We consider a status update system consisting of one source, one server, and one sink. The source generates packets according to a Poisson process and the packets are served according to a generally distributed service time. We consider a system with a capacity of one packet, i.e., there is no waiting buffer in the system, and model it as an M/G/1/1 queueing system. We introduce a probabilistically preemptive packet management policy and calculate the moment generating functions (MGFs) of the age of information (AoI) and peak AoI (PAoI) under the policy. According to the probabilistically preemptive policy, when a packet arrives, the possible packet in the system is replaced by the arriving packet with a fixed probability. Numerical results show the effectiveness of the packet management policy.

We address the problem of real-time remote tracking of a partially observable Markov source in an energy harvesting system with an unreliable communication channel. We consider both sampling and transmission costs. Different from most prior studies that assume the source is fully observable, the sampling cost renders the source partially observable. The goal is to jointly optimize sampling and transmission policies for two semantic-aware metrics: i) a general distortion measure and ii) the age of incorrect information (AoII). We formulate a stochastic control problem. To solve the problem for each metric, we cast a partially observable Markov decision process (POMDP), which is transformed into a belief MDP. Then, for both AoII under the perfect channel setup and distortion, we express the belief as a function of the age of information (AoI). This expression enables us to effectively truncate the corresponding belief space and formulate a finite-state MDP problem, which is solved using the relative value iteration algorithm. For the AoII metric in the general setup, a deep reinforcement learning policy is proposed. Simulation results show the effectiveness of the derived policies and, in particular, reveal a non-monotonic switching-type structure of the real-time optimal policy with respect to AoI.

We study the problem of remote tracking in an energy-harvesting enabled status update system consisting of an information source, a sampler, a transmitter, and a monitor. The information source is modeled as a finite-state Markov chain. The sampler samples the source, and the transmitter transmits the taken samples to the monitor. We consider both sampling and transmission costs, and thus, the source is not fully observable. The primary objective is to determine the optimal joint sampling and transmission policies based on a goal-oriented metric, defined by a generic distortion function. We first formulate a stochastic optimization problem and cast it into a partially observable Markov decision process (POMDP) problem. Subsequently, we employ the notion of belief state and characterize the belief space through the age of information (AoI) to convert the problem into a finite-state MDP problem, which is then solved via the relative value iteration algorithm. We also explore different estimation strategies at the monitor and examine their impact on the system performance. The simulation results show the effectiveness of the derived policy and reveal that, depending on the source dynamic, the choice of estimation strategy itself can significantly influence the overall performance.

We consider a multi-source relaying system where independent sources randomly generate status update packets which are sent to the destination with the aid of a relay through unreliable links. We develop transmission scheduling policies to minimize the weighted sum average age of information (AoI) subject to transmission capacity and long-run average resource constraints. We formulate a stochastic control optimization problem and solve it using a constrained Markov decision process (CMDP) approach and a drift-plus-penalty method. The CMDP problem is solved by transforming it into an MDP problem using the Lagrangian relaxation method. We theoretically analyze the structure of optimal policies for the MDP problem and subsequently propose a structure-aware algorithm that returns a practical near-optimal policy. Using the drift-plus-penalty method, we devise a near-optimal low-complexity policy that performs the scheduling decisions dynamically. We also develop a model-free deep reinforcement learning policy for which the Lyapunov optimization theory and a dueling double deep Q-network are employed. The complexities of the proposed policies are analyzed. Simulation results are provided to assess the performance of our policies and validate the theoretical results. The results show up to 91% performance improvement compared to a baseline policy.

We consider an energy harvesting (EH) IoT network, where users make on-demand requests to a cache-enabled edge node to send status updates about various random processes, each monitored by an EH sensor. The edge node serves each user's request by either commanding the corresponding sensor to send a fresh status update or retrieving the most recently received measurement from the cache. We aim to find a control policy at the edge node that minimizes the average on-demand age of information (AoI) over all sensors subject to per-slot transmission and energy constraints under partial battery knowledge at the edge node. Namely, the limited radio resources (e.g., bandwidth) causes that only a limited number of sensors can send status updates at each time slot (i.e., per-slot transmission constraint) and the scarcity of energy for the EH sensors imposes an energy constraint. Besides, the edge node is informed of the sensors' battery levels only via received status update packets, leading to uncertainty about the battery levels for the decision-making. We develop a low-complexity algorithm - termed relax-then-truncate - and prove that it is asymptotically optimal as the number of sensors goes to infinity. Numerical results illustrate that the proposed method achieves significant gains over a request-aware greedy policy and show that it has near-optimal performance even for moderate numbers of sensors.

We study status updating under two-way delay in a system consisting of a sampler, a sink, and a controller residing at the sink. The controller drives the sampling process by sending request packets to the sampler. Upon receiving a request, the sampler generates a sample and transmits the status update packet to the sink. Transmissions of both request and status update packets encounter random delays. We develop optimal control policies to minimize the average age of information (AoI) using the tools of Markov decision processes in two scenarios. We begin with the system having at most one active request, i.e., a generated request for which the sink has not yet received a status update packet. Then, as the main distinctive feature of this paper, we initiate pipelined-type status updating by studying a system having at most two active requests. Furthermore, we conduct AoI analysis by deriving the average AoI expressions for the Zero-Wait-1, Zero-Wait-2, and Wait-1 policies. According to the Zero-Wait-1 policy, whenever a status update packet is delivered to the sink, a new request packet is inserted into the system. The Zero-Wait-2 policy operates similarly, except that the system can hold two active requests. According to the Wait-1 policy, whenever a status update packet is delivered to the sink, a new request is sent after a waiting time which is a function of the current AoI. Numerical results illustrate the performance of each status updating policy under varying system parameter values.

We consider a resource-constrained IoT network, where users make on-demand requests to a cache-enabled edge node to send status updates about various random processes, each monitored by an energy harvesting sensor. The edge node serves users’ requests by either commanding the corresponding sensor to send a fresh status update or retrieving the most recently received measurement from the cache. We aim to find a control policy at the edge node to minimize the average age of information (AoI) of the received measurements upon requests, i.e., average on-demand AoI, subject to per-slot transmission and energy constraints. We develop a low-complexity algorithm – termed relax-then-truncate – and prove that it is asymptotically optimal as the number of sensors goes to infinity. Numerical results assess the performance of the proposed method.

We consider a multi-source status update system consisting of multiple independent sources, one server, and one sink. The packets of the sources are generated according to Poisson processes and served according to a generally distributed service time. We consider a system with no waiting buffer and model it as a multi-source M/G/1/1 queueing model. We introduce a source-aware preemptive packet management policy and subsequently derive the moment generating functions (MGFs) of the age of information (AoI) and peak AoI of each source. According to the policy, when a packet arrives, the possible packet of the same source in the system is replaced by the fresh packet. Simulation results show the performance of the packet management policy.

