Artificial intelligence of things (AIoT) is an emerging paradigm integrating artificial intelligence (AI) technologies within the Internet of Things (IoT) paradigm. However, deploying deep-learning models on IoT devices is challenging due to their inherent computational, communications, and security constraints. To address these challenges, we propose Flexible Neuron Selection (FlexNS), a computation- and communication-efficient personalised multi-task transfer learning framework for AIoT.FlexNSenables IoT devices to train their private task-specific shallow models by leveraging a multi-task, deep-learning model pre-trained by a cloud server.FlexNSsignificantly reduces IoT devices’ computational and communications resource demands by selecting a subset of neurons in an early layer of the server’s public model to be connected to the private models of multiple IoT devices. The neurons need to be carefully selected to ensure effective and efficient knowledge transfer to the fine-tuned private models tailored to each IoT device’s specific task. Experimental results show thatFlexNS-based private models achieve 104.3% and 98.4% model accuracy compared to the public model for two datasets on network intrusion detection and image classification tasks, with 99.5% and 98.0% reduction in training and inference time.

Deep Neural Networks (DNNs) have demonstrated remarkable success across various applications, yet some studies reveal their vulnerability to backdoor attacks, where attackers manipulate models under specific conditions using triggers. It significantly compromise the model integrity. Addressing this critical security issue requires robust defence mechanisms to ensure the reliability of DNN models. However, most existing defence mechanisms heavily rely on specialized defence datasets, which are often difficult to obtain due to data privacy and security concerns. This highlights the urgent need for effective data-free defence strategies. In this work, we propose Lipschitzness Precise Pruning (LPP), a novel data-free backdoor defence algorithm that leverages the properties of Lipschitz function to detect and mitigate backdoor vulnerabilities by pruning neurons with strong backdoor correlations while fine-tuning unaffected neurons. Our approach optimizes the computation of the Lipschitz constant using dot product properties, allowing for efficient and precise identification of compromised neurons without the need of clean defence data. This method addresses the limitations of existing data-free defences and extends the scope of backdoor mitigation to include fully connected layers, ensuring comprehensive protection of DNN models. As our approach does not require data exchange, it can be implemented efficiently and effectively in diverse environments. Extensive experiments demonstrate that LPP outperforms state-of-the-art defence approaches without the need for additional defence datasets. We release our code at: https://github.com/LMBTough/LPP

Multiview systems have become a key technology in modern computer vision, offering advanced capabilities in scene understanding and analysis. However, these systems face critical challenges in bandwidth limitations and computational constraints, particularly for resource-limited camera nodes. This paper presents a novel approach for communication-efficient distributed multiview detection and tracking using masked autoencoders (MAEs). We introduce a semantic-guided masking strategy that leverages pre-trained segmentation models and a tunable power function to prioritize informative image regions. This approach, combined with an MAE, reduces communication overhead while preserving essential visual information. We evaluate our method on both virtual and real-world multiview datasets, demonstrating comparable performance in terms of detection and tracking performance metrics compared to state-of-the-art techniques, even at high masking ratios. Our selective masking algorithm outperforms random masking, maintaining higher accuracy and precision as the masking ratio increases. Furthermore, our approach achieves a significant reduction in transmission data volume compared to baseline methods, thereby balancing multiview tracking performance with communication efficiency.

The utility of multiple reinforcement learning (RL) agents collaboratively training within a shared environment, all working towards common objectives, is increasingly evident within the Internet of Vehicles (IoV). The multi-agent Advantage Actor-Critic (MA2C) algorithm is a prominent example of such a Multi-Agent Reinforcement Learning (MARL) system. However, MA2C requires agents to share policies, such as pairs of states and actions and even trained models, among neighboring agents, to overcome the challenge of agents having only partial observations. Unfortunately, this requirement amplifies the communication overhead and raises privacy concerns. Federated learning (FL), as a privacy-preserving machine learning method, can be applied in the MARL context with a central server aggregating the weights of the agents' models. However, this technique assumes that all agents are capable of executing identical actions, which may be impractical. In this paper, we introduce a novel FL A2C algorithm called Advantage Actor Federated Critic (A2FC). The proposed algorithm streamlines the aggregation of agents' critic models while offloading the training of actor models to the individual agents' local machines. An experiment conducted in an adaptive traffic signal control (ATSC) system demonstrates the method's effectiveness in personalizing agents' actions, preserving agents' privacy during training, and mitigating communication overhead issues.

In federated learning (FL), local models are trained independently by clients, local model parameters are shared with a global aggregator or server, and then the updated model is used to initialize the next round of local training. FL and its variants have become synonymous with privacy-preserving distributed machine learning. However, most FL methods have maximization of model accuracy as their sole objective, and rarely are the clients’ needs and constraints considered. In this paper, we consider that clients have differing performance expectations and resource constraints, and we assume local data quality can be improved at a cost. In this light, we treat FL in the training phase as a game in satisfaction form that seeks to satisfy all clients’ expectations. We propose two novel FL methods, a deep reinforcement learning method and a stochastic method, that embrace this design approach. We also account for the scenario where certain clients can adjust their actions even after being satisfied, by introducing probabilistic parameters in both of our methods. The experimental results demonstrate that our proposed methods converge quickly to a lower cost solution than competing methods. Furthermore, it was found that the probabilistic parameters facilitate the attainment of satisfaction equilibria (SE), addressing scenarios where reaching SEs may be challenging within the confines of traditional games in satisfaction form.

Digital beamforming is a crucial technology that enables fifth-generation (5G) devices to operate on millimeter wave (mmW) radio frequencies. However, linearizing massive arrays at the transmitter (TX) side remains a significant challenge as the complexity scales with the number of antennas. This work focuses on the orthogonal frequency division multiplexing (OFDM) modulation in 5G and beyond networks and presents low-complexity linearization approaches that can be used before the precoder. These approaches do not scale with the number of antennas and provide flexible control of the linearization performance across different parts of the spectrum. Simulation results for a 64-element uniform linear array (ULA) demonstrate that the proposed techniques can achieve performance comparable to conventional multi-digital pre-distortion (DPD) with only 8.91% and 3.52% of the complexity.

The use of unmanned aerial vehicles (UAVs) for diverse activities has increased rapidly in recent years. Nonetheless, if operational cyber security is not handled effectively, these technologies offer a significant hazard which can cause catastrophic harm. Therefore, it is important to identify the potential attacks that can be implemented by an adversary. Traditional methods for data integrity designed for the Internet are not suitable for UAV assisted vehicular or wireless sensor networks due to the high communication overhead and latency required. This paper proposes a lightweight data integrity technique called MaDe to address this problem. Every device, at regular intervals, generates an authentication parameter that depends on the packets transmitted. The authentication parameters are only delivered to a central server or the device where the integrity of the packets is verified. At the server, MaDe takes the final decision about an UAV using a generalized likelihood ratio test. MaDe can identify malicious UAVs effectively as demonstrated through our performance analysis. The results show that MaDe detects malicious UAVs with minimum communication overhead and latency.

In this paper, we focus on the problem of remote state estimation in wireless networked cyber-physical systems (CPS). Information from multiple sensors is transmitted to a central gateway over a wireless network with fewer channels than sensors. Channel and power allocation are performed jointly, in the presence of a denial of service (DoS) attack where one or more channels are jammed by the attacker through the transmission of spurious signals. The attack policy is unknown and the central gateway has the objective of minimizing state estimation error with maximum energy efficiency. Therefore, the problem involves a novel combination of discrete and continuous action spaces. In addition, the state and action spaces have high dimensionality and the channel states are not fully known to the defender. We propose a novel model-free and off-policy deep reinforcement learning algorithm to address the problem. The proposed algorithm shows promise in solving online complex CPS problems, outperforming some other existing benchmark algorithms.

The Internet of Vehicles (IoV) may enhance road safety, improve traffic flow, etc. However, Internet-connected intelligent vehicles (IVs) are vulnerable to cyber-attacks. One of the important challenges in IoV is thus, verifying data integrity with strict latency requirements. The conventional way of providing data integrity in the Internet cannot be applied to IoV due to excessive overhead and latency. Therefore, most commercially available IVs do not use any security mechanisms for delay-sensitive traffic. However, if a road side unit (RSU) has been compromised, it can tamper with the data sent or received by IVs. To solve this issue, this article presents a light-weight mechanism called DRiVe to establish data integrity for the IVs and detect malicious RSUs. The DRiVe is based on a probabilistic model to identify malicious RSUs using specially constructed authentication techniques. The authentication parameters are only sent when a vehicle leaves the coverage area of one RSU and enters that of another. DRiVe does not employ any computationally intensive cryptographic primitives. This significantly reduces the security overhead introduced by sending message authentication codes (MACs) with each packet. A security and performance analysis shows that DRiVe can not only identify malicious RSUs effectively but can do so without introducing any significant communication overhead or latency. The proposed scheme reduces the number of bits transmitted by approximately 7% and decreases the latency incurred by 7.5%. For the scenario where malicious vehicles are present, the proposed scheme achieves a probability of detection close to 99%.

There has been a strong interest in using deep neural networks (DNNs) for modeling the power amplifier (PA) non-linearity and designing the digital pre-distortion (DPD) circuit. Most DNNs only accept real-valued inputs since the baseband signal has in-phase and quadrature (I/Q) components. As a result, their entire structures can be highly complex. In this paper, we are interested in reducing the complexity of such structures by exploiting both the envelope-dependent terms and residual learning. To acquire such an efficient structure, we propose a novel methodology executed over two consecutive steps; at first, we estimate the best input combinations to a shallow NN that allows it to achieve a threshold value of NMSE. Then, we exploit these combinations as inputs to our proposed structure and increase the network depth until we obtain our system's actual requirements. Finally, our optimized structure (ODNN) performance has been evaluated using MATLAB simulation and real measurements. For a 15 MHz test signal, ODNN achieves lower NMSE than conventional DNN by 2.13 dB and 3.08 dB for Doherty PA behavioral modeling and its DPD design, respectively. For a broader 40 MHz test signal, ODNN achieves lower NMSE by 0.94 dB and 1.94 dB. Moreover, in all previous scenarios, ODNN reduces the complexity of DNN by 26.40%.

A practical power amplifier (PA) has nonlinear characteristics that distort the output signal and hence increase the transmission error. Digital pre-distortion (DPD) has been widely accepted to compensate for the PA nonlinearity. However, in direct-conversion transmitters (DCTs), DPD performance is affected by in-phase and quadrature (IQ) imbalance. In this paper, we utilize the Iterative Learning Control (ILC) algorithm to design a DPD scheme to compensate for PA nonlinearity under IQ imbalance. We first prove that ILC is applicable in such a scenario. This proof is validated using simulations which show that ILC is able to estimate the PA ideal input. The estimated ideal input is then exploited in training a neural network (NN)-based DPD model. We provide the complexity estimation of our proposed scheme using the number of real multiplications. Finally, we demonstrate the performance advantage of our proposed scheme in comparison with other existing polynomial based approaches through simulations and measurements.

Compromised relays in clustered IoT networks can be used to launch attacks that cannot be easily detected using the traditional security algorithms. In this paper, we consider an attack where a compromised relay deliberately drops the packets received from the IoT devices it serves. Such an attack causes the IoT devices to retransmit more frequently, thereby increasing their processing load. As a result, their batteries will drain at a faster rate. The difficulty in differentiating a genuine packet drop event from a malicious packet drop event makes it necessary to develop a novel Intrusion Detection System (IDS) specially tailored for detecting such an attack. The IDS is installed in a special node called a sentinel, which monitors the network. The sentinel also estimates the packet retransmission rate of the IoT devices, a parameter required for the IDS. The effectiveness of the system is demonstrated experimentally on a clustered network.

Clustering Internet of Things (IoT) networks, to alleviate the network scalability problem, provides an opportunity for an adversary to compromise a set of nodes by simply compromising the relay they are associated with. In such scenarios, an adversary who has compromised the relay can affect the network's performance by deliberately dropping the packets transmitted by the IoT devices and/or by corrupting the packets to be forwarded by the relay. In this way, the adversary can successfully mimic a bad radio channel between the IoT devices and the relay, thereby requiring the IoT devices to retransmit more frequently. Such a strategy increases the processing load on the IoT devices and will drain their batteries at a faster rate. To detect such an attack, we present hybrid intrusion detection systems that rely on the monitoring of uplink and downlink packets transmitted between IoT devices and the relay. Specifically, we compare the observed packet drop probabilities against their long-term expected values. The detection rules proposed originate from the generalized likelihood ratio test, where the adversary parameters are estimated using maximum likelihood estimation. A semi-analytical approach to obtain the expressions for the false alarm probability is presented in order to determine the decision thresholds. Results presented show the effectiveness of the proposed detection systems, demonstrate the impact of the choice of adversary parameters on them, and validate the expressions obtained for the false alarm probability.

