We study the problem of uplink compression for cell-free multi-input multi-output networks with limited fronthaul capacity. In compress-forward mode, remote radio heads (RRHs) compress the received signal and forward it to a central unit for joint processing. While previous work has focused on a transform-based approach, which optimizes the transform matrix that reduces signals of high dimension to a static pre-determined lower dimension, we propose a rate-based approach that simultaneously finds both dimension and compression adaptively. Our approach accommodates for changes to network traffic and fronthaul limits. Using mutual information as the objective, we obtain the theoretical network capacity for adaptive compression and decouple the expression to enable decentralization. Furthermore, using channel statistics and user traffic density, we show different approaches to compute an efficient representation of side information that summarizes global channel state information and is shared with RRHs to assist compression. While keeping the information exchange overhead low, our decentralized implementation of adaptive compression shows competitive overall network performance compared to a centralized approach.

Refractive metasurfaces (RMTSs) offer a promising solution to improve energy efficiency of wireless systems. To address the limitations of single-layer RMTSs, stacked intelligent metasurfaces (SIMs), which form the desired precoder and combiner in the wave domain, have been proposed. However, previous analyses have overlooked hardware non-idealities that significantly affect SIM performance. In this paper, we study the achievable sum-rate of SIM antennas in an uplink scenario, accounting for hardware constraints. We propose a system model that includes noise and hardware effects, formulate a non-convex sum-rate optimization problem, and solve it using gradient ascent and interior point methods. We compare SIMs and digital phased arrays (DPAs) under Rayleigh fading and 3GPP channels with two conditions: an equal number of$\mathbf{R F}$chains and an equal physical aperture size. Our results show SIMs outperform DPAs under equal number of RF chains but underperform DPAs with equal aperture size.

In the user-centric cell-free massive MIMO (UC-mMIMO) network scheme, user mobility necessitates updating the set of serving access points to maintain the user-centric clustering. Such updates are typically performed through handoff (HO) operations; however, frequent HOs lead to overheads associated with the allocation and release of resources. This paper presents a deep reinforcement learning (DRL)-based solution to predict and manage these connections for mobile users. Our solution employs the Soft Actor-Critic algorithm, with continuous action space representation, to train a deep neural network to serve as the HO policy. We present a novel proposition for a reward function that integrates a HO penalty in order to balance the attainable rate and the associated overhead related to HOs. We develop two variants of our system; the first one uses mobility direction-assisted (DA) observations that are based on the user movement pattern, while the second one uses history-assisted (HA) observations that are based on the history of the large-scale fading (LSF). Simulation results show that our DRL-based continuous action space approach is more scalable than discrete space counterpart, and that our derived HO policy automatically learns to gather HOs in specific time slots to minimize the overhead of initiating HOs. Our solution can also operate in real time with a response time less than 0.4 ms.

Radio-frequency energy harvesting from ambient cellular energy and drone-based receivers close to the sensors can be effective tools to prolong the lifespan of wireless sensor networks (WSNs). Moreover, the energy management policy of a sensor plays a critical role in increasing the reliability of data transmission under severe energy constraints. Thus, in this paper, we develop an optimal transmission policy to reduce the outage such that a sensor decides on when to transmit and how much power to use given the uncertainties of the harvested energy. To this end, we model sensors with a finite-level battery and a buffer with discrete states, whereas the cellular base stations from which the energy is harvested are modeled according to a Poisson point process. The drone is assumed to be hovering at a fixed height above the field of sensors, and assume path loss and small scale fading with varying intensities depending on the line-of-sight nature of the link. An outage is assumed to occur due to incorrect reception of transmissions and buffer overflow. We formulate the problem as a Markov decision process, and utilize a Q-learning based algorithm to generate the optimal transmission policy. Our numerical results show that the proposed policy significantly outperforms the previously proposed policies under all conditions.

Placing selected content at the edge of the network close to the users, known as caching, is an important technique to improve the efficiency of content delivery in wireless networks. In this paper, we consider caching in a cloud radio access network (C-RAN) in which the primary fronthaul link operates in the mmWave range and may switch to microwave frequencies in the case of blockage. We aim to minimize the average long-term network cost by optimizing dynamic fetching and caching decisions. Importantly, we consider the realistic case of user request distributions and blockage rates being a priori unknown and not necessarily stationary. We introduce change point detection (CPD) to detect significant changes in the environment; we couple this step with reinforcement learning (RL): our key contribution, the proposed change point detection assisted reinforcement learning (CPRL) algorithm learns the environment and (re-)optimizes the caching policy to solve the associated Markov decision process (MDP) problem. Essentially, CPD allows our learning algorithm to adapt its caching strategy to the new environment which shows faster convergence. The numerical results show that our proposed approach improves the efficiency of caching in wireless networks, making it more adaptable to changing request patterns over time.

We study the problem of managing handoffs (HOs) in user-centric cell-free massive MIMO (UC-mMIMO) networks. Motivated by the importance of controlling the number of HOs and by the correlation between efficient HO decisions and the temporal evolution of the channel conditions, we formulate a partially observable Markov decision process (POMDP) with the state space representing the discrete versions of the large-scale fading and the action space representing the association decisions of the user with the access points (APs). We develop a novel algorithm that employs this model to derive a HO policy for a mobile user based on current and future rewards. To alleviate the high complexity of our POMDP, we follow a divide-and-conquer approach by breaking down the POMDP formulation into sub-problems, each solved separately. Then, the policy and the candidate pool of APs for the sub-problem that produced the best total expected reward are used to perform HOs within a specific time horizon. We then introduce modifications to our algorithm to decrease the number of HOs. The results show that half of the number of HOs in the UC-mMIMO networks can be eliminated. Namely, our novel solution can control the number of HOs while maintaining a rate guarantee, where a 47%-70% reduction of the cumulative number of HOs is observed in networks with a density of 125 APs per km2. Most importantly, our results show that a POMDP-based HO scheme is promising to control HOs.

We examine the problem of optimizing resource allocation in the uplink for a user-centric, cell-free, multi-input multi-output network. We start by modeling and developing resource allocation algorithms for two standard network operation modes. The centralized mode provides high data rates but suffers multiple issues, including scalability. On the other hand, the distributed mode has the opposite problem: relatively low rates, but is scalable. To address these challenges, we combine the strength of the two standard modes, creating a new semi-distributed operation mode. To avoid the need for information exchange between access points, we introduce a new quality of service metric to decentralize the resource allocation algorithms. Our results show that we can eliminate the need for information exchange with a relatively small penalty on data rates.

To meet the rising demand for data rates, the next generation of wireless communication will need to deploy more antennas at the base stations (BSs) while also addressing sustainability and power efficiency concerns. Recently, metasurfaces, specifically dynamic metasurface antennas (DMAs), have been suggested as potential solutions. Specifically, researchers have demonstrated the potential throughput performance of DMAs as basestation antennas. In this paper, we study the channel estimation problem for DMAs, modeled as correlated multiple input multiple output (MIMO) systems. We first propose a system model that details the noise sources in these systems. Then, exploiting the fast response time of DMAs, we consider the possibility of multiple channel measurements during a single symbol. Adopting a minimum-mean-square-error (MMSE) based approach and assuming availability of the long-term channel correlation matrices at the receiver and transmitters, then, we formulate the channel estimation problem. We first solve this problem assuming full-control over the DMA. We then propose a more practical algorithm to map the full-control solution to the DMA structure, thereby accounting for the limitations imposed by the DMA. Our numerical results demonstrate that the proposed practical algorithm suffers only a minor performance loss compared to the full-control case.

In this work, we study the impact of THz signaling on controlling protein conformational changes. Specifically, due to the imposed variability of the intra-body medium on signal propagation, we study the effect of the channel randomness on THz-induced protein interactions. To do so, we demonstrate how the impinging nanoantenna force affects the energy absorbed by the protein structure. We specifically focus on analyzing the impact of the slowly varying force on the capability to control the dynamics of the desired protein population. We later introduce the probability of selectivity outage as a metric that is calculated based on a pre-defined selectivity threshold. Our results indicate that a trade-off must exist in the system since the higher the selectivity threshold, the higher the probability of selectivity outage, which increases the constraints imposed on the intra-body system design. The presented work provides a better understanding and characterization of the electromagnetically triggered protein molecules, their micro-environment, and their interaction with surrounding particles.

Massive multiple-input multiple-output (MIMO) systems, wherein a massive number of antennas are deployed at the base stations, are expected to play a significant role in 5G networks. The drawback of using the massive MIMO technique is the need for advanced and complex signal processing schemes. In recent years, several iterative and learning-based techniques have been introduced to address the need for low-complexity signal detection in the uplink of a massive MIMO system. The complexity of the iterative methods is highly affected by the number of needed iterations. On the other hand, although the performance of low-complexity learning-based techniques is close to optimal, they need retraining after major changes in the wireless communication channel. In this paper, we introduce Hint, a robust learning-based technique that finds an initial vector tailored for the current realization of the wireless channel; this vector initializes the iterative detector to complete the task of massive MIMO detection.

Long-term average proportional-fair (LTAPF) throughput optimization through power control is a popular resource allocation problem which is typically approximated by weighted sum-rate (WSR) maximization. WSR optimization is non-convex and strongly NP-hard in general. In this letter, we demonstrate that, in fact, the original sum-log-average-throughput power control problem can be recast as a convex program and thus solved to global optimality efficiently. We also generalize our result to show that the long-term average$\alpha$-fair utility maximization problem can be recast as convex for$\alpha \in (1,\infty)$. Numerical results demonstrate a substantial gain in LTAPF throughput compared to state-of-the art algorithms used to solve the max-WSR problem.

Placing selected content at the edge of the network close to the users, known as caching, can lower network latency and congestion in the fronthaul link. Unlike most works that assume a fixed or limited variation in file popularities, to better address user requests, we consider a time-varying popularity resulting in hidden-mode Markov decision processes. In fact, each mode captures environmental changes, and we optimize the fetching and dropping (of files) decisions to minimize a long-term network cost in a cloud radio access network. Importantly, the primary fronthaul link is a millimeter (mmWave) link with large capacity supported by a microwave backup link in case of blockage. Since caching decisions are coupled over time and can affect the future, we introduce a dynamic programming approach to solve the caching problem. We approximate the future cost of each cache state in each mode. To reduce the complexity of calculating the future cost, we introduce two approximation approaches and illustrate the accuracy of the approximations under different environmental conditions. Finally, our simulation results confirm the effectiveness of our proposed algorithm in finding effective caching and fetching decisions to lower the total network cost while dealing with time-varying popularities.

