Probabilistic shaping (PS) techniques, when combined with forward error correction, enable reliable transmission at rates close to capacity by inducing a favorable probability distribution on channel input symbols. In this paper, we introduce the parallelized block-based distribution matching (PB-DM) shaping architecture. To facilitate high-throughput PS, the method operates by combining blocks of bits, generated in parallel by multiple binary distribution matchers, and mapping them to blocks of symbols from a non-binary alphabet. The sizes of bit and symbol blocks, and the mapping rule between such blocks, are key design parameters. The parameters bestow the PB-DM architecture with a high degree of customizability, which distinguishes it from other PS schemes. In particular, different PB-DM instances – each characterized by a particular trade-off between shaping performance, latency and memory footprint – can be constructed. Considering a scenario in which constraints are imposed on the allowable latency and memory usage, we propose a heuristic approach to determine design parameters that produce well-performing PB-DM schemes that satisfy the given constraints. We present simulation results over the additive white Gaussian noise channel, demonstrating the performance-complexity trade-offs realizable by different short-blocklength PB-DM designs, and compare them against the trade-offs achieved by other commonly used schemes.

Significant advances in wireless network technologies have given rise to use cases with unprecedented data rates, number of devices as well as low-latency constraints that thrive on such agility of networks. However, it also means that network management through appropriate resource allocation, coverage, and continuity of connection has become significantly more challenging compared to previous generations of networks. More recently, disaggregated network architectures have received increased attention through virtualization of important network functions and through the promise of significant value creation leveraging data-driven intelligent network control policies. In order to leverage the benefits of those versatile network technologies it is imperative that a fine balance be maintained between the resources allocated to a given set of users and the number of users that gain access to the network through intelligent admission control (AC). In this work, we propose and evaluate an admission control policy that leverages data-driven optimization to admit the maximum number of users while being acutely aware of quality of service (QoS) constraints of the users. This novel approach is achieved within the emerging O-RAN framework leveraging RAN Intelligent Controllers (RICs) to recommend appropriate policies enabling operators to maintain an efficient operating network. Our simulation results show that by intelligent tradeoff between the QoS and incoming UE request rejections, we can reduce the rejections by 2-3x in congested networks while maintaining acceptable QoS.

5G transport networks will support dynamic services with diverse requirements through network slicing. Elastic Optical Networks (EONs) facilitate transport network slicing by flexible spectrum allocation and tuning of transmission configurations. A major challenge in supporting dynamic services is the lack of priori knowledge of future slice requests. As a consequence, slice embedding can become sub-optimal over time, leading to spectrum fragmentation and skewed utilization. This in turn can block future slice requests, impacting operator revenue. To address this issue, operators can periodically re-optimize slice embedding for reducing fragmentation. In this paper, we address this problem of re-optimizing network slice embedding on EONs for minimizing fragmentation. The problem is solved in its splittable version, which significantly increases problem complexity, but also offers more opportunities for a larger set of re-configuration actions. We employ simulated annealing for systematically exploring the large solution space. We also propose a greedy algorithm to address the practical constraint of limiting the number of re-configuration steps. Moreover, we present a novel method based on Deep Reinforcement Learning (DRL) for determining when performing re-configuration is most effective. Our extensive simulations demonstrate that the greedy algorithm yields a solution very close to that obtained using simulated annealing while requiring orders of magnitude lesser re-configuration actions. Finally, we show that by applying the greedy algorithm periodically on the network according to the DRL-based time selection algorithm, a significant improvement in the total number of accepted slice requests can be achieved with only performing a limited number of re-configuration operations.

Probabilistic shaping methods induce a desired nonuniform distribution on the transmitted symbols in order to realize a favorable trade-off between the communication rate and average transmission energy. In this work, we study a probabilistic shaping architecture wherein the central component is a binary linear code, employed as a lossy source code. The rate-distortion performance of the linear code directly determines the realized shaping rate-energy performance. We use this connection to establish the rate-energy optimality of the investigated shaping architecture. Although the primary focus of this paper is on shaping for two-symbol alphabets, extensions to non-binary alphabets will be briefly discussed.