Byzantine attack is a severe security concern in network coding enabled wireless ad hoc networks, because the malicious nodes can easily inject bogus packets into the information flow and cause an epidemic propagation of pollution. In this paper, we address the Byzantine attack by proposing a malicious node identification scheme, which can achieve a high identification accuracy on malicious nodes and protect the benign nodes from being mis-identified as attackers. We consider two practical challenges, namely, 1) only a fraction of the intermediate nodes can be deployed as defenders; and 2) the malicious nodes are intelligent-they pretend to be legitimate nodes probabilistically to reduce the chances of being identified. Theoretical analysis and extensive simulations show that our scheme performs well even under the conditions mentioned above. Furthermore, we conduct a series of comparisons between our scheme and several existing schemes, which show that our scheme outperforms them in both identification accuracy and valid throughput during the identification procedure. Finally, we present a two-player game theory framework to find the optimal strategy for the defender, and also provide a case study of the defender's strategy optimization.

Mobile Edge Computing can be used to realize the low latency requirements of vehicular networks. However, by compromising the road side units (RSUs), an adversary can introduce an extra delay leading to various problems such as the wastage of edge computing resources and disruption of navigational and safety functions. The compromised RSU can for instance deliberately corrupt the PHY layer payload of the packets to be transmitted to the vehicles. With this simple attack, the adversary would increase latency and through that effect, create serious disruptions. Such an attack can affect many critical delay sensitive applications such as collision avoidance. To detect the presence of such an adversary, we propose a trust based detection system in this paper. Each vehicle transmits a feedback packet about every RSU it has interacted with to a central trusted server. Using the feedback obtained from multiple vehicles, at regular intervals, an aggregated trust value for each RSU in the network is obtained and is compared with a threshold to classify the RSU as authentic or malicious. We also present a mechanism to detect the presence of malicious vehicles reporting false feedback in the network. Simulation results presented demonstrate the effectiveness of the proposed detection mechanism and the impact of the choice of adversary parameters on the detection system.

For a radio resource-limited multi-tier machine-type communication (MTC) network, controlling random access congestion while satisfying the unique requirements of each tier (type) and guaranteeing fairness among nodes is always a challenge. In this paper, we study the network dimensioning and radio resource partitioning for the uplink of an MTC network with the signal-to-interference ratio-based clustering and relaying, where the MTC gateways (MTCGs) capture and forward the packets sent from MTC devices (MTCDs) to the base station (BS). Specifically, under transmission outage probability constraints, we investigate the trade-off between network utility (in terms of transmission capacity and revenue) and resource allocation fairness. With both outage probability constraints and minimum MTCD density constraints, we propose approaches to maximize the weighted sum of quality of experience of different tiers of MTCDs. Furthermore, a transmit power control strategy for MTCG-to-BS link is proposed to achieve a constant data rate.

We consider a multiple-access channel (MAC), where transmitters are powered by energy harvesting. They are equipped with batteries having non-ideal charging and discharging characteristics, resulting in a fractional loss of power driven into or drawn from them. Assuming that each user consumes constant power for circuit operation during transmission, we optimize the throughput region, the set of all tuples of the number of bits delivered by the users over a finite duration of time. When circuit powers are zero, it is known that a non-orthogonal multiple access (NOMA) strategy, called Pure-NOMA (P-NOMA), where user transmissions always overlap, achieves all points on the largest throughput region. We show that P-NOMA is no longer optimal with non-zero circuit power and propose a hybrid strategy called H-NOMA that combines P-NOMA with time-division multiple access (TDMA). H-NOMA allocates fixed time windows for single-user and non-orthogonal multi-user transmissions. We maximize the sum-throughput in H-NOMA with non-casual and causal knowledge of the harvested powers and channel power gains. With causal knowledge, we obtain the optimal online policy via dynamic programming and deduce some structural properties and propose a simpler suboptimal online policy that performs significantly better than a naive greedy policy. We numerically show the largest throughput regions of P-NOMA and TDMA are contained within that of H-NOMA.

We consider a multiple-access channel where the users are powered from batteries having non-negligible internal resistance. When power is drawn from the battery, a variable fraction of the power, which is a function of the power drawn from the battery, is lost across the internal resistance. Hence, the power delivered to the load is less than the power drawn from the battery. The users consume a constant power for the circuit operation during transmission but do not consume any power when not transmitting. In this setting, we obtain the maximum sum-rates and achievable rate regions under various cases. We show that, unlike in the ideal battery case, the TDMA (time-division multiple access) strategy, wherein the users transmit orthogonally in time, may not always achieve the maximum sum-rate when the internal resistance is non-zero. The users may need to adopt a hybrid NOMA-TDMA strategy which combines the features of NOMA (non-orthogonal multiple access) and TDMA, wherein a set of users are allocated fixed time windows for orthogonal single-user and non-orthogonal joint transmissions. We also numerically show that the largest achievable rate regions in NOMA and TDMA strategies are contained within the largest achievable rate region of the hybrid NOMA-TDMA strategy.

Constant envelope (CE) precoding is an appealing transmission technique, which enables highly efficient power amplification, and is realizable with a single radio frequency (RF) chain at the multiantenna transmitter.In this paper, we study the transceiver design for a point-to-point multiple-input multiple-output (MIMO) system with CE precoding.Both single-stream transmission (i.e., beamforming) and multi-stream transmission (i.e., spatial multiplexing) are considered.For single-stream transmission, we optimize the receive beamforming vector to minimize the symbol error rate (SER) for any given channel realization and desired constellation at the combiner output.By reformulating the problem as an equivalent quadratically constrained quadratic program (QCQP), we propose an efficient semi-definite relaxation (SDR) based algorithm to find an approximate solution.Next, for multi-stream transmission, we propose a new scheme based on antenna grouping at the transmitter and minimum mean squared error (MMSE) or zero-forcing (ZF) based beamforming at the receiver.The transmit antenna grouping and receive beamforming vectors are then jointly designed to minimize the maximum SER over all data streams.Finally, the error-rate performance of single-versus multi-stream transmission is compared via simulations under different setups.

The scale of connected devices in the modern communication network and its heterogeneous nature have made securing the network more challenging. However, with the advent of software defined networking (SDN), the algorithmic complexity is handled at a centralized control plane and the network elements perform only data forwarding based on control plane decisions. This enables researchers to design innovative security protocols at the control plane to dynamically defend against attacks. In this paper, we propose a dynamic attack-resilient routing (ARR) approach and develop an optimization formulation for fragmented multipath routing taking reliability and load into consideration for SDN-enabled networks. Though erasure encoding has been well studied for resilient data storage, it is rarely mentioned in the context of network routing owing to its complexity, redundancy, and difficulty of satisfying practical routing constraints. In this paper, we dynamically determine the optimal route for erasure-encoded fragments of the data, in terms of attack resilience, under the constraint on allowable encoding redundancy. Since the ARR algorithm is computationally prohibitive for larger networks, we develop a heuristic solution for the same using a multipath-tree. The proposed algorithm dynamically routes the data fragments along a set of reliable and lightly loaded paths to achieve multipath diversity and thereby improve data availability at the destination even in the presence of attacks. We demonstrate the effectiveness of our proposed approach in terms of weighted path reliability, resilience, and blocking performance through simulations.

To address the problem of multiple access for a large number of devices in machine-type communication (MTC), we study the use of full-duplex relays. In-band-full-duplex (IBFD) allows devices to receive and transmit concurrently on the same frequency band, potentially doubling the spectral efficiency. On the downside, the use of IBFD raises mutual interference due to an increased density of concurrently transmitting nodes. This paper deals with the tradeoff between the increased spectral efficiency and increased interference in utilizing the IBFD uplink relays in a densely deployed MTC network. Using stochastic geometry, we develop a framework to evaluate the end-to-end outage probability and the uplink data transmission rate in a single-hop relay network for MTC. A fraction of MTC devices (MTCDs) can be scheduled to transmit in the same band as the relay nodes, with the rest transmitting on an orthogonal band. The relay nodes, therefore, operate either as IBFD or out-band-full-duplex (OBFD) depending on their associated MTCD and forward packets to base stations (BSs). The BSs employ linear zero-forcing filters to cancel the interference from their associated relays. We calculate the optimal fraction of in-band MTCDs and relative transmit powers of relay and MTCDs to maximize the area spectral efficiency. The appeal of in-band scheduling increases as more antennas are deployed at the BS.

We consider rate maximization for an energy harvesting node transmitting delay-constrained information over a slow fading channel corrupted by additive white Gaussian noise. Time is divided into frames of fixed duration equal to the channel coherence block length, the time duration for which the channel power gain remains constant before changing to a different value, independently. We assume that the transmitter does not know the exact channel state but has access to the channel statistics. The transmitter is equipped with a battery having non-zero internal resistance. Using superposition coding, we formulate and study an average rate maximization problem with non-causal knowledge of the harvested power. Further, assuming statistical knowledge and causal information of the harvested power variations, we propose a sub-optimal algorithm, and compare with the stochastic dynamic programming based solution and a greedy policy.

For a radio resource limited multi-tier Machine-type Communication (MTC) network, controlling random access congestion while satisfying the unique requirements of each tier and guaranteeing fairness among nodes is always a challenge. In this paper, we propose three approaches for network dimensioning and radio resource partitioning for the uplink of an MTC network with signal-to-interference ratio (SIR)-based clustering and relaying, where MTC gateways (MTCGs) capture and forward the packets sent from MTC devices (MTCDs) to the base station (BS). Assuming that each tier has a different outage probability constraint, our first resource allocation method achieves maximum sum network capacity, while the other two methods achieve suboptimal sum network capacity with higher degrees of fairness. The proposed methods are also applicable when the objective is the network operator's total revenue instead of sum network capacity.

In this paper, we study a single-user multiple-input single-output (MISO) system with constant envelope (CE) transmission. To enable the nonlinear mapping from a fixed receiver signal constellation to the transmitter CE signal vectors, the availability of perfect channel state information at the transmitter (CSIT) is assumed in existing literature. However, traditionally, CSIT needs to be acquired at the cost of additional channel training and feedback overhead, which increases with the number of transmit antennas. In this paper, we propose a novel adaptive online signal constellation design for MISO CE transmission with significantly reduced training time and feedback complexity compared to the traditional training with fixed constellation set. Numerical results show that our proposed scheme outperforms the traditional scheme in terms of average throughput and yet with less training time required.

It is well known that clustering sensor nodes and letting those with superior resources act as cluster heads help to address the network scalability problem in wireless sensor networks (WSNs). However, the clustered architecture provides the opportunity for an adversary to attack sensor nodes within a cluster by simply compromising the cluster head. This paper investigates the attack detection issues in clustered WSNs where cluster heads are exposed to hostile environments. We propose a novel cross- layer scheme that detects abnormal cluster head behaviors. The proposed approach only makes use of the transmitted information of sensor nodes and does not require additional energy consumption at sensor nodes to monitor the cluster heads. Analytical results for detection statistics and simulations are presented to show the effectiveness of the proposed scheme.

To address massive access in machine-type communication (MTC), we consider in-band full- duplex (IBFD) relays to aggregate MTC packets. IBFD allows devices to receive and transmit concurrently on the same frequency band, potentially doubling the spectral efficiency. On the downside, the use of IBFD raises the interference due to an increased density of simultaneously transmitting nodes. This paper deals with a key trade-off between the increased spectral efficiency and interference in utilizing IBFD uplink relays in a densely deployed MTC network. Using stochastic geometry, we develop a framework to evaluate the end-to-end outage probability and uplink data aggregation rate. The model consists of MTC devices and IBFD relay nodes that forward MTC packets to base stations (BSs) over single-input-multiple-output channels. The BSs employ linear zero-forcing filters to cancel the interference from their associated relay nodes which may transmit simultaneously on the same frequency band. We show the somewhat surprising result that IBFD relaying does not lead to performance gains over half-duplex relays, when devices and relays are independently and homogeneously distributed in space.