We consider a resource-constrained IoT network, where multiple users make on-demand requests to a cache-enabled edge node to send status updates about various random processes, each monitored by an energy harvesting sensor. The edge node serves users’ requests by deciding whether to command the corresponding sensor to send a fresh status update or retrieve the most recently received measurement from the cache. Our objective is to find the best actions of the edge node to minimize the average age of information (AoI) of the received measurements upon request, i.e., average on-demand AoI, subject to per-slot transmission and energy constraints. First, we derive a Markov decision process model and propose an iterative algorithm that obtains an optimal policy. Then, we develop an asymptotically optimal low-complexity algorithm – termed relax-then-truncate – and prove that it is optimal as the number of sensors goes to infinity. Simulation results illustrate that the proposed relax-then-truncate approach significantly reduces the average on-demand AoI compared to a request-aware greedy policy and a weighted AoI policy, and also depict that it performs close to the optimal solution even for moderate numbers of sensors.

We consider a multi-source status update system, where each source generates status update packets according to a Poisson process which are then served according to a generally distributed service time. For this multi-source M/G/1/1 queueing model, we consider a self-preemptive packet management policy and derive the moment generating functions (MGFs) of the age of information (AoI) and peak AoI of each source. According to the policy, an arriving fresh packet preempts the possible packet of the same source in the system. Furthermore, we derive the MGFs of the AoI and peak AoI for the globally preemptive and non-preemptive policies, for which only the average AoI and peak AoI have been derived earlier. Finally, we use the MGFs to derive the average AoI and peak AoI in a two-source M/G/1/1 queueing model under each policy. Numerical results show the effect of the service time distribution parameters on the average AoI. The results also highlight the importance of higher moments of the AoI.

We consider a status update system where multiple sensors communicate timely information about various random processes to a sink. The sensors share orthogonal sub-channels to transmit such information in the form of status update packets. A central controller can control the sampling actions of the sensors to trade-off between the transmit power consumption and information freshness which is quantified by the Age of Information (AoI). We jointly optimize the sampling action of each sensor, the transmit power allocation, and the sub-channel assignment to minimize the average total transmit power of all sensors, subject to a maximum average AoI constraint for each sensor. To solve the problem, we develop a dynamic control algorithm using the Lyapunov drift-plus-penalty method and provide optimality analysis of the algorithm. According to the Lyapunov drift-plus-penalty method, to solve the main problem, we need to solve an optimization problem in each time slot which is a mixed integer non-convex optimization problem. We propose a low-complexity sub-optimal solution for this per-slot optimization problem that provides near-optimal performance and we evaluate the computational complexity of the solution. Numerical results illustrate the performance of the proposed dynamic control algorithm and the performance of the sub-optimal solution for the per-slot optimization problem versus the different parameters of the system. The results show that the proposed dynamic control algorithm achieves more than$60~\%$saving in the average total transmit power compared to a baseline policy.

We consider a multi-source relaying system where the sources independently and randomly generate status update packets which are sent to the destination with the aid of a buffer-aided relay through unreliable links. We formulate a stochastic optimization problem aiming to minimize the sum average age of information (AAoI) of sources under per-slot transmission capacity constraints and a long-run average resource constraint. To solve the problem, we recast it as a constrained Markov decision process (CMDP) problem and adopt the Lagrangian method. We analyze the structure of an optimal policy for the resulting MDP problem that possesses a switching-type structure. We propose an algorithm that obtains a stationary deterministic near-optimal policy, establishing a benchmark for the system. Simulation results show the effectiveness of our algorithm compared to benchmark algorithms.

We consider a multi-source status update system in which status updates are transmitted as packets containing the measured value of the monitored process and a time stamp representing the time when the sample was generated. The packets of each source are generated according to a Poisson process and served according to an exponentially distributed service time. We assume that the received status update packets need further processing before being used (hence, computation intensive). This is mathematically modeled by an additional server at the sink. The sink server serves the packets according to an exponentially distributed service time. We introduce two packet management policies, a preemptive policy and a blocking policy, and derive the moment generating function (MGF) of the AoI of each source under the both policies. In the both policies, the system can contain at most two packets, one at the transmitter server and one at the sink server. In the preemptive policy, a new arriving packet preempts any possible packet that is currently under service regardless of the packet’s source index. In the blocking policy, when a server is busy at the arrival instant of a packet, the arriving packet is blocked and cleared. We assume that the same preemptive/blocking policy is employed in both the transmitter and sink server. Numerical results are provided to assess the results.

Information freshness is crucial for time-critical IoT applications, e.g., monitoring and control. We consider an IoT status update system with users, energy harvesting sensors, and a cache-enabled edge node. The users receive time-sensitive information about physical quantities, each measured by a sensor. Users demand for the information from the edge node whose cache stores the most recently received measurements from each sensor. To serve a request, the edge node either commands the sensor to send an update or retrieves the aged measurement from the cache. We aim at finding the best actions of the edge node to minimize the average AoI of the served measurements at the users, termed on-demand AoI. We model this problem as a Markov decision process and develop reinforcement learning (RL) algorithms: model-based value iteration and model-free Q-learning. We also propose a Q-learning method for the realistic case where the edge node is informed about the sensors’ battery levels only via the status updates. The case under transmission limitations is also addressed. Furthermore, properties of an optimal policy are characterized. Simulation results show that an optimal policy is a threshold-based policy and that the proposed RL methods significantly reduce the average cost compared to several baselines.

We study the information freshness under three different source aware packet management policies in a status update system consisting of two independent sources and one server. The packets of each source are generated according to the Poisson process and the packets are served according to an exponentially distributed service time. We derive the average age of information (AoI) of each source using the stochastic hybrid systems (SHS) technique for each packet management policy. In Policy 1, the queue can contain at most two waiting packets at the same time (in addition to the packet under service), one packet of source 1 and one packet of source 2. When the server is busy at an arrival of a packet, the possible packet of the same source waiting in the queue (hence, source-aware) is replaced by the arrived fresh packet. In Policy 2, the system (i.e., the waiting queue and the server) can contain at most two packets, one from each source. When the server is busy at an arrival of a packet, the possible packet of the same source in the system is replaced by the fresh packet. Policy 3 is similar to Policy 2 but it does not permit preemption in service, i.e., while a packet is under service all new arrivals from the same source are blocked and cleared. Numerical results are provided to assess the fairness between sources and the sum average AoI of the proposed policies.

We consider a status update system consisting of two independent sources, one server, and one sink. The packets of different sources are generated according to the Poisson process and the packets are served according to an exponentially distributed service time. We consider the following packet management policy. When the system is empty, any arriving packet immediately enters the server; when the server is busy, a packet of a source waiting in the queue is replaced if a new packet of the same source arrives. We derive the average age of information (AoI) of the considered M/M/1 queueing model by using the stochastic hybrid systems (SHS) technique. Numerical results are provided to show the effectiveness of the proposed policy.

