The broadcasting nature of the wireless medium makes exposure to eavesdroppers a potential threat. Physical Layer Security (PLS) has been widely recognized as a promising security measure complementary to encryption. It has recently been demonstrated that PLS can be implemented using off-the-shelf equipment by spectrum-programming enhanced Software-Defined Networking (SDN), where a network controller is able to execute intelligent access point (AP) selection algorithms such that PLS can be achieved and secrecy capacity optimized. In this paper we provide a basic system model for such implementations. We also introduce a novel secrecy capacity optimization algorithm, in which we combine intelligent AP selection with the addition of Friendly Jamming (FJ) by the not-selected AP.

It has been known that the insufficiency of linear coding in achieving the optimal rate of the general index coding problem is rooted in its rate’s dependency on the field size. However, this dependency has been described only through the two well-known matroid instances, namely the Fano and non- Fano matroids, which, in turn, limits its scope only to the fields with characteristic two. In this paper, we extend this scope to demonstrate the reliance of linear coding rate on fields with characteristic three. By constructing two index coding instances of size 29, we prove that for the first instance, linear coding is optimal only over the fields with characteristic three, and for the second instance, linear coding over any field with characteristic three can never be optimal. Another main contribution of this paper is to reduce the key constraints on the space of the linear coding for each index coding instance of size 29 into a matroid instance with the ground set of size 9, whose linear representability is dependent on the fields with characteristic three. The proofs and discussions provided in this paper through using these two relatively small matroid instances will shed light on the underlying reason causing the linear coding to become insufficient for the general index coding problem.

Industrial Internet of Things (IIoT) is a high-value cyber target due to the nature of the devices and connectivity protocols they deploy. They are easy to compromise and, as they are connected on a large scale with high-value data content, the compromise of any single device can extend to the whole system and disrupt critical functions. There are various security solutions that detect and mitigate intrusions. However, as they lack the capability to deal with an IIoT’s co-existing heterogeneity and interoperability, developing new universal security solutions to fit its requirements is critical. This is challenging due to the scarcity of accurate data about IIoT systems’ activities, connectivities, and attack behaviors. In addition, owing to their multiplatform connectivity protocols and multivendor devices, collecting and creating such data are also challenging. To tackle these issues, we propose a holistic approach for generating an appropriate intrusion data set for an IIoT called X-IIoTID, a connectivity-agnostic and device-agnostic intrusion data set for fitting the heterogeneity and interoperability of IIoT systems. It includes the behaviors of new IIoT connectivity protocols, activities of recent devices, diverse attack types and scenarios, and various attack protocols. It defines an attack taxonomy and consists of multiview features, such as network traffic, host resources, logs and alerts. X-IIoTID is evaluated using popular machine and deep learning algorithms and compared with 18 intrusion data sets to verify its novelty.

In this paper, we propose a new scalar linear coding scheme for the index coding problem called update-based maximum column distance (UM CD) coding scheme. The central idea in each transmission is to code messages such that one of the receivers with the minimum size of side information is instantaneously eliminated from unsatisfied receivers. One main contribution of the paper is to prove that the other satisfied receivers can be identified after each transmission, using a polynomial-time algorithm solving the well-known maximum cardinality matching problem in graph theory. This leads to determining the total number of transmissions without knowing the coding coefficients. Once this number and what messages to transmit in each round is found, we then propose a method to determine all coding coefficients from a sufficiently large finite field. We provide concrete instances where the proposed UM CD scheme has a better broadcast performance compared to the most efficient existing linear coding schemes, including the recursive scheme (Arbabjolfaei and Kim, 2014) and the interlinked-cycle cover scheme (Thapa et al., 2017).

Insufficiency of linear coding for the network coding problem was first proved by providing an instance which is solvable only by nonlinear network coding (Dougherty et al., 2005). Based on the work of Effros et al., 2015, this specific network coding instance can be modeled as a groupcast index coding (GIC) instance with 74 messages and 80 users (where a message can be requested by multiple users). This proves the insufficiency of linear coding for the GIC problem. Using the systematic approach proposed by Maleki$et$al., 2014, the aforementioned GIC instance can be cast into a unicast index coding (UIC) instance with more than 200 users, each wanting a unique message. This confirms the necessity of nonlinear coding for the UIC problem, but only for achieving the entire capacity region. Nevertheless, the question of whether nonlinear coding is required to achieve the symmetric capacity (broadcast rate) of the UIC problem remained open. In this paper, we settle this question and prove the insufficiency of linear coding, by directly building a UIC instance with only 36 users for which there exists a nonlinear index code outperforming the optimal linear code in terms of the broadcast rate.

