal with the problem of limited CSIT feedback for MIMO-OFDM systems with correlated subcarriers. Based on the framework, we obtain the optimal transmission and CSI feedback strategies given the limited CSI feedback constraint. We propose a MIMO-OFDM design with combined adaptive power control and beam-forming framework for optimizing MIMO-OFDM link capacity with limited feedback in frequency selective fading channels. We derive a computationally efficient algorithm which exploits subcarrier correlation to search for the design of the optimal transmission and feedback strategy. We found that with a small number of bits for CSIT feedback, there is already significant capacity gain in the MIMO-OFDM systems

In this paper, we examine and compare the potential maximum sum capacity of downlink multiple-input-multiple-output orthogonal frequency division multiple access (MIMO-OFDMA) and multiple-input-multiple-output multicarrier code division multiple access (MIMO-MC-CDMA) in a single-cell multiuser environment with channel side information at the transmitter, with and without a fairness constraint. The resource allocation is formulated as a cross-layer optimization framework and optimal power allocation and user selection algorithms are proposed for both scenarios. We find that for delay-sensitive applications, where fairness is imposed, the performance gain of OFDMA over MC-CDMA is quite large at moderate path loss exponents and number of antennas. However, for delay-insensitive applications, the benefits of OFDMA over MC-CDMA are significantly reduced when the path loss exponent or the number of antennas is large

By employing spatial multiplexing, multiple-input multiple-output (MIMO) wireless antenna systems provide increases in capacity without the need for additional spectrum or power. Zero-forcing (ZF) detection is a simple and effective technique for retrieving multiple transmitted data streams at the receiver. However the detection requires knowledge of the channel state information (CSI) and in practice accurate CSI may not be available. In this letter, we investigate the effect of channel estimation error on the performance of MIMO ZF receivers in uncorrelated Rayleigh flat fading channels. By modeling the estimation error as independent complex Gaussian random variables, tight approximations for both the post-processing SNR distribution and bit error rate (BER) for MIMO ZF receivers with M-QAM and M-PSK modulated signals are derived in closed-form. Numerical results demonstrate the tightness of our analysis

In most existing works, perfect knowledge of channel state information at the transmitter (CSIT) is assumed to realize the potential benefit of cross-layer scheduling and spatial multiplexing gains of MIMO/OFDMA systems. However, perfect knowledge of CSIT is not easy to achieve in practice due to estimation noise or delay in feedback. In this paper, we shall focus on the cross-layer design of downlink multi-antenna OFDMA systems with imperfect CSIT for slow fading channels. We shall show that our proposed cross-layer scheduler can exploit the multiuser diversity and spatial multiplexing gain even in the presence of moderate CSIT error.

Few works have addressed the effect of CFO (carrier frequency offset) on channel estimation performance. In this paper, compact analytical expressions on the mean square error of channel estimation in the presence of CFO are derived for OFDM (orthogonal frequency division multiplexing) systems employing single and multiple antennas. Concise upper bounds are also derived for SISO-OFDM systems. It is revealed that channel estimation MSE (mean square error) increases at the rate of approximately the square of the CFO and increasing the number of pilots does not contribute to better suppression of the MSE caused by CFO. In addition, it is observed that LMMSE (linear minimum mean square error) channel estimation is more resistant to CFO compared to LS (least square) in terms of channel estimation MSE. Furthermore, from the results derived herein, a clue for the pilot design of OFDM systems with CFO can be obtained

Blind carrier frequency synchronization for orthogonal frequency division multiplexing (OFDM) systems with receive diversity is proposed in this paper. This method utilizes the fact that different receive branches contain uncorrelated observations about the same information bits. Due to receive diversity, our synchronizer can perform well without virtual subcarriers when channel state information (CSI) is known. In addition, we shall analyze the effects of CSI errors on the carrier frequency offset (CFO) estimation and show that the proposed algorithm is robust to CSI error. In particular, we derive the Cramer-Rao bound (CRB) from the mean square error (MSE) of the carrier frequency synchronizer. Computer simulation results show the performance improvement of our synchronizer to the single antenna systems and to the existing method.

In wireless networks with centralized base station, cross layer scheduling to adapt user selection, power allocation and rate allocation can achieve a tremendous gain due to multiuser selection diversity. However, conventional cross layer designs all require channel state information at the base station (CSIT), which is difficult to obtain in practice. In this paper, we propose a closed loop cross layer design with no CSIT. The optimal power, rate allocation and user selection has high complexity as it involves stochastic programming. Instead, we would like to focus on two suboptimal algorithms, namely CP-TX and OP, which are based on ACK/NAK feedbacks from selected mobiles. Under practical circumstances, OP can achieve more than 88% goodput of the optimal algorithm.

Previous works on joint, both transmit and receive, antenna selection in MIMO systems have placed the computation burden to the receiver side, where perfect channel state information (CSI) at the receiver is assumed and the selected transmit antenna indices are sent back to the transmitter. This solution might not be practical, since with bursty packet transmission, the selected antenna subset may no longer be a good choice at the instant of actual transmission. In light of this, we formulate the problem such that the transmitter is performing the selection based on the CSI estimated a while back, or outdated CSI. A space-time coded MIMO channel with correlated flat fading and joint antenna selection is considered. Our closed-form optimal selection criterion incorporating the outdatedness of the CSI outperforms the existing solutions which treat the outdated CSI as the actual CSI.

Cross layer designs for OFDMA systems have been shown to offer significant gains of spectral efficiency by exploiting multiuser diversity over the temporal and frequency domains. However, in most existing designs, perfect knowledge of channel state information at the transmitter (CSIT) is assumed. When we have imperfect CSIT and unknown interference at the receiver, there may be packet transmission outage (error) and it is a tricky problem for cross layer design with imperfect CSIT and unknown interference. In this paper, we shall propose a robust optimal cross layer design for downlink TDD-OFDMA systems with imperfect channel state information (CSIT) and unknown interference in slow fading channels. Exploiting the ACK/NAK (1-bit) feedbacks from the mobiles, the proposed cross layer design does not require knowledge of the CSIT error nor interference statistics. Furthermore, for sufficiently large number of feedbacks, the system will converge to steady state (as if perfect CSIT were available). Simulation results illustrate that the performance of the proposed closed-loop cross layer design is very robust with respect to imperfect CSIT, unknown interference, model mismatch as well as channel variations due to Doppler.

This paper investigates a cross layer scheduling scheme for OFDMA wireless system with heterogeneous delay requirements. Unlike most existing cross layer designs which take a decoupling approach, our design considers both queueing theory and information theory in modeling the system dynamics. The cross layer design is formulated as an optimization of total system throughput, subject to individual user's delay constraint and total base station transmit power constraint. The optimal scheduling algorithm for the delay-sensitive cross layer optimization is to dynamically allocate radio resources based on users' channel state information, source statistics and delay requirements. Specifically, optimal power allocation was found to be multilevel water-filling where urgent users have higher water-filling filinglevels, while optimal subcarrier allocation strategy is shown to be achievable by low complexity greedy algorithm. Simulation results also show the proposed jointly optimal power and subcarrier allocation policy can provide substantial throughput gain with all delay constraints being satisfied.