We consider an IoT sensing network with multiple users, multiple energy harvesting sensors, and a wireless edge node acting as a gateway between the users and sensors. The users request for updates about the value of physical processes, each of which is measured by one sensor. The edge node has a cache storage that stores the most recently received measurements from each sensor. Upon receiving a request, the edge node can either command the corresponding sensor to send a status update, or use the data in the cache. We aim to find the best action of the edge node to minimize the average long-term cost which trade-offs between the age of information and energy consumption. We propose a practical reinforcement learning approach that finds an optimal policy without knowing the exact battery levels of the sensors. Simulation results show that the proposed method significantly reduces the average cost compared to several baseline methods.

Information freshness is crucial in a wide range of wireless applications where a destination needs the most recent measurements of a remotely observed random process. In this paper, we study the information freshness of a single-server multi-source M/M/1 queueing model under a first-come first-served (FCFS) serving policy. The information freshness of the status updates of each source is evaluated by the average age of information (AoI). We derive an exact expression for the average AoI for the multi-source M/M/1 queueing model. Simulation results are provided to validate the derived exact expression for the average AoI.

The authors consider the problem of compressive sensed video recovery via iterative thresholding algorithm. Traditionally, it is assumed that some fixed sparsifying transform is applied at each iteration of the algorithm. In order to improve the recovery performance, at each iteration the thresholding could be applied for different transforms in order to obtain several estimates for each pixel. Then the resulting pixel value is computed based on obtained estimates using simple averaging. However, calculation of the estimates leads to significant increase in reconstruction complexity. Therefore, the authors propose a heuristic approach, where at each iteration only one transform is randomly selected from some set of transforms. First, they present simple examples, when block‐based 2D discrete cosine transform is used as the sparsifying transform, and show that the random selection of the block size at each iteration significantly outperforms the case when fixed block size is used. Second, building on these simple examples, they apply the proposed approach when video block‐matching and 3D filtering (VBM3D) is used for the thresholding and show that the random transform selection within VBM3D allows to improve the recovery performance as compared with the recovery based on VBM3D with fixed transform.

Freshness of status update packets is essential for enabling services where a destination needs the most recent measurements of various sensors. In this paper, we study the information freshness of single-server multi-source queueing models under a first-come first-served (FCFS) serving policy. In the considered model, each source independently generates status update packets according to a Poisson process. The information freshness of the status updates of each source is evaluated by the average age of information (AoI). We derive an exact expression for the average AoI for the case with exponentially distributed service time, i.e., for a multi-source M/M/1 queueing model. Moreover, we derive three approximate expressions for the average AoI for a multi-source M/G/1 queueing model having a general service time distribution. Simulation results are provided to validate the derived exact average AoI expression, to assess the tightness of the proposed approximations, and to demonstrate the AoI behavior for different system parameters.

We study rate-distortion (RD) performance of various single-sensor compressed sensing (CS) schemes for acquiring sparse signals via quantized/encoded noisy linear measurements, motivated by low-power sensor applications. For such a quantized CS (QCS) context, the paper combines and refines our recent advances in algorithm designs and theoretical analysis. Practical symbol-by-symbol quantizer based QCS methods of different compression strategies are proposed. The compression limit of QCS - the remote RDF - is assessed through an analytical lower bound and a numerical approximation method. Simulation results compare the RD performances of different schemes.

In the context of the next generation wireless networks, freshness of status update packets is essential for enabling the services where a destination needs the most recent measurements of various sensors. In this paper, we study the information freshness of a multi-source M/G/1 first-come first-served (FCFS) queueing model, where each source independently generates status update packets according to a Poisson process. The information freshness of the status updates of each source is evaluated using the average age of information (AoI). To this end, we derive a closed-form expression for the average AoI of each source. As particular cases of our general expressions, we also derive closed-form expressions of the average AoI for both multi-source M/M/1 and single-source M/G/1 queueing models.

We consider the problem of channel estimation in hybrid transceiver architectures operating in millimeter wave (mmWave) band. Due to the dynamic features of the environment and the sensitivity of mmWave bands to blockage and deafness, it is important to estimate mmWave channels with a low complexity and high performance algorithm. In this regard, we exploit the sparse structure of the frequency-selective mmWave channels and formulate the channel estimation problem as a sparse signal reconstruction in frequency domain. In order to solve the estimation problem, we propose a multi-stage based low complexity algorithm. Simulation results show that the proposed algorithm significantly reduces the computational complexity while preserving the quality of the estimation.

Block compressed sensing (BCS) is used to reduce the acquisition and processing complexity for large images. The standard BCS algorithms recover each image block independently and then use post-processing techniques to reduce the blocking artifacts affecting the reconstructed image. In contrast, we propose a post-processing free recovery method which uses {\it lapped transforms} based sparse image representations in order to reduce the blocking artifacts. Specifically, instead of recovering each image block independently, we derive an iterative reconstruction method where a small number of adjacent measurement blocks are jointly processed for recovering every image block.

The issue considered in the current study is the problem of adaptive distributed estimation based on diffusion strategy which can exploit sparsity in improving estimation error and reducing communications. It has been shown that distributed estimation leads to a good performance in terms of the error value, convergence rate, and robustness against node and link failures in wireless sensor networks. However, the main focus of many works in the field of distributed estimation research is on convergence speed and estimation error, neglecting the fact that communications among the nodes require a lot of transmissions. In this work, the focus is on a solution based on sparse diffusion least mean squares (LMS) algorithm, and a new version of sparse diffusion LMS algorithm is proposed which takes both communications and error cost into account. Also, the computation complexity and communication cost for every node of the network, as well as performance analysis of the proposed strategy, is provided. The performance of the proposed method in comparison with the existing methods is illustrated by means of simulations in terms of computational and communicational cost, and flexibility to signal changes.

This paper addresses lossy distributed source coding for acquiring correlated sparse sources via compressed sensing (CS) in wireless sensor networks. Noisy CS measurements are separately encoded at a finite rate by each sensor, followed by the joint reconstruction of the sources at the decoder. We develop a novel complexity-constrained distributed variable-rate quantized CS method, which minimizes a weighted sum between the mean square error signal reconstruction distortion and the average encoding rate. The encoding complexity of each sensor is restrained by pre-quantizing the encoder input, i.e., the CS measurements, via vector quantization. Following the entropy-constrained design, each encoder is modeled as a quantizer followed by a lossless entropy encoder, and variable-rate coding is incorporated via rate measures of an entropy bound. For a two-sensor system, necessary optimality conditions are derived, practical training algorithms are proposed, and complexity analysis is provided. Numerical results show that the proposed method achieves superior compression performance as compared with baseline methods, and lends itself to versatile setups with different performance requirements.