We propose to control handoffs (HOs) in user- centric cell-free massive MIM 0 networks through a partially observable Markov decision process (POMDP) with the state space representing the discrete versions of the large-scale fading (LSF) and the action space representing the association decisions of the user with the access points. Our proposed formulation accounts for the temporal evolution and the partial observability of the channel states. This allows us to consider future rewards when performing HO decisions, and hence obtain a robust HO policy. To alleviate the high complexity of solving our POMDP, we follow a divide-and-conquer approach by breaking down the POMDP formulation into sub-problems, each solved individually. Then, the policy and the candidate cluster of access points for the best solved sub-problem is used to perform HOs within a specific time horizon. We control the number of HOs by determining when to use the HO policy. Our simulation results show that our proposed solution reduces HOs by 47% compared to time- triggered LSF-based HOs and by 70% compared to data rate threshold-triggered LSF-based HOs. This amount can be further reduced through increasing the time horizon of the POMDP.

In this paper, we propose a theoretical intra-body propagation model for signal transmission in the THz frequency band. The channel of interest is a blood vessel composed of red blood cells (RBCs), where propagation occurs between a nanoantenna transmitter and a protein receiver. The presented model accounts for signal losses due to molecular absorption and scattering through both line-of-sight (LOS) and non-line-of-sight (NLOS) links. The RBCs between the antenna and the protein act as obstacles that attenuate the signal power giving rise to shadowing. By conducting Monte Carlo simulations, we develop the random characteristics of the transmission medium. Inspired by radar systems, an expression for the received power is derived using the bistatic radar model and the total path loss is computed through the different links. The results are validated by means of electromagnetic wave propagation simulations. The presented work indicates that a reliable communication link exists between the nanoantenna and the protein through both the LOS and NLOS transmission. Our work facilitates the accurate design of in-vivo wireless nanosensor networks and paves the path towards selective intra-body interactions.

We study the problem of distributed user scheduling and beamforming in multi-user, multi-cell, multiple-input multiple-output (MIMO) networks to maximize the weighted sum-rate. While previous work has focused on optimizing the signal-to-leakage-plus noise ratio (SLNR) or the signal-to-interference-plus-noise ratio (SINR), we propose a new signal-to-leakage-plus-interference-plus-noise ratio (SLINR) metric which hybridizes the SINR and SLNR by incorporating the intra-cell interference and inter-cell leakage. Using fractional programming and the Hungarian algorithm, we construct an iterative resource allocator that performs user scheduling and beamforming while accounting for channel estimation errors. Furthermore, we show different approaches for calculating the leakage which vary in terms of practicality and scalability. These approaches decrease the complexity compared to the standard method of leakage calculation, while providing comparable performance. Our results show that resource allocation based on the SLINR metric is a promising solution for decentralized implementation.

We consider the analysis and design of distributed wireless networks wherein remote radio heads (RRHs) coordinate transmissions to serve multiple users on the same resource block (RB). Specifically, we analyze two possible multiple-input multiple-output wireless fronthaul solutions: multicast and zero forcing (ZF) beamforming. We develop a statistical model for the fronthaul rate and, coupled with an analysis of the user access rate, we optimize the placement of the RRHs. This model allows us to formulate the location optimization problem with a statistical constraint on fronthaul outage. Our results are cautionary, showing that the fronthaul requires considerable bandwidth to enable joint service to users. This requirement can be relaxed by serving a low number of users on the same RB. Additionally, we show that, with a fixed number of antennas, for the multicast fronthaul, it is prudent to concentrate these antennas on a few RRHs. However, for the ZF beamforming fronthaul, it is better to distribute the antennas on more RRHs. For the parameters chosen, using a ZF beamforming fronthaul improves the typical access rate by approximately 8% compared to multicast. Crucially, our work quantifies the effect of these fronthaul solutions and provides an effective tool for the design of distributed networks.

In this paper, we optimize user scheduling, power allocation and beamforming in distributed multiple-input multiple-output (MIMO) networks implementing user-centric clustering. We study both the coherent and non-coherent transmission modes, formulating a weighted sum rate maximization problem for each; finding the optimal solution to these problems is known to be NP-hard. We use tools from fractional programming, block coordinate descent, and compressive sensing to construct an algorithm that optimizes the beamforming weights and user scheduling and converges in a smooth non-decreasing pattern. Channel state information (CSI) being crucial for optimization, we highlight the importance of employing a low-overhead pilot assignment policy for scheduling problems. In this regard, we use a variant of hierarchical agglomerative clustering, which provides a suboptimal, but feasible, pilot assignment scheme; for our cell-free case, we formulate anarea-basedpilot reuse factor. Our results show that our scheme provides large gains in the long-term network sum spectral efficiency compared to benchmark schemes such as zero-forcing and conjugate beamforming (with round-robin scheduling) respectively. Furthermore, the results show the superiority of coherent transmission compared to the non-coherent mode under ideal and imperfect CSI for the area-based pilot-reuse factors we consider.

We develop two distributed downlink resource allocation algorithms for user-centric, cell-free, spatially-distributed, multiple-input multiple-output (MIMO) networks. In such networks, each user is served by a subset of nearby transmitters that we call distributed units or DUs. The operation of the DUs in a region is controlled by a central unit (CU). Our first scheme is implemented at the DUs, while the second is implemented at the CUs controlling these DUs. We define a hybrid quality of service metric that enables distributed optimization of system resources in a proportional fair manner. Specifically, each of our algorithms performs user scheduling, beamforming, and power control while accounting for channel estimation errors. Importantly, our algorithm does not require information exchange amongst DUs (CUs) for the DU-distributed (CU-distributed) system, while also smoothly converging. Our results show that our CU-distributed system provides 1.3- to 1.8-fold network throughput compared to the DU-distributed system, with minor increases in complexity and front-haul load - and substantial gains over benchmark schemes like local zero-forcing. We also analyze the trade-offs provided by the CU-distributed system, hence highlighting the significance of deploying multiple CUs in user-centric cell-free networks.

Licensed spectrum sharing has been a promised approach to provide mobile network operators (MNOs) with required spectrum at times of increased traffic, or to improve the mobile user data rate with limited spectral resources. In this paper, we investigate the use of multi-user multiple-input, multiple-output (MIMO) techniques to enable licensed spectrum sharing. Specifically, we present a fair spectrum sharing system between two MNOs in multi-cell multi-user MIMO networks. We impose fairness by ensuring that each MNO receives spectrum in proportion to the amount it contributes. We formulate a constrained optimization problem to determine resource allocation and user scheduling across two MNOs. Since the problem is non-convex, we develop an algorithm to provide an effective solution through fractional programming and block coordinate descent. Our numerical results illustrate that the proposed spectrum sharing scheme can achieve up to 60 % improvement in terms of the average user rate among the two operators while ensuring that neither MNO is exploited for participating in the sharing mechanism. This improvement is in relation to the baseline of each MNO using multi-user MIMO communications on its own. In addition, most users, especially cell-center users close to the BSs, take advantage of our proposed spectrum sharing framework.

Wireless sensor networks (WSNs) can utilize radio frequency energy harvesting from ambient power sources for continuous operation, while drones acting as sink nodes increase the reliability of transmission and decrease a sensor's energy usage. In this paper, we attempt to optimize the transmit power of a sensor such that the overall outage probability is minimized. The sensors are assumed to have a finite-level battery and a buffer with discrete states. Energy is harvested from ambient energy arising from cellular base stations distributed according to a Poisson point process. The sensor's data is transmitted to a drone whenever its buffer becomes full. We consider two scenarios for the drone: i) hovering, and ii) moving on a fixed trajectory. Moreover, we utilize different path loss and fading models for the sensor-drone links due to their line-of-sight nature. An outage occurs due to both transmission outage and buffer overflow. We formulate the problem as a Markov decision process, and utilize a Q-learning based algorithm to learn the power control policy. Our numerical results show that the proposed policy significantly outperforms both the full and single-step energy usage policies. Moreover, it is robust enough to handle environmental/channel changes without a need for re-training.

The recent applications in communications are required to meet low-latency transmission with high traffic rates and reliabilities. From the latency point of view, most of the state-of-the-art techniques consider the average latency which does not directly apply to delay-sensitive scenarios. In this paper, we propose a novel approach to tackle the scheduling problem by directly addressing the max-delay constraint; this is an NP-hard problem. Our main contributions are first, proposing the Super State Monte-Carlo Tree Search (SS-MCTS) as a version of regular MCTS modified for large-scale probabilistic environments with less computational complexity, and second, addressing the scheduling problem with maximum delay constraint on flows. Our numerical results demonstrate that the proposed approach significantly improves the packet delivery rate while meeting the maximum delay constraint in large-scale scenarios compared to the state-of-the-art technologies.

In this paper, we study the deployment decisions and location optimization for the remote radio heads (RRHs) in coordinated distributed networks in the presence of a wireless backhaul. We implement a scheme where the RRHs use zero-forcing beamforming (ZF-BF) for the access channel to jointly serve multiple users, while on the backhaul the RRHs are connected to their central units (CUs) through point-to-point wireless links. We investigate the effect of this scheme on the deployment of the RRHs and on the resulting achievable spectral efficiency over the access channel (under a backhaul outage constraint). Our results show that even for noise-limited backhaul links, a large bandwidth must be allocated to the backhaul to allow freely distributing the RRHs in the network. Additionally, our results show that distributing the available antennas on more RRHs is favored as compared to a more co-located antenna system. This motivates further works to study the efficiency of wireless backhaul schemes and their effect on the performance of coordinated distributed networks with joint transmission.

We study the problem of user-scheduling and resource allocation in distributed multi-user, multiple-input multiple-output (MIMO) networks implementing user-centric clustering and non-coherent transmission. We formulate a weighted sum-rate maximization problem which can provide user proportional fairness. As in this setup, users can be served by many transmitters, user scheduling is particularly difficult. To solve this issue, we use block coordinate descent, fractional programming, and compressive sensing to construct an algorithm that performs user-scheduling and beamforming. Our results show that the proposed framework provides an 8- to 10-fold gain in the long-term user spectral efficiency compared to benchmark schemes such as round-robin scheduling. Furthermore, we quantify the performance loss due to imperfect channel state information and pilot training overhead using a defined area-based pilot-reuse factor.