Modern systems will increasingly rely on energy harvested from their environment. Such systems utilize batteries to smooth out the random fluctuations in harvested energy. These fluctuations induce highly variable battery charge and discharge rates, which affect the efficiencies of practical batteries that typically have non-zero internal resistance. In this paper, we study an energy harvesting communication system using a finite battery with non-zero internal resistance. We adopt a dual-path architecture, in which harvested energy can be directly used, or stored and then used. In a frame, both time and power can be split between energy storage and data transmission. For a single frame, we derive an analytical expression for the rate optimal time and power splitting ratios between harvesting energy and transmitting data. We then optimize the time and power splitting ratios for a group of frames, assuming non-causal knowledge of harvested power and fading channel gains, by giving an approximate solution. When only the statistics of the energy arrivals and channel gains are known, we derive a dynamic programming-based policy and propose three sub-optimal policies, which are shown to perform competitively. In summary, this paper suggests that battery internal resistance significantly impacts the design and performance of energy harvesting communication systems and must be considered.

To accommodate massive access for machine-type communication (MTC) in cellular networks, we propose two single-hop relaying schemes exclusively designed for the MTC: signal-to-interference ratio-based relaying and location-based relaying. The relay nodes capture one or more MTC device (MTCD) transmissions and forward them to the base station (BS), thus reducing the signaling overhead in the cellular network. To efficiently use the spectrum, MTCDs make concurrent transmissions, and therefore create interference with each other. Unlike related work assuming known interference values for each link, we use stochastic geometry to characterize the received signal and interference powers. We derive the outage probability expressions and analytically calculate the maximum density of MTCDs that can be supported under an outage constraint. Moreover, the optimal partitioning of spectrum resources into the MTCD-to-relay, and relay-to-BS links, for maximum transmission capacity is presented.

We consider the design of energy management policies for multimedia wireless sensor nodes that rely entirely on harvesting energy from the environment for both the data acquisition and transmission. In many high volume data sensing applications, the sampled data is compressed before transmission to meet the bandwidth and transmit power constraints. The compression results in data distortion, but it reduces the amount of data to be transmitted. As a consequence, the transmission energy is reduced, but excessive compression may consume more energy than what is saved by transmitting less data. This points to a trade-off between compression and transmission (in terms of both the energy and time allocated to these operations). Our goal is to identify the optimal energy management policies that minimize the long-term average distortion at the receiver. We first study the optimal solution in an off-line setting and then propose three on-line policies. We highlight the importance of the compression power, showing that, all other system parameters being equal, the average distortion decreases exponentially as the compression power is increased by processing at a faster rate.

In this paper, we study the receive beamforming design to minimize the symbol error rate (SER) in a point-to-point multiple-input multiple-output (MIMO) system with constant envelope (CE) precoding. In this case, a constellation is feasible at the combiner output of the receiver if and only if it can be scaled to lie in an annular region, whose boundaries are determined by channel realization, receive beamforming and per-antenna transmit power. By approximating the exact SER with its union bound, we aim to optimize the receive beamforming weights to maximize the minimum Euclidean distance (MED) between any two signal points at the combiner output for any desired constellation and given channel realization, subject to the feasibility constraint of the constellation. We first show that under the assumption of independent and identically distributed (i.i.d.) Rayleigh fading channel, this problem is feasible as long as there are no more transmit antennas than receive antennas. Then, we assume the aforementioned condition holds and reformulate this problem into an equivalent quadratically constrained quadratic program (QCQP), for which we find an approximate solution by applying the semidefinite relaxation (SDR) technique and a customized Gaussian randomization method. Numerical results show that our proposed receive beamforming scheme achieves significantly improved SER performance than other benchmark schemes.

In this paper, we propose a Fragmentation-based Multipath Routing (FMR) model for Software Defined Networks (SDNs) to enable attack-resilient data transfer. With the use of erasure encoding to fragment a message, the fragments are routed along multiple paths such that no intermediate node receives enough fragments required for message decoding. This ensures that, any intruder on a compromised node does not infer the original data from the received fragments. We develop an optimization programming formulation of the problem to choose reliable paths that provide resilience to attacks. Using FMR, the SDN controller dynamically routes the data fragments along a set of most reliable paths to achieve multipath diversity and hence improve data availability at the destination even in the presence of an attack. We carry out performance studies and demonstrate the effectiveness of our approach in terms of weighted path reliability and blocking performance.

In this paper, we propose an online energy management strategy to minimize the operational expenses incurred by cellular base stations powered by both renewable and conventional energy. Our proposed strategy uses an Auto Regressive Integrated Moving Average (ARIMA) time series model for inter-day forecasting, a Markov chain model for intra-day predictions, and linear programming techniques for optimizing the decision variables on a real-time basis. To the best of our knowledge, the potential of these techniques has not been sufficiently explored in the literature. We assume that the base station is equipped with a rechargeable battery and a solar panel. Moreover, we consider real-time electricity pricing, and the application of a net-metering policy, whereby consumers are allowed to add excess renewable energy to the grid and obtain kilowatt credits in return. To evaluate the performance of the proposed algorithm, we devise an offline strategy which assumes non-causal knowledge of renewable energy generation and hence provides an upper bound in performance. Finally, we present numerical results obtained using real meteorological data, practical solar panel specifications, and factual energy tariffs. Through simulations we benchmark the proposed algorithm against the genie-aided strategy, and show its robustness by considering random energy rates.

To accommodate massive access for machine-type communication (MTC) in cellular networks, we propose a signal-to-interference ratio based clustering for the MTC. The cluster heads operate as single hop relays between successfully decoded MTC devices (MTCDs) and the base station (BS), reducing the signaling overhead. MTCDs randomly select channels to transmit on, and therefore create interference to each other. Unlike related work assuming known interference values for each link, we use stochastic geometry to characterize the received signal and interference powers. Given limited spectrum resources for MTC, we derive the outage probability expression and analytically calculate the maximum density of MTCDs that can be supported under an outage constraint. Moreover, the optimal partitioning of spectrum resources into the MTCD-to-cluster head links and relay links for maximum transmission capacity is presented.

In this paper, we consider a novel mobile relaying technique, where the relay nodes are mounted on unmanned aerial vehicles (UAVs) and hence are capable of moving at high speed. Compared with conventional static relaying, mobile relaying offers a new degree of freedom for performance enhancement via careful relay trajectory design. We study the throughput maximization problem in mobile relaying systems by optimizing the source/relay transmit power along with the relay trajectory, subject to practical mobility constraints (on the UAV's speed and initial/final relay locations), as well as the information-causality constraint at the relay. It is shown that for the fixed relay trajectory, the throughput-optimal source/relay power allocations over time follow a “staircase” water filling structure, with non-increasing and non-decreasing water levels at the source and relay, respectively. On the other hand, with given power allocations, the throughput can be further improved by optimizing the UAV's trajectory via successive convex optimization. An iterative algorithm is thus proposed to optimize the power allocations and relay trajectory alternately. Furthermore, for the special case with free initial and final relay locations, the jointly optimal power allocation and relay trajectory are derived. Numerical results show that by optimizing the trajectory of the relay and power allocations adaptive to its induced channel variation, mobile relaying is able to achieve significant throughput gains over the conventional static relaying.

The transmission capacity of wireless networks is limited by the intensity of the interference received from concurrent transmissions. Interference causes serious performance degradation, particularly when no centralized controller within the network exists. Cognitive radio (CR) is a promising solution for distributed interference management as users with CR capabilities can acquire local activity and position information to achieve spatial reuse while limiting interference to neighboring users. Considering a two-tier network consisting of a licensed primary network overlaid by an unlicensed secondary tier, this paper proposes CR-based spectrum access schemes for secondary users (SUs). Acquiring the activity information of nearby users, the SUs are activated only when they are outside the exclusion zone of primary receivers. Additionally, the active secondary transmitters are separated from each other by forming secondary exclusion zones around themselves. Using stochastic geometry, primary and secondary exclusion zone sizes that maximize the transmission capacity under per-tier outage constraints are calculated. Analytical results supported by numerical simulations, suggest primary exclusion zones reduce predominantly the cross-tier interference while the secondary exclusion zone size is critical in mitigating the interference among SUs.

Constant envelope (CE) precoding is an appealing transmission technique, which enables the realization of high power amplifier efficiency. For CE precoding in a single-user multiple-input single-output (MISO) channel, a desired constellation is feasible at the receiver if and only if it can be scaled to lie in an annulus, whose boundaries are characterized by the instantaneous channel realization. Therefore, if a fixed receiver constellation is used for CE precoding in a fading channel, where the annulus is time-varying, there is in general a non-zero probability of encountering a channel that makes CE precoding infeasible, thereby causing a high probability of error. To tackle this problem, this paper studies the adaptive receiver constellation design for CE precoding in a single-user MISO flat-fading channel with an arbitrary number of antennas at the transmitter. We first investigate the fixed-rate adaptive receiver constellation design to minimize the symbol error rate (SER). Specifically, an efficient algorithm is proposed to find the optimal amplitude-and-phase shift keying (APSK) constellation with two rings that is both feasible and of the maximum minimum Euclidean distance, for any given constellation size and instantaneous channel realization. Numerical results show that by using the optimized fixed-rate adaptive receiver constellation, our proposed scheme achieves significantly improved SER performance over CE precoding with a fixed receiver constellation. Furthermore, based on the family of optimal fixed-rate adaptive two-ring APSK constellation sets, a variable-rate CE transmission scheme is proposed and numerically examined.

We consider the design of transmission policies for sensor nodes that rely entirely on harvesting energy from the environment. Nodes store the harvested energy in a storage element which has constraints on maximum discharge rate and charging efficiency. Non-zero circuit power is considered and limitations on channel bandwidth and processor clock-rate are incorporated. We assume an additive white Gaussian noise (AWGN) channel and that time is divided into frames, with a fixed number of symbols transmitted in the frame duration. We consider an energy arrival process in which energy arrives at a constant rate within a frame but varies stochastically and independently across frames. In this context, we propose a dual-path architecture for energy flow that is shown to mitigate the performance loss due to the discharge rate constraint. For a given frame, we determine the rate-optimal time sharing ratio between harvesting energy and transmitting data. We also propose three sub-optimal policies, including statistical directional water-filling, for determining the time sharing ratio for a group of frames and compare their performance with an upper bound. We highlight that the discharge rate constraint is an important limitation of the storage element that can potentially hinder the effective use of energy.

Constant envelope (CE) precoding is an appealing transmission technique which enables the use of highly efficient nonlinear radio frequency (RF) power amplifiers (PAs). For CE precoding in a single-user multiple-input single-output (MISO) channel, a desired constellation is feasible at the receiver if and only if it can be scaled to lie in an annulus, whose boundaries are characterized by the instantaneous channel realization. Therefore, if a fixed receiver constellation is used for CE precoding in fading channel, where the annulus is time-varying, there is a non-zero probability of encountering a channel that makes CE precoding infeasible. To tackle this problem, we study the fixed-rate adaptive receiver constellation design for CE precoding to minimize symbol error rate (SER) in a single-user MISO flat-fading channel with an arbitrary number of antennas at the transmitter. Specifically, this paper proposes an efficient algorithm to find the optimal two-ring amplitude-and-phase shift keying (APSK) constellation that is both feasible and of the maximum minimum Euclidean distance (MED), for any given constellation size and instantaneous channel realization. Numerical results show that by using the optimized adaptive receiver constellation, our proposed scheme achieves significantly improved SER performance than CE precoding with fixed receiver constellation. Furthermore, with the PA efficiency gain achieved by CE precoding, our proposed scheme requires less transmitter power consumption to achieve a desired SER level than linear precoding schemes under the less-stringent average per-antenna power constraint (PAPC).

In this paper, we propose an evolutionary algorithm to minimize the energy bill incurred by a cellular base station (CBS) that is equipped with a rechargeable battery. Assuming time-of-use electricity pricing, we formulate an optimization problem to minimize the energy expenditures incurred by the CBS. Unlike existing strategies, the proposed algorithm takes into account the non-linear properties of the rechargeable battery. More specifically, Peukert's law is used to model the energy loss incurred during the discharging operation. As a result, the energy-bill minimization problem is non-convex. We therefore use the concept of evolution strategy to devise an algorithm that minimizes the energy bill, and at the same time ensures maximum renewable energy usage. The proposed algorithm exhibits linear time complexity and is able to achieve convergence within a few hundreds of iterations. Finally, we use simulations to show that the proposed algorithm outperforms the solutions based on linearised models.

