In cache-assisted wireless edge networking, the proactive content caching policy is acknowledged as an effective way for data traffic relief. In this paper, we introduce an online content caching scheme for a mobile edge computing (MEC) system, which consists of multiple edge servers equipped with limited storage capacity. We formulate the collaborative content caching problem as the minimization of long-term average cost of the system under the uncertainties of users' demands and dynamic content popularity. Then, a two-phase proactive caching algorithm based on deep reinforcement learning (RL) is proposed, which successfully break the curse of high dimensionality by redesigning the output layer of the neural network, and adaptively updates the caching decisions for edge servers. The numerical results show that the proposed proactive caching algorithm is robust to large-scale caching scenarios, able to predict the users' future requests with a high accuracy and collaboratively update the caching policies. Compared to four well-known caching schemes, the proposed scheme outperforms on various performance metrics including the average system cost and overall cache hit rate.

Edge caching-enabled networks can efficiently alleviate data traffic and improve quality of service. However, effectively adapting to users' heterogeneous requests and coordinating among multiple edge servers remains a challenge. In this paper, we address the collaborative cache update and request delivery problem in an edge caching system, aiming to minimize the long-term average system cost under uncertainties of users' heterogeneous demands and dynamic content popularity. To overcome the curse of dimensionality, we decompose the formulated problem into two subproblems: the coordinated proactive cache updating and local request processing. Next, we propose a unified federated deep Q learning (DQL) caching scheme to tackle and coordinate these two subproblems. Particularly, our scheme features a scalable DQL approach with a two-phase action selection procedure to learn the heterogeneous user requests across distributed servers in an online manner. Furthermore, we develop a federated learning (FL)-empowered training process to improve coordination among multiple servers, in which a Thompson sampling (TS)-based algorithm is introduced for smart server selection. We evaluate the performance of our proposed caching scheme in both small-scale and large-scale scenarios through comprehensive experiments, which highlights the advantages of the proposed scheme in terms of caching performance, scalability and robustness.

In this paper, we address the problem of Device-to-Device communication (D2D) in the next generations of cellular networks, where the number of D2D pairs can grow large and, hence, improving spectral efficiency becomes a crucial design factor. More specifically, decentralized channel selection and power control by D2D pairs for interference mitigation without inflicting a heavy controlling overhead on the network become significant challenges in allocating resources. To this end, we introduce an online distributed reinforcement learning algorithm at D2D pairs to maximize network throughput, while guaranteeing both D2D users’ and cellular users’ (CUs) Quality of Service (QoS) under the dynamic wireless channel environment. To track and evaluate the performance of the proposed online algorithm, we define three metrics, i.e., D2D collision probability, D2D access rate and time-average network throughput. The simulation results confirm the convergence property of the proposed algorithm and shows improved performance in terms of three defined metrics as compared to the celebrated Q-learning-based method.

Mobile edge computing (MEC) is a promising technique which migrates computational intensive tasks from smart devices to edge servers, so as to increase the computational capacity of smart devices while saving the battery energy. In this paper, we consider a MEC network system where the smart device with energy harvesting module and electricity storage chooses an offloading rate to offload computational task to one of edge servers. We formulate the offloading problem as a joint minimization problem of energy consumption and delay in the long-term, while meeting the smart device’s quality of experience constraints. The challenge is that the varying renewable energy generation and the energy consumed by the current action will affect the next battery level. To address this problem, we use a reinforcement learning model to account for future dynamics in the environment. To this end, we develop an algorithm based on a deep Q noisy neural network that adjusts automatically the noise level at smart devices for exploration, and hence, replaces the epsilon-greedy policy, traditionally used in the Q-learning algorithm. Simulation results show that the proposed algorithm achieves lower energy consumption and better quality of experience, as compared to the celebrated deep Q-learning algorithm and the random scheme.

In this paper, we consider a multi-access edge computing (MEC) network with one base station (BS) and multiple users. A number of edge computing servers with limited computing and storage capability are attached to the BS to execute the computation tasks offloaded by the users. We develop an online mirror-prox optimization (OMO) algorithm to minimize the overall network delay for task computation. Solving the underlying optimization problem distributively across the users and over a long time horizon to obtain globally optimal decisions at individual users is challenging due to having to cope with time-varying cost function and constraints with unknown statistics. To make the proposed algorithm perform in a distributed and globally optimal manner at users, the BS broadcasts information based on the current states of the servers to individual users. We evaluate the performance of the algorithm using two performance metrics which are dynamic regret, assessing the closeness of the achievable cost against the dynamic optimal value, and aggregate violation, measuring the asymptotic satisfaction of the constraints. The simulation results indicate the effectiveness of the proposed algorithm in the long term and achieve considerable efficiency improvement in lower battery consumption at users' devices.

This work deals with the task offloading problem for multiple cellular edge devices in a multi-access edge computing (MEC) infrastructure attached to a base-station (BS). In order to minimize the overall task computing-communication delay through coping with time-varying cost and constraint functions with unknown statistics on-the-go, we propose a novel distributed bandit optimization (DBO) algorithm which runs based on the projected dual gradient iterations and a single broadcast communicating the MEC states to the SDs at the end of each time-slot. To track the performance of the proposed online learning algorithm over time, we define a dynamic regret to assess the closeness of the underlying delay cost of the DBO to a clairvoyant dynamic optimum and an aggregate violation metric to evaluate the asymptotic satisfaction of the constraints. We derive lower and upper bounds for dynamic regret as well as an upper-bound for the aggregate violation and show that the upper-bounds are sub-linear under sub-linear accumulated hindsight variations. The simulation results and comparisons confirm the effectiveness of the proposed algorithm in the long run.

In this paper, we address the problem of overall delay minimization in a cellular multi-access edge computing (MEC) network, where servers with limited computing and storage resources are co-located with a base station (BS) to execute the computation tasks offloaded by the users. We formulate this problem as a distributed mirror prox (DMP) optimization at individual users and the MEC servers' local controller (LC) over a long-time horizon and develop an online algorithm to ensure queue stability of the overall network in the long run. Both, the cost and the constraint functions are time-varying with unknown statistics. We evaluate the performance of the proposed algorithm using two performance metrics: the dynamic regret to assess the closeness of the achievable cost against the dynamic optimal value; and the aggregate violation to measure the asymptotic satisfaction of the constraints. Given the optimum hindsight variation is sub-linear, we prove that both of the dynamic regret and the aggregate violation are sub-linear in the long run. The simulation results confirm the superiority of the proposed DMP algorithm over the stochastic dual gradient in terms of delay minimization, dynamic regret, aggregate violation and energy efficiency in battery-powered user devices.