Energy harvesting is a key enabling technology to prolong the lifetime of wireless sensor devices, while a sink node should be properly placed to efficiently gather sensor data. In our model, sensors distributed randomly based on a Matern cluster process harvest energy from a Poisson point process of base stations and use this energy for their transmissions to a stationary aerial sink such as a tethered drone. Moreover, each sensor is assumed to have a battery with multiple energy levels and a packet buffer with a finite capacity. We aim to comprehensively characterize the harvesting and outage performance of radio frequency energy harvesting sensor devices with an aerial receiver. We derive the ambient power at a sensor device using a moment generating function based approach, and the harvested energy is characterized. For the scheme where a sensor transmits whenever the buffer becomes full irrespective of the battery level, we derive the interrelated steady-state probabilities of the different battery states and packet buffer levels using Markov chains. Finally, the outage probability is derived when a sensor transmits its packets to an aerial sink node. Our numerical results illustrate that the numbers of energy and buffer states significantly impact the energy harvesting performance and the overall outage.

It has been established that interfacing Terahertz (THz) band signals with protein molecules excites their resonant modes. In this work, we develop a model that bridges the mechanical system of proteins, modeled as harmonic oscillators, and the probability of protein conformal changes. We deploy the Langevin stochastic equation under the influence of an external force to capture the protein dynamics. The average energy driving the protein to alter its conformation is derived and used to determine the probability of protein folding. Our numerical analysis results show that the energy transferred from the nanoantenna and stored in the protein is capable of inducing a conformal change in the protein. This illustrates the power of THz waves in enabling protein interactions with high selectivity. It also sheds light on various opportunities that impact applications concerning targeted therapy, biosensing as well as disease control and prevention.

This paper addresses the problem of sub-carrier and user association in a downlink cloud based radio access network (C- RAN), considering fronthaul and access radio frequency (RF) links and orthogonal frequency division multiple access (OFDMA). Our problem is most relevant to scenarios where bandwidth resources must be shared between the fronthaul and access links. In order to assign users to their appropriate cells and to allocate frequency resources, we maximize the sum throughput. Importantly, we consider fronthaul and inter-cell interference. The resulting optimization problem is based on joint fronthaul and access frequency resource allocation and user association and is non-convex. To tackle the non-convexity of the problem, a successive convex approximation method is proposed. In order to guarantee integer solutions, we introduce a term, called virtual interference, into the problem formulation. Numerical results validate the effectiveness of proposed algorithm in jointly allocating resources of fronthaul and access links. The results confirm improved total network throughput by considering full interference scheme and sharing resources between fronthaul and access links.

In this paper, we study the channel estimation problem in microwave correlated massive multiple-input-multiple-output systems with reduced number of radio-frequency chains. We exploit the knowledge of the transmit and receive correlation between the antennas. Leveraging the fact that the channel entries are uncorrelated in its eigen-domain, we seek to estimate the channel in this domain. Due to reduced number of radio-frequency chains, channel estimation is performed in multiple time slots. Under a total energy budget, we aim to optimally design the hybrid precoder and combiner in each training time slot, in order to estimate the channel using the minimum mean squared error criterion. We show that the optimal precoder and combiner in each time slot are aligned to transmitter and receiver eigen-directions, respectively. The energy allocation of each eigen-direction determines the significance of each eigen-direction; more energy is allocated to the stronger eigen-directions. At low training energy budget, only significant part of the channel needs to be estimated. At high training energy budget, the energy is equally distributed among all eigen-directions. Simulation results show that the proposed channel estimation scheme can efficiently estimate correlated massive multiple-input-multiple-output channels within a few training time slots.

Nanosized devices operating inside the human body open up new prospects in the healthcare domain. On the one hand, molecular communication enables biological nanomachines to communicate by exchanging molecules and performing application-dependent tasks. On the other hand, electromagnetic (EM) nano-communication points to the Terahertz Band (0.1-10 THz) as the frequency range for communication among nano-biosensors. In this paper, we propose a stimuli-responsive paradigm which integrates EM and molecular communication by stimulating proteins in the human body. Our model capitalizes on the fact that proteins act as an interface between both mediums, in which triggering proteins by THz waves changes their conformational structure. This allows biochemical and biomechanical activities to be carried out in a controlled manner. The stochasticity involved in the folding and unfolding of proteins is modeled using a Markov chain. A closed form expression for the mutual information rate by which proteins receive information is derived and maximized to find the capacity. By illustrating the information rates theoretically achievable, we hope to spark research into the EM-based control of protein networks.

For a multi-cell, multi-user, cellular network downlink sum-rate maximization through power allocation is a nonconvex and NP-hard optimization problem. In this article, we present an effective approach to solving this problem through single- and multi-agent actor-critic deep reinforcement learning (DRL). Specifically, we use finite-horizon trust region optimization. Through extensive simulations, we show that we can simultaneously achieve higher spectral efficiency than state-of-the-art optimization algorithms like weighted minimum mean-squared error (WMMSE) and fractional programming (FP), while offering execution times more than two orders of magnitude faster than these approaches. Additionally, the proposed trust region methods demonstrate superior performance and convergence properties than the Advantage Actor-Critic (A2C) DRL algorithm. In contrast to prior approaches, the proposed decentralized DRL approaches allow for distributed optimization with limited CSI and controllable information exchange between BSs while offering competitive performance and reduced training times.

Optimizing the sum-log-utility for the downlink of multi-frequency band, multiuser, multiantenna networks requires joint solutions to the associated beamforming and user scheduling problems through the use of cloud radio access network (CRAN) architecture; optimizing such a network is, however, non-convex and NP-hard. In this paper, we present a novel iterative beamforming and scheduling strategy based on fractional programming and the Hungarian algorithm. The beamforming strategy allows us to iteratively maximize the chosen objective function in a fashion similar to block coordinate ascent. Furthermore, based on the crucial insight that, in the downlink, the interference pattern remains fixed for a given set of beamforming weights, we use the Hungarian algorithm as an efficient approach to optimally schedule users for the given set of beamforming weights. Specifically, this approach allows us to select the best subset of users (amongst the larger set of all available users). Our simulation results show that, in terms of average sum-log-utility, as well as sum-rate, the proposed scheme substantially outperforms both the state-of-the-art multicell weighted minimum mean-squared error (WMMSE) and greedy proportionally fair WMMSE schemes, as well as standard interior-point and sequential quadratic solvers. Importantly, our proposed scheme is also far more computationally efficient than the multicell WMMSE scheme.

This paper considers information-theoretic aspects of mechanosensitive based communication signals in living organisms such as plants and bacteria. Mechanosensitive ion channels function as mechanosensitive transducers, which generate an activation signal in response to a mechanical stimulus. This activation signal is necessary for performing different functions, such as the coordination of growth or determining a response to a stress. The activation signal works by releasing a number of protein molecules carrying information in the transmitter cell which is the input signal of the system. These protein molecules propagate to the receiver cells through a diffusion based medium and react with receptors to produce output protein molecules, forming a molecular communication system. In this paper we develop a communication system model for mechanosensitive systems, and study the mutual information and the information propagation speed associated with their signals.

This paper focuses on different properties of an electrochemical signal based model for inter-cellular communication in plants. The input signal in this communication system can be either composed of (a) a number of fast moving charged molecules or (b) randomly diffusing molecules, which are driven by an action potential (AP) signal. APs are usually generated by an external stimulus such as change in temperature or light. We use a model of AP generation in this paper from previous works. The three main contributions of this paper are: firstly we consider the impact of multiple AP signals on the mutual information of the system for multiple (varying) numbers of receiver cells in different configurations, second, we compare the impact of different propagation mechanisms (i.e. fast active movement and random diffusion of molecules) on the output molecules and mutual information of the system, and finally we study the impact of multiple APs on the information propagation speed of the system and compare it for the different receiver cell configurations.

This paper considers an electro-chemical signal based model for inter-cellular communication in plants. The input signal, composed of fast moving charged molecules is driven by an action potential (AP). APs, generated by an external stimulus, are part of the communication mechanism in plants. We extend the simple model for AP generation presented in previous work to incorporate the AP signal arriving from neighboring cells. Furthermore in this model we study the transfer of information between cells via fast moving ions. Unlike previous work, this paper does not consider diffusion but only reactions between molecules at each step. We then use an information-theoretic analysis to compute the mutual information between the input and output of this system. The key aim is to study the impact of an increase in population of cells on the mutual information. We calculate the mutual information for a large group of cells (up to 100) in three different topologies i.e., parallel, series and mixed. Finally we study the impact of a single AP on multiple cells in the system.

The availability of accurate channel state information (CSI) is essential in multiuser massive multiple-input multiple-output (MIMO) systems. However, most published works focus on frequency-flat channel estimation which requires that the estimation must be repeated for every frequency slot, e.g., subcarrier, in a broadband system. Since the channels in the frequency-domain are the Fourier transform of a small set of time-domain channel coefficients, the time-domain channel estimation requires that fewer parameters be estimated. In this paper, we propose a semi-blind, time-domain, channel estimation technique for frequency-selective massive MIMO systems. Our solution depends on the subspace spanned by the signal eigenvectors of the received signal covariance matrix. Importantly, since the receiver samples at the symbol rate, time-domain-based estimation inherently has available enough samples for an accurate matrix estimate. To avoid asymptotic assumptions, we express each channel vector as a linear combination of the signal eigenvectors. We estimate the linear combination using a set of training symbols. Given the many samples of the received signal in the time-domain, we obtain a better channel estimate compared to conventional subcarrier-wise frequency-domain channel estimation. Unlike the previous published results, in this paper, we do not assume orthogonality of users' channels or knowledge of large-scale fading coefficients. In addition, our estimation procedure does not require orthogonality between the training symbols of the users in all cells.