This paper studies a multiple-input single-output (MISO) broadcast channel (BC) featuring simultaneous wireless information and power transfer (SWIPT), where a multi-antenna access point (AP) delivers both information and energy via radio signals to multiple single-antenna receivers simultaneously, and each receiver implements either information decoding (ID) or energy harvesting (EH). We characterize the capacity region for ID receivers under given energy requirements for EH receivers, by solving a sequence of weighted sum-rate (WSR) maximization (WSRMax) problems subject to a maximum sum-power constraint for the AP, and a set of minimum harvested power constraints for individual EH receivers. The problem corresponds to a new form of WSRMax problem in MISO-BC with combined maximum and minimum linear transmit covariance constraints (MaxLTCCs and MinLTCCs), which has not been addressed in the literature and is challenging to solve. By extending the general BC-multiple access channel (MAC) duality, which is only applicable to WSRMax problems with MaxLTCCs, and applying the ellipsoid method, we propose an efficient algorithm to solve this problem globally optimally. Numerical results are presented to validate our proposed algorithm.

In this paper, we propose an energy cost minimization strategy for cellular base stations (CBSs) that are jointly powered by renewable and conventional energy. Energy tariffs follow time-of-use and consumption-based block pricing. In addition, consumers are allowed to return energy to the utility, e.g. following a net metering policy. Assuming that the CBS is equipped with a finite-capacity battery, we formulate an optimization problem to minimize the energy expenses incurred over a finite time horizon. Through convex analysis, we show that this optimization problem is equivalent to a linear program. We then use the equivalent formulation to solve the original problem and thus to find the optimal battery management policy. We evaluate the performance of the proposed strategy in terms of pricing parameters such as the consumption limits and their associated financial penalties. Using simulations we show that the proposed strategy can effectively minimize the energy bill.

This paper studies a multiple-input–single-output (MISO) broadcast channel (BC) featuring simultaneous wireless information and power transfer, where a multiantenna access point (AP) delivers both information and energy via radio signals to multiple single-antenna receivers simultaneously, and each receiver implements either information decoding (ID) or energy harvesting (EH). In particular, pseudorandom sequences that area prioriknown and therefore can be cancelled at each ID receiver are used as the energy signals, and the information-theoretically optimal dirty paper coding is employed for the information transmission. We characterize the capacity region for ID receivers by solving a sequence of weighted sum-rate (WSR) maximization (WSRMax) problems subject to a maximum sum-power constraint for the AP, and a set of minimum harvested power constraints for individual EH receivers. The problem corresponds to a new form of WSRMax problem in MISO-BC with combined maximum and minimum linear transmit covariance constraints (MaxLTCCs and MinLTCCs), which differs from the celebrated capacity region characterization problem for MISO-BC under a set of MaxLTCCs only and is challenging to solve. By extending the general BC–multiple-access-channel duality, which is only applicable to WSRMax problems with MaxLTCCs, and applying the ellipsoid method, we propose an efficient iterative algorithm to solve this problem globally optimally. Furthermore, we also propose two suboptimal algorithms with lower complexity by assuming that the information and energy signals are designed separately. Finally, numerical results are provided to validate our proposed algorithms.

In this paper, we minimize the total energy used in a two-tier heterogeneous cellular network (HCN), through the optimization of resource partitioning and user association, assuming that both macro and small-cell base stations may be put into sleep mode. With resource partitioning, one tier of base stations can be put into sleep mode on a fraction of available time/frequency resources to reduce network power consumption. A user association scheme is adopted to alleviate the SINR degradation due to severe inter-tier interference for users close to tier boundaries. By deriving tractable throughput characterizations, we formulate the network-wide energy usage minimization problem and determine the optimal user association and resource partitioning strategies. The maximum achievable network coverage probabilities using the optimal strategies are also investigated. Numerical results show that the proposed resource allocation and user association scheme reduces network energy consumption and improves coverage probability in co-channel heterogeneous networks.

The cloud radio access network (C-RAN) concept, in which densely deployed access points (APs) are empowered by cloud computing to cooperatively support mobile users (MUs), to improve mobile data rates, has been recently proposed. However, the high density of active APs results in severe interference and also inefficient energy consumption. Moreover, the growing popularity of highly interactive applications with stringent uplink (UL) requirements, e.g., network gaming and real-time broadcasting by wireless users, means that the UL transmission is becoming more crucial and requires special attention. Therefore in this paper, we propose a joint downlink (DL) and UL MU-AP association and beamforming design to coordinate interference in the C-RAN for energy minimization, a problem which is shown to be NP hard. Due to the new consideration of UL transmission, it is shown that the two state-of-the-art approaches for finding computationally efficient solutions of joint MU-AP association and beamforming considering only the DL, i.e., group-sparse optimization and relaxed-integer programming, cannot be modified in a straightforward way to solve our problem. Leveraging on the celebrated UL-DL duality result, we show that by establishing a virtual DL transmission for the original UL transmission, the joint DL and UL optimization problem can be converted to an equivalent DL problem in C-RAN with two inter-related subproblems for the original and virtual DL transmissions, respectively. Based on this transformation, two efficient algorithms for joint DL and UL MU-AP association and beamforming design are proposed, whose performances are evaluated and compared with other benchmarking schemes through extensive simulations.

In this paper, we propose an exclusion zone around each femtocell in a two-tier heterogeneous network, in which macro users enjoy exclusive interference-free access to a frequency band. The femto users, and macro users outside this region, share the remaining spectrum resource. The exclusion zone is a disc centered on the femto base station because here we only consider closed-access femtocells, meaning that the macro users within a femtocell cannot be off-loaded to the femto base station. Under the assumption that the bandwidth dedicated to macro users is proportional to the average probability that a macro user lies in the exclusion zone, the optimal exclusion zone radius that minimizes macro base station power consumption while satisfying all users' outage constraints is found.

In cognitive radio networks, a pair of communicating nodes are required to rendezvous or meet on a common available channel prior to control information exchange. Maintaining a common control channel (CCC), has several drawbacks such as channel saturation, vulnerability to jamming attacks, and spatial variation in the CCC availability. Although channel-hopping (CH) approaches have been proposed to avoid these problems, most existing work on CH schemes assume that the nodes hop to one channel at a time. This paper proposes asynchronous CH schemes where the nodes listen to or transmit on an arbitrary number of channels at a time. The proposed schemes enable any pair of nodes to rendezvous on all common available channels in the shortest period of time. Simulations are performed to evaluate the expected time-to-rendezvous and robustness of our schemes to primary network activity.

Cloud radio access network (C-RAN) has been recently proposed, in which densely deployed access points (APs) are empowered by cloud computing, to achieve enormous mobile data rates. However, close proximity of many active APs results in more severe interference and also inefficient energy consumption. To tackle this problem, we propose a joint downlink (DL) and uplink (UL) user-AP association and beamforming design in this paper to coordinate interference in the C-RAN for energy minimization. The design problem is shown to be NP hard, but exhibits an interesting “group-sparse” property. By establishing a virtual DL transmission for the original UL transmission based on the celebrated UL-DL duality result, we convert the problem to an equivalent DL problem in C-RAN with two inter-related subproblems for the original and virtual DL transmissions, respectively, and obtain an efficient solution through “group-sparse” optimization.

In this paper, we study the problem of minimizing the energy cost incurred by a Cellular Network Operator (CNO) in a Smart Grid (SG) environment. We consider a CNO that deploys several Cellular Base Stations (CBS) to serve a given geographical area. Each CBS is equipped with a limited-capacity battery and can be powered either by the SG or by a renewable-energy (RE) harvester. Given this topology, two-way energy flow is allowed between each CBS and the SG and between any pair of CBSs in the network through the SG. The space-time-dependent energy-buying and energy-sharing costs and the energy-selling prices are made known to the CNO in advance. Therefore, in order to minimize the total cost incurred by the CNO, we find the optimal energy-management strategy by solving a constrained optimization problem. The proposed strategy ensures that the instantaneous energy demand of each CBS and the constraints imposed by each battery are satisfied at every point in time. We evaluate the performance of the proposed solution using simulations. Our results show that a significant cost reduction can be achieved by implementing the proposed strategy.

In this paper, we formulate and solve a weighted-sum transmitter and receiver energy minimization (WSTREMin) problem in the downlink of an orthogonal frequency division multiplexing (OFDM) based multiuser wireless system. The proposed approach offers the flexibility of assigning different levels of importance to base station (BS) and mobile terminal (MT) power consumption, with the BS being connected to the grid and the MT relying on batteries. To obtain insights into the problem, we first consider two extreme cases separately, i.e., weighted-sum receiver-side energy minimization (WSREMin) for MTs and transmitter-side energy minimization (TEMin) for the BS. It is shown that Dynamic TDMA (D-TDMA), where MTs are scheduled for single-user OFDM transmissions over orthogonal time slots, is the optimal transmission strategy for WSREMin at MTs, while OFDMA is optimal for TEMin at the BS. As a hybrid of the two extreme cases, we further propose a new multiple access scheme, i.e., Time-Slotted OFDMA (TS-OFDMA) scheme, in which MTs are grouped into orthogonal time slots with OFDMA applied to users assigned within the same slot. TS-OFDMA can be shown to include both D-TDMA and OFDMA as special cases. Numerical results confirm that the proposed schemes enable a flexible range of energy consumption tradeoffs between the BS and MTs.

In this paper, we consider three transmit strategies for the fading three-node two-way relay network, namely, physical-layer network coding (PNC), digital network coding (DNC), and codeword superposition (CW-Sup). The aim is to minimize the total average energy needed to deliver a given pair of required average rates. Full channel state information is assumed to be available at all transmitters and receivers. The optimization problems corresponding to the various strategies in fading channels are formulated, solved, and compared. For the DNC-based strategies, a simple time sharing of transmission of the network-coded message and the remaining bits of the larger message (DNC-TS) is considered first. We extend this approach to include a superposition strategy (DNC-Sup), in which the network-coded message and the remainder of the longer source message are superimposed before transmission. It is theoretically demonstrated that DNC-Sup outperforms DNC-TS and CW-Sup in terms of total average energy usage. More importantly, it is shown in the simulation that DNC-Sup performs better than PNC if the required rate is low and worse otherwise. Finally, an algorithm to select the optimal strategy in terms of energy usage subject to different rate pair requirements is presented.

In this paper, we consider a three-node, two-way relay network with digital network coding over fading channels. The aim is to minimize total energy consumption for a given pair of required rates through this network. The uplink is a multiple access channel. In the downlink, we first consider orthogonal time sharing of network coded broadcasting and one way forwarding. We extend this approach to include a superposition strategy for the downlink, in which the network coded message and the remaining bits of the larger message are superimposed. This network coding superposition strategy is shown to always decrease total energy usage. We verify our findings via a series of numerical computations of the total energy consumption of the two strategies.

In this paper we study the problem of energy management in cellular base stations powered by smart grids and renewable energy. The utility company, through the smart grid, offers hourly-varying electricity prices made known a day ahead to the base station. We formulate an optimization problem in which the cost function is defined as the billing cost of the energy consumed each day. We seek to minimize the cost function while meeting the energy demand of the base station. We assume that the base station is equipped with a finite-capacity battery and a renewable source of energy such as a solar panel. The battery incurs charging and discharging losses which are accounted in the problem formulation. We find the optimal energy management policy using linear programming techniques. Furthermore, we study how the optimal cost is affected by several system parameters such as: initial state of the battery, its capacity, maximum charging/discharging rates, losses, smoothness of the price profile and correlation between the price and the consumption profiles. Our results show that significant cost savings can be achieved by properly scheduling the battery.

Improving energy efficiency is key to network providers maintaining profit levels and an acceptable carbon footprint in the face of rapidly increasing data traffic in cellular networks in the coming years. The energy-saving concept studied in this paper is the adaptation of a base station's (BS's) transmit power levels and coverage area according to channel conditions and traffic load. Cell coverage is usually pre-designed based on the estimated peak traffic load. However, traffic load in cellular networks exhibits significant fluctuations in both space and time. We design short- and long-term power control (STPC and LTPC respectively) policies for the OFDMA-based downlink of a single-cell system, where bandwidth is dynamically and equally shared among a random number of mobile users (MUs). STPC is a function of all MUs' channel gains that maintains the required user-level quality of service (QoS), while LTPC is a function of traffic density that minimizes the long-term energy consumption at the BS under a minimum throughput constraint. We first develop a power scaling law that relates the (short-term) average transmit power at BS with the given cell range and MU density. Based on this result, we derive the optimal (long-term) transmit adaptation policy by considering a joint range adaptation and LTPC problem. Finally, we compare our proposed adaptation scheme with suboptimal schemes of lower complexity to demonstrate the potential energy saving in broadcast channels.