In this work, we study the secure index coding problem where there are security constraints on both legitimate receivers and eavesdroppers. We develop two performance bounds (i.e., converse results) on the symmetric secure capacity. The first one is an extended version of the basic acyclic chain bound (Liu and Sadeghi, 2019) that takes security constraints into account. The second converse result is a novel information-theoretic lower bound on the symmetric secure capacity, which is interesting as all the existing converse results in the literature for secure index coding give upper bounds on the capacity.

This work considers the problem of fronthaul offloading in a multi-hop fog network where the user devices (denoted as F-UEs) enhanced by learning capabilities, can communicate and collaborate with each other using network coded packets. The paper studies the achievable offloading in relation to different transmission deadlines and network densities comparing it to a single-hop communication. The F-UEs use Q-learning to find autonomously the optimal paths which are defined as the paths requiring the least amount of energy for a single transmission. However, due to the fact that the F-UEs are half-duplex devices and the multi-hop nature of the communication, transmission collisions are inevitable. Thus, the central processing unit is tasked not only with finding the best schedule of transmitting/receiving F-UEs and files in two transmission phases, but also with resolving any possible conflicts between the F-UEs. The central processing unit is able to detect those collisions and resolve them by adjusting the schedule while obeying the time deadlines and thus, maintaining high quality of service (QoS). Results obtained in simulations prove that allowing multi-hop communication achieves better fronthaul offloading than the single-hop communication.

This work presents a novel framework for analyzing the outage performance of the amplify-and-forward (AF) protocol-based spatially diverse multi-source multi-relay multi-user two-hop cooperative relay networks (TCRNs) with potential applications in characterizing unmanned aerial vehicle (UAV) and maritime TCRNs. We consider spatially distributed MIMO communications between arbitrary shaped spatial regions populated with randomly distributed sources, i.e., base stations (BSs), and users, i.e., mobile stations (MSs). It is assumed that the cooperative BSs in the BS region broadcast signals to the MSs in the MS region through multiple relay stations (RSs) that are located randomly within the RS region. We utilize the properties of Hilbert space to analyse such systems and provide a contemporary perspective on the transmit diversity and spatial multiplexing transmission techniques. We then propose relay-selection criteria to choose the best relay which maximizes the system signal-to-noise ratio (SNR) and derive asymptotic closed-form expressions of the outage probability in Rayleigh fading. Both the theoretical predictions and numerical modeling clearly indicate that the outage probabilities of the proposed TCRNs are limited by the sizes of the spatial regions and by exploiting spatial diversity, a significant gain in system's SNR is attainable for both transmission techniques.

This paper considers a device-based Fog Radio Access Network (F-RAN), where users’ smart devices (denoted by F-UEs) can be exploited to cache popular files so as to communicate them when requested by other F-UEs among them. This architecture restricts the involvement of the central baseband processing unit (BBU) to serving those F-UEs not immediately served by their peers, thus offloading the BBU's fronthaul spectrum and increasing the overall capacity of the network. This paper aims to further maximize fronthaul offloading in this architecture using network coding (NC). The latter exploits both the cached and previously received files by different F-UEs as side information to serve more requesting F-UEs in each transmission from their peers or the BBU so as to maximize fronthaul offloading. Being half-duplex devices (they can only either send or receive in any given time), the problem of BBU fronthaul offloading in this setting is formulated over two transmission phases on an NC graph. After showing that this problem is NP-hard, the paper proposes two novel heuristic algorithms to solve it in real-time. In addition, a lower-bound on the offloading gain of these algorithms is derived for a special file placement case, their asymptotic optimality is proven, and their complexities are analyzed. Simulation results both show that these proposed algorithms perform closely to the optimal solution and quantify the significant offloading gains achieved by them.