We investigate lossy compressed sensing (CS) of a hidden, or remote, source, where a sensor observes a sparse information source indirectly. The compressed noisy measurements are communicated to the decoder for signal reconstruction with the aim to minimize the mean square error distortion. An analytically tractable lower bound to the remote rate-distortion function (RDF), i.e., the conditional remote RDF, is derived by providing support side information to the encoder and decoder. For this setup, the best encoder separates into an estimation step and a transmission step. A variant of the Blahut-Arimoto algorithm is developed to numerically approximate the remote RDF. Furthermore, a novel entropy coding based quantized CS method is proposed. Numerical results illustrate the main rate-distortion characteristics of the lossy CS, and compare the performance of practical quantized CS methods against the proposed limits.

The problem of admission control in a multicell downlink multiple-input single-output system is considered. The objective is to maximize the number of admitted users subject to the signal-to-interference-plus-noise ratio constraint for each admitted user and a transmit power constraint at each base station. We cast the admission control problem as an l0minimization problem. This problem is known to be combinatorial NP-hard. Hence, we have to rely on suboptimal algorithms to solve it. We first approximate the l0minimization problem via a non-combinatorial one. Then, we propose centralized and distributed algorithms to solve the non-combinatorial problem. To develop the centralized algorithm, we use the sequential convex programming method. The distributed algorithm is derived by using the alternating direction method of multipliers in conjunction with sequential convex programming. We show numerically that the proposed admission control algorithms achieve a near-to-optimal performance. Next, we extend the admission control problem to provide fairness, where a long term fairness among the users is guaranteed. We focus on proportional and max-min fairness and propose dynamic control algorithms via Lyapunov optimization. It is shown numerically that the proposed fair admission control algorithms guarantee fairness among the users.

Compressive sensing theory asserts that, under certain conditions, a high dimensional but compressible signal can be recovered from a small number of random linear projections by utilizing computationally efficient algorithms. The a priori knowledge of the basis in which the signal of interest is sparse is the key assumption utilized by such algorithms. However, the basis in which the signal is the sparsest is unknown for many natural signals of interest. Instead there may exist multiple bases which lead to a compressible representation of the signal: e.g., an image is compressible in different wavelet transforms. We show that a significant performance improvement can be achieved by utilizing multiple estimates of the signal using sparsifying bases in the context of signal reconstruction from compressive samples. Further, we derive a customized interior-point method to jointly obtain multiple estimates of a 2-D signal (image) from compressive measurements utilizing multiple sparsifying bases as well as the fact that the images usually have a sparse gradient.

The total variation (TV) minimization can be utilized in a compressive sensing framework to recover a signal from a small number of measurements by searching for a signal with a sparse gradient. However, many natural signals of interest, such as natural images, generally have sparse representations in known transforms. Hence, the performance of the signal reconstruction procedure can be improved by also taking into account this transform-domain sparsity of the signal. Thus, the TV minimization problem can be modified by introducing an $l_1$-norm penalty term. The $l_1$-regularized TV minimization problem searches for a signal with a sparse gradient and a sparse representation in the given transform. The main contribution of this paper is the derivation of a customized interior-point method for solving the $l_1$-regularized TV minimization problem that computes the search direction of the Newton method efficiently by exploiting the specific structure of the Hessian.

The problem of spectrum sharing between two operators in a dynamic network is considered. We allow both operators to share (a fraction of) their licensed spectrum band with each other by forming a common spectrum band. The objective is to maximize the gain in profits of both operators by sharing their licensed spectrum bands rather than using them exclusively, while considering the fairness among the operators. This is modeled as a two-person bargaining problem, and cast as a stochastic optimization. To solve this problem, we propose centralized and distributed dynamic control algorithms. At each time slot, the proposed algorithms perform the following tasks: 1) determine spectrum price for the operators; 2) make flow control decisions of users data; and 3) jointly allocate spectrum band to the operators and design transmit beamformers, which is known as resource allocation (RA). Since the RA problem is NP-hard, we have to rely on sequential convex programming to approximate its solution. To derive the distributed algorithm, we use alternating direction method of multipliers for solving the RA problem. Numerically, we show that the proposed distributed algorithm achieves almost the same performance as the centralized one. Furthermore, the results show that there is a trade-off between the achieved profits of the operators and the network congestion.

Lossy compressed sensing (CS) of a sparse source is studied. A lower bound to the best achievable compression performance in a finite rate CS setup is established by providing support side information to the encoder and decoder. The rate-distortion problem is formulated via remote source coding and conditional rate-distortion theory. The best encoder separates into an estimation step and a rate-dependent transmission step. Numerical results illustrate the rate-distortion behavior of the scheme.

We consider a communication system in which status updates arrive at a source node, and should be transmitted through a network to the intended destination node. The status updates are samples of a random process under observation, transmitted as packets, which also contain the time stamp to identify when the sample was generated. The age of the information available to the destination node is the time elapsed, since the last received update was generated. In this paper, we model the source-destination link using the queuing theory, and we assume that the time it takes to successfully transmit a packet to the destination is an exponentially distributed service time. We analyze the age of information in the case that the source node has the capability to manage the arriving samples, possibly discarding packets in order to avoid wasting network resources with the transmission of stale information. In addition to characterizing the average age, we propose a new metric, called peak age, which provides information about the maximum value of the age, achieved immediately before receiving an update.

The problem of robust weighted sum-rate maximization (WSRMax) in multicell downlink multi-input single-output systems is considered. We assume that the channel state information (CSI) of all users is imperfectly known at the base stations. The problem is known to be NP-hard even in the case of perfect CSI. Assuming a bounded ellipsoidal model for the CSI errors, we maximize the worst-case weighted sum-rate and proposed a fast but possibly suboptimal algorithm. The proposed algorithm is based on alternating optimization technique and sequential convex programming. Numerical results show that the convergence speed of the proposed algorithm is fast, and it finds a close-to-optimal solution in only a few iterations.

We propose a reactive route discovery protocol for energy efficient transmission in a wireless multihop network with multiple input multiple output (MIMO) channels. We focus on networks consisting of MIMO nodes that use time division scheduling where the source needs to transmit a certain amount of data to the destination in a fixed amount of time. We show how the intermediate nodes find the route distributively. This protocol takes into account the quality of the MIMO channels and allocates power to them so that the end-to-end transmission energy is minimized. The resulting protocol is easy to implement and provides the most energy efficient route and the power allocation for the data transmission. We perform simulations on Rayleigh-fading channels to show the significant energy benefits of the proposed route discovery protocol against a dynamic source routing (DSR)-based routing protocol with optimal power allocation.