In this paper, we consider a three-node, two-way relay system with digital network coding over static channels. For a given pair of random packet arrival rates, we aim to minimize total energy consumption while ensuring queue stability at all nodes. A set of transmission modes is considered and we solve for the optimal fraction of resources allocated to each mode, including a multi-access uplink transmission mode and a network coded broadcasting mode. For the downlink, we further consider whether it is more energy-efficient, to superimpose the excess bits of the larger message for one user with the network coded message for both users. We formulate and solve the corresponding optimization problem, deriving conditions under which it is better to use superposition coding. Finally, we present a detailed analysis of the queues at each node using a random scheduling method that closely approximates the theoretical design, through a Markov chain model.

In this paper, we consider a three-node, two-way relay system with digital network coding. The aim is to minimize total energy consumption while ensuring queue stability at all nodes, for a given pair of random packet arrival rates. Specifically, we allow for a set of transmission modes and solve for the optimal fraction of resources allocated to each mode. First, we formulate and solve the static-channel problem, where all link gains are constant over the duration of transmission. Then, we solve the fading-channel problem, where link gains are random. We call the latter the ergodic energy efficiency problem and show that its solution has a water-filling structure. Finally, we provide a detailed analysis of the queues at each node when a random scheduling method that closely approximates the theoretical design is used.

In this paper, the design of a wireless communication device relying exclusively on energy harvesting is considered. Due to the inability of rechargeable energy sources to charge and discharge at the same time, a constraint we term the energy half-duplex constraint, two rechargeable energy storage devices (ESDs) are assumed so that at any given time, there is always one ESD being recharged. The energy harvesting rate is assumed to be a random variable that is constant over the time interval of interest. A save-then-transmit (ST) protocol is introduced, in which a fraction of time ρ (dubbed the save-ratio) is devoted exclusively to energy harvesting, with the remaining fraction 1-ρ used for data transmission. The ratio of the energy obtainable from an ESD to the energy harvested is termed the energy storage efficiency, η. We address the practical case of the secondary ESD being a battery with η <; 1, and the main ESD being a super-capacitor with η = 1. Important properties of the optimal save-ratio that minimizes outage probability are derived, from which useful design guidelines are drawn. In addition, we compare the outage performance of random power supply to that of constant power supply over the Rayleigh fading channel. The diversity order with random power is shown to be the same as that of constant power, but the performance gap can be large. Finally, we extend the proposed ST protocol to wireless networks with multiple transmitters. It is shown that the system-level outage performance is critically dependent on the number of transmitters and the optimal save-ratio for single-channel outage minimization.

Improving energy efficiency is key to network providers maintaining profit levels and an acceptable carbon footprint in the face of rapidly increasing data traffic in cellular networks in the coming years. The energy-saving concept studied in this paper is the adaptation of a base station's (BS's) transmit power levels and coverage area according to channel conditions and traffic load. Cell coverage is usually pre-designed based on the estimated static (e.g. peak) traffic load. However, traffic load in cellular networks exhibits significant fluctuations in both space and time, which can be exploited, through cell range adaptation, for energy saving. In this paper, we design short- and long-term BS power control (STPC and LTPC respectively) policies for the OFDMA-based downlink of a single-cell system, where bandwidth is dynamically and equally shared among a random number of mobile users (MUs). STPC is a function of all MUs' channel gains that maintains the required user-level quality of service (QoS), while LTPC (including BS on-off control) is a function of traffic density that minimizes the long-term energy consumption at the BS under a minimum throughput constraint. We first develop a power scaling law that relates the (short-term) average transmit power at BS with the given cell range and MU density. Based on this result, we derive the optimal (long-term) transmit adaptation policy by considering a joint range adaptation and LTPC problem. By identifying the fact that energy saving at BS essentially comes from two major energy saving mechanisms (ESMs), i.e. range adaptation and BS on-off power control, we propose low-complexity suboptimal schemes with various combinations of the two ESMs to investigate their impacts on system energy consumption. It is shown that when the network throughput is low, BS on-off power control is the most effective ESM, while when the network throughput is higher, range adaptation becomes more effective.

This paper tackles the downlink user scheduling and transmit beamforming problems in MIMO-OFDMA by extending a recent algorithm that maximizes the weighted sum rate (WSR) to all users in MIMO flat fading channels. The proposed method has a complexity that is proportional to the number of OFDMA subcarriers, which makes it practically attractive. Having assigned users to each subcarrier and designed beamformers for each user in each subcarrier, it remains to find the best, in terms of rate maximization, adaptive modulation and coding (AMC) mode to use for each data stream. The latter problem is solved in the second half of the paper through viewing the channel from the base station to the k-th receiver as a multiple access channel (MAC) with Nk“users”, where Nkis the number of antennas at receiver k. The proposed method maps the available AMC modes to the space of allowed theoretical rates, using the signal-to-noise ratio (SNR) gap to capacity concept, and selects the operating point with the largest sum-rate.

In this paper, the design of a wireless communication device relying exclusively on energy harvesting is considered. Due to the inability of rechargeable energy sources to charge and discharge at the same time, a constraint we term the energy half-duplex constraint, two rechargeable energy storage devices (ESDs) are assumed so that at any given time, there is always one ESD being recharged. The energy harvesting rate is a random variable that is constant over the time interval of interest. A save-then-transmit (ST) protocol is introduced, in which a fraction of time ρ (dubbed the save ratio) is devoted exclusively to energy harvesting, with the remaining fraction 1 - ρ used for data transmission. The ratio of the energy obtainable from an ESD to the energy harvested is termed the ESD efficiency, η. We address the practical case of the secondary ESD being a battery with η <; 1, and the main ESD being a super-capacitor with η = 1. The optimal save-ratio that minimizes outage probability is derived, from which some useful design guidelines are drawn. Numerical results are provided to validate our proposed study.

In this paper we study the effect of receiver phase noise on single carrier frequency division multiple access (SC-FDMA). We show that common phase error rotates all the symbols by a certain angle and that the higher order frequency components of phase noise result in inter-carrier interference, or ICI and draw parallels to orthogonal frequency division multiple access (OFDMA). We then study the effect of phase noise on the performance of linear receivers that are often used in practice. In particular, we show that the amount of ICI affecting the sub-carriers depends closely on the allocation of sub-carriers among different users and prove that the performance of linear receivers in the presence of receiver phase noise deteriorates much more in the case of interleaved SC-FDMA than in the case of localized SC-FDMA. We identify the association of the significant phase noise components with the components of multi-user interference to be the fundamental reason behind the performance gap between interleaved and localized SC-FDMA.

In this paper, we maximize the throughput of a cognitive radio (CR) network with respect to the frame length when quickest sensing is used. The amount of interference to the primary network, measured by the probability of collision with primary users (PUs), is constrained below a certain maximum. The corresponding problem when CRs use block sensing is also solved, assuming that collision with PUs causes a drop in throughput for the CR - this important assumption was missing in prior work. We then compare the maximum achievable throughput with block and quickest sensing schemes and show that for a practical range of protection levels for the primary network, the quickest sensing approach results in significantly higher average throughput.

We consider the problem of designing linear precoders for Gaussian multiple input, single output (MISO) broadcast (BC) channels with a random channel matrix, where the randomness models imperfect channel knowledge due to feedback delay, channel time variations, limited training, or quantized feedback. First, we introduce a design framework where the goal is to minimize outage probability (or risk) or maximize rate. It is shown that full-power precoders, i.e. those that meet the transmit power constraint with equality, satisfy the design criterion. Next, we motivate and solve a related mean-variance optimization problem by supposing the channel density is a Gaussian mixture model (GMM) and deriving efficient formulae to compute the objective and constraint functions.

We study the effect of receiver phase noise (PHN) on single carrier frequency division multiple access (SC-FDMA). We show that in the case of SC-FDMA, common phase error (CPE) rotates all the symbols by a certain angle, while the higher order frequency components of PHN result in inter-carrier interference (ICI). We then study the effect of PHN on the performance of linear receivers used in the detection of SC-FDMA/OFDMA symbols. In particular, we show that the amount of ICI affecting the sub-carriers depends closely on the allocation of sub-carriers amongst different users and further show that the performance of linear receivers in the case of interleaved SC-FDMA deteriorates much more than in the case of localized SC-FDMA in the presence of receiver PHN.

This paper addresses the issue of blind estimation of common phase error (CPE) in OFDM systems affected by phase noise (PHN). Common approaches to blind CPE detection detect the symbols, and estimate the phase noise in an iterative manner. An important assumption that these decision-directed algorithms make is that a majority of the symbols detected in the first iteration, while ignoring the presence of phase noise, have been detected correctly. This assumption fails to hold under scenarios of high CPE and leads to a premature error floor. In this paper we dispense with the assumption that most of the symbols have been detected correctly and instead associate with each symbol a certain probability of having been detected correctly. Through the introduction of an auxiliary binary variable that indicates whether the right decision on a symbol has been made or not, we design a new algorithm to estimate CPE. This algorithm is robust to high CPE scenarios and is able to lower the error floor seen at high SNRs.

A novel linear precoding technique for the two user broadcast channel is presented. The performance measure used is the cross rate, defined as the difference between the sum throughputs of the system and its transpose. We show that the cross rate optimization problem can be reformulated as an alternating optimization of the steering matrix and power allocation. We solve the power allocation problem in closed form and give an efficient algorithm to compute the steering matrix. The algorithm is iterative and consists of simple operations, only requiring a single eigen-decomposition of a certain matrix. Simulation results indicate a 10% achievable rate gain over the regularized zero forcing algorithm with similar complexity, over a wide range of signal to noise (SNR) values.

In this paper, the variational expectation-maximization (VEM) algorithm is used to provide channel estimates in a space-time decoder. The proposed estimator works for many types of space-time codes (STC), including full-rate full-diversity (FRFD) codes, Bell Laboratories layered space-time architecture (BLAST) and orthogonal STC's. The principle idea is to treat the channel coefficients as the unknown parameters, and the transmitted symbols as the unobserved variables in the EM algorithm. The posterior distribution of the symbols is approximated by a factorized distribution, whose Kullback-Liebler divergence with the true distribution is minimized, thereby explaining the ``variational'' aspect of the technique. The channel estimates may then be used for coherent decoding -- here we use the K-best detector for a 2×2 system. Simulation results show that the new channel estimator can reduce system complexity greatly compared with traditional schemes, while achieving near-ideal performance.

This work proposes a per-subband multiple input multiple output (MIMO) precoder selection technique for point-to-point MIMO orthogonal frequency division multiplexing (OFDM) based bit interleave coded modulation (BICM) systems with the soft-output minimum mean square error (MMSE) receiver. Given a pre-designed precoder codebook, the codeword/precoder that maximizes the mean of the mutual information per coded bit (MMIB) on all subcarriers within a subband is selected. The main advantages of this technique are the following: i) the precoder selection metric is explicitly related to BICM performance, thus it outperforms the previously proposed precoding techniques; ii) with commonly used unitary precoding codebooks, this technique works for an arbitrary number of transmit streams unlike the minimum singular value based method which does not work when the number of input streams is the same as the number of transmit antennas; iii) when multiple packets are transmitted and one precoder is used for these transmitted packets, an algorithm that combines the MMIB of each packet is proposed using an upper bound on the average packet error rate.

In this letter, we propose multi-antenna based spectrum sensing methods for cognitive radios (CRs) using the generalized likelihood ratio test (GLRT) paradigm. The proposed methods utilize the eigenvalues of the sample covariance matrix of the received signal vector from multiple antennas, taking advantage of the fact that in practice, the primary user signals to be detected will either occupy a subspace of dimension strictly smaller than the dimension of the observation space, or have a non-white spatial spectrum. These methods do not require prior knowledge of the primary user signals, or the channels from the primary users to the CR. By making different assumptions on the availability of the white noise power value at the CR receiver, we derive two algorithms that are shown to outperform the standard energy detector.

In this paper, a new opportunistic feedback and user selection protocol is proposed for the multiple antenna downlink. The proposed scheme uses the structure of zero-forcing dirty paper coding (DPC) at the transmitter and selects users using a contention-based feedback channel. The user selection procedure is divided into stages and within each stage, a user is selected for one spatial channel (defined by the zero-forcing dirty paper coding strategy). The user feedback is controlled by a feedback probability and a feedback threshold. This method of feedback significantly reduces the resources used for feedback compared to dedicating feedback resources for each user. We optimize an approximation to the sum rate expression with respect to these parameters and illustrate the sum rate performance by simulations. The simulation shows that we can achieve good sum rate performance despite the reduction in feedback resources.