We study the problem of reducing the download time of multiple files requested by multiple clients from multiple cloud/fog storage servers. Given possible previous file downloads by the clients, network coding can be efficiently exploited to expedite the download process. Since each client can tune to only one server at a time, the sets of clients served by the different servers must be disjoint in order to guarantee a maximum reduction in download time. To accomplish disjoint download mechanisms, a dual conflict network coding graph is proposed. Given the intractability of the long-term optimal solution, we propose an online algorithm using the designed dual conflict graph. For the case of one file request per client, both asymptotic lower and upper bounds of the performance of the proposed conflict-free algorithm are derived. Simulation results show that this proposed algorithm exhibits near optimum performance compared to the optimum solution, and a significant reduction in download time as compared to the per-server network coding scheme. Furthermore, imperfect feedback environment scenarios are investigated. A maximum likelihood approach is employed at the server to estimate the network state, which is then incorporated in our proposed algorithm to reduce the download time in such scenarios.

Future distributed data networks are expected to be assisted by users cooperation and coding schemes. Given the explosive increase in the end‐users’ demand for download of the content from the servers, in this paper, the implementation of instantly decodable network coding (IDNC) is considered in full‐duplex device‐to‐device (D2D) cooperative fog data networks. In particular, this paper is concerned with designing efficient transmission schemes to offload traffic from the expensive backhaul of network servers by employing IDNC and users cooperation. The generalized framework where users send request for multiple packets and the transmissions are subject to erasure is considered. The optimal problem formulation is presented using the stochastic shortest path (SSP) technique over the IDNC graph with induced subgraphs. However, as the optimal solution suffers from the intractability of being NP‐hard, it is not suitable for real‐time communications. The complexity of the problem is addressed by presenting a greedy heuristic algorithm used over the proposed graph model. The paper shows that by implementing IDNC in a full‐duplex cooperative D2D network model significant reduction in the number of downloads required from the servers can be achieved, which will result in offloading of the backhaul servers and thus saving valuable servers’ resources. It is also shown that the performance of the proposed heuristic algorithm is very close to the optimal solution with much lower computational complexity.

This paper studies the problem of offloading the fronthaul of Fog Radio Access Networks (F-RANs) with smart user equipments (denoted by F-UEs) that can cache files and cooperate with each other to retrieve their requested files. This cooperation between the F-UE devices will reduce the load on the fronthaul of the central baseband processing unit (BBU), thus increasing the overall capacity of the network. By smartly employing network coding (NC), this paper aims to minimize the number of transmissions required from the BBU given a realistic half-duplex transmission scenario. In this setting, F-UE devices can only send or receive at a time, and thus must receive their requested files in maximum two time-slots in order to achieve high quality of experience (QoE). The above problem is first formulated over an NC graph. Being NP-hard, two heuristic algorithms are proposed to solve the problem in real-time. Simulation results show that these proposed heuristics perform closely to the optimal solution. They also demonstrate the significant fronthaul offloading gains achieved by our proposed algorithms.

In random machine-to-machine (M2M) networks, the network topology along with the variation of the wireless channel are the main sources of uncertainty that affect the network performance. Unlike most prior works, which assume a reference receiver such as aggregator located at a certain fixed point, this paper discusses a more general case where the intended receiving node can be any randomly-located node distributed uniformly in a circle. Each machine node transmits a backlog of data in time duration based on slotted-synchronous ALOHA multiple access control (MAC) scheme. A closed-form expression for the order statistics of the interference power produced by a randomly located interfering node for Nakagami-m fading channel model is obtained. Due to the dependency of the inter-node distances in uniform deployment, deriving a closed-form expression for outage probability from randomly-selected node's perspective is demanding. Using the order statistics notion, a tractable lower bound is derived for the outage probability seen from any randomly-located node's perspective inside the network region. The numerical results validate the accuracy of analytical ones and tightness of the lower bound for the broad range of the network parameters.

This paper studies the problem of broadcasting layered video streams over heterogeneous single-hop wireless networks using feedback-free random linear network coding (RLNC). We combine RLNC with unequal error protection (UEP) and our main purpose is twofold: to systematically investigate the benefits of UEP+ RLNC layered approach in servicing users with different reception capabilities and to study the effect of not using feedback, by comparing feedback-free schemes with idealistic full-feedback schemes. To these ends, we study “expected percentage of decoded frames” as a key content-independent performance metric and propose a general framework for calculation of this metric, which can highlight the effect of key system, video, and channel parameters. We study the effect of number of layers and propose a scheme that selects the optimum number of layers adaptively to achieve the highest performance. Assessing the proposed schemes with real H.264 test streams, the trade-offs among the users' performances are discussed and the gain of adaptive selection of number of layers to improve the trade-offs is shown. Furthermore, it is observed that the performance gap between the proposed feedback-free scheme and the idealistic scheme is very small and the adaptive selection of number of video layers further closes the gap.