The problem of robust weighted sum-rate maximization (WSRMax) in multicell downlink multi-input single-output systems is considered. We assume that channel state information (CSI) of all users is imperfectly known at the base stations. The problem is known to be NP-hard even in the case of perfect CSI. We propose optimal and suboptimal but fast-converging algorithms for WSRMax problem with CSI errors. Assuming bounded ellipsoidal model for the CSI errors, we optimize the worst-case weighted sum-rate. The proposed optimal algorithm is based on branch and bound (BB) technique, and it globally solves the worst-case WSRMax problem with an optimality certificate. As the convergence speed of the BB method can be slow for large networks, we also provide a fast but possibly suboptimal algorithm based on alternating optimization technique and sequential convex programming. The optimal BB based algorithm can be used to provide performance benchmarks for any suboptimal algorithm. Numerical results show that the convergence speed of the suboptimal algorithm is fast, and it finds a close-to-optimal solution in only a few iterations.

We consider network layer cooperation in spectrum sharing networks whereby some secondary users relay primary users' packets, in return for more favorable spectrum access rules. Under this cooperative scheme, we investigate how primary and secondary networks can be stabilized without explicit knowledge of the packet arrival rates. We consider a primary packet generation process wherein a packet is formed by aggregating constant amount of bits that arrive in every time slot from upper-layers of the primary transmitter. For this primary packet generation model we develop a relaying and scheduling algorithm using Lyapunov drift techniques that does not require knowledge of packet arrival rates. We also construct a guaranteed stability region representing packet generation rates for which the algorithm can stabilize the network. The set of secondary packet generation rate vectors for which the network can be stabilized do not decrease under cooperation when the primary packet generation rate is lower than what can be maximally supported without cooperation. For higher primary packet generation rates the algorithm stabilizes the network for a non-empty set of secondary packet generation rate vectors.

This paper considers sequential compressed acquisition and progressive reconstruction of spatially and temporally correlated sensor data streams in wireless sensor networks (WSNs) via compressed sensing (CS). We develop a sequential framework based on sliding window processing, in which the sink can efficiently reconstruct the current sensors' readings from a sequence of periodically delivered CS measurements by exploiting the joint compressibility via Kronecker sparsifying bases. Specifically, we derive a recursive CS recovery method which utilizes the estimates from the preceding decoding instants via a regularization and reweighted ℓ1-minimization to improve the reconstruction accuracy of sensor data streams while reducing the necessary communications. As beneficial features, the method produces estimates for the current sensors' readings without additional decoding delay, and, via adjusting the window size, it can dynamically trade-off between the CS recovery performance and decoding complexity. Numerical results show that our proposed method achieves higher reconstruction accuracy with a smaller number of required transmissions, and with lower decoding delay and complexity as compared to those of the state of the art CS methods.

This paper addresses compressed acquisition and progressive reconstruction of spatially and temporally correlated signals in wireless sensor networks (WSNs) via compressed sensing (CS). We propose a novel method based on sliding window processing, where the sink periodically collects CS measurements of sensor samples, and then, instantaneously reconstructs current WSN samples by exploiting the spatio-temporal correlation via Kronecker sparsifying bases. By using previous estimates as prior information, the method can progressively improve the reconstruction accuracy of the signal ensemble. Furthermore, the method can control the trade-off between decoding delay and complexity. Numerical results demonstrate that the proposed method can recover WSN data samples from CS measurements with higher reconstruction accuracy, yet with lower decoding delay and complexity, as compared to the state of the art methods.

We consider a system in which random status updates arrive at a source node, and should be transmitted through a wireless network to the intended destination node. The status updates are samples of a random process, transmitted as packets, containing the time stamp to identify the moment the sample was generated. The time it takes to successfully transmit a packet to the destination is modeled as an exponentially distributed service time. The status update age at the receiver is the time elapsed since the last received update was generated. In this paper, we analyze the age in the case that the source node has the capability to manage the arriving samples and decide which packets will be transmitted to the destination. In addition to the average age, we investigate the average of a new metric, called peak age, which provides information about the maximum value of the Age, achieved immediately before receiving an update.

We discuss the reconstruction of streaming signals from compressive measurements. We propose to use an algorithm based on sparse Bayesian learning to reconstruct the streaming signal over small shifting intervals. The proposed algorithm utilizes the previous estimates to improve the accuracy of the signal estimate and the speed of the recovery algorithm. Simulation results show that the proposed algorithm can achieve better signal-to-error ratios compared with the existing l1-homotopy based recovery algorithm.

This paper considers network-layer cooperation in cognitive radio networks whereby secondary users can relay primary user's packets, in return for a more favorable spectrum access rules. Under this cooperative scheme, the paper investigates whether, and under what conditions, the primary and secondary networks can be stabilized without explicit knowledge of the packet arrival-rates. We consider a deterministic and periodic primary packet arrival process and develop a relaying and scheduling algorithm using Lyapunov drift techniques that does not require knowledge of primary and secondary packet arrival rates. The algorithm is then shown to stabilize the transmission queues in the network for all secondary packet arrival rates that lie in the interior of a certain region. The region includes all secondary arrival-rate vectors that can be supported when the secondary nodes do not cooperate. Furthermore, when the primary data arrival-rate is greater than what could have been supported without relays but less than what can be maximally supported with relays, the algorithm stabilizes the network for a non-empty set of secondary arrival-rate vectors. The significance of these results is that they show that properly designed cooperation may result in a win-win scenario for both primary and secondary users (and not just for one type of users). Finally we extend our analysis to the case of a deterministic but aperiodic primary packet arrival process.

In this paper, we study the stability property of a cognitive radio system comprised of a set of source-destination pairs having different priorities. In particular, we focus attention on the effect of imperfect sensing on the stability region of the system, which has been overlooked in most of related previous work. The adopted cognitive access protocol allows the secondary user not only to exploit the idle slots of the primary user but also to transmit along with the primary user with some probability. This is aimed at achieving the full utilization of the shared channel with capture, i.e., a transmission can be correctly decoded at the destination, even in the presence of other transmissions, if the received signal-to-interference-plus-noise ratio (SINR) exceeds a certain threshold for successful decoding. The abolition of strong primacy, however, requires the secondary user to properly regulate its multi-access probability in order not to impede the primary user's stability guarantee. To this end, the maximum stability region of the system is characterized which describes the theoretical limit on rates that can be pushed into the system while maintaining the queues stable. Interestingly, we found that even with non-zero sensing error rates, there exists a condition for which we can achieve the identical stability region that is achieved with perfect sensing. This is when the destinations enjoy fairly strong capture, and if then sensing errors do not affect the stability region for the queueing system. For the case when the specified condition does not hold, we precisely quantify the loss due to the imperfect sensing in terms of the size of the stability region. Finally, we study the problem of controlling the operating point of the sensing device over its receiver operating characteristic (ROC) and summarize some key aspects observed in the control.