This paper studies the problem of joint edge cache placement and content delivery in cache-enabled small cell networks in the presence of spatio-temporal content dynamics unknown a priori. The small base stations (SBSs) satisfy users' content requests either directly from their local caches, or by retrieving from other SBSs' caches or from the content server. In contrast to previous approaches that assume a static content library at the server, this paper considers a more realistic non-stationary content library, where new contents may emerge over time at different locations. To keep track of spatio-temporal content dynamics, we propose that the new contents cached at users can be exploited by the SBSs to timely update their flexible cache memories in addition to their routine off-peak main cache updates from the content server. To take into account the variations in traffic demands as well as the limited caching space at the SBSs, a user-assisted caching strategy is proposed based on reinforcement learning principles to progressively optimize the caching policy with the target of maximizing the weighted network utility in the long run. Simulation results verify the superior performance of the proposed caching strategy against various benchmark designs.

This paper focuses on joint edge cache placement and content delivery problem at a base station (BS) in the presence of spatio-temporal unknown content dynamics, where the BS can satisfy its users' content demands either directly from its local cache or by fetching from the content server. Unlike the previous works that assume a static content library, we consider a more realistic non-stationary scenario, where new contents are emerging over time at the content library and might be cached at users. We propose that the new contents cached at local users can be utilized by the BS to timely update its flexible portion of cache memory in addition to its routine off-peak main cache update from the content server. We model the caching problem as a non- stationary bandit problem and introduce a user-aided caching algorithm that accounts for the traffic demand variations and the limited caching space at the BS. The proposed algorithm progressively improves the caching policy, with the target of maximizing the weighted content delivery rate to the users in the long run. Simulation results validate that the proposed strategy outperforms various benchmark designs.

This paper studies the problem of max-min fairness power allocation in distributed small cell networks operated under the same frequency bandwidth. We introduce a calibrated learning enhanced time division multiple access scheme to optimize the transmit power decisions at the small base stations (SBSs) and achieve max-min user fairness in the long run. Provided that the SBSs are autonomous decision makers, the aim of the proposed algorithm is to allow SBSs to gradually improve their forecast of the possible transmit power levels of the other SBSs and react with the best response based on the predicted results at individual time slots. Simulation results validate that in terms of achieving max-min signal-to-interference-plus-noise ratio, the proposed distributed design outperforms two benchmark schemes and achieves a similar performance as compared to the optimal centralized design.

This paper introduces a combined calibrated learning and bandit approach to online distributed power control in small cell networks operated under the same frequency bandwidth. Each small base station (SBS) is modelled as an intelligent agent who autonomously decides on its instantaneous transmit power level by predicting the transmitting policies of the other SBSs, namely the opponent SBSs, in the network, in real-time. The decision making process is based jointly on the past observations and the calibrated forecasts of the upcoming power allocation decisions of the opponent SBSs who inflict the dominant interferences on the agent. Furthermore, we integrate the proposed calibrated forecast process with a bandit policy to account for the wireless channel conditions unknowna priori, and develop an autonomous power allocation algorithm that is executable at individual SBSs to enhance the accuracy of the autonomous decision making. We evaluate the performance of the proposed algorithm in cases of maximizing the long-term sum-rate, the overall energy efficiency and the average minimum achievable data rate. Numerical simulation results demonstrate that the proposed design outperforms the benchmark scheme with limited amount of information exchange and rapidly approaches towards the optimal centralized solution for all case studies.

The pilot contamination problem creates a limitation to the potential benefits of massive multiple input multiple output (MIMO) systems. To mitigate the pilot contamination, in this study, the authors propose a novel channel estimation for massive MIMO systems, using sparse Bayesian learning (SBL) based on a pattern‐coupled hierarchical Gaussian framework. In the proposed technique, the sparsity of each channel coefficient is controlled by its own hyperparameter and the hyperparameters of its immediate neighbours. The simulation results show that the channel coefficients can be estimated more efficiently in contrast to the conventional channel estimators in terms of channel estimation with pilot contamination. Furthermore, they derive the mean square error (MSE) analytical expression for the proposed technique and based on that MSE expression, a pilot design criterion is proposed to design the optimal pilot to improve the estimation accuracy of the proposed algorithm using the Lagrange multiplier optimisation method. Results show that they can reduce the MSE of the SBL estimator by employing the optimal pilot sequence.

Time-varying wireless channel as well as the variability of renewable energy supply and energy prices are practically unknown in advance. To address such dynamic statistics of wireless networks, this letter develops an adaptive strategy inspired by combinatorial multiarmed bandit model for energy storage management and cost-aware coordinated load control at the base stations. The proposed strategy makes online foresighted decisions on the amount of energy to be stored in storage to minimize the average energy cost over long-time horizon. Simulation results validate the superiority of the proposed strategy over a recently proposed storage-free learning-based design.

This paper studies a robust beamforming optimization problem of minimizing total transmit power in a distributed manner in the presence of imperfect channel state information (CSI) in multicell interference networks. Due to the fact that worst- case is a rare occurrence in practical network, this problem is constrained to satisfying a set of signal-to-interference-plus-noise-ratio (SINR) requirements at user terminals with certain SINR outage probabilities. This problem is numerically intractable due to the cross-link coupling effect among base stations (BSs) operating under the same frequency bandwidth and the robust constraints that involve instantaneous CSI uncertainties. The intractable problem is first converted to a semidefinite programming form with linear matrix inequality constraints via Schur complement, S-procedure and semidefinite relaxation technique, and then decomposed into a set of independent subproblems at individual BSs and solved via subgradient iterations with a light inter-BS communication overhead. Simulation results demonstrate the advantage of the proposed strategy in terms of providing larger SINR operational range as compared with recent proposed designs.

This paper addresses the problem of maximizing the weighted signal-to-interference-plus-noise-ratio (SINR) targets at user terminals in a distributed manner in multicell interference networks. The optimization is constrained to strict individual base station (BS) transmit power limitations in the presence of imperfect channel state information (CSI). This problem is numerically intractable due to the coupling effect among a cluster of BSs operating under the same frequency bandwidth and robust constraints that involve the imperfect CSI. We first convert the original problem into a dual total transmit power minimization problem subject to a set of robust SINR constraints in the centralized worst-case scenario. Then the resulting global optimization problem is decomposed into a set of independent subproblems at individual BSs. Finally, a multi-arm bandit based algorithm is proposed to optimally scale the SINR targets in a distributed manner based on individual BS power budgets, and coordinate intercell interference among the BSs with a light inter-BS communication overhead. Simulation results demonstrate the advantage of the proposed scheme in terms of providing larger SINR operation range and robustness to the CSI uncertainties.

In this paper, we propose a coordinated base-station energy management (CoBEM) technique in which the BSs in a coordinated multipoint (CoMP) with rate-limited backhaul links collaborate to keep the demand and supply balanced using local renewable energies. We formulate two sparse beamforming techniques as ¿0-norm optimisation problems and apply a method that can replace ℓ0-norm with ℓ1-norm and iteratively updated the weight factors to obtain a sufficient sparsity solution called reweighted ℓ1-norm minimisation. For user-centric clustering technique, each user terminal selects a cluster of BSs, whereas, for BS-centric clustering technique, the BSs with a shortage of power budget are authorised to select an optimal number of user terminals based on their available energy budget. In both techniques, we investigate the optimal tradeoff between the BS-receiver cooperation links, the overall energy consumption by the BSs and the energy purchased by the retailer from the realtime market, whilst accounting for the quality-of-service (QoS) requirements for simultaneous wireless information and power transfer (SWIPT). Extensive simulation results confirm that the proposed sparse beamforming techniques significantly improve the infeasibility of a full cooperation in CoMP-SWIPT networks and reveal that the BS-centric clustering is more profitable than the user-centric clustering in real-time energy balancing.