Multiple antennas can be used for increasing the packet reliability (spatial diversity) or the spectral efficiency (spatial multiplexing) in wireless communication systems operating under a slow fading environment. Zheng and Tse showed that both types of gains can be simultaneously obtained for a given multiple antenna channel, but there is a fundamental tradeoff between spatial diversity and spatial multiplexing. They considered the case of no Channel State Information at the Transmitter (CSIT) but perfect Channel State Information at the Receiver (CSIR). On the other hand, when perfect CSIT is available at the transmitter, infinite diversity order can be achieved through rate adaptation. However, perfect CSIT is very difficult to obtain and in practice, we shall have imperfect CSIT (which lies between the two extremes of no CSIT versus perfect CSIT). In this paper, we analyze the role of CSIT on the fundamental performance tradeoff for a MISO/SIMO link. Defining CSIT quality order as α = - log σ2δh/log SNR, we derived the optimal rate adaptation as well as the optimal tradeoff between the CSIT quality order, α, the average diversity order, d and the average multiplexing gain, r, as d(α, r) = (1 + α - r)n, where n is the number of transmit or receive antennas. The relationship suggests that imperfect CSIT can also provide additional diversity order and interprets the CSIT quality order as the maximum achievable spatial multiplexing gain with n diversity order when α < 1.

In this paper, an exact expression has been derived for the outage probability and the total system goodput of multi-user detection (MUD) based on successive interference cancellation (SIC) for multi-user communication system. We consider a single antenna wireless system with n mobile users and a base station. We focus on the uplink direction with perfect channel knowledge (CSIR) at the base station. We consider slow fading channels where packet outage is the primary concern even if capacity achieving coding is applied. To capture the effect of packet outage, we consider the packet outage probability and the total system goodput of the n users. The analytical expressions for packet outage probability and system goodput are very important for cross layer optimizations of multi-user systems with MUD. However, due to the SIC processing in the MUD, the outage probability analysis is not trivial as the outage probability of the n users are coupled together. In this paper, we shall propose an analytical approach to evaluate the exact expressions of the packet outage probabilities and the system goodputs for the n users based on ordered statistics. We consider adaptive SIC where an optimal decoding order for the MUD is found per fading slot. Simulation results and discussions are given to verify the analytical expressions. From the results, the system goodput increases with the average user SNR and the number of the users with diminishing returns at large SNR and number of users.

We address the problem of designing a linear precoder for space-time coded multiple-input multiple-output (MIMO) system with partial knowledge of channel state information at the transmitter (CSIT), subject to a total transmit power constraint. Assuming an uncorrelated flat-fading channel and using a maximum-likelihood detector at the receiver, we show that the derived precoder is a function of the noise variance, the eigenvalues of the estimated channel matrix and the eigenvalues of the codeword error matrix. From the results derived herein, it is observed that the power allocation on the eigenvalues of the precoder follows the water-pouring policy. Simulation results are provided to illustrate the significant performance gain achieved by using the proposed precoder even at moderate to large CSIT errors.

Cross-layer design for multi-antenna system has been shown to offer high spectral efficiency for multiuser system due to the multi-user diversity and the spatial multiplexing in wireless fading channels. Yet, most of the existing works assumed homogeneous user type (pure delay-insensitive data application). The effect of source statistics, queueing delay and application level requirements were completely ignored. In this paper, we shall propose an analytical cross layer design framework for multi-user multi-antenna systems for wireless multimedia applications with heterogeneous delay requirements. To take delay sensitive users into consideration, we shall utilize both queueing theory and information theory to model the system dynamics. A novel cross layer scheduler is designed to exploit the spatial multiplexing gain as well as the multi-user selection diversity gain, and at the same time maintain the delay constraints of the delay sensitive users. Simulation results indicate that the proposed scheduling design provides desirable delay performance and high throughput gain compared to other heuristic schemes.

Multiple antennas can be used for increasing the packet reliability (spatial diversity) or the spectral efficiency (spatial multiplexing) in wireless communication systems operating under a slow fading environment. Zheng and Tse showed that both types of gains can be simultaneously obtained for a given multiple antenna channel, but there exist a fundamental tradeoff between spatial diversity and spatial multiplexing. They considered the case of no channel state information at the transmitter (CSIT) but perfect channel state information at the receiver (CSIR). In this paper, we provide an alternative derivation of the fundamental tradeoff with no CSIT and extend the framework to analyze the role of CSIT on the fundamental performance tradeoff for a MIMO link. Defining CSIT quality order as α = - log σΔ2/log ρ, where σΔ2is the CSIT error variance, we showed that the diversity order d_(α, r_) is a piecewise linear function interpolating the points (k, (m-k)(n-k) + αmax(m,n), for k = 0,1,..., min(m,n) where m is the number of transmit antennas and n is the number of receive antennas. The relationship suggests that if the imperfect CSIT is asymptotically "good", (α ≠ 0) then an additional diversity order of αmax(m, n) can be realized compared to the no CSIT case. This additional diversity gain is independent of the average spatial multiplexing gain

Multiple antennas can be used for increasing the packet reliability (spatial diversity) or the spectral efficiency (spatial multiplexing) in wireless communication systems with slow fading. Zheng and Tse showed that both types of gains can be simultaneously obtained for a given multiple antenna channel, but there is a fundamental tradeoff between spatial diversity and spatial multiplexing. They considered the case of no CSIT but perfect CSIR. In this paper, we focus on the role of channel state information at the transmitter (CSIT) on the diversity multiplexing tradeoff. When there is perfect CSIT, there is no packet outage in slow fading channels through proper rate adaptation and hence, the diversity order can be regarded as infinity. However, perfect CSIT is not realistic. Thus, we consider the diversity and multiplexing tradeoff with imperfect CSIT for slow fading MIMO channels. The imperfect CSIT is parameterized by the CSIT estimation error sigma2DeltaH. Hence, the results obtained are the generalizations of the Zheng-Tse results. We show that when SNRsigma2DeltaHrarr infin as SNR rarr infin, the availability of the imperfect CSIT does not improve the diversity multiplexing tradeoff

In this paper, we investigate the optimal multi-cell systems design involving multi-user detection. Specifically, we analyze the optimal intra-cell and inter-cell multi-access strategies in slow fading channels and the role of multi-user detection (MUD) in the multi-cell system design based on an information theoretical framework. We adapt Wyner's model and consider a multi-cell system of M cells with K single antenna homogeneous users. At each of the base stations, both zero forcing and the optimal MUD are considered as the base station receiver. We consider slow fading channels where the instantaneous channel capacity is unknown to the transmitters. Hence, packet outage will occur whenever the data rate of packet transmissions is less than the instantaneous channel capacity. Conventional performance measure by ergodic capacity may not be useful in this situation and we consider the overall network goodput, which measures the average number of bits/sec/Hz successfully delivered to the base station by the K users, as the capacity measure. Based on the theoretical framework, we derive closed-form expressions on the total network goodput with respect to different multi-cell system design schemes. The optimal intra-cell and inter-cell channel partitioning schemes with respect to the network goodput in various scenarios is found. We conclude that the multi-user detection plays a important role in multi-cell systems by improving the efficiency resource reuse within a cell