We consider scheduling and resource allocation in long-term evolution (LTE) networks across voice over the LTE (VoLTE) and best-effort data users. The difference between these two is that VoLTE users get scheduling priority to receive their required quality of service. As we show, strict priority causes data services to suffer. We propose new scheduling and resource allocation algorithms to maximize the sum or proportional fair (PF) throughout amongst data users while meeting VoLTE demands. Essentially, we use VoLTE as an example application with both a guaranteed bit-rate and strict application-specific requirement. We first formulate and solve the frame-level optimization problem for throughput maximization; however, this leads to an integer problem coupled across the LTE transmission time intervals (TTIs). We then propose a TTI-level problem to decouple scheduling across TTIs. Finally, we propose a heuristic scheme, with extremely low complexity. The formulations illustrate the details required to realize resource allocation in an implemented standard. The numerical results show that the performance of the TTI-level scheme is very close to that of the frame-level upper bound. Similarly, the heuristic scheme works well compared to TTI-level optimization and a baseline scheduling algorithm. Finally, we show that our PF optimization retains the high fairness index characterizing PF-scheduling.

This paper considers an electro-chemical based communication model for intercellular communication in plants. Many plants, such as Mimosa pudica (the "sensitive plant"), employ electrochemical signals known as action potentials (APs) for communication purposes. In this paper we present a simple model for action potential generation. We make use of the concepts from molecular communication to explain the underlying process of information transfer in a plant. Using the information-theoretic analysis, we compute the mutual information between the input and output in this work. The key aim is to study the variations in the information propagation speed for varying number of plant cells for one simple case. Furthermore we study the impact of the AP signal on the mutual information and information propagation speed. We further aim to explore the impact of increasing number of cells on the information propagation speed.

Channel state information (CSI) is essential in massive multiple-input mulitple-output (MIMO) systems. However, most of the literature focuses on frequency-flat channel estimation; in a realistic broadband setting this implies that the estimation must be repeated for each subcarrier. In this paper, we propose a new channel estimation technique for frequency-selective channels in the time-domain. Time-domain CSI acquisition requires the estimation of fewer parameters. Importantly, since the receiver samples at the symbol rate, time-domain based estimation yields many samples of the received signal. Our approach depends on the estimation of the subspace spanned by users' channels, requiring a large number of antennas to ensure that the required rank constraints are met. Unlike previous published results, in this paper, we do not make any assumption on orthogonality of users' channel vectors or knowledge of large-scale fading coefficients. Additionally, our channel estimation, does not require orthogonality between the training symbols of the users in all cells.

This paper develops a new beamforming method using focused frequency time reversal (FFTR) matrices as well as the time reversal-based MUltiple SIgnal Classification (MUSIC) algorithm to focus spatially on the location of possible tumor for microwaves-based breast cancer detection. A key feature of the proposed method is that there is no need for prior knowledge of the constitutive properties of the breast tissue for breast clutter suppression; sliding sub-aperture data processing is used to focus on tumor location. Results from our electromagnetic finite difference time domain (FDTD) simulations demonstrate the accuracy in estimating both the tumor location as well as in suppressing the breast clutter using time reversal.

A base-station (BS) equipped with multiple antennas can use its spatial dimensions in three different ways: 1) to serve multiple users, thereby achieving a multiplexing gain; 2) to provide spatial diversity in order to improve user rates; and 3) to null interference in neighboring cells. This paper answers the following question: What is the optimal balance between these three competing benefits? We answer this question in the context of the downlink of a cellular network, where multi-antenna BSs serve multiple single-antenna users using zero-forcing beamforming with equal power assignment, while nulling interference at a subset of out-of-cell users. Any remaining spatial dimensions provide transmit diversity for the scheduled users. Utilizing tools from stochastic geometry, we show that, surprisingly, to maximize the per-BS ergodic sum rate, with an optimal allocation of spatial resources, interference nulling does not provide a tangible benefit. The strategy of avoiding inter-cell interference nulling, reserving some fraction of spatial resources for multiplexing, and using the rest to provide diversity, is already close-to-optimal in terms of the sum-rate. However, interference nulling does bring significant benefit to cell-edge users, particularly when adopting a range-adaptive nulling strategy where the size of the cooperating BS cluster is increased for cell-edge users.

Hardware calibration is essential to restore the uplink/downlink channel reciprocity for multi-user massive multiple-input multiple-output (MIMO) systems operating in a time division duplexing mode. Unfortunately, due to the associated overhead, calibration cannot be performed frequently; furthermore, any calibration procedure leaves behind a residual mismatch between the uplink and downlink channels. In this paper, we study the effects of these calibration errors on the achievable rates in the downlink of a multi-cell, multi-user, and distributed massive MIMO system. Specifically, we develop accurate, yet simple, lower-bounds on the per-user achievable rate, assuming either zero-forcing (ZF) or matched filtering (MF) are used. We also introduce a performance loss coefficient as a measure of sensitivity of the performance to the calibration errors. Using this measure, we identify the conditions under which ZF precoding is more sensitive to calibration errors than MF. Finally, we consider the robust weighted sum-rate maximization problem to mitigate the degrading effects of non-ideal calibration. Our numerical experiments show that the rate lower-bounds developed in this paper accurately quantify the impacts of non-ideal calibration on performance. Also, the proposed robust beamforming scheme improves the average sum-rate by up to 42% compared with the other available schemes.

We propose algorithms for cloud radio access networks that not only provide heterogeneous quality of-service (QoS) for rate- and, importantly, delay-sensitive applications, but also jointly optimize the frequency reuse pattern. Importantly, unlike related works, we account for random arrivals, through queue awareness and, unlike the majority of works focusing on a single frame only, we consider QoS measures averaged over multiple frames involving a set of closed loop controls. We model this problem as multi-cell optimization to maximize a sum utility subject to the QoS constraints, expressed as minimum mean-rate or maximum mean-delay. Since we consider dynamic interference coordination jointly with dynamic user association, the problem is not convex, even after integer relaxation. We translate the problem into an optimization of frame rates, amenable to a decomposition into intertwined primal and dual problems. The solution to this optimization problem provides joint decisions on scheduling, dynamic interference coordination, and, importantly, unlike most works in this area, on dynamic user association. Additionally, we propose a novel method to manage infeasible loads. Extensive simulations confirm that the design responds to instantaneous loads, heterogeneous user and AP locations, channel conditions, and QoS constraints while, if required, keeping outage low when dealing with infeasible loads. Comparisons to the baseline proportional fair scheme illustrate the gains achieved.

The distributed power control algorithms available in this paper assume perfect estimation of interference power at the receivers and often, the availability of perfect local channel state information (CSI). However, perfect estimation of interference power or acquisition of channel information is impractical. In this paper, we consider a generic class of distributed power control algorithms and analyze the resulting performance loss when faced with errors in CSI and/or interference level estimation. We approximate such power control algorithms with linearized state space dynamics wherein the errors in the estimation of received interference or the CSI, can be modeled as additive observation noise. Based on the proposed model, we derive lower bounds on the norm of the errors in the transmit power vector and receive signal-to-interference-plus-noise ratio (SINR) vector. Our analysis suggests that when the target SINR vector approaches the Pareto-optimal bound, the errors in the transmit vectors could grow without bound. As a consequence, to maintain a performance loss within an acceptable range, either much longer estimation times or lower target SINRs are required. Our numerical results confirm the validity of our analysis and the validity of the proposed bounds.

Hardware calibration is a necessary procedure to establish the reciprocity of the uplink (UL) and downlink channels (DL) in a time division duplex (TDD) mode of operation. In practice, any calibration scheme is prone to errors. Such errors result in a residual mismatch between UL and DL channels, which, in turn, can significantly affect the quality of DL transmission. In this paper, we study the effects of the hardware calibration errors on the per-user achievable ergodic rate in the DL of a single cell multi-user massive MIMO system. We consider zero-forcing (ZF) precoding under two different scenarios: first we assume that the UL channel state information (CSI) is known perfectly at the BS; we then consider the case where CSI is acquired with minimum mean squared error (MMSE) estimation. We verify the accuracy of our analytical results through numerical simulations.

There are now several works on the use of the additive inverse Gaussian noise (AIGN) model for the random transit time in molecular communication (MC) channels. The randomness invariably causes inter- symbol interference (ISI) in MC, an issue largely ignored or simplified. In this paper we derive an upper bound and two lower bounds for MC based on amplitude shift keying (ASK) in presence of ISI. The Blahut-Arimoto algorithm (BAA) is modified to find the input distribution of transmitted symbols to maximize the lower bounds. Our results show that over wide parameter values the bound are close.

We analyze the uplink of coordinated multi-point (CoMP) networks in which cooperation can be amongst N = 2 or N = 3 base stations (BSs). We consider a 2-D network of BSs on a regular hexagonal lattice wherein the cooperation tessellates the 2-D plane into cooperation regions (CRs); specifically, we analyze the impact of the interference between the CRs in the network. Our model accounts realistic propagation conditions, particularly including shadowing. We obtain accurate, closed-form, approximations for the user capacity coverage probability (CCP) and the ergodic capacity at each point within the CR. To provide a network-level analysis, we focus on the locations within each CR with the minimum CCP - “the worst-case point(s)”. The worst-case CCP and/or ergodic capacity can be used in parametric studies for network design. Here, the analysis is applied to obtain the relationship between cell size and CCP and, thereby, the required density of BSs to achieve a chosen target capacity coverage. The analysis also allows for a comparison between different orders of BS cooperation, quantifying the reduced required BS density from higher orders of cooperation. Comprehensive simulations are used to illustrate the accuracy of our analysis, including the approximations used for analytic tractability.

Assuming a network of infinite extent, several researchers have analyzed small-cell networks using a Poisson point process (PPP) location model, leading to simple analytic expressions. The general assumption has been that these results apply to finite-area networks as well. However, do the results of infinite-area networks apply to finite-area networks? In this paper, we answer this question by obtaining an accurate approximation for the achievable signal-to-interference-plus-noise ratio (SINR) and user capacity in the downlink of a finite-area network with a fixed number of access points (APs). The APs are uniformly distributed within the area of interest. Our analysis shows that, crucially, the results of infinite-area networks are very different from those for finite-area networks of low-to-medium AP density. Comprehensive simulations are used to illustrate the accuracy of our analysis. For practical values of signal transmit powers and AP densities, the analytic expressions capture the behavior of the system well. As an added benefit, the formulations developed here can be used in parametric studies for network design. Here, the analysis is used to obtain the required number of APs to guarantee a desired target capacity in a finite-area network.