Without a proper observation of the energy demand of the receiving terminals, the retailer may be obliged to purchase additional energy from the real-time market and may take the risk of losing profit. This paper proposes two combinatorial multi-armed bandit (CMAB) strategies in green cloud radio access network (C-RAN) with simultaneous wireless information and power transfer under the assumption that no initial knowledge of forthcoming energy demand and renewable energy supply are known to the central processor. The aim of the proposed strategies is to find the set of optimal sizes of the energy packages to be purchased from the day-ahead market by observing the instantaneous energy demand and learning from the behaviour of cooperative energy trading, so that the total cost of the retailer can be minimized. Two novel iterative algorithms, namely, ForCMAB energy trading and RevCMAB energy trading are introduced to search for the optimal set of energy packages in ascending and descending order of package sizes, respectively. Simulation results indicate that CMAB approach in our proposed strategies offers the significant advantage in terms of reducing overall energy cost of the retailer, as compared to other schemes without learning-based optimization.

We propose a robust coordinated game theoretic approach that distributively minimizes the aggregate downlink transmit power in a multicell interference network in the presence of imperfect channel state information (CSI). The optimization is constrained to satisfying the signal-to-interference-plus-noise-ratio (SINR) requirements at individual user terminals within certain predefined SINR outage probabilities. This problem is numerically intractable due to the cross-link coupling effect among a cluster of base stations (BSs) as well as the robust constraints that involve the second order statistical CSI estimation error. By employing cumulative distribution function of standard normal distribution, Lemma 2 and semidefinite relaxation technique, we first convert the original problem to a linear matrix inequality form. Then, we introduce an iterative subgradient algorithm that decomposes the multicell-wise general problem into a set of parallel subproblems at individual BSs to find the global optimality. We show that the proposed design co-ordinates intercell interference among the BSs with a light inter-BS communication overhead. Simulation results demonstrate the advantages of the proposed chance-constrained distributed design in terms of power efficiency and achievable robustness trade-off.

We introduce a downlink robust optimization approach that minimizes a combination of total transmit power by a multiple antenna base station (BS) within a cell and the resulting aggregate inter-cell interference (ICI) power on the users of the other cells. This optimization is constrained to assure that a set of signal-to-interference-plus-noise ratio (SINR) targets are met at user terminals with certain outage probabilities. The outages are due to the uncertainties that naturally emerge in the estimation of channel covariance matrices between a BS and its intra-cell local users as well as the other users of the other cells. We model these uncertainties using random matrices, analyze their statistical behavior, and formulate a tractable probabilistic approach to the design of optimal robust downlink beamforming vectors. The proposed approach reformulates the original intractable non-convex problem in a semidefinite programming (SDP) form with linear matrix inequality (LMI) constraints. The resulting SDP formulation is convex and numerically tractable under the standard rank relaxation. We compare the proposed chance-constrained approach against two different robust design schemes as well as the worst-case robustness. The simulation results confirm better power efficiency and higher resilience against channel uncertainties of the proposed approach in realistic scenarios.

In this paper, we propose a robust transmission strategy for multi-cell networks equipped with multiple-antenna base stations (BSs) under universal frequency reuse and in the presence of channel estimation error.We propose a distributed optimization scheme, where each BS individually minimizes a combination of its total transmit power and its resulting overall interference inflicted on the users of the adjacent cells, subject to maintaining a desired quality of service at its local users.We transform the proposed scheme to a robust optimization problem for the worst case of errors and derive a semidefinite programming (SDP) using rank-relaxation.We prove that the derived SDP always yields exact rank-one optimal solutions.This is in contrast to the standard rank-relaxed SDP technique that requires an additionally high computational complexity to approximate the solutions with sufficient accuracies, required for an effective beamforming.A comparison of simulation results show that the proposed transmission strategy can expand the signal-tointerference-plus-noise-ratio operational range with significantly reduced power consumption levels at BSs and perform closely to its centralized counterpart.

With enormously increasing demand for mobile data and high data rates, the aggregated power requirements by user terminals may exceed the amount of power budget at the remote radio heads (RRHs). Hence, the retailer has to purchase additional energy from the real-time market, i.e., the grid, and may take a risk of losing the profit. To address the aforementioned issue, this paper studies the real-time energy management for green cloud radio access network (C-RAN) using the local renewable energy generation at RRHs. We develop three different cooperative real-time energy trading strategies, namely, power-shortage management by partial cooperation, power-shortage management by full cooperation, and overall network energy management by full cooperation, to jointly minimize the energy consumption and the real-time energy trading under the constraints of demand and supply power balancing at RRHs and quality of service satisfaction at user terminals. For the first strategy, we formulate a sparse beamforming problem as an ℓ0-norm optimization problem and solve it using semidefinite relaxation (SDR) and iterative reweighted ℓ1-norm approximation of ℓ0-norm. We formulate the second and the third strategies as numerically tractable optimization problems and solve them using the SDR approach. Our simulation results show that the second and the third strategies perform closely up to medium signal-to-interference-plus-noise ratio range and both outperform the first strategy in terms of reducing the total energy cost of the retailer. Furthermore, in terms of cost reduction, all of our three joint cooperative transmission and energy trading strategies achieve significant performance gain, as compared to a baseline scheme that separately optimize the cooperative transmission and energy trading.

Recently, equipping the base stations (BSs) with renewable energy harvesters for green communications has been considered as a promising technique to benefit both the environment and the retailer. However, the uneven distribution of renewable energy generation and the drastically increasing demand for mobile data has put forward the new challenge for the retailer. To tackle this problem, we propose a real-time base-station energy management (RBEM) strategy using renewable energy sources for green communications in coordinated multipoint networks with simultaneous wireless information and power transfer, where all the BSs collaborate to maintain the real-time supply and demand power balancing at the BSs, in order to reduce the overall energy cost of the retailer. The RBEM strategy is formulated as a non-convex linear combinational optimization problem to jointly minimize the energy consumption and energy trading, whilst accounting for the quality-of-service requirements for information receiving terminals and power transfer requirements for energy receiving terminals (ETs). Moreover, an iterative algorithm is proposed to determine the sources of energy harvested by the ETs. The original non-convex problem can be transformed into a numerically tractable form and solved using the semidefinite relaxation approach. Our simulation results confirm that the proposed RBEM strategy significantly reduces overall energy cost of the retailer.