In this paper, we consider the effect of CFO (carrier frequency offset) on the performances of LS (least square) and LMMSE (linear minimum mean square error) channel estimation algorithms for SISO (single input single output) OFDM (orthogonal frequency division multiplexing) systems. Compact analytical expressions on the MSE (mean square error) of the channel estimation under the effect of CFO are derived, while conventional works mainly rely on Cramer-Rao bound or Monte Carlo simulations. Based on the expressions derived, it is observed that the MSE contributed by CFO increases at the rate about the square of CFO and it can not be reduced by using more training symbols. What is more, it is also found that LMMSE is more resistant to CFO compared to LS

We consider the problem of creating signal constellations for trellis-coded unitary space–time communication links, where neither the transmitter nor the receiver knows the fading gains of the channel. Our study includes design techniques for trellis-coded schemes with and without parallel paths, which allows us to find a tradeoff between low complexity and high performance. We present a new formulation of the constellation design problem for trellis-coded unitary space–time modulation schemes. The two key differences in our approach against those of other authors are that we not only combine the constellation design and mapping by set partitioning into one step, but we also use directly the Chernoff bound of the pairwise error probability as a design metric. By novelly employing a theorem for the Clarke subdifferential of the sum of the$k$largest singular values of the unitary matrix, we also present a numerical optimization procedure for finding signal constellations resulting in high-performance communications systems. To demonstrate the advantages of our new design method, we report the best constellations found for trellis-coded unitary space–time modulation systems. Simulation results show that these constellations achieve 1 dB coding gain against the usually used constellations.

We consider the problem of creating signal constellations for trellis-coded unitary space-time communication links, where neither the transmitter nor the receiver knows the fading gains of the channel. Our study includes constellation-design techniques for trellis-coded schemes with and without parallel paths, which allows us to find a tradeoff between low complexity and high performance. We present a new formulation of the constellation design problem for trellis-coded unitary space-time modulation (TCUSTM) schemes. The two key differences in our approach against those of other authors are that we not only combine the constellation design and mapping by set partitioning into one step, but we also use directly the Chernoff bound of the pairwise error probability as a design metric. By novelly employing a theorem for the Clarke subdifferential of the sum of the k largest singular values of the unitary matrix, we also present a numerical optimization procedure for finding signal constellations resulting in high-performance communications systems. To demonstrate the advantages of our new design method, we report the best constellations found for TCUSTM systems. Simulation results show that these constellations achieve a 1-dB coding gain at a bit-error rate of 10-4against usually used constellations

It is well-known that wireless scheduling algorithm could exploit multi-user diversity to enhance the network capacity of wireless systems. However, the advantage of scheduling with respect to network coverage is a relatively unexplored topic and it is the focus of this paper to study optimal scheduler design with respect to network coverage. We consider a wireless system with an access point or base station equipped with nTtransmit antennas as well as K mobiles with single receive antenna. We first extend the conventional concept of coverage and proposed a utility-based coverage. We consider two examples of coverage utility functions, namely the network centric utility and the user centric utility. Based on the generalized concept of network coverage, we propose a systematic framework based on information theoretical approach and formulate the scheduling design as a mixed concave and combinatorial optimization problem. As a result, we found that multi-user selection diversity, spatial multiplexing and spatial diversity due to the nTantennas are the major factors contributing to network coverage gain. Due to the huge search space, the complexity of the optimal algorithm is enormous. We consider a genetic-based scheduler design, which offers a reasonable complexity-performance tradeoff

This letter studies the performance of uplink cross layer design for multi-antenna systems with outdated channel state information (CSI). We consider a multi-user system with one base station (with n/sub R/ receive antennas) and K mobile users (each with single transmit antenna). The multi-user physical layer is modeled based on information theoretical framework and the cross layer design can be cast as an optimization problem. In view of the high computation complexity in the optimal solution, we propose a low complexity genetic algorithm as suboptimal solution. We found that with outdated CSI, there is significant degradation in the spatial multiplexing and multi-user diversity gain due to potential packet transmission outage as well as misscheduling. To address the poor performance in the presence of outdated CSI, we propose two simple but effective empirical solutions, namely the rate quantization and rate discounting, to tackle the packet outage problem. For instance, it is well-known that rate quantization imposes system capacity loss in systems with perfect CSI. However, we found that rate quantization can enhance the robustness of system capacity with respect to outdated CSI.

In this paper, we investigate the downlink scheduling and rate adaptation design in multiuser multiple-input single-output (MISO) systems with imperfect channel state information at the transmitter (CSIT). We shall propose a systematic design framework to address the multi-user scheduling and rate adaptation combined design based on information theoretical framework. We assume time division duplex (TDD) systems with quasi-static fading channels where packet outage is an important concern with imperfect CSIT. We introduce the average system goodput as the measure of system performance in the presence of CSIT error and effective albeit suboptimal solutions are proposed to maximize the average system goodput. Given any target outage probability /spl isin/, a Gaussian approximation on the outage probability is derived and the closed-form solution is obtained for the cross-layer scheduling design based on this approximation. Numerical results demonstrate that, by considering the statistic of CSIT errors into the design, the proposed schemes provide significant goodput enhancement.

For slow fading channels, the MIMO performance is limited by packet outage even if powerful component codes are used at the transmitter. It is well-known that MIMO links in slow fading channels can benefit significantly through rate and power adaptations when there is knowledge of channel state information at the transmitter (CSIT). However, practical MIMO transmitter can only support finite combinations of the channel encoding rates and modulation constellation levels due to the finite number of bits allocated in the control overhead (embedded in the packet transmission) to inform the receiver of the current transmission mode. As a result of this practical limitation, there are several design issues concerning MIMO adaptation design. In this paper, we shall address various practical MIMO adaptation design issues such as how many different transmission modes are needed, how to determine the specific channel encoding rates and modulation constellation levels for each transmission modes as well as how to choose select a transmission mode for the current packet transmission given a particular CSIT. We propose a systematic design framework to address the MIMO adaptation design based on information theoretical framework. We assume time division duplex (TDD) systems with quasi-static fading channels where packet outage is an important concern with imperfect CSIT. The design problem is cast into an optimization problem similar to the classical vector quantization problem with a modified distortion measure. As an illustration, we apply the design framework to a 2 times 2 MIMO link and compare the goodput performance with the optimal performance predicted by the theory. We found that the goodput performance of the MIMO link predicted by the theoretical framework matches the actual performance closely

In this paper, we present a new formulation of the problem of signal design for trellis-coded unitary space-time communication systems. Our approach differs from other methods in that we not only combine the constellation design and set partitioning process together, we also use directly the Chernoff bound of the pairwise probability of error as design metric, which is a better figure of merit than the previously used chordal distance, for unitary constellation design. By novelly employing a theorem regarding the Clarke subdifferential of the sum of the k largest singular values of the unitary matrix, we present a numerical optimization procedure for finding good constellations and report the best signal constellations found. Simulation results show that these constellations achieve 1 dB gain against the previously used constellations of unitary space-time signals.