This paper analyzes the impact of user mobility in multi-tier heterogeneous networks. We begin by obtaining the handoff rate for a mobile user in an irregular cellular network with the access point locations modeled as a homogeneous Poisson point process. The received signal-to-interference-ratio (SIR) distribution along with a chosen SIR threshold is then used to obtain the probability of coverage. To capture potential connection failures due to mobility, we assume that a fraction of handoffs result in such failures. Considering a multi-tier network with orthogonal spectrum allocation among tiers and the maximum biased average received power as the tier association metric, we derive the probability of coverage for two cases: 1) the user is stationary (i.e., handoffs do not occur, or the system is not sensitive to handoffs); 2) the user is mobile, and the system is sensitive to handoffs. We derive the optimal bias factors to maximize the coverage. We show that when the user is mobile, and the network is sensitive to handoffs, both the optimum tier association and the probability of coverage depend on the user's speed; a speed-dependent bias factor can then adjust the tier association to effectively improve the coverage, and hence system performance, in a fully-loaded network.

In this paper, we examine the problem of throughput maximization in an energy-harvesting two-hop amplify-and-forward relay network. This problem is investigated over a finite time horizon and in an online setting, where the causal knowledge of the harvested energy and that of fading are available. We use Markov decision process (MDP) formulation to present a mathematically tractable solution to the throughput maximization problem. In this solution, optimal power-use policy is obtained using backward induction algorithm of the corresponding discrete dynamic programming problem. We also present properties of the optimal policy for an important special case, where the power control at transmitters is limited to on-off switching. These properties facilitate the implementation of the MDP based solution. Our numerical simulations show that the proposed method outperforms existing solutions to this problem.

We consider a coordinated multi-point (CoMP) uplink cellular network with a Poisson point process (PPP) model for the position of BSs. Our model assumes cooperation amongst two BSs and the required density is obtained under shadowing and Rayleigh fading for different LTE-A path loss models. We obtain accurate closed-form approximations for the worst-case rate coverage probability within the cooperation region. The approximations presented are useful for a rapid assessment of network performance and can be utilized in parametric studies for network design. Here, they are applied to obtain the required density of BSs to achieve a target rate coverage probability. As an added benefit, the formulation here quantifies the penalty in moving from a regular BS deployment (the grid model) to a random BS deployment (the PPP model).

This paper addresses the problem of throughput maximization in an energy-harvesting two-hop amplify-and-forward relay network. We obtain optimal policies for transmission power for two cases. First, we assume non-causal knowledge of the harvested energy and that of the fading channel states. Then, we assume that this information is known only causally. We propose an effective algorithm to solve the power-use problem in the non-causal (offline) case. For the causal (online) case, we cast the problem as a Markov decision process (MDP) and solve the resulting optimization problem using only causal knowledge of the fading and the harvested energy. This MDP approach yields good performance, but at the cost of computational complexity. To address this issue, we consider the case where the power control at the transmitting nodes is limited to on-off switching. We derive interesting properties for the optimal solutions to the MDP formulation for this special case. Furthermore, using these properties, we propose a computationally simple power allocation scheme. The performances of the proposed schemes are evaluated using computer simulations and are compared to existing methods which address the same problem.

We propose a four-stage hierarchical resource allocation scheme for the downlink of a large-scale small-cell network in the context of orthogonal frequency-division multiple access (OFDMA). Since interference limits the capabilities of such networks, resource allocation and interference management are crucial. However, obtaining the globally optimum resource allocation is exponentially complex and mathematically intractable. Here, we develop a partially decentralized algorithm to obtain an effective solution. The three major advantages of our work are as follows: 1) as opposed to a fixed resource allocation, we consider load demand at each access point (AP) when allocating spectrum; 2) to prevent overloaded APs, our scheme is dynamic in the sense that as the users move from one AP to the other, so do the allocated resources, if necessary, and such considerations generally result in huge computational complexity, which brings us to the third advantage: 3) we tackle complexity by introducing a hierarchical scheme comprising four phases: user association, load estimation, interference management via graph coloring, and scheduling. We provide mathematical analysis for the first three steps modeling the user and AP locations as Poisson point processes. Finally, we provide the results of numerical simulations to illustrate the efficacy of our scheme.

In this paper we investigate the benefit of base station (BS) cooperation in the uplink of coordinated multi-point (CoMP) networks. Our figure of merit is the required BS density required to meet a chosen rate coverage. Our model assumes a 2-D network of BSs on a regular hexagonal lattice in which path loss, lognormal shadowing and Rayleigh fading affect the signal received from users. Accurate closed-form expressions are first presented for the sum-rate coverage probability and ergodic sum-rate at each point of the cooperation region. Then, for a chosen quality of user rate, the required density of BS is derived based on the minimum value of rate coverage probability in the cooperation region. The approach guarantees that the achievable rate in the entire coverage region is above a target rate with chosen probability. The formulation allows comparison between different orders of BS cooperation, quantifying the reduced required BS density from higher orders of cooperation.

Employing centralized resource management schemes is generally infeasible in large-scale networks. The deployment of heterogeneous Femtocell Access Points (FAPs) over the cellular licensed spectrum is therefore challenging. In particular, the resulting inter-node interference inhibits the network performance. In this paper, we design a hierarchical, distributed, interference management scheme that exploits the benefits of clustering. First, in order to reduce the cross-tier interference, each FAP independently identifies vacant subbands for potential transmission. Then, by exchanging some simple messages with its immediate neighbors in an iterative fashion, coalition clusters are formed. Given the small population of each group, centralized resource management is subsequently performed to avoid intra-cluster interference. Different clusters, however, may still share a fraction of common idle channels, which degrades system performance. Therefore, this paper further considers inter-cluster interference management to determine the set of privileged FAPs that can share a subband via solving a binary power control optimization problem. While the optimal solution requires prohibitive complexity, this paper provides tight bounds on the sum rate of the binary power control problem. The simulation results show that, in a high interference regime, inter-cluster coordination provides a significant performance improvement compared to the case of no coordination.

In femtocell networks, large numbers of femtocell access points (FAPs) are deployed and integrated into a cellular network. In this paper, a novel architecture for femtocell networks is proposed, known as fractional cooperation. Using this method, connections from mobile users to multiple FAPs are permitted. This architecture is analogous to fractional cooperation in wireless relay networks. Analytical and simulation results are presented, which indicate that large gains are possible over conventional cellular-style architectures.

Over the last decade, the development of resource allocation algorithms for cooperative communication networks has focused on optimal, or acceptable suboptimal, solutions assuming full knowledge of channel state information (CSI) in the network. This assumption is, unfortunately, unlikely to be satisfied in practice. Here we turn our attention to the effects of limited CSI on the performance of a cooperative network. We show that the performance gap between the fully centralized approaches and fully distributed algorithms falls exponentially with the number of CSI bits per link available at the central node. We use this to show that, for a large class of objective functions, a close-to-optimal solution is achievable with limited CSI. We also show that proper allocation of the CSI bits to different links in the network is a crucial issue. We propose a simple upper bound on the performance gap and a bit allocation algorithm that minimizes the upper bound. Our numerical results confirm that through optimal bit allocation considerable savings in CSI bits is achieved.

This paper presents a hierarchical approach to resource allocation in open-access femtocell networks. The major challenge in femtocell networks is interference management which in our system, based on the Long Term Evolution (LTE) standard, translates to which user should be allocated which physical resource block (or fraction thereof) from which femtocell access point (FAP). The globally optimal solution requires integer programming and is mathematically intractable. We propose a hierarchical three-stage solution: first, the load of each FAP is estimated considering the number of users connected to the FAP, their average channel gain and required data rates. Second, based on each FAP's load, the physical resource blocks (PRBs) are allocated to FAPs in a manner that minimizes the interference by coloring the modified interference graph. Finally, the resource allocation is performed at each FAP considering users' instantaneous channel gain. The two major advantages of this suboptimal approach are the significantly reduced computation complexity and the fact that the proposed algorithm only uses information that is already likely to be available at the nodes executing the relevant optimization step. The performance of the proposed solution is evaluated in networks based on the LTE standard.

In molecular communication, messages are conveyed in patterns of particles (e.g., arranged in time), which propagate from transmitter to receiver by means of Brownian motion. If there is drift from transmitter to receiver, the first arrival time of the particles has the Inverse Gaussian distribution, leading to the additive inverse Gaussian noise channel. In this paper, we give a closed-form upper bound on capacity for this channel when the maximum waiting time is constrained, building on previous work in which only the mean waiting time was constrained.

In this paper, we consider molecular communication, with information conveyed in the time of release of molecules. These molecules propagate to the transmitter through a fluid medium, propelled by a positive drift velocity and Brownian motion. The main contribution of this paper is the development of a theoretical foundation for such a communication system; specifically, the additive inverse Gaussian noise (AIGN) channel model. In such a channel, the information is corrupted by noise that follows an IG distribution. We show that such a channel model is appropriate for molecular communication in fluid media. Taking advantage of the available literature on the IG distribution, upper and lower bounds on channel capacity are developed, and a maximum likelihood receiver is derived. Results are presented which suggest that this channel does not have a single quality measure analogous to signal-to-noise ratio in the additive white Gaussian noise channel. It is also shown that the use of multiple molecules leads to reduced error rate in a manner akin to diversity order in wireless communications. Finally, some open problems are discussed that arise from the IG channel model.

This paper uses generalized singular value decomposition (GSVD) to reduce decoding complexity in an amplify-andforward, multiple-input multiple-output, two-way relay channel (TWRC). The key is diagonalization of the effective channels at the source nodes; this formulation also simplifies the optimization problem of designing linear transceivers at each node to maximize the achievable sum rate (ASR). For the given diagonalized structure, we first align the entries of the diagonalized channels using a permutation followed by power allocation, provided in closed form. Simulation results demonstrate that the proposed GSVD-based relaying scheme, with the signal alignment and power allocation, significantly improves the ASR while retaining the diagonalized channel structure.