We consider a single-macrocell heterogeneous multiple-input multiple-output network, where the macrocell shares the same frequency band with the femto network. The interference power to the macro users from the femto base stations is kept below a threshold to guarantee that the performance of the macro users does not degrade due to the femto network. We consider the problem of finding the set of all achievable power-rate tuples for this setting. We first formulate a two-dimensional vector optimization problem in which we consider maximizing the sum-rate and minimizing the sum-power, subject to maximum power and interference threshold constraints. The considered problem is NP-hard. We provide a method to solve the problem by using the relationship between the weighted sum-rate maximization and weighted-sum-mean-squared-error minimization problems. Furthermore, using the proposed algorithm, we evaluate the impact of imposing interference threshold constraints and the impact of co-channel deployment in heterogeneous networks. The proposed algorithm can be used to evaluate the performance of real heterogeneous networks via off-line numerical simulations.

In this work, we consider a hybrid of interweave and underlay modes of operation for cognitive radio systems with random connectivity and bursty packet arrivals. Under the designed hybrid access policy, the secondary communication system is allowed to operate in the interweave mode only when its transmission has no harm on the primary communication. This is when the primary communication system is idle or the interference link from the secondary source to the primary destination is disconnected. The secondary communication system can optionally operate in the underlay mode, although when it is inevitable to interfere with the primary communication. The underlay mode is activated with some probability, called the hybrid rate. We analyze the stability of the hybrid access policy and show that it is not always beneficial when compared against the interweave-only mode. Thus, the condition for which the hybrid access policy can outperform is specified.

We present a transmission energy efficient power allocation method for different end-to-end rates in a Multiple Input Multiple Output (MIMO) multihop network. This method is intended for a network consisting of MIMO nodes that use simple time division scheduling. We formulate the power allocation problem for the various degrees of freedom of MIMO hops as an energy minimization problem and present our iterative solution. Our method calculates the power allocations to the known routes for a requested data transmission time and takes the quality of the channels into account. We perform simulations to show the energy efficiency of these routes.

We consider a distributed total transmit power minimization in a multi-hop single-sink data gathering wireless sensor network by jointly optimizing the resource allocation and the routing with given source rates. An inherent coupling in optimal routing and resource allocation is taken into account via cross-layer optimization to increase the energy efficiency of the network. Instead of distributing the solution process horizontally by commonly used dual decomposition, we apply consensus optimization in conjunction with the alternating direction method of multipliers (ADMM). By duplicating flow variables, the problem decomposes into node specific subproblems with local variables. These variables are iteratively driven into consensus via the ADMM. Numerical examples show that the proposed algorithm converges significantly faster as compared to the state of the art methods based on the dual decomposition. Additionally, the algorithm is appealing for practical implementation due to its low local communication overhead, robust operation in slightly changing channel conditions and scalability to large networks.

This paper proposes a fast distributed optimization algorithm for total transmit power minimization in single-sink data gathering wireless sensor networks. Many of the existing decentralized optimization algorithms addressing cross-layer design over the physical and network layer are based on dual decomposition. Our design includes joint power and routing optimization with given source rates by using consensus mechanism in conjunction with alternating direction method of multipliers (ADMM). Thus, the problem is decoupled across the nodes via introducing local copies of the variables, which are then iteratively driven into consensus with the ADMM. By the numerical experiments, the proposed distributed algorithm is shown to converge significantly faster to near optimal solutions with a small amount of local variable exchange as compared to the existing methods based on the dual decomposition.

We consider the problem of MaxWeight scheduling in wireless multihop networks. This problem is known to be NP-hard. We propose a solution method, based on the branch and bound technique, which solves globally the MaxWeight scheduling problem with an optimality certificate. Efficient analytic bounding techniques are introduced as well.

We consider the problem of weighted sum-rate maximization (WSRMax) in wireless networks. This problem is known to be NP-hard and it plays a central role in resource allocation, link scheduling or in finding achievable rate regions for both singlecast and multicast networks. We propose a solution method, based on the branch and bound technique, which solves globally the WSRMax problem with an optimality certificate. Efficient bounding techniques are introduced as well.

This paper addresses the problem of power allocation for single carrier point-to-point multiple input multiple output (MIMO) systems with iterative frequency-domain (FD) soft cancellation (SC) minimum mean squared error (MMSE) equalization. Two novel heuristic power allocation methods are proposed. The proposed methods explicitly take into account the convergence properties of the iterative equalizer while transmission power is minimized. The proposed heuristic schemes are based on, convergence constraint power allocation (CCPA), technique that decouples the spatial interference between streams using singular value decomposition (SVD), and minimize the transmission power while achieving the target mutual information for each stream after iterations at the receiver side. The proposed heuristic transmission schemes are inspired by well-known greedy algorithm resulting in a simple and efficient solutions to the power allocation problem. Numerical results show that the proposed heuristic schemes can achieve close to optimal performance in the terms of equalizer convergence as well as the total transmission power.

In a wireless network, the problem of self interference arises whenever a node transmits and receives simultaneously in the same frequency band. So far only two extreme approaches to circumvent this problem were thoroughly investigated in the literature. The first one prevents any node to transmit and receive simultaneously which may lead to a too conservative design. The second one assumes perfect self interference cancelation which can be too optimistic since it ignores all possible technological limitations. To fill this gap, we provide a method to evaluate the network layer benefits from simultaneous transmission and reception when the network nodes employ self interference cancelation techniques with different degrees of accuracy. From a network design perspective, the provided method can be used to find the required level of accuracy for the self interference cancelation such that certain gains are achieved at the network layer. Numerical results suggest that the accuracy of existing self interference cancelation techniques can provide significant gains for certain network setups.

We consider the cross-layer utility maximization problem for wireless networks. It is well known that the optimal network control policy can be decomposed in three separate subproblems: 1) flow control at the network layer, 2) routing and scheduling at the network layer, and 3) resource allocation (RA) at the medium access control and physical layers. The main contribution of this paper is a power and rate control algorithm for the RA subproblem. In the case of single-hop networks, the proposed algorithm provides a locally optimal solution for the RA subproblem. Even though the global optimality of the solution cannot be guaranteed due to the nonconvexity of the problem, the numerical results show that the algorithm can provide significant gains at the network layer in terms of end-to-end rates and network congestion as compared to the optimal time division multiple access (TDMA) based RA. For solving the RA subproblem in the case of multi-hop networks, the proposed algorithm must be used in conjunction with an exhaustive search for the optimal set of transmitter nodes. However, the numerical results show that even with a random selection of the set of transmitter nodes, the proposed algorithm can provide significant improvement at the network layer in terms of end-to-end rates and network congestion as compared the optimal TDMA based RA.