Using stochastic geometry, the downlink (DL) and uplink (UL) coverage probabilities are derived for co-channel packet multi-tier heterogeneous cellular networks (HCNs). The spatial locations of base stations (BSs) as well as user equipments (UEs) are modeled as independent spatial homogeneous Poisson point processes. The decoupled association is evaluated where the UE may connect to different BSs in the UL and DL transmissions. Unlike most of the existing works, the packet transmission slots are not synchronized, that is, the starting and ending points of the slots are not aligned. We investigate fundamental performance metrics of dynamic packet HCN for two traffic models, namely, the slotted-asynchronous and exponential-interarrival. Furthermore, tight lower bounds for the DL and UL coverage probabilities for two traffic models are obtained. The derived bounds are tight especially in high data rate regimes. The analysis provided herein enables us to determine the performance limits of packet-based HCNs with possible asynchronous time-slots. Simulation results are conducted to verify the analytical derivations. Furthermore, the performance comparison between pure synchronous and asynchronous packet-based systems is provided. The results confirm that the synchronous case outperforms the asynchronous one in terms of the UL and DL coverage probabilities at the cost of higher computational complexity.

This paper proposes a new technique to concatenate 1-bit compressed sensing with a convolutional channel encoder for transmission of sparse signals over a memoryless AWGN channel. At the reconstruction part, an iterative decoder, referred to as turbo-CS decoder, has been proposed. At the turbo-CS decoder, the sparse signal is decoded through iterations between an a posteriori probability decoder and a softin/soft-out 1-bit compressed sensing decoder. By numerical experiments, we show that the turbo-CS decoder outperforms the state-of-the-art algorithms for 1-bit compressed sensing reconstruction in the presence of AWGN channel by more than 10 dB in terms of signal reconstruction performance.

Using stochastic geometry, downlink (DL) coverage probability in slot-based two-tier heterogeneous cellular networks (HCNs) is studied. Two traffic patterns namely slotted-asynchronous and exponential-interarrival are introduced to capture user activity, and the DL coverage probability based on max-SINR association is derived. In contrast to many works, the transmission slots of base stations (BS) are not synchronous. This causes a packet transmitted in one slot to experience varying levels of interference during the time slot interval. The SINR statistical distribution during one slot is derived for both traffic patterns. The numerical results are presented to illustrate the analytical ones. The comparison between asynchronous and conventional synchronous HCNs is provided to illustrate the asynchronous effect on DL performance. It is shown that to meet high coverage requirements, synchronizing slots from different BSs help to boost the coverage performance. Furthermore, it is shown that densifying networks with low-power BSs improves coverage performance and the improvement is more pronounced at higher path-loss exponents.

Next-generation cellular networks are expected to be assisted by femtocaches (FCs), which collectively store the most popular files for the clients. Given any arbitrary non-fragmented placement of such files, a strict no-latency constraint, and clients' prior knowledge, new file download requests could be efficiently handled by both the FCs and the macrocell base station (MBS) using opportunistic network coding (ONC). In this paper, we aim to find the best allocation of coded file downloads to the FCs so as to minimize the MBS involvement in this download process. We first formulate this optimization problem over an ONC graph, and show that it is NP-hard. We then propose a greedy approach that maximizes the number of files downloaded by the FCs, with the goal to reduce the download share of the MBS. This allocation is performed using a dual conflict ONC graph to avoid conflicts among the FC downloads. Simulations show that our proposed scheme almost achieves the optimal performance and significantly saves on the MBS bandwidth.

In this paper, we design a conflict free instantly decodable network coding (IDNC) solution for file download from distributed storage servers. Considering previously downloaded files at the clients from these servers as side information, IDNC can speed up the current download process. However, transmission conflicts can occur since multiple servers can simultaneously send IDNC combinations of files to the same client, which can tune to only one of them at a time. To avoid such conflicts and design more efficient coded download patterns, we propose a dual conflict IDNC graph model, which extends the conventional IDNC graph model in order to guarantee conflict free server transmissions to each of the clients. We then formulate the download time minimization problem as a stochastic shortest path problem whose action space is defined by the independent sets of this new graph. Given the intractability of the solution, we design a channel-aware heuristic algorithm and show that it achieves a considerable reduction in the file download time, compared to applying the conventional IDNC approach separately at each of the servers.