This paper considers a multi-source orthogonal frequency division multiplexing (OFDM)-based network of access points wherein dedicated relays use the decode-and-forward relaying. We investigate the joint problem of transmission strategy selection (relaying v/s direct), relay assignment, and power allocation to maximize the minimum rate across sources in two different cooperative scenarios: subcarrier-based and block-based relaying. In the subcarrier-based scheme, each subcarrier is treated as an independent transmission; however, in addition to the synchronization problems caused, it is likely impractical for a relay to decode a subset of subcarriers. Thus, we study relay selection for the entire OFDM block. The key difference from previous work is that we consider resource allocation across source-relay, relay-destination, and source-destination channels. Second, a simple, distributed, block-based scheme is proposed. Simulation results using the COST-231 channel model reveals that the performance of this heuristic scheme tracks that of the optimum block-based scheme while it significantly decreases the required computational complexity.

In the context of a time-varying multiuser multiple-input-multiple-output (MIMO) system, we design recursive least squares based adaptive predictors and differential quantizers to minimize the sum mean squared error of the overall system. Using the fact that the scalar entries of the left singular matrix of a Gaussian MIMO channel becomes “almost” Gaussian distributed even for a small number of transmit antennas, we perform adaptive differential quantization of the relevant singular matrix entries. Compared to the algorithms in the existing differential feedback literature, our proposed quantizer provides three advantages: first, the controller parameters are flexible enough to adapt themselves to different vehicle speeds; second, the model is backward adaptive i.e., the base station and receiver can agree upon the predictor and variance estimator coefficients without explicit exchange of the parameters; third, it can accurately model the system even when the correlation between two successive channel samples becomes as low as 0.05. Our simulation results show that our proposed method can reduce the required feedback by several kilobits per second for vehicle speeds up to 20 km/h (channel tracker) and 10 km/h (singular vector tracker). The proposed system also outperforms a fixed quantizer, with same feedback overhead, in terms of bit error rate up to 30 km/h.

In this paper, we examine the coverage gains made possible by a macrocell network assisted by hybrid-access femtocells; the analysis is in the context of the LTE-standard. The interference between the macrocell and femtocell is completely eliminated using orthogonal spectrum allocation. The interference between femtocells is almost negligible due to random spectrum allocation to femtocells as well as low transmit power of femtocell base stations. Proposed designs and analysis of femtocells have, so far, focused on closed-access, i.e., only the femtocell owner can use the access point. Our goal here is to illustrate the large performance gains made possible by hybrid-access policies that share resources between the owner and outdoor opportunistic users. Our analysis is in context of the LTE standard.

We consider the problem of optimizing the allocation of available energy across training and data symbols under linear precoding in multiuser downlink systems with imperfect channel state information (CSI). Our figure of merit is the sum rate across all users. This paper derives a lower bound on achievable rate under linear precoding and extends existing precoder designs to the case of imperfect CSI. Optimality and separability results for the energy allocation and precoder design problems that were found previously for sum-MSE minimization are extended to the problem of sum-rate maximization when channels are modelled using uncorrelated Rayleigh block fading with equal variances of the fading coefficients. Simulation results suggest significant improvements in achievable rate under the proposed algorithm.

The authors investigate quantisation and feedback of channel state information in a multiuser (MU) multiple-input multiple-output (MIMO) system. Each user may receive multiple data streams. The authors design minimises the sum mean squared error (SMSE) while accounting for the imperfections in channel state information (CSI) at the transmitter. This study makes three contributions: first, the authors provide an end-to-end SMSE transceiver design that incorporates receiver combining, feedback policy and transmit precoder design with channel uncertainty. This enables the proposed transceiver to outperform the previously derived limited feedback MU linear transceivers. Second, the authors remove dimensionality constraints on the MIMO system, for the scenario with multiple data streams per user, using a combination of maximum expected signal combining and minimum MSE receiver. This makes each user's feedback independent of the others and the resulting feedback overhead scales linearly with the number of data streams instead of the number of receiving antennas. Finally, the authors analyse the SMSE of the proposed algorithm at high signal-to-noise ratio (SNR) and large number of transmit antennas. As an aside, the authors show analytically why the bit error rate, in the high SNR regime, increases if quantisation error is ignored.

In the multiuser downlink, power allocation for linear precoders that minimize the sum of mean squared errors under a sum power constraint is a non-convex problem. Many existing algorithms solve an equivalent convex problem in the virtual uplink and apply a transformation based on uplink-downlink duality to find a downlink solution. In this letter, we analyze the optimality criteria for the power allocation subproblem in the virtual uplink, and demonstrate that the optimal solution leads to identical power allocations in the downlink and virtual uplink. We thus extend the known duality results and, importantly, simplify the existing algorithms used for iterative transceiver design.

The maximum multiplexing gain of orthogonal relaying protocols has, so far, been limited to 1/2. We propose a new orthogonal decode-and-forward protocol that employs rotated n-dimensional constellations. With a single relay, the proposed protocol achieves a linear diversity-multiplexing tradeoff (DMT) connecting the points (0,2) and (n/n+1,0), where n+1 is the frame length. With NRrelays, our proposed protocol achieves a linear DMT connecting (0, NR+1) and (n/n+NR,0).

We design an end-to-end linear transceiver in the downlink of a multi-user multiple-input multiple-output (MIMO) system with multiple data streams per user and quantized channel state information at the transmitter. We minimize the sum mean squared error (SMSE) under a sum power constraint with quantization based on the mean squared inner product. We make three contributions: (i) we remove dimensionality constraints on the MIMO configuration and the resulting feedback overhead scales linearly with the number of data streams; (ii) we use the combination of eigenmode combining and minimum mean square error receiver that makes user's feedback mutually independent; (iii) we analyze SMSE at high signal-to-noise ratio and large number of transmit antennas and derive an approximate SMSE floor.

We consider a multi-source mesh network of static access points wherein sources use decode-and-forward to cooperate with each other. All transmissions use orthogonal frequency division multiplexing (OFDM). Our objective is to maximize the minimum achievable rate across all flows. We find a tight upper bound on the performance of the subcarrier-based cooperation and show that selecting a single relay for each subcarrier is optimal for almost all subcarriers. The solution to the related optimization problem simultaneously solves the relay, power, and subcarrier assignment problems. Second, unlike previous works, we also consider relay selection for the entire OFDM block. This addresses the fact that, in addition to the synchronization problems caused, it is likely impractical for a relay to only decode a subset of subcarriers. We propose three selection-based cooperation schemes to relay the entire OFDM block with varying complexity. Simulation results show that under the COST-231 channel model, the performance of the simplest scheme almost exactly tracks that of an exhaustive search.

We consider a half-duplex mesh network wherein a single source communicates to a destination with the help of N potential decode-and-forward relays. We develop the optimal selection of a relaying subset and allocation of transmission time. This resource allocation is found by maximizing over the rates achievable for each possible subset of active relays; in turn, the optimal time allocation for each subset is obtained by solving a linear system of equations. An assumed relay numbering imposes a causality constraint. We also present a recursive algorithm to solve the optimization problem which reduces the computational load of finding the required matrix inverses and the number of required iterations. We show that (i) optimizing transmission time significantly improves achievable rate; (ii) optimizing over the channel resources ensures that more relays are active over a larger range of signal-to-noise ratios; (iii) linear network constellations significantly outperform grid constellations; (iv) the achievable rate is robust to node ordering.

We consider a system with a single base station communicating with multiple users over orthogonal channels while being assisted by multiple relays. Several recent works have suggested that, in such a scenario, selection, i.e., a single relay helping the source, is the best relaying option in terms of the resulting complexity and overhead. However, in a multiuser setting, optimal relay assignment is a combinatorial problem. In this paper, we formulate a related convex optimization problem that provides an extremely tight upper bound on performance and show that selection is, almost always, inherent in the solution. We also provide a heuristic to find a close-to-optimal relay assignment and power allocation across users supported by a single relay. Simulation results using realistic channel models demonstrate the efficacy of the proposed schemes, but also raise the question as to whether the gains from relaying are worth the additional costs.

We present the optimal relay-subset selection and transmission-time for a decode-and-forward, half-duplex cooperative network of arbitrary size. The resource allocation is obtained by maximizing over the rates obtained for each possible subset of active relays, and the unique time allocation for each set can be obtained by solving a linear system of equations. We also give a recursive algorithm which reduces the number of operations as well as the computational load of finding the required matrix inverses. Our results, in terms of outage rate, confirm the benefit of adding potential relays to a small network and the diminishing marginal returns for a larger network. Furthermore, optimizing over the channel resources ensures that more relays are active over a larger SNR range.

Linear precoding for multiuser multiple input multiple output (MIMO) cellular systems has generally focused on a single isolated cell. A crucial tool in algorithm development has been a downlink/uplink duality. Here we investigate downlink multiuser communications across multiple cells. Previous work identified the resulting asynchronous interference as a key issue. We prove the existence of a downlink/uplink duality requiring time reversal. This duality is used to develop an effective linear precoding algorithm for multiuser, multi-cell, MIMO systems. Simulation results illustrate the importance of accounting for, and to quantify the loss due to, asynchronous interference.

In fractional cooperation, many relays simultaneously assist the source, and each relay is responsible to relay only a fraction of the source transmission. In this paper, the problem of fractional cooperation is considered in the presence of multiple sources and multiple relays. In particular, optimization problems are formulated that can be used to allocate the relay resources between multiple sources to either minimize the energy consumed to achieve a given probability of error threshold, or minimize the maximum probability of error experienced by each source node.

We consider a system with a single base station communicating with multiple users over orthogonal channels while being assisted by multiple relays. Several recent works have suggested that selection, i.e., a single relay helping the source, is the best option in terms of the resulting complexity and overhead. However, in a multiuser setting, optimal relay assignment is a combinatorial problem. In this paper, using the sum rate as our design metric, we develop a convex optimization problem that provides an extremely tight upper bound on performance. We also provide a heuristic to find a close-to-optimal relay assignment. Simulation results using realistic channel models demonstrate the efficacy of the proposed scheme.

Communication between a transmitter and a receiver using electromagnetic waves does not scale to nano-sizes. To enable communication between nano-sized devices separated by a short distance, molecular communication has recently been proposed as a feasible scheme. The transmitter disperses molecules into the medium, which propagate to, and are sensed by, the receiver. In this paper, we wish to mathematically model such a system and subsequently characterize the information theoretic capacity of this channel. We present basic results on characterizing the mutual information between the transmitter and the receiver when information is encoded in the time of release of the molecule. To do so, we model the propagation of the molecule in this medium as Brownian motion, and derive the probability density function of the arrival time of the molecule at the receiver.