This paper quantifies the information rate of multiple-input multiple-output (MIMO) systems with finite rate channel state feedback and power on/off strategy. In power on/off strategy, a beamforming vector (beam) is either turned on (denoted by on-beam) with a constant power or turned off. We prove that the ratio of the optimal number of on-beams and the number of antennas converges to a constant for a given signal-to-noise ratio (SNR) when the number of transmit and receive antennas approaches infinity simultaneously and when beamforming is perfect. Based on this result, a near optimal strategy, i.e., power on/off strategy with a constant number of on-beams, is discussed. For such a strategy, we propose the power efficiency factor to quantify the effect of imperfect beamforming. A formula is proposed to compute the maximum power efficiency factor achievable given a feedback rate. The information rate of the overall MIMO system can be approximated by combining the asymptotic results and the formula for power efficiency factor. Simulations show that this approximation is accurate for all SNR regimes

MIMO-OFDMA is a promising technology to accommodate multi-user transmission over frequency selective fading channels with high spectral efficiency. In multi-user MIMO-OFDMA systems, cross layer scheduling is very important to exploit the multi-user selection diversity over the spatial and frequency domains. In this paper, we focus to investigate the design and performance of downlink multi-user MISO-OFDMA cross layer scheduler for time-division duplex (TDD) systems. We propose a systematic framework for the optimal design of the cross layer scheduling with imperfect knowledge of CSIT. We first exam the optimal solution for the scheduling problem. Because of its huge complexity, some sub-optimal algorithms are proposed to achieve reasonable performance-complexity tradeoff. We found that while the ideal schedulers (designed for perfect CSIT) fails to exploit the spatial multiplexing and cross-layer gains, the proposed cross-layer scheduling algorithms designed for imperfect CSIT achieves significant spectral efficiency even at large CSIT errors

We give a new formulation of the problem of signal design based on the design criteria for trellis coded unitary space-time communication system. Two key differences in our approach from that of previous method are that we not only combine the constellation design and set partition process together, we also use directly the Chernoff bound of the pairwise probability of error as design metric, which is a better figure of merit than previously used chordal distance, for unitary constellation design. By novelly employing a theorem for the Clarke subdifferential of the sum of the k largest singular values of the unitary matrix, we present a numerical optimization procedure for finding good constellations and report the best signal constellations found. Simulation results show that these constellations improve significantly upon the previously used constellations of unitary space-time signals.

We extend the proportional fair (PF) scheduling algorithm to systems with multiple antennas. There are K client users (each with a single antenna) and one base station (with n/sub R/ antennas). We focus on the reverse link of the system, and assume a slow-fading channel where clients are moving with pedestrian speed. Qualcomm's original PF scheduling algorithm satisfies the PF criteria only when the communication is constrained to one user at a time with no power waterfilling. However, the original PF algorithm does not generalize easily when we have n/sub R/ receive antennas at the base station. In this paper, we shall formulate the PF scheduling design as a convex optimization problem. One challenge is in the optimal power allocation over the multiantenna multiaccess capacity region, which is still an open problem. For practical consideration, we consider multiuser minimum mean-square error processing at the base station. To obtain first-order insight, we propose an asymptotically optimal PF scheduling solution. Using the proposed PF solution for a multiantenna base station, the system capacity is enhanced by exploiting the multiuser selection diversity, as well as the distributed multiple-input multiple-output configuration. It is found that the PF scheduler achieves a good balance between fairness and system capacity gain.

To efficiently support tetherless applications in ad hoc wireless mobile computing networks, a judicious ad hoc routing protocol is needed. Much research has been done on designing ad hoc routing protocols and some well-known protocols are also being implemented in practical situations. However; one major imperfection in existing protocols is that the time-varying nature of the wireless channels among the mobile-terminals is ignored; let alone exploited. This could be a severe design drawback because the varying channel quality can lead to very poor overall route quality in turn, resulting in low data throughput. Indeed, better performance could be achieved if a routing protocol dynamically changes the routes according to the channel conditions. In this paper, we first propose two channel adaptive routing protocols which work by using an adaptive channel coding and modulation scheme that allows a mobile terminal to dynamically adjust the data throughput via changing the amount of error protection incorporated. We then present a qualitative and quantitative comparison of the two classes of ad hoc routing protocols. Extensive simulation results indicate that channel adaptive ad hoc routing protocols are more efficient in that shorter delays and higher rates are achieved, at the expense of a higher overhead in route set-up and maintenance.

In this paper, we consider a system with K single-antenna client users, n/sub B/ base stations (each base station has n/sub R/ antennas), as well as a centralized controller. A client user could be associated with a single base station at any time. All the base stations operate at the same frequency and have optimal multiuser detection per base station which cancels intracell interference only. We consider a general problem of uplink macroscopic resource management where the centralized controller dynamically determines an appropriate association mapping of the K users with respect to the n/sub B/ base stations over a macroscopic time scale. We propose a novel analytical framework for the above macroscopic scheduling problems. A simple rule is to associate a user with the strongest base station (camp-on-the-strongest-cell), and this has been widely employed in conventional cellular systems. However, based on the optimization framework, we found that this conventional approach is in fact not optimal when multiuser detection is employed at the base station. We show that the optimal macroscopic scheduling algorithm is of exponential complexity, and we propose a simple greedy algorithm as a feasible solution.

We consider the optimal downlink MISO scheduling design framework for a general class of convex utility functions. The access point or base station is equipped with n/sub T/ transmit antennas. There are K mobiles in the system with single receive antenna. For practical reasons, we constraint the physical/link layer of the base station and mobile devices to linear processing complexity in signal transmission/reception respectively. We shall apply the design framework to two common utility functions, namely the maximum throughput and the proportional fair. The scheduling problem is a mixed nonlinear integer programming problem and therefore, the complexity of the optimal solution is enormous. Greedy algorithms is widely used in today's wireless data systems (3GIX, HDR, UMTS) and is optimal when n/sub T/ = 1. However, we found that there is a large performance penalty of greedy algorithms (relative to optimal performance) when n/sub T/ > 1 and this motivates the search for more efficient heuristics. In this paper, we address genetic-based heuristics and discuss their complexity-performance tradeoff.

This paper studies the performance of proportional fair space time scheduling in the presence of imperfect channel state information (CSI). We consider the uplink scheduling in the multiuser single-input-multiple-output (SIMO) system. There are K mobiles (with single antenna) and a base station (with n/sub R/ receive antennas) in the system. The space time scheduling problem is formulated with proportional fair (PF) utility function and a novel heuristic solution is presented. We found that in general, with perfect CSI, the system performance improves as n/sub R/ increases due to the extra spatial dimensions. However, at moderate CSI errors, the system performance saturates with respect to SNR and n/sub R/. This is due to the misscheduling and packet outage problem. To address the poor performance in the presence of CSI error, we proposed a low-complexity enhancement, namely proportional rate discounting, to enhance the scheduler's robustness in the presence of CSI error.

In this paper, we consider a system with K single-antenna client users, n/sub B/ base stations (each base station has n/sub B/ antennas) as well as a centralized controller. All the base stations operate at the same frequency and have optimal multi-user detection per base-station. A client user could be associated with a single base station at any time. We consider a general problem of uplink macroscopic scheduling where the centralized controller dynamically determines an appropriate association mapping of the K users with respect to the n/sub B/ base stations over a macroscopic time scale. We propose a novel analytical framework for the above macroscopic scheduling problems. A simple rule is to associate a user with the strongest base station (camp-on-the-strongest-cell) and this has been widely employed in conventional cellular systems. However, based on the optimization framework, we found that this conventional approach is in fact not optimal when multi-user detection is employed at the base station. We show that the optimal macroscopic scheduling algorithm is of exponential complexity and we propose a simple greedy algorithm as a feasible solution. It is shown that the macroscopic scheduling gain relative to the conventional approach increases with increasing n/sub B/ (due to the extra degree of freedom introduced by multiple antennas) and decreasing path loss exponent (due to large area of overlapping).