Orthogonal frequency division multiple access and coordinated multipoint transmission/reception are two key techniques employed in LTE-Advanced to provide high-speed connectivity to mobile users regardless of their locations within the cells. The combination of the two techniques in a cellular network with large cells however, results in timing asynchronization. This leads to symbol timing offset (STO) and, thus, degrades users' performance. In this paper, we propose a novel user position aware (UPA) algorithm to tackle the timing offset problem by partitioning the coverage area into different zones based on users' received power requirements. Serving base stations in each zone are assigned such that their distances to a user within the zone are bounded. Hence, the time difference of signals arrived at the user is controlled and, therefore, the effect of STO is mitigated. We analyze the performance of the proposed method by deriving bounds on the STO, and signal-to-interference-plus-noise ratio (SINR) in different zones. Simulation results confirm that the proposed UPA algorithm significantly reduces the STO and improves users' SINR.

In this paper, we investigate power efficient resource allocation for the uplink of LTE networks under delay Quality-of-Service (QoS) constraints. We formulate the resource allocation problem as the minimization of sum power in the uplink under statistical delay QoS provisioning, which is complicated due to the specific constraints of SC-FDMA (uplink air interface in LTE networks). We solve the problem using Canonical duality theory. Numerical results which are obtained using the Invasive Weed Optimization algorithm, show that the proposed resource allocation algorithm not only outperforms classical algorithms in terms of power efficiency while satisfying the QoS requirements, but also performs closer to the optimal solution.

We consider the problem of power-efficient transmit beamforming design at multi-antenna base stations (BSs) of a multicell network, when the channel state information (CSI) at both transceiver ends are imperfect. We introduce an optimization problem that accounts for robustness at, both, the constraints and the objective function. The robust constraints guarantee the quality of service (QoS) at mobile users (MUs) by ensuring that a set of signal-to-interference-plus-noise ratio (SINR) targets are met, despite the presence of erroneous CSI at both the BS and the MU sides. The robust objective function minimizes a linear combination of total transmit power at each BS and the overall inflicted interference power on the other users of the other cells under the worst-case of channel uncertainties. As the proposed problem is NP-hard, in general, we reformulate the problem into a semidefinite programming (SDP) with linear matrix inequality (LMI) constraints using the standard rank relaxation and the S-procedure. The simulation results confirm the effectiveness of the proposed robust beamforming design in terms of power efficiency at BSs and QoS guarantee at MUs, when compared with the conventional method in the presence of imperfect CSI.

Recently, energy efficiency in wireless networks has become an important objective. Aside from the growing proliferation of smartphones and other high-end devices in conventional human-to-human (H2H) communication, the introduction of machine-to-machine (M2M) communication or machine-type communication into cellular networks is another contributing factor. In this paper, we investigate quality-of-service (QoS)-driven energy-efficient design for the uplink of long term evolution (LTE) networks in M2M/H2H co-existence scenarios. We formulate the resource allocation problem as a maximization of effective capacity-based bits-per-joule capacity under statistical QoS provisioning. The specific constraints of single carrier frequency division multiple access (uplink air interface in LTE networks) pertaining to power and resource block allocation not only complicate the resource allocation problem, but also render the standard Lagrangian duality techniques inapplicable. We overcome the analytical and computational intractability by first transforming the original problem into a mixed integer programming (MIP) problem and then formulating its dual problem using the canonical duality theory. The proposed energy-efficient design is compared with the spectral efficient design along with round robin (RR) and best channel quality indicator (BCQI) algorithms. Numerical results, which are obtained using the invasive weed optimization (IWO) algorithm, show that the proposed energy-efficient uplink design not only outperforms other algorithms in terms of energy efficiency while satisfying the QoS requirements, but also performs closer to the optimal design.

This paper focuses on multicell coordinated beamforming in the presence of channel state information (CSI) errors, where base stations (BSs) collaboratively mitigate their intercell interference (ICI). Assuming that the CSI errors are hyper-spherically bounded, we consider an optimization problem that minimizes the overall transmission power of BSs subject to signal-to-interference-plus-noise-ratio (SINR) constraints at each mobile station (MS). We solve this problem in a distributed manner with a limited information exchange among BSs. Using semidefinite relaxation (SDR) and the S-Lemma, we first reformulate our optimization problem into a numerically tractable one. Since the SINR constraints are coupled, we introduce an algorithm by which each BS can obtain a local version of its coupling variables via a small data exchange with other BSs. Then, we propose an iterative algorithm that employs the projected gradient method to coordinate ICI across multiple BSs. Finally, we extend the application of the proposed algorithm to solve the problem of robust and distributed per-user SINR maximization under per-BS power constraints. Simulation results confirm the effectiveness of the proposed algorithm in terms of power efficiency and convergence in the presence of CSI uncertainties.

We introduce a downlink robust optimization problem to minimize an objective function which is a combination of a cost characterizing the total transmit power at each multiple antenna base station (BS) and a penalty term due to the induced aggregate intercell interference power across the multicell network. This optimization is constrained to assure that a set of target signal-to-interference-plus-noise ratio (SINR) levels are maintained at the intracell user terminals with adjustable outage probabilities. The involved uncertainties are due to the errors incurred in estimating both direct downlink channels within each cell and the interfering channels between a BS and the remote user terminals in other cells. By introducing a slack variable, we reformulate the problem so that the probability of imperfection in the penalty term is confined within an adjustable outage. To maintain the tractability of the robust solutions, we derive an equivalent semidefinite programming (SDP) formulation that is convex under the standard rank relaxation. Our simulation results show that at fixed outage probabilities, the proposed scheme is considerably more sensitive to imperfection in direct downlink channels than the imperfection in the interfering channels at a given BS. This observation is due to the presence of the interfering channels within the penalty term that is minimized as a part of the proposed objective function.

In this paper, we study the problem of downlink multicell processing (MCP) when a channel state information is used to design transmit beamforming. This kind of design entails the complexity that requires extra signalling overhead. However, the channel state information (CSI) may be subjected to estimation and quantization errors. To acquire reduction in signalling overhead, we propose a robust multicell downlink beamforming that minimizes a combination of the sum-power, used by each base station (BS) to transmit data to its local users, and the worst-case of the resulting overall interference induced on the other users of the adjacent cells in the presence of imperfect channel state information, whilst also guaranteeing that the worst-cases of the signal-to-interference-plus-noise ratio (SINR) remains above the required level. We consider a spherical uncertainty set to model the imperfection in CSI between the true and the estimated channel coefficients. The original non-convex problem is formulated as the second-order cone programming (SOCP) and then recast the convex constraints in linear matrix inequality (LMI) form. Also, we reformulate a coordinated beamforming with imperfect instantaneous CSI based on spherical uncertainty set using the standard semidefinite relaxation (SDR). Simulation results confirm the efficiency of the proposed method at BSs, compared with the conventional method in the presence of imperfect CSI to validate the theoretical analysis.