For a multiuser bit interleaved coded OFDM (BIC-OFDM) system, we study the methods of time domain equalization and per-subcarrier maximum likelihood (ML) frequency domain equalization when there is timing asynchronism among the users. For the time domain equalizer structure, a coded pairwise error probability (PEP) approximation is derived, which leads to a training-based space-time design method that takes the error rate performance into consideration. We compare the time domain equalization and per-subcarrier ML frequency domain equalization methods, and conclude that at high SNR's, the time domain equalization approach is preferable.

In this paper, cooperative quickest spectrum sensing for cognitive radios is studied. Various cooperative schemes are considered based on the cumulative sum (CUSUM) algorithm, for different memory and communication constraint scenarios. The optimal CUSUM statistics are derived for each of these cooperative sensing schemes in the noisy channel scenario. In practice, due to unknown parameters in the distribution of the observations, the CUSUM-based approaches are not directly applicable to cognitive radios. Therefore, we propose to apply a linear test, which does not require any prior knowledge or estimates of the unknown parameters, for quickest spectrum sensing of cognitive radios. We derive linear-based CUSUM statistics for different cooperative sensing scenarios. The proposed approach results in fast and simple algorithms for cooperative quickest detection with unknown parameters, while maintaining a performance close to that of the perfectly known parameter schemes.

In this paper, we present a composite hypothesis testing approach for cooperative spectrum sensing. We derive the optimal likelihood ratio test (LRT) statistic based on the Neyman-Pearson (NP) criterion at the fusion center for both hard (one-bit) and quantized (multi-bit) local decisions. We show that the LRT statistic depends on the modulation type and second- and fourth- order statistics of the primary signal. However, such side information is not commonly available to the secondary network. Therefore, we propose to apply composite hypothesis testing methods, such as the Rao test, which do not require any prior knowledge about the primary signal, in a cooperative sensing scenario. We derive a modified Rao test statistic for decision making at the fusion center for both cases of hard and quantized local decisions. We also apply the locally most powerful (LMP) detector at the fusion center for weak primary signals and derive its corresponding test statistic. These methods are much simpler than the optimal NP-based method and do not require estimation of the primary signal statistics while having a very close performance to the optimal method.

Transmitter precoding strategies achieve a large portion of the capacity promised in broadcast MIMO systems. However, these schemes generally require perfect channel information at the transmitter. In this paper, the impact of Gaussian additive noise in the channel information is investigated for a downlink system where the transmitter uses a zero forcing precoding strategy. It is shown that noise in the channel state information (CSI) induces T-distributed multiplicative noise at the receiver. The multiplexing gain and power offset of the scheme are calculated and compared against the corresponding perfect feedback system.

Transmitter precoding strategies in broadcast systems generally assume perfect knowledge of channel state information (CSI) at the transmitter. In this paper, we study linear precoding with arbitrary error in the CSI from an estimation theoretic point of view. We derive a Bayesian Cramer-Rao type bound on the sum mean squared error (SMSE) achievable at the receiver for any linear precoding scheme for arbitrary feedback noise and channel fading. We next specialize this result to power constrained precoders. It is shown that the regularity conditions of the bound may be significantly weakened for power constrained precoders. Interestingly, we obtain a bound whose validity depends on rather weak conditions of continuity and differentiability on the joint distribution of the channel and feedback. We demonstrate the bound by applying it to Gaussian, Nakagami-m and Weibull fading models, with the assumption of feedback corrupted by additive Gaussian noise.

We use the sum-product algorithm (SPA) to propose a reduced-complexity detector for serially concatenated continuous phase modulated (SCCPM) signals with noisy channel state information (CSI) at the receiver. Two channel effects-phase noise (PN) and flat fading-are tackled using the statistical model and associated factor graph (FG) of the problem. Although optimal performance is not assured due to the loopy nature of the FG, as well as simplifying assumptions made in the algorithm development, simulations show that very significant performance gains are possible, and the gap between systems with noiseless and noisy CSI can be reduced to about 1 dB using the proposed methods. The increase in complexity compared to the conventional decoder, which treats CSI estimates as if they were equal to their true values, is small.

We propose a unified framework for deriving and studying soft-in soft-out (SISO) detection in multiple-access channels using the concept of variational inference. The proposed framework may be used in multiple-access interference (MAI), intersymbol interference (ISI), and multiple-input multiple-output (MIMO) channels. Without loss of generality, we will focus our attention on turbo multiuser detection, to facilitate a more concrete discussion. It is shown that, with some loss of optimality, variational inference avoids the exponential complexity ofaposterioriprobability (APP) detection by optimizing a closely related, but much more manageable, objective function calledvariationalfreeenergy. In addition to its systematic appeal, there are several other advantages to this viewpoint. First of all, it provides unified and rigorous justifications for numerous detectors that were proposed on radically different grounds, and facilitates convenient joint detection and decoding (utilizing the turbo principle) when error-control codes are incorporated. Second, efficient joint parameter estimation and data detection is possible via the variational expectation maximization (EM) algorithm, such that the detrimental effect of inaccurate channel knowledge at the receiver may be dealt with systematically. We are also able to extend BPSK-based SISO detection schemes to arbitrary square QAM constellations in a rigorous manner using a variational argument.

This paper discusses cooperative protocols based on fountain codes in a relay network. Unlike traditional fixed-rate codes, fountain codes are capable of adapting the bit rate to the channel realization blindly, i.e., without channel information at the transmitter. This makes them attractive in fading wireless channels. We first describe several rateless cooperative protocols, and then implement them using Raptor codes (a type of fountain code) and 16-QAM over a slow, deeply-interleaved fading relay channel. Given no delay constraints, the achievable rate of these protocols are found when the source and relay are close to each other, and when the relay is half-way between the source and destination. Furthermore, to reduce decoding complexity, we propose a soft-decision version of the adaptive demodulation (ADM) method recently introduced in the literature at each receiver. Finally, we analyze the practical scenario of data streaming with delay constraints, and find that the time-division (TD) protocol retains its relative superiority over the space-time one.

In this paper, we propose several spectrum sensing methods designed using the generalized likelihood ratio test (GLRT) paradigm, for application in a cognitive radio network. The proposed techniques utilize the eigenvalues of the sample covariance matrix of the received signal vector, taking advantage of the fact that in practice, the primary signal in a cognitive radio environment will either occupy a subspace of dimension strictly smaller than the dimension of the observation space, or have a spectrum that is non-white. We show that by making various assumptions on the availability of side information such as noise variance and signal space dimension, several feasible algorithms result which all outperform the standard energy detector.

In previous work, we studied the structure and performance of optimum maximum-likelihood receivers for binary antipodal and orthogonal signals in the presence of Gaussian-distributed channel estimation error and additive white Gaussian noise for flat Rayleigh fading channels. In this paper, we investigate the structure and performance of these receivers for an application where Gaussian-distributed channel estimation error arises: minimum mean-square error channel estimation in quasi- static Rayleigh fading channels. Exact closed-form analytical expressions are derived for the average bit error probability (BEP). We quantify the impact of number of pilot symbols in each frame as well as the ratio of the power of the pilot symbol to the power of data on the average BEP. We derive conditions under which orthogonal signalling results in a lower average BEP compared with binary antipodal signalling.

This correspondence investigates iterative soft-in-soft-out (SISO) detection in coded multiple access channels, with Gray-codedM-ary quadrature amplitude modulation (QAM) for the channel symbols. The proposed solution may be summarized as a generic iterative detection scheme called bit-level equalization and soft detection (BLESD), which is an extension of a unified variational inference framework for binary SISO detection proposed in our prior work. This new strategy fundamentally differs from the conventional symbol detector, in that data symbols are transparent to the new detector. Rather, soft estimates of the bits that make up the symbols are directly and naturally obtained by the detector in terms of posterior probabilities given the channel observation, facilitating efficient message-passing in joint detection and decoding. Case studies that illustrate the applications of the proposed scheme are presented for turbo multiuser detection (MUD) for multiple-access interference (MAI) channels and turbo equalization for inter-symbol interference (ISI) channels.

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

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

An exact closed-form expression is derived for the average bit-error probability (BEP) of binary phase-shift keying signals with multiple-antenna reception. We consider maximal ratio combining technique in the presence of multiple cochannel interferers with identical or different powers and additive white Gaussian noise. It is assumed that both the desired signal and interference are subject to flat Rayleigh fading, and the fading channels of different users are independent of each other. In this paper, the derivation of the average BEP is different from the conventional PDF-based approach and is based on the decision variable at the output of the maximal ratio combiner conditioned only on the fading channel of the desired user. The analytical result is verified by Monte Carlo simulations.

This paper investigates the soft-in soft-out (SISO) equalization of multilevel QAM symbols in coded inter-symbol interference (ISI) channels. Unlike the conventional approach of performing equalization at the symbol level, the proposed scheme targets the channel bits directly. This solution can be seen to belong to a family of SISO detection schemes which we call Bit-Level Equalization and Soft Detection (BLESD), stemming from the minimization of variational free energy given different postulates about the prior and posterior distributions of the channel bits. Simulation results demonstrate that the bit-level approach outperforms the symbol-level alternative in terms of error rate in the Porat-Friedlander channel.

This paper develops a practical design method for implementing Tomlinson-Harashima precoding (THP) in a downlink channel with multiple antennas at the transmitter and a single antenna at each receiver. A two-step design process is proposed for minimizing the total transmit power while satisfying every user's minimum data rate and maximum bit-error rate (BER) requirements. First, the BER and rate requirements are converted to "virtual rate" requirements, which account for the gap-to-capacity introduced by practical quadrature amplitude modulation (QAM) and THP. The second step is to determine the transmit covariance matrices (which specify the entire THP system) that will provide these virtual rates at the minimum total transmit power. As one of the main features in the proposed scheme, an algorithm for finding the optimal user encoding (or presubtraction) order in polynomial time is proposed. In addition, we also propose an algorithm that finds a near-optimal order, but which is much less complex. The proposed method outperforms existing zero-forcing-based THP systems in term of power efficiency

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

The optimum detection of binary antipodal signals in additive white Gaussian noise (AWGN) channels with Gaussian channel estimation error has been studied in prior work. In this paper, we present the optimum detector based on the maximum- likelihood criterion for binary orthogonal signals in the presence of Gaussian distributed channel estimation error and AWGN. It is shown that the optimum detector is a linear combination of the optimum coherent and optimum noncoherent detectors. We derive the exact closed-form expression of the average bit error probability of the proposed optimum detector in Rayleigh fading channels with AWGN. It is found that if the variance of channel estimation error for a given average SNR is greater than a threshold, then orthogonal signalling outperforms antipodal modulation, and the analytical expression of this threshold is derived.

We propose an efficient training-based OFDM channel impulse response (CIR) and carrier frequency offset (CFO) estimation algorithm that addresses the problem of phase noise (PHN), assuming that the PHN has a known prior distribution. The optimal joint estimation of CIR, PHN and CFO was described in an earlier work of ours. In this paper, we focus on the case where a training symbol consists of two identical halves in the time domain, and propose a variant to Moose's CFO estimation algorithm that accounts for PHN in CFO estimation. This is followed by an optimal joint CIR and PHN estimation scheme tailored for this "repeating training symbol" setup. It is assumed that the PHN process is Gaussian with known mean and covariance matrix. This encompasses both Wiener PHN and Gaussian PHN. It is shown through simulations that the proposed algorithm performs almost as well as the optimal JCPCE algorithm at much lower complexity. To further reduce the complexity of the proposed scheme, the conjugate gradient (CG) method is used and we show that it can be realized using the Fast Fourier Transform (FFT).

We use the sum-product algorithm to handle the problem of phase noise (PN) in the detection of a serially concatenated continuous phase modulated system. Although optimal performance is not assured due to the loopy nature of the factor graph describing the problem, as well as simplifications made in the algorithm development, simulations show that significant performance gains are possible, and the gap between systems with and without PN can be reduced to about 1 dB using the proposed method. The increase in complexity compared to ignoring PN is small, and no periodic pilot symbols are inserted within a code word, although we assume the availability of a phase-locking mechanism to set the initial phase offset to a small range around zero. We use a different Gaussian approximation from what has been previously proposed, and this results in a less complex algorithm

Building upon a unified framework for CDMA multiuser detection proposed in our prior work, we investigate the detection of M-QAM symbols in a multiuser CDMA channel. The solution proposed may be summarized as a generic iterative detection scheme for coded interference channels called bit-level equalization and soft detection (BLESD). It is shown that this novel approach avoids the exponential complexity of a posteriori probability (APP) detection by optimizing a closely-related, but much more manageable, objective function called variational free energy. It also fundamentally differs from the conventional symbol detector, in that data symbols are transparent to the new detector. Instead, soft estimates of the bits that make up the symbols are directly and naturally obtained at the detector output, in terms of posterior probabilities given the channel observation, facilitating efficient message-passing in joint detection and decoding

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

This paper analyzes the performance of maximal ratio combining (MRC) and compares it with the performance of optimum combining (OC) in the presence of channel estimation errors and multiple interferers in a flat Rayleigh fading environment. The probability density function (PDF) of the signal-to-interference-plus-noise ratio at the output of the maximal ratio combiner has been derived in prior work, assuming Gaussian channel estimation errors. We use that PDF to derive analytical expressions for a number of important performance measures such as the outage probability and the average bit error probability for different modulation formats in interference-limited systems. These expressions are used to show that the simpler MRC method can outperform the more complex OC receiver when the channel estimator performs poorly, and quantify the threshold of correlation between the true and estimated channels at which the cross-over occurs.