This paper studies the problem of transmitting multiple independent layered video streams over single-hop wireless networks using network coding (NC). We combine feedback-free random linear NC (RLNC) with unequal error protection (UEP) and our goal is to investigate the benefits of coding across streams, i.e. inter session NC. To this end, we present a transmission scheme that in addition to mixing packets of different layers of each stream (intra-session NC), mixes packets of different streams as well. Then, we propose the analytical formulation of the layer decoding probabilities for each user and utilize it to define a theoretical performance metric. Assessing this performance metric under various scenarios, it is observed that inter-session NC improves the trade-off among the performances of users. Furthermore, the analytical results show that the throughput gain of inter-session NC over intra-session NC increases with the number of independent streams and also by increasing packet error rate, but degrades as network becomes more heterogeneous.

In this paper, we study the problem of decoding delay reduction for instantly decodable network coding (IDNC) in broadcast cooperative systems, where a group of closely located clients cooperate with each other to obtain their missing packets. In such cooperative systems, one of the clients (referred to as the leader) decides the transmitting client and the packet combination for each transmission. We consider intermittent system status update (SSU) at the leader such that all other clients feed back their packet reception status to the leader after several cooperative transmissions. We first introduce an intermittent local IDNC (IL-IDNC) graph to represent all potential packet combinations for a transmitting client. We then formulate the joint client and packet selection problem that results in the minimum expected decoding delay in each cooperative transmission as a maximum weight clique problem over all the IL-IDNC graphs. Since solving the formulated problem is computationally complex, we propose a heuristic algorithm to select the transmitting client and the packet combination that can reduce the decoding delay. Simulation results show that the proposed heuristic algorithm can achieve a tolerable degradation compared to the full SSU performance while using a smaller number of SSUs.

Deterministic linear network coding (DLNC) is an important family of network coding techniques for wireless packet broadcast. In this paper, we show that DLNC is strongly related to and can be effectively studied using matroid theory without bridging index coding. We prove the equivalence between the DLNC solution and matrix matroid. We use this equivalence to study the performance limits of DLNC in terms of the number of transmissions and its dependence on the finite field size. Specifically, we derive the sufficient and necessary condition for the existence of perfect DLNC solutions and prove that such solutions may not exist over certain finite fields. We then show that identifying perfect solutions over any finite field is still an open problem in general. To fill this gap, we develop a heuristic algorithm which employs graphic matroids to find perfect DLNC solutions over any finite field. Numerical results show that its performance in terms of minimum number of transmissions is close to the lower bound, and is better than random linear network coding when the field size is not so large.

This paper studies the complicated interplay of the completion time (as a measure of throughput) and the decoding delay performance in instantly decodable network coded (IDNC) systems over wireless broadcast erasure channels with memory. We propose two new algorithms that enable a tradeoff for an improved balance between completion time and decoding delay of broadcasting a block of packets. We first formulate the IDNC packet selection problem that improves the balance between completion time and decoding delay as a statistical shortest path (SSP) problem. However, since finding such packet selection policy using the SSP technique is computationally complex, we employ its geometric structure to find some guidelines and use them to propose two efficient heuristic packet selection algorithms for broadcast erasure channels with a wide range of memory conditions. It is shown that each one of the two proposed algorithms is superior for a specific range of memory conditions. Furthermore, we show that the proposed algorithms achieve an improved fairness in terms of the decoding delay across all receivers.

In this paper, we consider the issue of throughput and packet drop rate (PDR) optimization as two performance metrics for delay sensitive applications in network coded time division duplex (TDD) satellite systems with large round trip times (RTTs). We adopt random linear network coding (RLNC) and our purpose is to obtain the optimum RLNC-based transmission strategy. We start with a single-user case and propose a systematic framework to investigate the advantage of using feedback by comparing feedback-free and feedback schemes. Showing analytically that the feedback-free scheme gives better performance for our system of interest, we extend it to multi-user broadcast case. To this end, we consider a number of different broadcast scenarios and optimize the system parameters such that the best overall performance is achieved. Furthermore, the complicated interplay of the mean throughputs and PDRs of different users with different packet erasure conditions is discussed. Finally, it is shown that the optimized feedback-free RLNC broadcast scheme works close enough to an idealistic RLNC scheme, where the complete and immediate knowledge about the reception status of all users is assumed to be available at the sender.