We consider the problem of cross-layer utility maximization subject to stability constraints for a multicommodity wireless network where all links are sharing a single channel. We assume a time slotted network and only one node is allowed to transmit at any given slot. The optimal cross-layer network control policy can be decomposed into three subproblems: (1) flow control at the transport and network layers, (2) routing and scheduling at the network layer, and (3) resource allocation (RA) at the medium access control and physical layers. Every time slot, a network controller decides the transmitter node, amount of each commodity data admitted to the network layer, schedules different commodities over networks' links and controls the power and rate allocated to every link. In this paper we provide solutions for the RA subproblem for two scenarios: (1) when the nodes are equipped with standard single user receivers, and (2) when the nodes are equipped with multiuser receivers performing successive interference cancelation. In addition we propose decentralized algorithms to obtain the solution of RA subproblem for each scenario. The numerical results show that as the signal-to-noise ratio increases, using multiuser receivers can provide significant gains at the network layer in terms of end-to-end rates and network congestion as compared to that of single user receivers.

The uplink-downlink SINR duality theorem is a key tool which simplify substantially the problem of joint design of the linear transmit and receive beamformers in multiple-input multiple-output (MIMO) downlink channels. The theorem has been proved previously under the assumption that the cross- coupling matrix between the users is primitive or, alternatively, by postulating the nonnegativity of the resolvent. By using the Lagrange duality theory, we first give an alternative proof which holds for arbitrary cross-coupling matrices. The proof does not only extend the result to a larger set of practical applications, but it also reveal more insight on the sum power minimization problem under a set of minimum SINR requirements for data streams. As a practical application, we apply the uplink-downlink SINR duality to derive a general method for MIMO downlink linear transceiver optimization according to different system performance criteria, including weighted sum rate maximization, weighted sum mean square error minimization, and minimum SINR maximization. The proposed method can handle multiple antennas at the BS and at the mobile user with single and/or multiple data streams per scheduled user. The numerical simulations show that the sum rate achieved by the sum rate maximization algorithm is within 0.5-1.5 bits/second/Hz close to the sum capacity. When compared to the traditional zero forcing based solutions, the proposed method provides more than 4 dB SNR gain and up to 3.5 bits/sec/Hz better spectral efficiency.

Cooperative processing of transmitted signal from several multiple-input multiple-output (MIMO) base stations (BS) is considered for users located within a soft handover (SHO) region. The downlink resource allocation problem with different BS power constraints is studied for the orthogonal frequency division multiplexing system with adaptive MIMO transmission. Joint design of the linear transmit and receive beamformers in a MIMO multiuser transmission subject to per BS power constraints is considered. A solution for the weighted sum rate maximization problem is proposed. The proposed algorithm is shown to provide a very efficient solution despite of the fact that the global optimality cannot be guaranteed due to the non-convexity of the optimization problem. Moreover, efficient resource allocation method based on zero forcing transmission is provided. The impact of the size of a SHO region, the overhead from the increased resource utilization, and different inter-cell interference distributions due to the SHO are evaluated by system level simulations. Although the overhead from the SHO processing can be significant, it can be mitigated by using space division multiple access for users having an identical SHO active set composition. The users located at the SHO region may enjoy from greatly increased transmission rates. This translates to significant overall system level gains from the cooperative SHO processing.

Joint design of linear multiuser multiple-input multiple-output (MIMO) transceiver subject to different quality of service constraints per user and with per antenna or antenna group power constraints is considered. A solution for finding a maximum weighted common rate achievable for each scheduled user with minimum rate requirements per user is proposed. The proposed joint transceiver optimization algorithms are compared to corresponding optimal nonlinear transmission methods as well as to zero forcing transmission solutions. The proposed algorithms provide efficient solutions for difficult non-convex transceiver optimization problems.

We consider a single-cell multiple-input multiple-output (MIMO) downlink channel where linear transmission and reception strategy is employed. The base station (BS) transmitter is equipped with a scheduler using a simple opportunistic beamforming strategy, which associates an intended user for each of the transmitted data streams. For the case when the channel of the scheduled users is available at the BS, we propose a general method for joint design of the transmit and the receive beamformers according to different optimization criteria. The proposed method can handle multiple antennas at the BS and at the mobile user with single and/or multiple data streams per scheduled user. By exploiting the uplink-downlink SINR duality, we decompose the original optimization problem as a series of simpler optimization problems which can be efficiently solved by using standard convex optimization tools. The simulations show that the algorithms converge fast to a solution, which can be a local optimum, but is still efficient. Only one iteration of the proposed method is enough to substantially outperform the zero forcing based solution.

Joint cooperative processing of transmitted signal from several multiple-input multiple-output (MIMO) base station (BS) antenna heads is considered for users located within a soft handover (SHO) region. Downlink resource allocation problem with different BS power constraints is studied. The mathematical framework for the SHO based MIMO system is derived and the joint design of linear transmit and receive beamformers in a MIMO multiuser transmission according to weighted sum rate maximization criterion and subject to per BS power constraints is considered. The proposed algorithm is shown to provide very efficient solutions despite of the fact that there is no guarantee of achieving the global optimum due to the non-convexity of the problem. Moreover, practical and efficient resource allocation method based on generalized zero forcing transmission is provided.

We consider a single-cell multiple-input multiple-output (MIMO) downlink channel where linear transmission and reception strategy is employed. The base station (BS) transmitter is equipped with a scheduler using a simple opportunistic beamforming strategy, which associates an intended user for each of the transmitted data streams. For the case when the channel of the scheduled users is available at the BS, we propose a general method for joint design of linear transmit and receive beamformers, according to weighted sum rate maximization criteria. The proposed method can handle multiple antennas at the BS and at the mobile users with an arbitrary number of data streams per scheduled user. It can also handle a fairly general set of practical power constraints for the transmit beamformers, i.e., we can impose sum power constraints for different subsets of the transmit antennas.

The joint cooperative processing of transmitted signal from several multiple-input multiple-output (MIMO) base station (BS) antenna heads is considered for users located within a soft handover (SHO) region. The mathematical framework for the SHO based MIMO system is derived and the joint design of linear transmit and receive beamformers in a MIMO multiuser transmission subject to per BS power constraints is considered. Solution for the maximization of the minimum weighted SINR per data stream criterion is proposed. The proposed algorithm is shown to provide very efficient solutions despite of the fact that the global optimum cannot be guaranteed due to the non-convexity of the problem. Moreover, a less complex but still efficient allocation method based on zero forcing transmission is provided for the same optimization criterion.