In this paper, we consider a wireless access network with K mobile devices and an access point with n/sub T/ transmit antennas. We shall propose a novel mobile-assisted scheduling algorithm for the multibeam phase-sweep transmit diversity (MB-PSTD) systems. We consider the downlink system performance with respect to maximal-throughput and proportional-fair utility functions. We show that the MB-PSTD scheme could significantly enhance the downlink system performance compared to the conventional PSTD schemes by fully utilizing the n/sub T/ degrees of freedom in the system. With the mobile-assisted scheduling algorithm, the feedback requirement is substantially reduced. The beam-sweeping facilitates the multiuser selection diversity between slow mobility users while the multibeam fully utilize the intrinsic system degree of freedom. As we increase n/sub T/, the MB-PSTD system performance is gradually limited by the multibeam interference because it more and more difficult to find a set of users perfectly aligned with the n/sub T/ orthogonal beams. Yet, the multibeam interference could be reduced by increasing the number of active users (K) in the system. Asymptotically at large n/sub T/ and K, we show that the system capacity scales linearly instead of logarithmically with respect to the transmitted power.

It has been shown that with perfect feedback (CSIT), the optimal multiple input/multiple output (MIMO) transmission strategy is a cascade of channel encoder banks, power control matrix, and eigen-beamforming matrix. However, the feedback capacity requirement for perfect CSIT is 2n/sub T//spl times/n/sub R/, which is not scalable with respect to n/sub T/ or n/sub R/. In this letter, we shall compare the performance of two levels of partial power-feedback strategies, namely, the scalar symmetric feedback and the vector feedback, for MIMO block fading channels. Unlike quasi-static fading, variable rate encoding is not needed for block fading channels to achieve the optimal channel capacity.

In this paper, we propose a combined adaptive power control and beamforming framework for optimizing multiple-input/multiple-output (MIMO) link capacity in the presence of feedback-link capacity constraint. The feedback channel is used to carry channel state information only. It is assumed to be noiseless and causal with a feedback capacity constraint in terms of maximum number of feedback bits per fading block. We show that the hybrid design could achieve the optimal MIMO link capacity, and we derive a computationally efficient algorithm to search for the optimal design under a specific average power constraint. Finally, we shall illustrate that a minimum mean-square error spatial processor with a successive interference canceller at the receiver could be used to realize the optimal capacity. We found that feedback effectively enhances the forward channel capacity for all signal-to-noise ratio (SNR) values when the number of transmit antennas (n/sub T/) is larger than the number of receive antennas (n/sub R/). The SNR gain with feedback is contributed by focusing transmission power on active eigenchannel and temporal power waterfilling . The former factor contributed, at most, 10log/sub 10/(n/sub T//n/sub R/) dB SNR gain when n/sub T/>n/sub R/, while the latter factor's SNR gain is significant only for low SNR values.

A coding theorem is proved for memoryless channels when the channel state feedback of finite cardinality can be designed. Channel state information is estimated at the receiver and a function of the estimated channel state is causally fed back to the transmitter. The feedback link is assumed to be noiseless with a finite feedback alphabet, or equivalently, finite feedback rate. It is shown that the capacity can be achieved with a memoryless deterministic feedback and with a memoryless device which select transmitted symbols from a codeword of expanded alphabet according to current feedback. To characterize the capacity, we investigate the optimization of transmission and channel state feedback strategies. The optimization is performed for both channel capacity and error exponents. We show that the design of the optimal feedback scheme is identical to the design of scalar quantizer with modified distortion measures. We illustrate the optimization using Rayleigh block-fading channels. It is shown that the optimal transmission strategy has a general form of temporal water-filling in important cases. Furthermore, while feedback enhances the forward channel capacity more effectively in low-signal-to noise ratio (SNR) region compared with that of high-SNR region, the enhancement in error exponent is significant in both high- and low-SNR regions. This indicates that significant gain due to finite-rate channel state feedback is expected in practical systems in both SNR regions.

Ad hoc routing is important for mobile devices, when they are out of each others transmission range, to communicate in an IEEE 802.11b based wireless LAN using the distributed coordination function. While traditional table-based or on-demand routing protocols can be used, it is much more efficient to use a routing protocol that is channel-adaptive - judiciously selecting links that can transmit at higher data rates to form a route. However, devising channel-adaptive routing protocols is still largely unexplored. In this paper, we propose a reactive ad hoc routing algorithm, called RICA (receiver-initiated channel-adaptive) protocol, to intelligently utilize the multi-rate services (based on different modulation schemes) provided by the IEEE 802.11b standard. Our NS-2 simulation results show that the RICA protocol is highly effective.

In this paper, we focus on investigating the optimal MIMO transmission strategy for forward channel capacity and forward error exponent of the MIMO block fading channels when the feedback link is causal and has a capacity constraint. In this paper, we assume a forward MIMO block fading channel where the channel state information is estimated at the receiver and partially feedback to the transmitter. The feedback link is assumed to be noiseless and causal with a feedback capacity constraint in terms of maximum number of feedback bits per fading block. We show that the design of the optimum feedback scheme is identical to the design of vector quantizer (Lloyd's algorithm [S.P. Lloyd, 1982]) with a modified distortion measure. It is shown that in the general case, the optimal feedback strategy has a general form of power water-filling cascaded with beamforming matrix as well. Furthermore, we show that the SNR gain with feedback is contributed by focusing transmission power on active eigen-channel and temporal power water-filling. The former factor contributed at most log/sub 10/(n/sub T/)n/sub R/ dB SNR gain when n/sub T/ > n/sub R/ in all SNR region while the latter contribution is significant only at low SNR region. Finally, MMSE receiver could be used to achieve the optimal capacity in the general case of partial feedback.

Multiple access control (MAC) of the uplink in a wireless mobile computing system is one of the most important resource allocation problems in that the response time and throughput of user applications (e.g., wireless web surfing) are critically affected by the efficiency of the MAC protocol. Compared with a traditional MAC problem (e.g., wireline Ethernet), there are two important new challenges in a modern wireless network: (1) multimedia data with diverse traffic requirements are involved; and (2) the wireless channel has a time-varying quality for each user. Furthermore, a more prominent user requirement is fairness among different users, possibly, with different traffic demands. While some protocols have been suggested to handle multimedia data and/or tackling the time-varying channel, there are a number of drawbacks in these existing protocols. The most notable drawback is that the channel model is rather unrealistic - just using a two state Markov chain instead of relying on accurate models of multipath fading and shadowing effects. Another common deficiency is that fairness is ignored. In this paper, we propose to use a new notion of fairness that can capture a realistic channel model, and to integrate a fair queuing scheduling algorithm in a MAC protocol to optimize performance while maintaining fairness among users regardless of their channel states and data types.