In this paper, we introduce a downlink beamforming strategy in a cognitive cell located at the boarder of two adjacent cells of a multicell network to support the local cell-edge users of both cells. The proposed strategy is formulated as an optimization problem to minimize a linear combination of total transmit power of the cognitive base station (BS) and the resulting total interference on the other users located outside of the cognitive cell, so that the signal-to-interference-plus-noise ratio (SINR) targets of the cell-edge users are maintained. In a realistic scenario where CSI may be imperfect, the beamforming design for the cognitive BS based on perfect channel state information (CSI) can easily end up violating the tolerable interference levels of the users falling outside of the cognitive cell. We reformulate the proposed strategy as a robust optimization problem with outage-probability based constraints to account for the imperfection in CSI. Using the S-Procedure, we transform the intractable probabilistic constraints to a computationally tractable set of conservative deterministic constraints. Finally, applying the rank relaxation, we rewrite the resulting problem in semidefinite programming (SDP) form that can be solved using the standard convex optimization packages. The simulation results confirm the effectiveness of the proposed robust scheme in power-efficiently expanding the range of achievable SINR targets for the cell-edge users.

In this paper, cooperative physical layer network coding (CPLNC) is introduced as an energy efficient transmission strategy for future wireless networks. Implementation framework for CPLNC is presented and its performance is analyzed in Rayleigh fading channels. To demonstrate the effectiveness of the proposed scheme in saving energy, we incorporate CPLNC in two major energy-efficient routing algorithms. Simulation results demonstrate the effectiveness of the proposed algorithms in reducing energy consumption.

This paper introduces a transmit beamforming strategy taking into account the positions of primary, secondary victim and intended secondary receivers, to achieve underlay secondary access in multihop cognitive radio networking. The transmit beamforming strategy defines a novel path optimization scheme that deviates from a preselected path given by the routing module, based on local information and according to a relay selection metric. This metric is designed to improve both coexistence with primary/secondary victim receivers and performance of the secondary cognitive network. Simulations compare the proposed strategy with a baseline solution that does not adopt beamforming, and with a strategy that applies beamforming on each hop without modifying the original path. Results show that the proposed strategy is capable of improving coexistence with primary/secondary victims, and highlight that a trade-off exists between the meeting of coexistence constraints and maximisation of secondary network performance.

In this paper, we consider an optimum multicell downlink beamforming that minimizes a combination of the sum-power, used by each base station (BS) to transmit data to its local users, and the worst-case of the resulting overall interference induced on the other users of the adjacent cells in the presence of imperfect channel state information (CSI). The aim is to ensure that the worst-cases of the signal-to-interference-plus-noise ratio (SINR) at each user remains above the required level. The imperfection in CSI is modeled to be bound within an ellipsoidal set, which is used to model the uncertainty between the true and the estimated channel coefficients. Using the S-procedure, we cast the original non-convex problem into a tractable formulation with convex constraints in linear matrix inequality (LMI) form and solve it using the standard semidefinite relaxation (SDR). The results confirm the effectiveness of the proposed robust scheme, in terms of power efficiency at BSs, compared with the conventional method in the presence of imperfect CSI.

In this paper, we focus on robust design of downlink beamforming vectors for multiple antenna base stations (BSs) in a multi-cell interference network. We formulate a robust optimization problem where an individual BS within a cell designs its beamforming vectors to minimize a combination of its sum-power, used for assuring a desired quality of service at its local users, and its aggregate induced interference on the users of the other cells, to balance inter-cell interference across the multiple cells. The proposed robust formulation uses spherical uncertainty sets to model imperfections in the second-order statistical channel knowledge between the BS and the users. To maintain tractability of the robust solutions, we derive an equivalent semidefinite programming (SDP) formulation that is convex under standard rank relaxation. The numerical results confirm the effectiveness of the proposed algorithm under various sizes of uncertainty set and the fact that the attained robust solutions always satisfy the rank constraint.

This paper addresses the problem of strong intercell interference on cell-edge users in conventional cellular networks by deploying cognitive cells within the vicinity of primary cell borders. The cognitive base stations serve primary cell-edge users within the cognitive cells. In return, the cognitive base stations are rewarded by the same spectrum allocated to the primary base stations to serve secondary users. We propose a strategy that is formulated as an optimization problem for the cognitive cell to minimize the total transmit power of the cognitive base station. This optimization problem is subjected to maintain a controlled level of interference at the primary outer-cell users falling outside of the cognitive cell and to assure required levels of signal-to-noise-plus-interference-ratio (SINR) at all primary cell-edge and secondary users within the cognitive cell. Simulation results confirm that the beamforming scheme in conjunction with the proposed cognitive structure lead to a significant reduction in overall power transmitted in the network.

In this paper, we focus on energy efficient routing in wireless ad hoc networks. We exploit modulo combining of continuous-time signals arriving at a receiving node from a number of synchronized transmitting nodes and introduce a cooperative physical layer network coding (CPLNC) scheme. To demonstrate the effectiveness of the proposed scheme in reducing energy consumption, we propose two routing algorithms that utilize CPLNC as a basic building block. Through analysis, we show that with sufficiently long packets, the proposed algorithms can achieve energy saving gains of up to 37% and 62% with respect to the conventional shortest-path routing algorithm in regular line and grid networks, respectively, using less number of hops. We incorporate CPLNC into two major energy efficient routing algorithms and derive threshold distances that enable the current node to intelligently decide whether it should use CPLNC or point to point transmission to the next hop. Simulation results confirm that the proposed and the modified algorithms based on CPLNC outperform their conventional counterparts in terms of achieving significant energy saving gains in networks of randomly distributed nodes.

In this paper, we propose a downlink transmission strategy based on intercell interference pricing and a distributed algorithm that enables each base station (BS) to design locally its own beamforming vectors without relying on downlink channel state information of links from other BSs to the users. This algorithm is the solution to an optimization problem that minimizes a linear combination of data transmission power and the resulting weighted intercell interference with pricing factors at each BS and maintains the required signal-to-interference-plus-noise ratios (SINR) at user terminals. We provide a convergence analysis for the proposed distributed algorithm and derive conditions for its existence. We characterize the impact of the pricing factors in expanding the operational range of SINR targets at user terminals in a power-efficient manner. Simulation results confirm that the proposed algorithm converges to a network-wide equilibrium point by balancing and stabilizing the intercell interference levels and assigning power optimal beamforming vectors to the BSs. The results also show the effectiveness of the proposed algorithm in closely following the performance limits of its centralized coordinated beamforming counterpart.

In this paper, we focus on energy efficient routing in wireless ad hoc networks. We exploit modulo combining of continuous-time signals arriving at a receiving node from a number of synchronized transmitting nodes and introduce a cooperative physical layer network coding (CPLNC) scheme. To demonstrate the effectiveness of the proposed scheme in saving energy, we incorporate CPLNC in two major energy-efficient routing algorithms. In the resulting modified algorithms, we derive threshold distances that enable the current node to intelligently decide whether it should use CPLNC or point to point transmission for the next hop. Simulation results confirm that the energy saving gains of the proposed new algorithms significantly outperform that of the baseline energy-efficient routing algorithms.