A novel low-complexity and provably convergent algorithm is proposed to find the sum capacity for vector Gaussian broadcast channels. Unlike the recently proposed sum-power constraint iterative waterfilling (SPC-IWF) algorithms, it has lower complexity and requires no additional precautions to ensure the convergence. We have proved analytically the convergence with probability one, and the computer simulations show that the proposed algorithm converges faster than the earlier variants of SPC-IWF algorithms. We formulate the problem in the context of a multiple-input multiple-output orthogonal frequency-division multiplexing system and discuss the simplifications provided by the block-diagonal channel structure

In a time-division-duplex communication system, the channel knowledge can be obtained at the transmitter side due to channel reciprocity and it can be used to increase the spectral efficiency of a multiple-input multiple-output (MIMO) communications. However, the interference structure between transmission directions does not necessarily correlate. The obtained quality of service at the receiver may differ significantly from the desired one if the transmission parameters are assigned based on the reverse link measurements only. In this paper, the performance of an orthogonal frequency division multiplexing (OFDM) cellular system with adaptive MIMO transmission is studied in the presence of non-reciprocal inter-cell interference when the downlink interference structure is known at the receiver and only limited feedback information about the interference is available at the transmitter. The results are compared to those with perfectly known interference structure per each sub-carrier. The system level impact of realistic interference non-reciprocity scenarios is studied via network simulations. Linear minimum mean squared error (MMSE) filter is applied at the receiver to suppress the impact of structured inter-cell interference together with a simple and bandwidth efficient closed-loop compensation algorithm. Both link and system level simulation results show that the proposed compensation algorithm with a simple scalar power offset feedback combined with interference suppression at the receiver results in nearly the same performance as the ideal case

The joint cooperative processing of transmitted signal from several multiple-input multiple-output (MIMO) base station (BS) antenna heads is considered for users located within a soft handover (SHO) region. The system level gains and trade-offs from cooperative SHO processing are investigated. The impact of the size of the SHO region, overhead from the increased hardware and physical (time, frequency) resource utilization, and different non-reciprocal inter-cell interference distributions due to SHO are evaluated. Practical user, bit and power allocation method with different BS power constraints is provided for the proposed cooperative multiuser MIMO transmission. The overhead from SHO processing can be significant, and the call blocking probability can be dramatically increased. However, the overhead can be mitigated by using space division multiple access for users that have identical SHO active set composition. Also, the dropping probability is decreased, and thus, the total outage probability with SHO is less than without SHO. The users located at the SHO region may enjoy from greatly increased transmission rates. This translates to significant overall system level gains from the cooperative SHO processing. The proposed soft handover scheme can be used to provide more evenly distributed service over the entire cellular network.

A MIMO broadcast channel with linear transceiver schemes is considered in this paper. The base station (BS) is equipped with a scheduler, using a simple opportunistic beamforming strategy, which associates an intended user for each of the transmitted data streams. For the case when the channel of the scheduled users is available at the BS we propose an iterative algorithm for joint design of the transmit and the receive beamformers according to mean square error minimization criterion. The proposed method can handle multiple antennas at the BS and at the mobile user with single and/or multiple data streams per scheduled user. The optimization problems encountered in the beamformer design (e.g., covariance rank constraint) are not convex in general. Therefore, the problem of finding the global optimum is intrinsically non-tractable. However, by exploiting the uplink-downlink SINR duality, we decompose the original optimization problem as a series of simpler optimization problems which can be efficiently solved by using standard convex optimization tools. There is no guarantee that the global optimum has been found due to the nonconvexity of the problem, but, the simulations show that the algorithms converge fast to a solution, which can be a local optimum, but is still efficient

The joint cooperative processing of transmitted signal from several multiple-input multiple-output (MIMO) base station (BS) antenna heads is considered for users located within a soft handover (SHO) region. Downlink space-frequency bit and power allocation problem with different BS power constraints is studied for the considered adaptive MIMO-OFDM system. The performance of the proposed heuristic loading method is shown to be close to the optimal convex optimization method with per BS power constraints. It is shown that the highest SHO gains are achieved with a small power imbalance between the received BS powers at low signal-to-noise ratio (SNR), where the achievable rates can be even doubled. On the other hand, the gain from joint processing in SHO quickly diminishes as the imbalance increases, especially at low SNR. Moreover, the results indicate that the joint processing can be even detrimental for the system performance if a coarse phase synchronization between BS antenna head is not guaranteed, as some additional fading on the target SNR values is introduced

Adaptive MIMO-OFDM systems employing eigenmode based signalling have a great potential to increase the spectral efficiency when the channel state information (CSI) is accurately known at transmitter (TX) side. However, the perfect CSI is a too strong assumption for a wireless system operating in frequency selective channels. In the presence of CSI errors, the eigenmodes orthogonality is lost and a spatial equalizer is used at each subcarrier to remove the inter-eigenmodes interference. In this paper we propose using a first order matrix inversion approximation (based on truncated Neumann expansion) to find an upper hound for the covariance matrix of the decision variable at equalizer's output. Based on this upper-bound we are able to find a new bit and power loading algorithm which maximizes the throughput subject to maximum transmit power and maximum frame error rate constraints. The effect of CSI errors on the achievable spectral efficiency is studied by computer simulations for different antenna correlation setups. The results clearly show that the proposed method is robust against CSI errors and channel spatial correlation. The achieved spectral efficiency at low and medium SNR is larger that the outage capacity with no CSI at TX side.

The knowledge of channel state information (CSI) at the transmitter (TX), which in case of time division duplex (TDD) can be easily obtained due to radio channel reciprocity, can dramatically increase the spectral efficiency of a multiple-input multiple-output (MIMO) system. This paper presents a robust link adaptation method for TDD systems employing MIMO-OFDM eigenmode based signalling. We propose a rather simple logarithm-free bit and power loading algorithm which requires low signalling overhead. For unbalanced MIMO systems with larger number of transmit than receive antennas, the achieved spectral efficiency at low and medium SNR is higher than the outage MlMO capacity with unknown CSI at the TX. The simulation results show that for a constant frame error rate, the throughput degradation comparing to the universally accepted Hughes-Hartogs algorithm is negligible.

In a time-division-duplex (TDD) system, the channel state information can be easily obtained at the transmitter side due to channel reciprocity and it can be used to dramatically increase the spectral efficiency of a MIMO system. However, the interference structure between transmission directions does not necessarily correlate at all. In such a case, the modulation/coding parameters assigned based on the reverse link measurements may lead to excessively high frame error rates. If the interference structure between transmission directions differs significantly, A simple closed-loop method to compensate the non-reciprocity between uplink and downlink interference structure is introduced. The simulation results clearly show that in the presence of non-reciprocal interference, some feedback to the transmitter is always needed in order to maintain the desired quality of service at the receiver. The gain from the fast feedback depends on the interference structure and the number of transmitter and receiver antennas.