This paper studies the impact of imperfect estimation of the desired user's channel as well as the interfering users' channels on the performance of optimum combining for space diversity reception with multiple interferers in a flat Rayleigh fading environment. The probability density function (PDF) of the signal-to-interference ratio (SIR) at the output of the optimum combiner is derived in prior work, assuming Gaussian channel estimation errors. We utilize this PDF expression to derive analytical expressions for some important measures of performance such as the moments of SIR, the outage probability and the average bit error probability for binary differential phase-shift keying and non-coherent frequency-shift keying modulations. These expressions are used to evaluate the performance of the optimum combiner in the presence of channel estimation errors, and are convenient substitutes for time-consuming simulations. The analytical results are verified by Monte Carlo simulations

We propose an optimal training-based OFDM channel impulse response (CIR) estimation algorithm that addresses the phase noise (PHN) and carrier frequency offset (CFO) problem. If left unattended, these combined problems severely degrade the accuracy of the channel estimate and ultimately the quality of the wireless link. The solution involves the joint optimization of a complete log-likelihood function over the unknown CIR, PHN and CFO. To reduce the complexity of the proposed algorithm, a simplification based on the conjugate gradient method is introduced, yielding an efficient realization using the fast Fourier transform (FFT) with only minor performance degradation

In this paper, we present a unified mathematical framework to analyze the bit-error rate (BER) performance of general linear coherent multiuser receivers with diversity reception and imperfect channel estimation for doubly selective Rician-fading asynchronous code-division multiple-access channels. BERs of linear receivers with channel state information and data-aided channel estimation are analyzed, and both exact and low-complexity approximate BER evaluation formulas are presented. Furthermore, by using a Markov chain steady-state analysis, a tight BER approximation for receivers with decision-directed channel estimation is proposed. Numerical and simulation results verify the accuracy of the proposed BER evaluation methods.

In this paper, a narrowband multiple-channel transmission scheme with multiple transmit antennas is proposed and analyzed. The channelization is based on space-time signature matrices, which do not expand bandwidth, unlike conventional schemes such as code-division or time-division multiplexing. The channels can be used by multiple independent users in an uplink or downlink scenario (multiple access or broadcast channels, respectively), or by one user in a multiplexing scenario. The data transmitted on each channel is convolutionally encoded, interleaved, and then space-time block encoded before space-time channelization. Each channel has a unique interleaver and space-time signature, but the convolutional encoder and space-time block code encoder can be identical across channels. It is shown that asymptotic single-user-like performance can be achieved with optimal detection and decoding in a Rayleigh fading channel. Practical receiver algorithms are developed based on the iterative (turbo) detection technique. The simulation results demonstrate that these suboptimal receivers achieve single-user performance at moderate signal-to-noise ratios, and moderate user loads. In the single-user multiplexing case, a significant performance gain over single-channel transmission with the same data rate is obtained.

In this paper, we study the downlink of a multiuser system, in which antenna arrays are employed at both the transmitter (base station) and the receivers (clients). A space-time modulation technique that can be seen as two-dimensional spreading is introduced. It provides full transmit diversity for every user, and accommodates Nttimes the number of users as a single-antenna code-division multi-access (CDMA) scheme, where Ntis the number of transmit antennas. Thus multiple access is provided through spatial as well as code dimensions. In addition, the scheme forms groups of users that are orthogonal to each other. This feature translates into simplified detection strategies without loss of performance. The main detector structure of interest is a two-stage interference canceller because of its low complexity compared to other joint detectors. We will demonstrate that in conjunction with an unequal power allocation scheme, this receiver provides full diversity and suffers from only a small performance loss compared to the full-complexity maximum likelihood (ML) receiver. In a single-user multiple antenna system, the same spreading scheme and unequal power allocation yields a new approach to designing full-rate, full-diversity space-time codes having good performance with successive interference cancellers

We propose a unified approach for deriving and studying multiuser detection algorithms using the concept of variational free energy minimization. Under this generalized framework, we readily arrive at many popular multiuser detection schemes. In addition to its systematic appeal, there are several other advantages of this viewpoint. First of all, by condensing the design of multiuser detectors into the selection of a few key probability distributions, namely p(b), p(r|b) and Q(b), we provide rigorous justifications for numerous detectors that were proposed on heuristic grounds and recommend new and improved designs. Furthermore, the free energy formulation facilitates convenient joint detection and decoding (utilizing the turbo principle) when error-control codes are incorporated, as well as efficient parameter estimation via the variational expectation maximization (EM) algorithm.

We propose a systematic probabilistic framework to address the phase noise (PHN) problem in OFDM. In addition to deriving the optimal data detection scheme in the presence of PHN, we introduce a series of suboptimal approaches to blindly cancel the effect of PHN without the aid of pilot symbols. Not only do these algorithms provide the means to efficiently eliminate the effect of PHN in OFDM, they also open the door to much wider applications of advanced probabilistic inference algorithms in solving communications problems.

This paper investigates the effect of imperfect channel estimates on the distribution of signal to interference ratio (SIR) at the output of the optimum combiner for space diversity reception with multiple interferers in a flat Rayleigh fading environment. Channel estimation errors result in flawed optimum combiner weights degrading the system performance. We consider interference-limited systems in which the number of interferers is no less than the number of antenna elements. It is assumed that the channel estimation errors are circularly symmetric Gaussian distributed, and the interferers have equal powers. The main contribution of the paper is applying multivariate statistical analysis to derive an exact closed-form expression for the probability density function of the output SIR. The analytical result is a useful tool to find the impact of imperfect channel estimation on several measures of performance such as average SIR, outage probability and average bit error probability. The analytical expression is verified by Monte Carlo simulations.

Assuming perfect channel knowledge at the transmitter, we study space-time beamforming for the downlink in a multiuser multi-input multi-output (MIMO) channel while a nonlinear interference pre-cancellation is presumed at the transmitter. The antenna arrays may be employed at both the transmitter and the receivers. The optimum transmit/receive beam vectors are obtained based on a minimum mean-squared error (MMSE) criterion and a per-user power constraint. In frequency selective fading channels, where orthogonal frequency division multiplexing (OFDM) is applied, the precoding and beamforming design is extended over space and frequency dimensions as well. In fact the proposed algorithm offers a unique method for assigning frequency bins in a MIMO-OFDMA system. The bit error rate performance of the proposed algorithm is assessed by computer simulations.

This paper proposes a novel bandwidth-efficient multiuser scheme with multiple antennas. By assigning each user a two-dimensional signature matrix and user-specific interleaver, K users can can occupy the same bandwidth as the single user. Both theoretical analysis and simulation results show that the single-user like performance can be achieved asymptotically.

We study the downlink of a MIMO multi-user system when no information about the channel is assumed at the transmitter. Instead of applying direct-sequence code-division multiple access (DS-CDMA) over the codewords of conventional space-time coding (STC) schemes, a modulation technique that can be seen as two-dimensional space-time spreading (2D-STSC) is described. It is based on well-known Walsh codes, provides full transmit diversity and high spectral efficiency, and produces groups of users that are orthogonal to each other. This last point translates into simplified detection strategies without loss of performance. The main detector structure of interest is a two-stage interference canceller which employs serial interference cancellation (SIC) in the first stage. We will demonstrate that in conjunction with an unequal power allocation scheme, this receiver is able to provide full diversity and suffers from only a small performance loss compared to the full-complexity maximum likelihood receiver. The proposed scheme compares favorably with related ones in terms of spectral efficiency, bit error probability, and complexity at the receiver.

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

Iterative methods to solve a linear equation system can be used to derive multistage interference cancellers (ICs) that converge to the decorrelating or the minimum mean-square error linear detectors. The link between linear multistage IC and iterative solution methods is well known; however, the parameters needed for fastest convergence are functions of the eigenvalues of the channel matrix. In this letter, we propose new methods for finding optimal parameters without eigendecomposition.

We propose a novel spreading code scheme, transmitter-receiver-based code, for wireless ad hoc networks. The design facilitates collision resolution using multiuser detection at each node, and is more bandwidth efficient than creating orthogonal channels in time or frequency. A subspace-based receiver structure is introduced, which identifies users of interest, or "active" users, with minimal prior information on the spreading code ensemble. A subspace-based blind multiuser detector can then be implemented to suppress multiaccess interference. The performance of the proposed active user identifier is studied by investigating its false alarm rate P/sub f/ and miss rate P/sub m/. Tradeoffs between P/sub f/ and P/sub m/ are discussed, and a graphical method to determine the threshold value d/sub th/ of the decision statistic used in discriminating between active and inactive channels is introduced.

Conventional matched-filter detectors for code-division multiple-access (CDMA) systems suffer from multiple-access interference (MAI) caused by nonzero correlation between spreading codes at the receiver. A host of advanced detector structures have been proposed to reduce the effect of MAI and, hence, improve performance. However, most multiuser detectors suffer from their relatively complex implementations. A simple method is proposed to improve the performance of the conventional detector by detecting and correcting decision errors at its output without the use of forward error correcting (FEC) codes. The proposed post-detection error control method is shown to substantially improve the performance of the conventional detector, but has a much lower complexity than most other multiuser detectors.

In this letter, we study differentially modulated, iteratively decoded CDMA. The iterative multiuser receiver proposed consists of an additional soft-input soft-output (SISO) differential decoder, when compared to turbo multiuser detectors for absolutely modulated systems. Algorithms for iterative decoding with and without phase information at the receiver are developed. The resulting turbo receivers with differential modulation outperform coherent receivers with absolute modulation at moderate to high signal to noise ratios due to the interleaver gain associated with recursive inner encoders in serially concatenated encoding structures.

In this paper, we study the performance of a general linear coherent multiuser receiver with decision-directed channel estimation (DDCE) for doubly-selective Rician-fading CDMA channels. Employing the multivariate Gaussian approximation (MGA) and a decision variable-based moment generating function (DV-MGF) approach, both exact and low-complexity approximate bit error probability (BEP) formulas for a linear detector with genie-aided DDCE are provided. Furthermore, using a Markov chain steady-state analysis, a tight BEP approximation for non-ideal DDCE is provided taking into account the error propagation in the decision feedback. Some new findings are illustrated by simulation results.

RicianAccurate performance analysis for linear receivers over frequency- and time-selective asynchronous code-division multiple-access Rician-fading channels is very useful and a general approach to this topic is very desirable. In this paper, by using a decision variable-based moment generating function approach, we provide a unified bit-error probability (BEP) analysis framework for different linear detectors with binary or quaternary differential phase-shift keying and postdetection combining over Rician-fading channels, taking into account the effects of the spreading code correlation, the system and fading-channel parameters, diversity combining, and branch correlation. To reduce the complexity of the exact BEP evaluation, we furthermore provide an approximate multivariate Gaussian assumption (MGA)-based method which entails a low complexity for BEP evaluation. Ideal and approximate linear minimum mean-squared error diversity receivers for correlated Rician-fading channels are proposed. Numerical results show that the phases of the line-of-sight (LOS) components of the desired user significantly affect the receiver performance over correlated multipath Rician channels, and this may be exploited to improve performance. Also, when the LOS components are affected by a significant Doppler shift, automatic frequency control is very useful in improving the receiver performance.

This paper explores the use of differential modulation as a rate-1 recursive code to improve performance in an iterative (or turbo) CDMA receiver in a convolutionally encoded system. We would expect an interleaver gain from the recursive nature of the differential encoder, and indeed such a gain over an absolutely encoded CDMA system is easily demonstrated through simulations. The iterative receiver proposed consists of an additional soft-input soft-output (SISO) differential decoder, when compared to turbo multiuser detectors for absolutely modulated systems. Algorithms for iterative decoding with and without phase information at the receiver are developed. The resulting coherent and non-coherent receivers with differential modulation both outperform coherent receivers with absolute modulation at moderate to high signal to noise ratios.