In this paper, we tackle the problem of intercell-interference in the absence of cooperation amongst base stations (BSs). The problem originates from the simultaneous transmissions of several BSs to their local users within their corresponding cells using the same frequency band. We propose a decentralized downlink beamforming scheme in cellular interference channels so that individual BSs can independently design their own beamforming vectors based on the locally attainable second order statistical channel state information at each BS. In this scheme, while each BS minimizes a combination of its total transmit power and the resulting total interference on the other vulnerable users of the adjacent cells, it assures that certain targeted signal-to-interference-plus-noise ratio (SINR) levels are achieved at its local users. Using the reciprocity of wireless network and the uplink-downlink duality, we develop a fast-converging iterative algorithm for downlink transmissions by distributed BSs. Finally, we derive a feasibility condition for the existence of a unique solution to the proposed optimization problem. This condition can be used to develop a scheduling algorithm for choosing a set of feasible users within each cell. Simulation results confirm the effectiveness of the proposed decentralized algorithm in closely following the performance curve of its centralized counterpart.

In this paper, we focus on energy efficient cooperative routing in wireless mesh networks of cellular base stations. Our aim is to design a cooperative routing algorithm which is sensitive to both, the power consumed in transmitting the message between the base stations and the power consumed in operating the radio electronics. We introduce the concept of supernode with improved power efficiency and propose a power aware cooperative routing algorithm. Simulation results show a significant performance improvement over well known power saving routing schemes.

In this paper, we study a coordinated beamforming (CBF) scheme for a multi-cell scenario where precoding/beamforming vectors for all coordinating base stations (BSs) are jointly designed in a manner that each BS transmits to its local users. We minimise the total transmit power across coordinating BSs while keeping the signal-to-interference-plus-noise ratio (SINR) at each user above the required level. We formulate the optimisation problem for CBF in a standard semidefinite programming form using instantaneous channel state information. Then, taking into consideration the uncertainty in the estimation of channel covariance matrix, we formulate the CBF problem based on the second order statistical properties of channel so that the robustness of the designed system parameters is guaranteed within a tolerable channel estimation error. We show that, although this robustness comes at the expense of increased overall power, a significant reduction in signaling overhead can be attained with a minor increase in total transmit power at moderately low SINRs.

Recent results on interference alignment (IA) in a K-user n × n MIMO interference channel, have shown that the ratio of total degrees of freedom d̂ to the single user degrees of freedom varies from d̂/n <; 2 in the case of arbitrary MIMO channels (i.e., channels with no structure) to K/2 in the case of channels with diagonal structure (i.e., channels with time or frequency varying coefficients). These results confirm that the information theoretic upper bound on degrees of freedom with no channel extension can only be achieved for K ≤ 3 with an overwhelming overhead of the global channel knowledge or an extensive number of back-and-forth iterations between the transmitting and the receiving nodes [3]. In this paper, we show that this limit on the number of users can be extended to K ≤ 5 as a result of introducing a partial cooperation into the interference channel. This partial cooperation is such that the message of the kth transmitter or base station (BS) is known to the [k - 1)th transmitter (mod K) and each receiver or user is simultaneously served by two transmitters. We introduce a one-shot algorithm that utilizes the reciprocity of wireless network to achieve interference alignment with no iterations between the transmitting and the receiving nodes when d̂/n ≤ 2. Monte-Carlo simulation results show that the proposed partial cooperation used with the one-shot algorithm can considerably enhance the sum rate in an interference channel system at practical SNR levels as a result of improved spatial multiplexing gain.

This study focuses on the problem of cell-edge user coverage in the context of cognitive radio networks operating within the vicinity of primary cell borders. Two strategies are introduced such that the primary cell-edge users get assisted by the cognitive base station (BS) to receive a consistent quality of service (QoS) because of their long distance from the primary BS. In return, the cognitive BS is rewarded by using the same spectrum that has already been allocated to the primary users’ link to serve a group of cognitive users. In the first strategy that we call cooperative, the cognitive BS relays the primary cell-edge users’ data, sent by the primary BS, through spatial multiplexing and beamforming, while transmitting towards its cognitive users. In the second strategy that we call soft interference shaping, the cognitive BS serves cognitive users as well as primary cell-edge users by spatial multiplexing and beamforming, while forming controlled nulls towards the primary users located outside but within the close vicinity of the cognitive cell border. This technique is done to avoid the interference towards the primary users surrounding the cognitive cells border.

In this paper, we address the problem of severe performance degradation of cell-edge users in conventional cellular networks via multicell processing with improved spectral efficiency. Using uplink-downlink duality, we propose an iterative multicell beamforming algorithm that minimises the total transmit power within a virtual cell of 3 adjacent sectors of 3 neighbouring BSs. We derive and employ a channel model that includes the angular spread caused by random local scatterers in the vicinity of a user. Simulation results confirm that the proposed algorithm converges fast to the optimal solution.

Implementing a distributed antenna system (DAS) improves system capacity and reduces transmit power. One challenge in a DAS is intercell interference due to simultaneously supporting multiple users with the same carrier frequency. We propose a multicell beamforming (MBF) technique to remove intercell interference amongst coordinated cells within a DAS. As the MBF technique requires circulations of users' data amongst groups of cells, we introduce a user position aware (UPA) MBF algorithm to reduce backhaul burden. Our proposed UPA MBF scheme guarantees a consistent quality of service to users irrespective of their locations within a cell with minimum total transmit power across base stations of a DAS. An effective sum rate formula is also derived to evaluate the backhaul effects on these schemes.

Various facets of cognitive radio technology have been studied in recent years. However, one possibility that is only thus-far marginally investigated is the ability to perform secondary access at the same time, band and location as ongoing primary transmissions through beamforming to secondary users. Clearly one reason why this has not been at the forefront of research is that it could imply a significant interference to primary users. This paper, however, demonstrates that such an approach is not only feasible, but derives an optimisation problem to constrain interference to primary users while keeping the received signal-to- interference-plus-noise ratio at the secondary users to above a threshold to maintain an acceptable level of quality of service, even taking into account positioning errors. It analyses the described concept and shows good performance against effective beam width increases in secondary transmissions, enacted to improve coverage to account for angular positioning error.

Existing power-aware routing algorithms are fixed and unaware of channel fading dynamics. They are suitable for interconnection of base station subsystem in large cellular networks, where the impact of fading is negligible. However, the height of base station towers could be reduced as the cell size shrinks. In such scenarios, and, in particular, in urban areas, the impact of fading cannot be ignored due to possibility of multipath reflections. Thus, when deployed in real world scenarios, these algorithms suffer from severe performance degradation in terms of power consumption. In this paper, we propose a routing algorithm that takes into account the real channel conditions such as multipath fading. The algorithm is optimised based on location knowledge of nodes and dynamic variation of the channel localised to each node. The results show a power saving gain of around 35% over the baseline scheme.

Optimising the quality-of-service for secondary users and minimising interference to the primary users are two important objectives of cognitive radio networks. In this study, the authors address these issues by introducing interference shaping criteria into orthogonal transmit beamforming (OTBF) strategies. Then, the authors formulate this problem using semi-definite programming (SDP) and find the optimal beamforming weight vectors at the secondary base station. The authors also introduce a resource allocation strategy which enables the base station to ensure a weighted fairness among the secondary users, hence, optimising the network for heterogeneous traffic patterns. The authors show that in terms of satisfying the signal-to-interference-plus-noise-ratio (SINR) requirements at the secondary users, their modified maximin SINR and modified minimum transmit power strategies produce fairly close results at the same transmit powers. However, in terms of minimising interference seen by the primary users, the former outperforms the latter. Finally, the authors study the impact of various interference shaping margins (ISMs) on the OTBF strategies. The authors show that the allocated power by the modified minimum transmit power strategy and the SINRs achieved by the modified maximin SINR strategy are inversely and directly proportional with the ISMs, respectively.