In fractional cooperation, each available relay node selects a small fraction of the source's transmission to be relayed. In previous work, it was assumed that every node relayed the same number of source symbols, and large diversity order gains were observed. In this paper, a theoretical basis is developed for irregular fractional cooperation, in which each node relays a different number of symbols. A general expression of the system diversity order is derived. A bound is introduced on the system performance of fractional cooperation, known as the erasure channel bound. Distributions of diversity order using this bound are given when the fraction relayed by each user is random.

In this paper, we investigate the performance of a single-relay cooperative scenario where the source, relay, and destination terminals are equipped with multiple transmit/receive antennas. We particularly focus on the so-called blind amplify- and-forward relaying in which the availability of channel state information at the relay terminal is not required. Through the derivation of pairwise error probability, we quantify analytically the impact of multiple antenna deployment assuming various scenarios which involve relay location and power allocation assumptions imposed on the cooperating nodes.

We consider linear precoding and decoding in the downlink of a multiuser multiple-input, multiple-output (MIMO) system, wherein each user may receive more than one data stream. We propose several mean squared error (MSE) based criteria for joint transmit-receive optimization and establish a series of relationships linking these criteria to the signal-to-interference-plus-noise ratios of individual data streams and the information theoretic channel capacity under linear minimum MSE decoding. In particular, we show that achieving the maximum sum throughput is equivalent to minimizing the product of MSE matrix determinants (PDetMSE). Since the PDetMSE minimization problem does not admit a computationally efficient solution, a simplified scalar version of the problem is considered that minimizes the product of mean squared errors (PMSE). An iterative algorithm is proposed to solve the PMSE problem, and is shown to provide near-optimal performance with greatly reduced computational complexity. Our simulations compare the achievable sum rates under linear precoding strategies to the sum capacity for the broadcast channel.

A simplified method of analysis and design based on the Bhattacharyya parameter (BP) in conjunction with the union bound and weight enumeration is presented for relay channels using coded cooperation. This method is particularly suitable for low-complexity relay systems employing demodulate-and-forward, focusing on the problems of relay selection and outage analysis. These applications are chosen to illustrate the use of the BP in scenarios where analytical solutions are otherwise unattainable. In terms of relay selection, it is shown that BP-based relay selection has essentially the same performance as density evolution, though with much lower complexity. It is further shown that BP-based relay selection can be applied to fractional cooperation, where each relay only forwards a fraction of the source codeword. In terms of analysis, it is shown that weight enumeration with BP can be used to provide a close approximate to the upper bound on the outage probability of fractional cooperation, again with much lower computational complexity than density evolution.

In this letter, we investigate the effect of imperfect channel estimation on the performance of distributed space-time block codes (DSTBCs) with amplify-and-forward relaying. Exploiting the orthogonality of the underlying code, we derive a maximum likelihood metric conditioned on the channel estimate acquired through the insertion of pilot symbols. For a large number of pilot symbols, we demonstrate that the proposed decoding rule coincides with the so-called mismatched receiver. On the other hand, as the number of pilot symbols decreases, the proposed decoder converges to a non-coherent detector. Through Monte-Carlo simulations, we further demonstrate that the performance of the proposed scheme lies within 0.8 dB of the genie receiver performance bound.

Relay systems have large and complex parameter spaces, which makes it difficult to determine the parameter region where the system achieves a given performance criterion, such as probability of frame error. In this paper, we show that the union bound (UB) and the Bhattacharyya parameter (BP) can be used for fast analysis of the parameter space when error-control coding is used. This is applicable when amplify-and-forward (AF) or demodulate-and-forward (DemF) are used. For a given code ensemble, the associated UB threshold is found and can be used to define the signal-to-noise region where a given frame error rate can be achieved. Using asymptotic results, the UB threshold can be used to specify the signal-to-noise ratio region where successful decoding can be achieved for large blocklength. In addition, the UB with BP can be used when fractional cooperation is used, where each relay only relays a fraction of the source codeword. This makes the UB with BP a valuable tool in the system design of relay networks.

Demodulate-and-forward (DmF) is an attractive approach when using cooperative diversity schemes in networks where only nodes with strict complexity constraints are allowed, such as sensor networks. In using DmF, the relay only demodulates, but does not decode, the received signal from the source node. Coding can be used at the relay to improve the performance over the relay-destination link. In unrelated work, relay selection has been shown to achieve full diversity order with low overhead by choosing the best relay node out of a pool of available relays to assist the source. A simple heuristic scheme for relay selection while using DmF is available, but this involves the exchange of channel parameters between the nodes, hence increasing the overhead. In this paper, we propose the use of the Bhattacharyya parameter (BP) to facilitate relay selection. The use of BP has the distinct advantage of incorporating the specific coding scheme used while retaining low computation load. As illustrated in our simulation results, the use of BP provides frame error rates quite similar to that obtained from exhaustive search. We should note that this BP-based relay selection scheme can also be applied to cooperation schemes where decoding is performed at the relay.

Recent work has developed single-carrier linear precoding for the multiuser downlink for multiple-input multiple-output (MIMO) systems. This paper addresses the problem of user selection in orthogonal frequency division multiplexing (OFDM) based MIMO systems using such multiuser precoding. We propose an effective scheme to assign multiple users, chosen from a larger set, to individual subcarriers. The scheme is developed in the context of a previously proposed single-carrier MIMO linear precoding scheme to minimize the sum mean squared error (SMSE) over multiple users. We further simplify the implementation of the proposed algorithm exploiting the inherent channel correlation between adjacent OFDM subcarriers. Simulations show that the proposed user assignment algorithm exhibits near optimal performance and that reducing the computational load causes minimal performance loss.

We consider the problem of beamforming codebook design for limited feedback half-duplex multiple-input multiple output (MIMO) amplify-and-forward (AF) relay system. In the first part of the paper, the direct link between the source and the destination is ignored. Assuming perfect channel state information (CSI), we show that the source and the relay should map their signals to the dominant right singular vectors of the source-relay and relay-destination channels. For the limited feedback scenario, we prove the appropriateness of Grassmannian codebooks as the source and relay beamforming codebooks based on the distributions of the optimal source and relay beamforming vectors. In the second part of the paper, the direct link is considered in the problem model. Assuming perfect CSI, we derive the optimization problem that identifies the optimal source beamforming vector and show that the solution to this problem is uniformly distributed on the unit sphere for independent and identically distributed (i.i.d) Rayleigh channels. For the limited feedback scenario, we justify the appropriateness of Grassmannian codebooks for quantizing the optimal source beamforming vector based on its distribution. Finally, a modified quantization scheme is presented, which introduces a negligible penalty in the system performance but significantly reduces the required number of feedback bits.

In a cooperative network with multiple potential relays and multiple simultaneous transmissions, we present selection cooperation wherein each source pairs with a single "best" relay. We analyze the outage probability of a simple and completely distributed selection scheme, requiring some feedback but no centralization, and show that it outperforms distributed space-time codes for networks with more than three relaying nodes. These gains are due to the more efficient use of power in networks using selection. We suggest two other more complex selection schemes based on increasing system intelligence and centralization, and show that for smaller network sizes their performance improvement over the simple selection scheme is not significant.

Wireless networks, and especially wireless sensor networks, have complexity and energy constraints, within which they must confront the challenging wireless fading environment. In this paper, fractional cooperation is introduced, which is shown to provide energy-efficient and low-complexity diversity gains for constant energy costs per bit throughout the network. To minimize complexity, cooperation is based on demodulate- and-forward, wherein the relay nodes encode demodulated, not decoded symbols. A scheme is presented for cooperative error- control coding in complexity-constrained networks, using low- density generator-matrix codes and repeat-accumulate codes, both chosen for being simple to encode, as well as for their easily adaptable rates. It is shown that these codes, coupled with fractional cooperation, are robust to system parameters and conditions, and introduce little added complexity at the receiver, while providing excellent performance.

A relay selection approach has previously been shown to outperform repetition-based scheduling for both amplify-and-forward (AF) and decode-and-forward (DF) cooperative networks. The selection method generally requires some feedback from the destination to the relays and the source, raising the issue of the interplay between performance and feedback rate. In this letter, we treat selection as an instance of limited feedback distributed beamforming in cooperative AF networks, and highlight the differences between transmit beamforming in a traditional multi-input single-output (MISO) system and the distributed case. Specifically, Grassmannian line packing (GLP) is no longer the optimal codebook design, and orthogonal codebooks are no longer equivalent to each other. We derive the high signal-to-noise ratio expressions for outage probability and probability of symbol error for unlimited-feedback and selection schemes, which are then used for performance comparisons. The selection protocol is compared to a limited-feedback distributed beamformer that assigns codebooks based on the Generalized Lloyd algorithm (GLA), and one that uses random beam-vectors. The main conclusion is that the performance improvement to be seen using the very complex GLA is small, and that many more feedback bits are required with random beamforming than selection for the same performance. These results indicate that the selection protocol is a very attractive protocol, with low complexity, that provides excellent performance relative to other known methods.

This paper explores relay selection and selection diversity for coded cooperation in wireless sensor networks, with complexity constraints for the sensor nodes. In previous work, a relaying scheme based on repeat-accumulate (RA) codes was introduced, where it was assumed that the relay does not perform decoding and simply uses demodulated bits to form codewords. However, in a network setting with multiple potential relays where relays do not decode the source transmission, it is not obvious how to select the best relay. The optimal choice involves finding the best relay possibly using density evolution, but is quite complex and time-consuming. It is shown here that the mutual information of the equivalent relay channel, which is much simpler than using DE, is a good selection heuristic. With surprisingly poor performance when a naive selection scheme is used, the importance of a good relay selection scheme is emphasized.

Since the introduction of cooperative diversity, many different implementations have been proposed to increase the reliability and/or power efficiency of distributed networks via relaying. One simple and flexible scheme introduced is coded demodulate-and-forward, where the relay only demodulates, instead of decodes, the received data, to create and forward a new codeword to the destination. This reduces the complexity of hardware as well as the energy consumption by the relay. In this paper, we consider another flexible feature of the coded demodulate-and-forward scheme, where the relay uses only a fraction of its codeword to assist the source, and saves the rest of the codeword for transmitting its own information. Previous schemes have generally focused on all-or-nothing cooperation where a relay either contributes all its resources or none at all to the source. Depending on the channel conditions, improved diversity order of the source codeword can be achieved with some small loss in the relay's own transmission performance. Here we identify the necessary criterion for the source to achieve a diversity order of 2.