This paper discusses the performance of communication systems using binary coherent and differential phase-shift keyed (PSK) modulation, in correlated Rician fading channels with diversity reception. The presence of multiple Rician-faded cochannel users, which may have arbitrary and nonidentical parameters, is modeled exactly. Exact bit error probability (BEP) expressions are derived via the moment generating functions (MGFs) of the relevant decision statistics, which are obtained through coherent detection with maximum ratio combining for coherent PSK modulation, and differential detection with equal gain combining (EGC) for differential modulation. Evaluating the exact expressions requires a complexity that is exponential in the number of interferers. To avoid this potentially time-consuming operation, we derive two low-complexity approximate methods each for coherent and differential modulation formats, which are more accurate than the traditional Gaussian approximation approach. Two new and interesting results of this analysis are: (1) unlike in the case of Rayleigh fading channels, increasing correlation between diversity branches may lead to better performance in Rician fading channels and (2) the phase distribution of the line-of-sight or static fading components of the desired user has a significant influence on the BEP performance in correlated diversity channels.

Adaptive least mean square (LMS) filters with or without training sequences, which are known as training-based and blind detectors respectively, have been formulated to counter interference in CDMA systems. The convergence characteristics of these two LMS detectors are analyzed and compared in this paper. We show that the blind detector is superior to the training-based detector with respect to convergence rate. On the other hand, the training-based detector performs better in the steady state, giving a lower excess mean-square error (MSE) for a given adaptation step size. A novel decision-directed LMS detector which achieves the low excess MSE of the training-based detector and the superior convergence performance of the blind detector is proposed.

A linear Kalman filter detector for code-division multiple access proposed earlier in the literature is extended to a structure that can handle arbitrary detection delays, through the mechanism of state augmentation. Because pre-detection RAKE combining is used in the detector, it is optimal for multipath channels, unlike the previous structure that performed post-detection combining. We also derive nonlinear Kalman detectors, which approximate the highly complex nonlinear minimum mean-squared-error detector, using the concept of "additional observations." Both linear and nonlinear detectors require processing at one or more times the chip rate, and knowledge of the spreading codes of interfering users. They have the advantage over many other multiuser detection algorithms of not requiring the spreading codes to be periodic at the symbol rate, or matrix inversion. In addition, two of the detectors are able to generate and update a posteriori probabilities of the transmitted symbols, making them interesting for iterative multiuser detection.

Provides an abstract of articles to be presented in a forthcoming issue.

The detection strategy usually denoted optimal multiuser detection is equivalent to the solution of a (0, 1)-constrained maximum-likelihood (ML) problem, a problem which is known to be NP-hard. In contrast, the unconstrained ML problem can be solved quite easily and is known as the decorrelating detector. In this paper, we consider the constrained ML problem where the solution vector is restricted to lie within a closed convex set (CCS). Such a design criterion leads to detector structures which are ML under the constraint assumption. A close relationship between a sphere-constrained ML detector and the well-known minimum mean square error detector is found and verified. An iterative algorithm for solving a CCS constraint problem is derived based on results in linear variational inequality theory. Special cases of this algorithm, subject to a box-constraint, are found to correspond to known, nonlinear successive and parallel interference cancellation structures, using a clipped soft decision for making tentative decisions, while a weighted linear parallel interference canceler with signal-dependent weights arises from the sphere constraint. Convergence issues are investigated and an efficient implementation is suggested. The bit-error rate performance is studied via computer simulations and the expected performance improvements over unconstrained ML are verified.

Adaptive filters used in code division multiple access (CDMA) receivers to counter interference have been formulated both with and without the assumption of training symbols being transmitted. They are known as training-based and blind detectors respectively. We show that the convergence behaviour of the blind minimum-output-energy (MOE) detector can be quite easily derived, unlike what was implied by the procedure outlined in a previous paper. The simplification results from the observation that the correlation matrix determining convergence performance can be made symmetric, after which many standard results from the literature on least mean square (LMS) filters apply immediately.

In this paper, we analyze the bit error probability (BEP) of binary and quaternary differential phase shift keying (2/4 DPSK) and noncoherent frequency shift keying (NCFSK) with postdetection diversity combining in arbitrary Rician fading channels. The model is quite general in that it accommodates fading correlation and noise correlation between different diversity branches as well as between adjacent symbol intervals. We show that the relevant decision statistic can be expressed in a noncentral Gaussian quadratic form, and its moment generating function (MGF) is derived. Using the MGF and the saddle point technique, we give an efficient numerical quadrature scheme to compute the BEP. The most significant contribution of the paper, however, lies in the derivation of a closed-form cumulative distribution function (cdf) for the decision statistic. As a result, a closed-form BEP expression in the form of an infinite series of elementary functions is developed, which is general and unifies previous published BEP results for 2/4 DPSK and NCFSK for multichannel reception in Rician fading. Specialization to some important cases are discussed and, as a byproduct, a new and general finite-series expression for the BEP in arbitrarily correlated Rayleigh fading is obtained. The theory is applied to study 2/4 DPSK and NCFSK performance for independent and correlated Rician fading channels; and some interesting findings are presented.

Linear parallel interference cancellation (PIC) schemes are described and analyzed using matrix algebra. It is shown that the linear PIC, whether conventional or weighted, can be seen as a linear matrix filter applied directly to the chip-matched filtered received signal vector. An expression for the exact bit-error rate (BER) is obtained, and conditions on the eigenvalues of the code correlation matrix and the weighting factors to ensure convergence are derived. The close relationship between the linear multistage PIC and the steepest descent method (SDM) for minimizing the mean squared error (MSE) is demonstrated. A modified weighted PIC structure that resembles the SDM is suggested which approaches the minimum MSE (MMSE) detector rather than the decorrelator. It is shown that for a K-user system, only K PIC stages are required for the equivalent matrix filter to be identical to the the MMSE filter. For fewer stages, techniques are devised for optimizing the choice of weights with respect to the MSE. One unique optimal choice of weights is found, which will lead to the minimum achievable MSE at the final stage. Simulation results show that a few stages are sufficient for near-MMSE performance.

An algorithm is proposed for estimating the directions of arrival (DOAs) of multipath signals received from multiple users on the uplink of a code-division multiple-access system. The correlation matrices of the received signal before and after code-matched filtering are used to provide unique estimates even when the number of required DOAs exceeds the number of antenna array elements. This scenario is well known to cause conventional direction-finding algorithms (such as MUSIC) to fail. Both intersymbol interference (ISI) and multiple-access interference (MAI) are modeled exactly, and so the algorithm performs much better than those which model ISI and MAI as Gaussian noise.

In this paper, we describe linear successive interference cancellation (SIC) based on matrix-algebra. We show that linear SIC schemes (single stage and multistage) correspond to linear matrix filtering that can be performed directly on the received chip-matched filtered signal vector without explicitly performing the interference cancellation. This leads to an analytical expression for calculating the resulting bit-error rate which is of particular use for short code systems. Convergence issues are discussed, and the concept of /spl epsiv/-convergence is introduced to determine the number of stages required for practical convergence for both short and long codes.

It is shown that a first-order linear state-space model applies to the asynchronous code-division multiple-access (CDMA) channel, and thus the Kalman filter produces symbol estimates with the minimum mean-squared error (MMSE) among all linear filters, in long- or short-code systems for a given detection delay. This result may be used as a benchmark against which to compare the performance of other linear detectors in asynchronous channels. It also reveals that a time-varying recursive filter with a fixed and finite complexity implements the fixed-lag linear MMSE (LMMSE) detector, which hitherto has been assumed to require a processing window (and hence complexity) that grows with time.

The convergence behaviour of the LMS CDMA multiuser detector is of great interest both in practice and in theory. The sampling rate for the tap-input of a LMS detector may be equal to or higher than the chip rate, and these correspond to chip-spaced (CS) and fractionally-spaced (FS) detection, respectively. It is shown in this paper that CS and FS detectors with the same time-span exhibit identical convergence behaviour, if the baseband received signal is strictly bandlimited to half the chip rate. Even in the practical case when this condition is not met, deviations from this observation are imperceptible unless the initial tap-weight vector gives an extremely large mean squared error (MSE). The inadequacy of the eigenvalue spread of the tap-input correlation matrix as an indicator of transient behaviour, and the influence of the initial tap weight vector on convergence speed, are highlighted. Specifically, initialization within the signal subspace or to the origin leads to very much faster convergence compared to initialization in the noise subspace.

Parallel interference cancellation (PIC) is a promising detection technique for code division multiple access (CDMA) systems. It has previously been shown that the weighted multistage PIC can be seen as an implementation of the steepest descent algorithm used to minimize the mean squared error (MSE). Following this interpretation, a unique set of weights, based on the eigenvalues of the correlation matrix, was found to lead to the minimum achievable MSE for a given number of stages in a short-code system. In this paper, we introduce a method for finding an appropriate set of time-invariant weights for systems using long codes. The weights are dependent on moments of the eigenvalues of the correlation matrix, exact expressions of which can be derived. This set of weights is optimal in the sense that it minimizes the ensemble averaged MSE over all code-sets. The loss incurred by averaging rather than using the optimal, time-varying weights is practically negligible, since the eigenvalues of sample correlation matrices are tightly clustered in most cases of interest. The complexity required for computing the weights increases linearly with the number of users but is independent of the processing gain, hence on-line weight updating is possible in a dynamic system. Simulation results show that a few stages is usually sufficient for near-MMSE performance.

We introduce a multiuser receiver based on the Kalman filter, which can be used for joint symbol detection and channel estimation. The proposed algorithm has the advantage of working even when the spreading codes used have a period larger than one symbol interval ("long codes"), unlike adaptive equalizer-type detectors. Simulation results which demonstrate the performance advantage of the proposed receiver over the conventional detector, the minimum mean squared error (MMSE) detector and a recursive least squares (RLS) multiuser detector are presented. A thorough comparison of the MMSE detector and the proposed detector is attempted because the Kalman filter also solves the MMSE parameter estimation problem, and it is concluded that, because the state space model assumed by the Kalman filter fits the code division multiple access (CDMA) system exactly, a multiuser detector based on the Kalman filter must necessarily perform better than a nonrecursive, finite-length MMSE detector. The computational complexity of the detector and its use in channel estimation are also studied.

Existing multiuser code-division multiple-access (CDMA) detectors either have to rely on strict power control or near-perfect parameter estimation for reliable operation. A novel adaptive multiuser CDMA detector structure is introduced. Using either an extended Kalman filter (EKF) or a recursive least squares (RLS) formulation, adaptive algorithms which jointly estimate the transmitted bits of each user and individual amplitudes and time delays may be derived. The proposed detectors work in a tracking mode after initial delay acquisition is accomplished using other techniques not discussed here. Through computer simulations, we show that the algorithms perform better than a bank of single-user (SU) receivers in terms of near-far resistance. Practical issues such as the selection of adaptation parameters are also discussed.

In this letter, we derive a recursive, additive metric for complexity-constrained maximum likelihood detection for multiuser CDMA using breadth-first detection algorithms. The metric requires linear filtering of the matched-filtered received signal vector. It is shown that a class of filters fulfilling certain requirements lead to identical performance.

Narrowband interference cancellation techniques described in the literature often overlook the possibility of using prior information known to the receiver in a commercial radio environment to improve their performance. This paper examines an adaptive receiver which exploits knowledge about the narrowband interference which is readily available in commercial CDMA systems, namely its frequency and signal pulse shape, to remove it. The proposed algorithm uses only one adaptive weight per interferer, as opposed to a transversal filter interference canceller which would require many more. Attendant advantages include faster convergence, removal of the need for filter order selection and reduced distortion of the desired signal.

Multicarrier CDMA and direct sequence CDMA have been suggested for both narrowband and broadband multiple access systems. A series of concepts have further been proposed for variable rate broadband transmission within a CDMA system. These principally different techniques exhibit certain common characteristics that can be exploited. We propose a generalised unifying discrete-time model for describing both MC-CDMA, DS-CDMA and hybrid DSMC-CDMA narrowband and variable rate broadband schemes. The model leads to a system description which is similar to the matrix algebraic formulation of a traditional DS-CDMA system.

An off-line or block B-spline interpolation algorithm is modified to an on-line or running form which handles growing data sequences more efficiently. Some loss of interpolation accuracy is suffered with lowpass signals but when the signal is broadband, the increase in interpolation error is negligible while a significant saving in computational effort is obtained.