We propose an enhancement to the original MAC (multiple access control) protocol in the IEEE 802.11 standard by improving the handshake mechanism and adding one more separate power control channel. With the control channel, the receiver notifies its neighbors about the noise tolerance. Thus, the neighbors can adjust their transmission power levels to avoid packet collision at the receiver. Through extensive simulations on the NS-2 platform, our power control mechanism is found to be effective in that network throughput can be increased by about 10%.

We focus on investigating the optimal MIMO transmission strategy and the optimal feedback strategy for forward channel capacity and forward error exponent of MIMO block fading channels when the feedback link is causal and has a capacity constraint. We assume a forward MIMO block fading channel where the channel state information is estimated at the receiver and partially fed back to the transmitter. The feedback link is assumed to be noiseless and causal with a feedback capacity constraint in terms of maximum number of feedback bits per fading block. We show that the design of the optimal feedback scheme is identical to the design of the vector quantizer - S.P. Loyld's algorithm (see IEEE Trans. Inf. Theory, 1982) - with a modified distortion measure. It is shown that in the general case, the optimal feedback strategy has a general form of power water-filling cascaded with a beamforming matrix as well. Furthermore, we show that the SNR gain with feedback is contributed by focusing transmission power on the active eigenchannel and temporal power water-filling. The former factor contributed at most log/sub 10/(n/sub T/)n/sub R/ dB SNR gain, when n/sub T/>n/sub R/ in all SNR regions, while the latter contribution is significant only in the low SNR region. Finally, the MMSE receiver could be used to achieve the optimal capacity in the general case of partial feedback.

In packet radio networks, especially an ad hoc wireless network using IEEE 802.11 as the MAC (media access control) protocol, power control is a crucial issue. By using a judicious power control mechanism, co-channel interference can be significantly reduced, thus improving the channel spatial reuse and network capacity. However, efficient power control in an IEEE 802.11 system is very challenging because according to the standard, fixed power is used for transmitting packets, and there is only one channel. In this paper, we propose an enhancement to the standard IEEE 802.11 MAC protocol by improving the handshaking mechanisms and adding one separate power control channel. With the control channel, the receiver notifies its neighbors its noise tolerance. Thus, the neighbors can adjust their transmission power levels to avoid packet collisions at the receiver. Through extensive simulations using NS-2, our proposed power control mechanism is found to be effective in that network throughput can be increased by about 10%, and the battery utilization can also be improved at the same time.

In this paper, we shall investigate the problem of optimal transmission and feedback strategies design when feedback link has a capacity constraint. Optimization is based on forward channel capacity and forward error exponent. Channel state information is estimated at the receiver and feedback to the transmitter. The feedback is assumed to be noiseless and casual with a feedback capacity constraint, C/sub fb/. We show that the design of the optimal feedback scheme is identical to the design of scalar quantizer (Lloyd's algorithm with a modified distortion measure). The feedback optimization algorithm could be applied to both the forward capacity and error exponent with modifications of distortion measure. The optimal tradeoff of forward channel capacity and forward channel error exponent versus the feedback link capacity could be clearly illustrated. It is shown that the optimal transmission strategy also has a general form of temporal water-filling. Furthermore, while feedback enhances the forward channel capacity more effectively in low SNR region compared with high SNR region, the enhancement in error exponent is significant in both high and low SNR region. This indicates that significant gain in practical coding design could be expected with partial feedback.

CDMA (code division multiple access) systems are critical building blocks of future high performance wireless and mobile computing systems. While CDMA systems are very mature for voice services, their potentials in delivering high quality data services are yet to be investigated. One of the most crucial component in an advanced wideband CDMA system is the judicious allocation of bandwidth resources to both voice and high data rate services so as to maximize utilization while satisfying the respective quality of service requirements. Specifically, in a multicode CDMA system, the problem is to intelligently allocate codes to the users' requests. While previous work in the literature has addressed this problem from a capacity point of view, the fairness aspect, which is also important from the users' point of view, is largely ignored. In this paper, we propose a new code allocation approach that is channel adaptive and can guarantee fairness with respect to the users' channel conditions. Simulation results show that out approach is more effective than the proportional fair approach.

To fully exploit the potential of a wideband CDMA-based mobile Internet computing system, an efficient algorithm is needed for judiciously performing rate allocation, so as to orchestrate and allocate bandwidth for voice services and high data rate applications. However, in existing standards (e.g., cdma2000), only a first-come-first-served equal sharing allocation algorithm is used, potentially leading to a low bandwidth utilization and inadequate support of high data rate multimedia mobile applications (e.g., video/audio files swapping, multimedia messaging services, etc.). In this paper, we first analytically model the rate allocation problem that captures realistic system constraints such as downlink power limits and control, uplink interference effects, physical channel adaptation, and soft handoff. We then suggest six efficient rate allocation schemes that are designed based on different philosophies: rate optimal, fairness-based, and user-oriented. Simulations are performed to evaluate the effectiveness of the rate allocation schemes using realistic system parameters in our model.

In a code-division multiple access (CDMA)-based wireless communication system, forward link is power limited and reverse link is interference limited. With power control and statistical multiplexing, voice services can be supported reasonably well. However, for high data-rate services, a more comprehensive scheduling mechanism is needed in order to achieve a high capacity while satisfying the forward and reverse link constraints. We formulate the high data-burst scheduling as a integer programming problem using a generic CDMA system model. We also suggest an optimal algorithm for generating scheduling solutions. With cdma2000 system details plugged in the proposed algorithm, it is found that our algorithm considerably outperforms several fast heuristics, including equal sharing, first-come-first-served, longest delay first, and shortest burst first.

We propose a novel proportional fair space time scheduling algorithm to enhance the reverse link capacity of a generic wireless access architecture. The access point is assumed to have n/sub R/ antenna while the client device has single transmit antenna. We also assume slow fading channel where clients are moving with pedestrian speed. System capacity is enhanced by exploiting the burstiness of data source, selection diversity of different client users as well as the distributed MIMO configuration when we have n/sub R/ antenna at the access point. We shall illustrate that the proposed algorithms could be applied to IEEE 802.11a wireless LAN system with slight modification of the MAC layer of client device. Significant gain in system capacity is found which is attributed to the distributed MIMO configuration between the access point and multiple client users as well as the selection diversity of different client users.

Wireless fair queueing algorithms have been extensively studied recently. However, a major drawback in existing approaches is that the channel model is overly simplified - a two states (good or bad) channel is assumed. While it is relatively easy to analyze the system using such a simple model, the algorithms so designed are of a limited applicability in a practical environment, in which the level of burst errors are time-varying and can be exploited by using channel adaptive coding and modulation techniques. In this paper, we first argue that the existing algorithms cannot cater for a more realistic channel model and the traditional notion of fairness is not suitable. We then propose a new notion of fairness, which bounds the actual throughput normalized by channel capacity of any two sessions. Using the new fairness definition, we propose a new fair queueing algorithm called CAFQ (channel adaptive fair queueing), which, as indicated in our numerical studies, outperforms other algorithms in terms of overall system throughput and fairness among error prone sessions.