This study derives upper bounds on the throughput of the wireless backhaul link within a cluster of inter-connected three base stations (BSs) or a cluster of a controlling BS and three fixed relay stations. The authors propose and analyse the Ring protocol for the former and the Star protocol for the latter using network coding concept. These protocols can be used individually or in an overlaid fashion in a coordinated multi-cell system or in a cellular-distributed-antenna system to exchange information in the backhaul. To avoid the weakest link acting as a bottleneck and determining the backhaul throughput, as a result, the authors derive throughput optimising time sharing factors to compensate for the resulting bit imbalances in the backhaul.

Here, the authors investigate dirty paper coding (DPC) in intercell cooperative multiple-input multiple-output-orthogonal frequency division multiplexing (MIMO-OFDM). Based on the multidimensional structure of intercell cooperative MIMO-OFDM, the DPC model has been modified by introducing a block QR decomposition (BQRD) algorithm which transforms the multiuser channel to a block lower triangular structure. The overall error performance of the proposed BQRD-based DPC is dominated by the error performance of the last precoded user. To improve the last user's performance, it was, first, shown that, in a system with U active users, the channel gain matrix remains unchanged for any user acting as the last user among all (U−1)! possible permutations of the other users. Then, a greedy algorithm that directly computes U channel gain matrices and obtains a near-optimal precoding order for the last user is proposed. The proposed greedy ordering scheme reduces the computational complexity by a factor of U!/2 compared with the brute force search. Our simulation results confirm that the performance of the greedy ordering scheme approaches that of the brute force search. Furthermore, the authors show that the proposed BQRD-based DPC expands the multiuser rate region and increases the sum rate over block diagonalisation based zero forcing method.

Cognitive radio is presently being researched as a means to greatly improve spectrum usage efficiency, connectivity and QoS among wireless devices and systems. In parallel, efficient and fair resource allocation strategies are being studied in order to address the requirements of future wireless technologies. In this paper, we assess a novel resource allocation scheme for OFDM systems which aims to maximize performance while limiting received interference at each user to below a defined threshold. We show that our scheme is relevant to cognitive radio, and demonstrate that through our approach, significant power savings can be achieved in wireless systems.

The combination of vertical Bell Labs layered space time (V-BLAST) and space time block coding (STBC), known as a Layered STBC (LSTBC) system, offers high spectral efficiency with a higher order of diversity. The system structure, computational complexity and error performance of the V-BLAST and LSTBC multiple-input multiple-output orthogonal frequency division multiplexing systems are analysed and compared. It is shown that, compared with V-BLAST, the overall diversity order of LSTBC increases two fold. This diversity gain is achieved at the expense of a four–fold increase in the computational complexity of the QR decomposition (QRD) algorithm, required at the receiver of both LSTBC and V-BLAST. The authors propose a modified QRD algorithm which reduces this four-fold complexity to two fold.

In this paper we develop a framework for resource allocation in a secondary spectrum access scenario, where a group of cognitive radios (CR) access the resources of a primary system. We assume the primary system is a cellular OFDM-based network operating in uplink. We develop an optimum resource allocation strategy, using cooperative game theory, which guarantees the primary's required QoS and allocates an achievable rate at a given bit error rate for the secondary, when possible. The proposed cognitive radio game (CRG) is a network-assisted resource management method, where users (both primary and secondary) inform the primary system's BS of their channel state information and power limitation and the base station calculates the optimum sub-channel and power allocation for all users. Using Game theoretic axiom of fairness, i.e., Nash Bargaining Solutions (NBS), we develop an alternative efficient and fair resource allocation and compare its performance with the proposed CRG method. We use Sequential Quadratic Programming (SQP) to solve the proposed non-linearly constrained CRG optimization problem.

In this paper we develop a framework for resource allocation in a secondary spectrum access scenario where a group of cognitive radios (CR) access the resources of a primary system. We assume the primary system is a cellular OFDM-based network. We develop the optimum resource allocation strategies which guarantee a level of QoS, defined by minimum rate and the target bit error rate (BER), for the primary system. Using the game theoretic axiom of fairness, i.e., Nash bargaining solutions (NBS), we show that by allocating a priority factor to all players an efficient and fair resource allocation can be achieved. We show how the priority factors are assigned in this scheme and outline a method to select the users who are allowed to share a specific sub-channel.

In this paper we develop a cellular cognitive radio network based on MC-CDMA, which is able to coexist with a number of legacy RANs in a shared band. Using the simple-to-obtain knowledge of RF environment, namely the bandwidth and frequency location of each legacy system, the cognitive MC-CDMA transmitter mitigates the interference in the corresponding receivers by adaptive transmission. We will develop an algorithm for calculating the adaptive transmission parameters and elaborate on error performance of this approach analytically and numerically.

Spectrum sharing methods, such as secondary spectrum access, are powerful candidates to improve spectrum utilization and efficiency. In this paper, we develop a novel resource allocation scheme for WiMAX networks, which is designed to maximize performance while limiting the received interference at each user. By defining different interference tolerances for different sets of users, the proposed allocation scheme can be exploited in a secondary spectrum access scenario where two WiMAX operators, one as the primary operator and the other one as the secondary operator, are using a shared band. The primary system benefits either by charging the secondary system for the use of its resources, or by some form of reciprocal arrangement allowing it to use the secondary system's licensed bands when needed. Numerical results show our resource allocation approach to achieve an excellent fairness, while incurring only a slight reduction in system throughput compared with the theoretical upper limit of opportunistic scheduling.

Efficient and fair resource allocation strategies are being extensively studied in current research in order to address the requirements of future wireless applications. A novel resource allocation scheme is developed for orthogonal frequency-division multiplexing (OFDM) networks designed to maximise performance while limiting the received interference at each user. This received interference is in essence used as a fairness metric; moreover, by defining different interference tolerances for different sets of users, the proposed allocation scheme can be exploited in various cognitive radio scenarios. As applied to the scheme, the authors investigate a scenario where two cellular OFDM-based networks operate as primary and secondary systems in the same band, and the secondary system benefits by accessing the unused resources of the primary system if additional capacity is required. The primary system benefits either by charging the secondary system for the use of its resources or by some form of reciprocal arrangement allowing it to use the secondary system's licenced bands in a similar manner, when needed. Numerical results show our interference-limited scheduling approach to achieve excellent levels of efficiency and fairness by allocating resources more intelligently than proportional fair scheduling. A further important contribution is the application of sequential quadratic programming to solve the non-convex optimisation problems which arise in such scenarios.