Our primary goal in this paper is to better understand and extend the achievable tradeoffs between the throughput and decoding delay performance of network coded wireless broadcast. To this end, we traverse the performance gap between two linear network coding schemes: random linear network coding (RLNC) and instantly decodable network coding (IDNC). Our approach is to appropriately partition a block of partially received data packets into subgenerations and broadcast them separately using RLNC. Through analyzing the factors that affect the performance of a generic partitioning scheme, we are led to develop a coding framework in which subgenerations are created from IDNC coding sets in an IDNC solution. This coding framework consists of a series of coding schemes, with classic RLNC and IDNC identified as two extreme schemes. We develop two basic partitioning guidelines, including disjoint partitioning and even partitioning. We design various implementations of this coding framework, such as partitioning algorithms and generation scheduling strategies, to further improve its throughput and decoding delay, to manage feedback frequency and coding complexity, or to achieve in-block performance adaption. Their effectiveness is verified through extensive simulations, and their performance is compared with an existing work in the literature.

In this paper, we propose a new compressive sensing (CS) based channel estimation method for high mobility orthogonal frequency division multiplexing (OFDM) systems. The proposed scheme offers the benefits of orthogonal matching pursuit (OMP) and subspace pursuit (SP) estimation methods combined with an inter-carrier interference (ICI) cancellation process. The proposed CS based channel estimation scheme, referred to as the hybrid pursuit (HP) based channel estimation method, operates in an iterative, decision-directed fashion. Here, in each iteration, once the channel is estimated, data symbols are detected and used to calculate the estimate of ICI, caused by the Doppler spread. After that, the ICI term is subtracted from the received signals. The whole process is then repeated, iteratively. The simulation results assess the performance gains achieved by the proposed scheme over the best known channel estimation methods.

This paper investigates the use of instantly decodable network coding (IDNC) for minimizing the mean decoding delay in multicast cooperative data exchange systems, where the clients cooperate with each other to obtain their missing packets. Here, IDNC is used to reduce the decoding delay of each transmission across all clients. We first introduce a new framework to find the optimum client and coded packet that result in the minimum mean decoding delay. However, since finding the optimum solution of the proposed framework is NP-hard, we further propose a heuristic algorithm that aims to minimize the lower bound on the expected decoding delay in each transmission. The effectiveness of the proposed algorithm is assessed through simulations.

In this paper, a new network coding model is proposed to unify instantly decodable network coding (IDNC) and random linear network coding (RLNC), which have been considered to be incompatible in the literature. This model is based on a novel definition of generation, which is built upon optimal IDNC solutions. Under this model, IDNC and RLNC are only two extreme cases with specific generation sizes. Throughput and delay properties of this model, measured by block completion time and packet decoding delay, respectively, are studied, which fill the gap between IDNC and RLNC and thus provide a good understanding on the throughput-delay tradeoff of network coding. An efficient adaptive scheme is then designed, which allows in-block switch among IDNC and different levels of RLNC, so that the system's throughput and delay can be fine-tuned to meet the real-time requirements of the application. Extensive simulations are performed to demonstrate how the proposed generation size interacts with the number of receivers and the channel quality to affect the overall system performance.

In this paper, we consider the problem of guaranteeing the quality of service (QoS) in delay sensitive network coded broadcast systems over time division duplex (TDD) satellite channels. We adopt feedback-less systematic random linear network coding (RLNC) and our goal is to design the system such that the required QoS is guaranteed. We focus on two classes of QoS requirements that necessitate the minimum/mean throughput of the users be higher than a threshold with a predefined probability and at the same time the packet drop rates (PDR) of the users be minimized within the delivery deadline requirements of the system. To this end, we start with formulating the probability and cumulative density functions (PDF and CDF) of users' throughputs. Then by utilizing the calculated functions, the optimum system design parameters that meet the QoS requirements can be obtained. The achieved results on the PDF and CDF of users' throughputs offer good insights about the performances of different users with different packet erasure conditions, and more importantly provide design guidelines for TDD satellite broadcast systems. Furthermore, the results show that the proposed feedback-less scheme performs nearly as well as an idealistic scheme using immediate and perfect feedbacks.