To support truly peer-to-peer applications in ad hoc wireless mobile computing networks, a judicious and efficient ad hoc routing protocol is needed. Much research has been done on designing ad hoc routing protocols and some well known protocols are also being implemented in practical situations. However, one major drawback in existing state-of-the-art protocols, such as the AODV routing protocol, is that the time-varying nature of the wireless channels among the mobile terminals is ignored, let alone exploited. This can be a severe design shortcoming because the varying channel quality can lead to very poor overall route quality, in turn result in low data throughput. In this paper, by using a previously proposed adaptive channel coding and modulation scheme which allows a mobile terminal to dynamically adjust the data throughput via changing the amount of error protection incorporated, we devise a new receiver-initiated algorithm for ad hoc routing that dynamically changes the routes according to the channel conditions. Extensive simulation results indicate that our proposed protocol are more efficient in that shorter delays and higher rates are achieved.

To support truly peer-to-peer applications in ad hoc wireless networks, a judicious and efficient ad hoc routing protocol is needed. Much research has been done on designing ad hoc routing protocols and some well known protocols are also being implemented in practical situations. However, one major drawback in existing state-of-the-art protocols, such as the AODV (ad hoc on demand distance vector) routing protocol, is that the time-varying nature of the wireless channels among the mobile terminals is ignored, let alone exploited. In this paper, by using a previously proposed adaptive channel coding and modulation scheme which allows a mobile terminal to dynamically adjust the data throughput via changing the amount of error protection incorporated, we devise a new ad hoc routing algorithm that dynamically changes the routes according to the channel conditions. Extensive simulation results indicate that our proposed protocol is more efficient in that shorter delays and higher rates are achieved.

Transmit diversity through multiple transmit antennas is generally regarded as beneficial to the link-level performance. In this paper, we shall investigate the role of transmit diversity with respect to the link level and the system-level performance. We focus on the reverse link analysis, where mobile users are assumed to have independent fading, and they are equipped with multiple transmit antennas (n/sub T/). Each mobile station is assumed to have an average power constraint. The base station is assumed to have n/sub R/ receive antennas. We consider two levels of partial feedback, namely, scalar feedback and per-antenna vector feedback. In both cases, the transmitter does not have full knowledge of the channel matrix. Based on the information theoretical analytical model, it is shown that transmit diversity could enhance link-level performance, but is harmful to the multiuser system performance when there is insufficient feedback information.

We consider the uplink access control problem in a mobile nomadic computing system, which is based on a cellular phone network in that a user can use the mobile device to transmit voice or file data. This resource management problem is important because an efficient solution to uplink access control is critical for supporting a large user population with a reasonable level of quality of service (QoS). While there are a number of recently proposed protocols for uplink access control, these protocols possess a common drawback in that they do not adapt well to the burst error properties, which are inevitable in using wireless communication channels. We propose a novel TDMA-based uplink access protocol, which employs a channel state dependent allocation strategy. Our protocol is motivated by two observations: (1) when channel state is bad, the throughput is low due to the large amount of FEC (forward error correction) or excessive ARQ (automatic repeated request) that is needed and (2) because of item 1, much of the mobile device's energy is wasted. The proposed protocol works closely with the underlying physical layer in that, through observing the channel state information (CSI) of each mobile device, the MAC protocol first segregates a set of users with good CSI from requests gathered in the request contention phase of an uplink frame. The protocol then judiciously allocates channel bandwidth to contending users based on their channel conditions. Simulation results indicate that the proposed protocol considerably outperforms five state-of-the-art protocols in terms of packet loss, delay, and throughput.

3rd generation mobile systems are mostly based on the wideband CDMA platform to support high bit rate packet data services. One important component to offer packet data service in CDMA is a burst admission control algorithm. In this paper, we propose and study a novel jointly adaptive burst admission algorithm, namely the jointly adaptive burst admission-spatial dimension algorithm (JABA-SD) to effectively allocate valuable resources in wideband CDMA systems to burst requests. In the physical layer, we have a variable rate channel-adaptive modulation and coding system which offers variable throughput depending on the instantaneous channel condition. In the MAC layer, we have an optimal multiple-burst admission algorithm. We demonstrate that synergy could be attained by interactions between the adaptive physical layer and the burst admission layer. We formulate the problem as an integer programming problem and derive an optimal scheduling policy for the jointly adaptive design. Both the forward link and the reverse link burst requests are considered and the system is evaluated by dynamic simulations which takes into account of the user mobility, power control and soft-handoff. We found that significant performance improvement, in terms of average packet delay, data user capacity and coverage, could be achieved by our scheme compared to the existing burst assignment algorithms.

On the verge of realizing truly ubiquitous access to high quality data (e.g., media, financial, etc.), an efficient burst admission control algorithm is crucial in third generation (3G) wireless communication systems based on wideband CDMA standards. In this paper, we propose and analyze the performance of a novel burst admission technique, called the multiple- burst admission-spatial dimension algorithm (MBA-SD) to judiciously allocate the precious channels in wideband CDMA systems to burst requests. The major contributions of the present paper are the novel formulation of the problem as an integer programming problem and the derivation of an optimal algorithm for scheduling the burst requests. Both the forward link and the reverse link burst requests are considered and the system is simulated by dynamic simulations which takes into account of the user mobility, power control, and soft hand-off. We found that significant performance improvement, in terms of data user capacity coverage, and admission and outage probabilities, could be achieved by our scheme compared to the existing burst assignment algorithms.

In our previous study, we have formulated the burst admission control problem for wideband CDMA systems as an integer programming problem. In this paper, we propose and analyze the performance of a novel burst admission technique, called the multiple-burst admission-spatial dimension algorithm (MBA-SD) to judiciously allocate the previous channels in wideband CDMA systems to burst requests. Both the forward link and the reverse link burst requests are considered and the system is simulated by dynamic simulations which takes into account the user mobility, power control and soft hand-off. We found that significant performance improvement, in terms of data user capacity, coverage, and admission and outage probabilities, could be achieved by our scheme compared to the existing burst assignment algorithms.

A bandwidth-efficient variable-rate adaptive channel coding scheme, ATCQAM, for time-varying flat-fading channels is proposed. In addition to the forward channel, a low-capacity feedback channel is needed to convey channel state information to the transmitter, possibly with delays and noise. A number of transmission modes, with varying throughputs, are incorporated at the transmitter. Appropriate transmission modes are selected based on the feedback channel states. Design issues for the ATCQAM are considered. A closed-loop control scheme to maintain mode synchronization between the transmitter and the receiver is discussed. The effects of feedback delay, a noisy feedback channel, and mobile speed are investigated. Analytical bounds are derived and simulations are performed to verify the results.

In this paper, we propose a bandwidth efficient error correction scheme, namely the variable rate adaptive bit-interleaved trellis coded modulation (ABICM), for wireless mobile channels. The code rate and modulation level are varied according to the current channel state to exploit the time-varying nature of the wireless channel. Design challenges to achieve symbol-by-symbol adaptation, component codes design, puncturing and interleaving design, adaptation thresholds determination are addressed. A multi-level puncturing scheme is proposed to tackle the problem of symbol-by-symbol adaptive puncturing and interleaving. We introduced the concept of equivalent distance spectrum for designing component codes of the ABICM system. Two operation modes, namely the constant throughput and the constant BER controls, are introduced. The design is illustrated with an example and it is found that there are significant gains relative to the fixed rate coding in terms of SNR and throughput. It is also found that the ABICM scheme is essentially not degraded in small interleaving depths. This makes the ABICM very suitable for real time applications.