In this paper, we propose a cognitive cellular system based on DS MC-CDMA to coexist with a number of narrow-band legacy systems in the same frequency range. If the bandwidth and the frequency location of each narrow-band system are known to this cognitive MC-CDMA, it is possible to mitigate their interfering effect in the cognitive receivers by adaptive transmission. We will develop an algorithm for calculating the adaptive transmission parameters and show that it combats interference in the channel effectively.

The log-normal probability distribution is commonly used in wireless communications to model the shadowing and more recently the small scale fading for indoor ultra wideband communications. In this paper, an accurate closed-form approximation of the average probability of error over a log-normal fading channel is derived for various constellation types and sizes. This expression can be used to evaluate and compare easily the symbol-error performance of communication systems over a log-normal fading channel.

Orthogonal space-time block codes (STBCs) are known to orthogonalise the multiple-input multiple-output (MIMO) wireless channel, thus reducing the space-time vector detection to a simpler scalar detection problem. The capacity of STBCs over correlated Rayleigh and Ricean flat-fading MIMO channels under different adaptive transmitting techniques is studied. Three adaptive schemes known as optimal power and rate allocation, total channel inversion with fixed rate policy and its truncated variant are studied. Taking into account the effect of channel correlation, closed-form expressions are obtained for the capacity of orthogonalised Rayleigh and Ricean MIMO channels under these adaptive transmission techniques in order to avoid Monte-Carlo simulations.

In this paper, we study the capacity of space time block codes (STBCs) over correlated Rayleigh flat-fading MIMO channels combined with adaptive transmitting techniques. We study three adaptive schemes known as optimal power and rate allocation (opra), optimal rate allocation (ora), total channel inversion with fixed rate policy (cifr) and its truncated variant (tifr). We obtain closed form capacity expressions of STBCs combined with these adaptive schemes, taking into account the effect of channel correlation, hence avoiding numerical integrations or Monte-Carlo simulations

In this paper, we study the effect of channel estimation error at the receiver on the mutual information of a multi user multiple input multiple output (MIMO) channel obeying Rayleigh fading. We assume that imperfect knowledge of the channel is available at the receiver and find the upper bound and the lower bound on mutual information for Gaussian input signals for the uplink of this system. We prove that when the input power at each user is uniformly distributed over its transmit antennas, the bounds on the mutual information are asymptotically tight for Gaussian input signals and this tightness increases when the number of users increases. Numerical simulations are conducted to corroborate theoretical results.

In this paper, we study the effect of channel estimation error on transmitter design in a multiple input multiple output (MIMO) channel. We assume that only knowledge of either the channel correlation or mean is available at the transmitter and find the transmitting strategy. Under covariance feedback, at the transmitter the channel is modelled as a matrix of zero mean circularly symmetric complex Gaussian (ZMCSCG) random variables with known covariances. We assume that only rows of channel matrix are correlated. Under mean feedback the covariance of channel is modelled as white. We determine the necessary and sufficient conditions under which a unit rank input covariance matrix, i.e. eigen-beamforming, can achieve capacity lower bound. Numerical simulations are conducted to corroborate theoretical results.

Ultra wideband (UWB) systems have attracted a lot of research interest lately, owing to their appealing features in short-range mobile communications. These features include low power peer-to-peer transmissions, multiple access communications, high data rates, and precise positioning capabilities. Space-time coding (STC) techniques, such as the block coding scheme, or the trellis coding scheme, are known to be simple and practical ways to increase the spectral efficiency in wireless communications. We aim to combine a pulse position modulation (PPM) impulse radio multiple access (IRMA) system with two different space-time coding techniques. Thus, we developed a space-time block code scheme and adapted a space-time trellis code scheme to UWB signalling, relying on the channel state information (CSI) at the receiver side. Then the added diversity produced by these techniques is exploited to enhance the performance of UWB systems. Analysis is conducted in a typical UWB environment, with fractionally-spaced (FS) coherent RAKE detection.

In this paper, we explore code division multiple access (CDMA) systems with multiple transmit and receive antennas combined with space-time trellis codes over a frequency selective channel. The conventional multi-user minimum mean square error (MMSE) detector is generalized to deal with multiple antennas and multiple paths and, then, extended to include the turbo principle in an iterative fashion, allowing interference regeneration and cancellation at the receiver. Iterative multi-user MMSE receivers employing chip level and symbol level detectors are derived and their equivalence is demonstrated. Computer simulations show that the proposed iterative MMSE equalizer completely remote the interference of the other users and transmit antennas in the system, providing a significant improvement compared to the conventional non-iterative multi-user MMSE detector and effectively achieving the single-user performance, even in a fully loaded system.

The bit error rate and frame error rate performance of space-time trellis codes are analysed. The analysis shows that for increasing number of states in the trellis codes, the FER performance improves; however, the BER performance does not always do so. By considering error event paths in trellises of different complexities, it is shown that this effect is owed to the increased recovery time from an erroneous decision needed by the trellis code of higher complexity. The results from this study also indicate certain design criteria when space-time trellis codes are concatenated with channel codes.

We propose a modified BLAST architecture which employs space-time trellis codes at each layer as constituent codes, and an iterative parallel interference canceller (PIC) at the receiver. The PIC performs both interference regeneration and cancellation; and furthermore, the scheme employs maximum-likelihood soft-output decoders, based on the Viterbi algorithm. These decoders calculate the a posteriori probabilities of the codewords in order to produce a soft output. Comparisons are drawn against the original BLAST architecture and simulation results are obtained. A significant gain is achieved in the case of Horizontal BLAST and a similar gain for the case of Diagonal BLAST, at a reduced computational complexity.

The magnitude spectrum of speech is sampled to extract the extremum (maximum) points representing the underlying sine-wave amplitudes. The resulting nonuniform samples are, first, interpolated using a cubic spline function and, then, modelled by an all-pole magnitude spectrum. The gain factor and the line spectral frequency (LSF) domain representation of the coefficients of the all-pole model are quantized at the encoder. A faithful reconstruction of the spectral extremum envelope is obtained at the decoder using the dequantized all-pole model parameters.

We present a novel class of forward error correcting codes constructed using the discrete Walsh transform. They are a class of double-error correcting codes defined on the field of real numbers. An iterative decoding algorithm for Walsh transform codes is developed and implemented. The error correcting performance of Walsh transform codes over an AWGN channel is evaluated. Selected Walsh transform code parameters are compared to those of the well-known BCH and RS codes.

We discuss the split band code-excited linear prediction (SB-CELP) speech coder which employs an iterative version of the harmonic sinusoidal coding algorithm to encode the periodic contents of speech signal. The speech spectrum is split into two frequency regions of harmonic and random components and a reliable fundamental frequency is estimated for the harmonic region using both speech and its linear predictive (LP) residual spectrum. The resulting sinusoidal parameters are interpolated to reconstruct the periodicity in speech waveform. The level of periodicity is controlled by computing a cutoff frequency between the harmonic and random regions of spectrum. The random part of spectrum and unvoiced speech are processed using the CELP coding algorithm. The SB-CELP speech coder which combines the powerful features of the sinusoidal and CELP coding algorithms yields a high quality synthetic speech at 4.05 kb/s.