In this paper, we consider the problem of minimizing the decoding delay of generalized instantly decodable network coding (G-IDNC) in persistent erasure channels (PECs). By persistent erasure channels, we mean erasure channels with memory, which are modeled as a Gilbert-Elliott two-state Markov model with good and bad channel states. In this scenario, the channel erasure dependence, represented by the transition probabilities of this channel model, is an important factor that could be exploited to reduce the decoding delay. We first formulate the G-IDNC minimum decoding delay problem in PECs as a maximum weight clique problem over the G-IDNC graph. Since finding the optimal solution of this formulation is NP-hard, we propose two heuristic algorithms to solve it and compare them using extensive simulations. Simulation results show that each of these heuristics outperforms the other in certain ranges of channel memory levels. They also show that the proposed heuristics significantly outperform both the optimal strict IDNC in the literature and the channel-unaware G-IDNC algorithms.

In this paper, we propose an iterative channel estimation and inter-carrier interference (ICI) cancellation method for highly mobile users in Long-Term-Evolution (LTE) systems. The proposed scheme estimates the wireless channel by using pilot symbols, estimates of the data symbols and Doppler spread information at the receiver. The wireless channel is expressed by a weighted time-domain channel interpolation, where the interpolation weights are designed based on the Doppler spread. The channel estimates are obtained by employing a least square (LS) method. A simplified parallel interference cancellation (PIC) scheme coupled with decision statistical combining (DSC) is used to cancel the ICI and to improve the data symbols detection. These data symbols are then utilized to refine the channel estimation further, iteratively. Simulation results are used to verify the effectiveness of the proposed method.

The new wireless standard, Long-Term-Evolution (LTE), needs to support high data rate orthogonal frequency division multiplexing (OFDM) transmission for highly mobile users. Due to users' mobility, the wireless channel becomes time-variant and frequency-selective. The symbol transmission is thus impaired by Doppler spread. As a consequence, the known channel estimation methods do not give satisfactory performances. In this paper, we propose an iterative channel estimation and intercarrier interference (ICI) cancellation method that estimates the wireless channel by utilizing pilot symbols, estimates of the data symbols and Doppler spread information at the receiver. The wireless channel is expressed by a weighted time-domain channel interpolation, where the interpolation weights are designed based on the Doppler spread and time-domain channel correlations. The channel estimates are obtained by employing a least square (LS) method. Once the channel is estimated, the ICI is cancelled by performing a zero-forcing technique. Data symbols are then estimated by a detector. The estimates of data symbols are used to refine the estimation of channel coefficients, iteratively. The simulation results show that the performance degradation of the proposed scheme, when users move at the speed of up to 324 Km/h, compared to a system when users are static and perfect channel state information (CSI) is available at the receiver, is minimal.

In this paper, we investigate a cooperative transmission method employing precoding, beamforming and power allocation for a multi-user multiple-input multiple-output (MU-MIMO) relay network. In the proposed scheme, the interference is canceled by using the combination of beamforming weights and Tomlinson-Harashima precoding (THP). To achieve symbol error rate (SER) fairness among different users and further improve the performance of MU-MIMO relay networks, we propose a low-complexity power allocation (LC-PA) that allocates power to each user so that the signal-to-interference-and-noise-ratio (SINR) for all users are equal. The simulation results show that the SER performance of the proposed scheme considerably outperforms other existing schemes under the same configuration.

In this paper, we investigate transmission methods for a multi-user multiple-input multiple-output (MIMO) network that utilizes base stations (BS) cooperation. To eliminate the interference between users, iterative zero forcing- (ZF) and iterative Tomlinson Harashima precoding-based (THP) schemes are proposed. In the iterative ZF-based scheme, all interference is cancelled by using the transmit-receive weights. To reduce the complexity of iterative ZF scheme, in the iterative THP-based scheme, the interference is cancelled by using transmit-receive weights and the THP. To achieve symbol error rate (SER) fairness among different users and further improve the performance of multi-user MIMO relay systems, we develop optimal and sub-optimal power allocation (PA) methods that ensure signal-to-interference-and-noise-ratio (SINR) across all users are equal, under the power constraints at both BSs and relay station (RS). In the optimal PA scheme, the PA is done at both BSs and RS. In the sub-optimal scheme, to reduce the computational complexity of optimal PA significantly, the PA is done only at BSs, and at the RS a power scaling is performed to satisfy the RS power constraint. The simulation results show that by using optimal and sub-optimal PAs, the iterative ZF-based scheme outperforms the iterative THP-based scheme by an average of 0.4 dB at the cost of a four times higher complexity.