A relay selection approach has previously been shown to outperform repetition-based scheduling for both amplify-and-forward (AF) and decode-and-forward (DF) cooperative networks. The selection method generally requires some feedback from the destination to the relays and the source, raising the issue of the interplay between performance and feedback rate. In this paper, we treat selection as an instance of limited- feedback distributed beamforming in cooperative AF networks, and highlight the differences between transmit beamforming in a traditional multi-input single-output (MISO) system and the distributed case. Specifically, Grassmanian line packing (GLP) is no longer the optimal codebook design, and orthogonal codebooks are no longer equivalent to each other. We derive the high signal-to-noise ratio expressions for outage probability and probability of symbol error for unlimited-feedback and selection schemes. The gap in performance between unlimited-feedback and selection beamforming is found analytically to grow rapidly with the number of relays. We compare the selection protocol to a limited-feedback distributed beamformer that assigns codebooks based on the generalized lloyd algorithm (GLA), and one that uses random beam-vectors. The main conclusion is that the performance improvement to be seen using the very complex GLA is small, and that many more feedback bits are required with random beamforming than selection for the same performance. These results indicate that the selection protocol is a very attractive protocol with low-complexity that provides excellent performance relative to other known methods.

We study the cross-layer problem of combining routing and cooperative diversity in multi-hop, bandwidth- constrained networks with dedicated multiple access. Previous work in cooperative diversity nearly always assumes cooperation to be a positive. We show that in a large scale multi-hop network, cooperation must only be used selectively. Our figure of merit is achievable data rate between a source and destination at a fixed probability of outage. We show that enforcing multiple hops is detrimental to performance, since each extra hop requires bandwidth expansion. This performance can be significantly improved by incorporating a selective cooperative diversity scheme on a one-hop link. On the other hand, the simulation results show that cooperative diversity does not improve performance over a dynamic routing protocol which searches for the optimal, non-diversity, route. Including the search for cooperative nodes into the dynamic route search, however, does further increase flow rates by decreasing the average number of hops and thus decreasing the required bandwidth expansion. This paper therefore points to the importance of an integrated approach to routing and the physical layer in cooperative networks.

This paper develops linear preceding schemes for the downlink in multiuser multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems with multiple data streams per user. We extend an existing multiuser MIMO algorithm, that jointly optimizes the power allocation and the transmit and receive filters, to MIMO- OFDM systems. One extension is to solve the resulting problem of joint power allocation across OFDM subcarriers. This paper also presents efficient methods to reduce the computational load of the algorithm by interpolating the precoding and decoding matrices corresponding to different OFDM subcarriers. The simulations show that the proposed interpolation scheme outperforms previously known schemes, but requires that the precoder for each subcarrier be tailored to the interpolated receiver.

Abstract — We analyse the characteristics of the Non-Coherent (NC) Multiple Transmit/Multiple Receive (MTMR) antenna aided Multi-Carrier (MC) DS-CDMA downlink employing a serial search based acquisition scheme, when communicating over spatially uncorrelated Rayleigh channels. The associated Mean Acquisition Time (MAT) performance trends are characterised as a function of both the number of antennas and that of the number of subcarriers. It is shown that the employment of both multiple transmit antennas and multiple subcarriers is typically detrimental in terms of the achievable NC acquisition performance, while that obtained by exploiting multiple receive antennas is always beneficial, regardless whether single-path or multi-path scenarios are considered. Based on our results justified by information theoretic considerations, our acquisition design guidelines are applicable to diverse NC MTMR antenna aided scenarios. Index Terms — MC-DS-CDMA, non-coherent, transmit/receive/ frequency diversity.

Given limitations with current technology, nodes in a sensor network have stringent energy and complexity constraints. This paper presents a scheme for cooperative error-control coding, using rateless and low-density generator-matrix codes, for sensor networks. Assuming knowledge of the source-relay channel quality, we show that the proposed scheme achieves good performance and a good energy tradeoff despite low computational complexity. The scheme exploits the flexibility of rateless and LDGM codes to permit, depending on the channel conditions, independent, relay and cooperative modes of operation. As a motivating example, we analyze networks of two cooperating nodes communicating with a more sophisticated receiver. We also discuss the generalization of our framework to a multi-node system.

In this paper we solve the problem of linear precoding for the downlink in multiuser multiple-input multiple-output (MIMO) systems. The transmitter and the receivers may be equipped with multiple antennas and each user may receive multiple data streams. Our objective is to jointly optimize the power allocation and transmit-receive filters for all users. We develop the optimization for two different criteria: (1) minimizing the total transmitted power while satisfying SINR constraints and (2) minimizing the sum mean squared error given a total power budget. We take advantage of the duality between the uplink and downlink to derive the solution.

This paper addresses the issue of stimulating cooperative diversity, using the amplify-and-forward protocol, among selfish nodes in commercial wireless ad hoc networks. For the relay, cooperation represents both a real cost of energy expenditure and an opportunity cost of possible delays for its own data. Since nodes are selfish, we propose a pricing game that stimulates cooperation via reimbursements to the relay. Specifically, given the price per channel use, the source and relay interact through reimbursement prices, transmitter power control and forwarding/protocol preferences such that their utilities are maximized. Our pricing game is shown to converge to a Nash equilibrium where cooperative diversity is induced at intuitively reasonable network geometries.

We consider an amplify-and-forward (AF) cooperative diversity system where a source communicates with a destination with the help of multiple relay nodes. The conventional system assumes all relay nodes participate, with the available channel and power resources equally distributed over all nodes. This approach being clearly sub-optimal, we first present an optimal power allocation scheme to minimize the outage probability for an AF system. Next, we propose a new selection scheme where only one, the "best" relay node is chosen to participate in the transmission. We show that at reasonable power levels the selection AF scheme maintains full diversity order, and has significantly better outage behavior and average throughput than the conventional scheme or that with optimal power allocation

Cooperative diversity schemes have been introduced in earlier works to achieve diversity in a block fading channel. However, most of these schemes ignore the quality of the source-relay (S-R) channel in the decoding process, even though it is this channel that limits the performance of cooperation schemes. This paper introduces a simple yet robust scheme for cooperative diversity based on message passing in the decoding process, which accounts for the quality of the S-R channel. In our scheme, the relay decodes the source symbols and forms parity bits, which are in turn used by the destination (D) to decode the source message. By accounting for the reliability of the parity bits, performance measures, such as bit error rate, are not limited by the S-R channel quality and improve with increasing signal-to-noise ratio on the S-D and R-D channels. With our scheme, feedback signals to the source node are not required, with only simple decoding and encoding required at the relay.

We propose here an analytical framework to quantify the impact of cooperative diversity on the energy consumption and lifetime of sensor networks. It is well accepted that cooperative diversity increases energy efficiency in fading environments. However, previous works have not analyzed, from a theoretical perspective, these benefits in a network setting. This paper presents a theoretical framework to model routing behavior and cooperative relay selection, using this information to predict the lifetime and energy consumption of the network

The paper presents a closed-form blind channel estimation scheme for Alamouti's and other orthogonal space-time block codes. Unlike other blind algorithms, the scheme is able to estimate the channels, to within a phase constant, in multiple-input single-output systems, i.e., systems that employ only one receive antenna. The channel matrix is estimated from the eigenvalue decomposition of the fourth order cumulant matrix of the output signal. The performance of the scheme depends upon the accuracy of the estimated cumulants, and thus a scheme to improve the cumulant matrix estimate is suggested. Using these improved cumulants, the algorithm performs very well in slowly fading channels.

An integrated beamforming (spatial processing) and multiuser detection (temporal processing) scheme is an effective approach to increase system capacity, but is also impractical due to the high associated computational costs. The authors previously proposed joint domain localized (JDL) processing which achieves significantly lower computational cost and a faster convergence rate in terms of number of training symbols. The paper justifies the choice of the transformation matrix that is the basis for the JDL algorithm. Building on JDL processing, we also introduce a new processor that combines JDL processing and zero forcing for multi-cell uplink CDMA systems. Simulations show that this approach achieves better performance and a faster convergence rate than the JDL algorithm as well as the reduced rank and iterative schemes introduced by other researchers. If restricted by short training sequences, it even outperforms the theoretically optimal processor.

In this paper we propose a novel method for joint transmit-receive linear optimization in the downlink of a multiuser MIMO communication system. This new method adapts existing joint linear optimization algorithms from the single user domain for application to the multiuser domain. The optimum transmit matrix is obtained using an iterative procedure based on a minimum mean-squared error (MMSE) criterion and a per-user power constraint; the optimum receive matrices for each user are then derived under an MMSE constraint. The proposed technique improves performance and increases data throughput in multiuser scenarios.

This paper develops the linear transformation (or precoding) of orthogonal space-time block codes (STBC) for minimizing probability of decoding error, when the channel covariance matrix is available at the transmitter. We build on recent work that stated the optimization problem without solving for the transformation. Specifically, we provide a closed-form solution for the multi-input single-output (MISO) systems, and a numerical solution for the multi-input multi-output (MIMO) systems. Our results confirm that eigen-beamforming is optimal at low SNR or highly correlated channels, and full diversity is optimal at high SNR or weakly correlated channels.

This paper investigates the impact of space-time coding on an IEEE 802.11a based power-efficient medium access control (MAC) protocol in a mobile ad hoc network (MANET). The new MAC protocol, MAC-2, preserves energy by sending information at the minimum power needed to reach the destination with a specified packet error rate (PER). The analysis of space-time coding is conducted in practical environments, including spatial correlations between the fades connecting the transmitter and receiver antenna elements. Combining improvements in the physical layer with the new MAC-2 protocol leads to tremendous savings in power and increase in overall network throughput, while maintaining a basic quality of service.

This paper presents a technique for the recovery of time delays associated with components of a signal in a multipath communication channel. Matrix pencil is used to recover these delays from the channel frequency response. This algorithm has some key advantages over traditional super-resolution techniques such as MUSIC. Most importantly, matrix pencil only requires a single channel estimate and can estimate the delays associated with coherent multiple components.