We proposed a novel interleaving technique, namely adaptive interleaving, which can break the bursty channel errors more effectively than traditional block interleaving. This new scheme resequences the transmitted symbols adaptively according to the instantaneous subcarrier channel state information. In this way, the bursty errors introduced by the channel can be broken more effectively, thus a better BER performance can be achieved. This technique is well-suited to OFDM systems because the CSI values of the whole frame could be estimated at once when transmitted symbols are framed in the frequency dimension. Computer simulation shows that significant SNR gains can be achieved, compared with traditional block interleaving.

The multiple access control (MAC) problem in a wireless network has intrigued researchers for years. For a broadband wireless multimedia network such as wireless ATM, an effective MAC protocol is very much desired because efficient allocation of channel bandwidth is imperative in accommodating a large user population with satisfactory quality of service. Indeed, MAC protocols for a wireless ATM network, in which user traffic requirements are highly heterogeneous (classified into CBR, VBR, and ABR), are even more intricate to design. Considerable research efforts expended in tackling the problem have resulted in a myriad of MAC protocols. While each protocol is individually shown to be effective by the respective designers, it is unclear how these different protocols compare against each other on a unified basis. We quantitatively compare seven previously proposed TDMA-based MAC protocols for integrated wireless data and voice services. We first propose a taxonomy of TDMA-based protocols, from which we carefully select seven protocols, namely SCAMA, DTDMA/VR, DTDMA/PR, D4RUMA, DPRMA, DSA++, and PRMA/DA, such that they are devised based on rather orthogonal design philosophies. The objective of our comparison is to highlight the merits and demerits of different protocol designs.

We consider the uplink access control problem in a mobile computing system, which is based on a cellular phone network in that a user can use the mobile device to transmit voice or file data. This resource management problem is important because efficient solution to uplink access control is critical for supporting a large user population with a reasonable level of quality of service (QoS). While there are a number of recently proposed protocols for uplink access control, these protocols possess a common drawback in that they do not exploit well the burst error properties, which are inevitable in a wireless communication system. In this paper, we propose a novel TDMA-based uplink access protocol, which employs a channel state dependent allocation strategy. Our protocol is motivated by two observations: (1) when channel state is bad, the throughput is low due to large amount of FEC (forward error correction) or excessive ARQ (automatic repeated request) is needed; and (2) because of (1), much of the mobile device's energy is wasted. The proposed protocol works closely with the underlying physical layer in that through observing the channel state information (CSI) of each mobile user, the MAC protocol first segregates a set of users with good CSI from requests gathered in the request contention phase of an uplink frame. The protocol then judiciously allocates channel bandwidth to contending users based on their channel conditions. Simulation results indicate that the proposed protocol considerably outperforms five state-of-the-art protocols in terms of packet loss, delay, and throughput.

In this paper, we propose a new multiple access control (MAC) protocol for wireless distributed multimedia systems based on ATM, in which user demands are highly heterogeneous and can be classified as CBR, VBR, and ABR. Our protocol is motivated by two of the most significant drawbacks of existing protocols: (1) channel condition is ignored or not exploited, and (2) inflexible or biased time slots allocation algorithms are used. Indeed, existing protocols mostly ignore the burst errors due to fading and shadowing, which are inevitable in a mobile and wireless communication environment. A few protocols take into account the burst errors but just “handle” the errors in a passive manner. On the other hand, most of the existing protocols employ an inflexible or biased allocation algorithm such that over-provisioning may occur for a certain class of users at the expense of the poor service quality received by other users. Our proposed protocol, called SCAMA (synergistic channel adaptive multiple access), does not have these two drawbacks. The proposed protocol works closely with the underlying physical layer in that through observing the channel state information (CSI) of each mobile user, the MAC protocol first segregates a set of users with good CSI from requests gathered in the request contention phase of an uplink frame. The MAC protocol then judiciously allocates information time slots to the users according to their traffic types, CSI, urgency, and throughput, which are collectively represented by a novel and flexible priority function.

The multiple access control (MAC) problem in a wireless network has intrigued researchers for years. An effective MAC protocol is very much desired because efficient allocation of channel bandwidth is imperative in accommodating a large user population with satisfactory quality of service. MAC protocols for integrated data and voice services in a cellular wireless network are even more intricate to design due to the dynamic user population size and traffic demands. Considerable research efforts expended in tackling the problem have resulted in a myriad of MAC protocols. While each protocol is individually shown to be effective by the respective designers, it is unclear how these different protocols compare against each other on a unified basis. In this paper, we quantitatively compare six recently proposed TDMA-based MAC protocols for integrated wireless data and voice services. We first propose a taxonomy of TDMA-based protocols, from which we carefully select six protocols, namely CHARISMA, D-TDMA/VR, D-TDMA/FR, DRMA, RAMA, and RMAV, such that they are devised based on rather orthogonal design philosophies. The objective of our comparison is to highlight the merits and demerits of different protocol designs.

We introduce a novel multiple access control (MAC) protocol for integrated wireless voice and data services on the uplink channel in a cellular wireless network. The proposed protocol is TDMA based and the uplink frame is divided into two subframes: a request subframe and an information subframe. Our scheme, called CHARISMA (Channel Adaptive Reservation-based Isochronous Multiple Access), works by first gathering users' request via the mini-slots in the request subframe and then decides on the allocation of the information slots in the information subframe based on the channel states ranking of the mobile users. Our extensive simulation results indicate that significant improvements in terms of throughput, delay, and packet loss probability are achieved using the CHARISMA protocol.

We have evaluated the information theoretical performance of variable rate adaptive channel coding for Rayleigh fading channels. The channel states are detected at the receiver and fed back to the transmitter by means of a noiseless feedback link. Based on the channel state informations, the transmitter can adjust the channel coding scheme accordingly. Coherent channel and arbitrary channel symbols with a fixed average transmitted power constraint are assumed. The channel capacity and the error exponent are evaluated and the optimal rate control rules are found for Rayleigh fading channels with feedback of channel states. It is shown that the variable rate scheme can only increase the channel error exponent. The effects of additional practical constraints and finite feedback delays are also considered. Finally, we compare the performance of the variable rate adaptive channel coding in high bandwidth-expansion systems (CDMA) and high bandwidth-efficiency systems (TDMA).

An adaptive coding scheme is introduced for a discrete sequence code-division multiple-access system. The system uses noncoherent M-ary orthogonal modulation with RAKE receiver and power control. Both a fast fading channel and a combined fast fading, shadowing and power control channel are considered. Analytical bounds and simulations are done to evaluate the performance of the system. It is found that there is significant improvement in the average throughput and the bit-error-rate performance in the adaptive coding scheme. The amount of improvement drops with the increase of diversity branches used. More importantly, it is found that adaptive coding scheme is relatively robust to shadowing, while fix-rate codes are ineffective in the shadowing environment. Finally, adaptive coding scheme is found to be robust to mobile speed, feedback delay, and finite interleaving depth.