We present a randomized schedule for alternating direction method of multipliers with linear programming (ADMM-LP) decoding of low-density parity check (LDPC) codes. The randomized schedule is based on horizontal layered decoding, where nodes are updated sequentially. Unlike existing layered decoding frameworks where all check nodes are updated exactly once per iteration, we propose a randomized schedule where more problematic nodes are updated more frequently. To do so, we sample from a probability mass function (PMF) over all check nodes and the check with sampled index is updated. The probability of each check to be updated is determined by its state. The PMF is constructed based on the distribution of replica (check) vectors inside or on the parity polytope. The randomized decoder usually converges faster than both standard and horizontal layered decoders, making it suitable for limited-iteration decoding in high-throughput applications.

Optical transmission performance measurement and prediction are key Digital Twin capabilities for the optical network. Recent advances in instrumentation and models that support optical transmission performance assessment are presented. Optical network operations automation use cases enabled by a transmission performance-focused Digital Twin are described or demonstrated. These include provisioning automation, transmission performance risk mapping, optimization-based planning and control, and generalized optical margin reduction.

5G transport networks will support dynamic services with diverse requirements through network slicing. Elastic Optical Networks (EONs) facilitate transport network slicing by flexible spectrum allocation and tuning of transmission configurations such as modulation format and forward error correction. A major challenge in supporting dynamic services is the lack of a priori knowledge of future slice requests. In consequence, slice embedding can become sub-optimal over time, leading to spectrum fragmentation and skewed utilization. This in turn can block future slice requests, impacting operator revenue. Therefore, operators need to periodically re-optimize slice embedding for reducing fragmentation. In this paper, we address this problem of re-optimizing network slice embedding on EONs for minimizing fragmentation. The problem is solved in its splittable version, which significantly increases problem complexity, but offers more opportunities for a larger set of re-configuration actions. We employ simulated annealing for systematically exploring the large solution space. We also propose a greedy algorithm to address the practical constraint to limit the number of re-configuration steps taken to reach a defragmentated state. Our extensive simulations demonstrate that the greedy algorithm yields a solution very close to that obtained using simulated annealing while requiring orders of magnitude lesser number of re-configuration actions.

Elastic Optical Networks (EONs) enable finer-grained resource allocation and tuning of transmission configurations for right-sized resource allocation. These features make EONs excellent choice for 5G transport networks supporting highly dynamic traffic with diverse Quality-of-Service (QoS) requirements. 5G network slices are expected to host applications with a dynamic nature (e.g., augmented/virtual reality broadcasting), which will result in slice resource requirement changing over time. The initial resource allocation to network slices has to be adapted to accommodate such changes without causing significant disruption to existing traffic and using minimal additional resources. In this paper, we address the problem of scaling bandwidth demand of network slices on an EON-enabled 5G transport network. In contrast to the state-of-the-art, we do not assume any specific technologies for minimizing disruption when accommodating the scaling request. Rather, we propose an Integer Linear Program (ILP) and a heuristic algorithm for accommodating scaling requests by choosing from a comprehensive set of reconfiguration actions. We carefully design a novel cost model for capturing traffic disruptions and additional resource usage by these different actions. Our extensive simulations using realistic network topologies shed light on the trade-off between additional resource usage and disruption while accommodating slice scaling requests by employing a comprehensive set of reconfiguration actions. Simulation results also show that our heuristic algorithm can find solutions that remain within 10% of ILP-based solutions, while executing several orders of magnitude faster than ILP.

Faster-than-Nyquist (FTN) transmission employs non-orthogonal signaling to improve spectral efficiency over conventional orthogonal transmission at the Nyquist rate. However, FTN signaling also introduces inter-symbol interference (ISI), which must be mitigated through additional signal processing.In this paper, we present a scalable FPGA-based architecture for a turbo maximum-a-posteriori (MAP) equalizer based on the Bahl-Cock-Jelinek-Raviv (BCJR) algorithm to mitigate the ISI. In contrast to many existing hardware implementations of BCJR, where the algorithm performs turbo decoding over a binary alphabet for an already-equalized channel, our FPGA design applies BCJR to non-binary signal constellations and for the task of equalization. To the best of our knowledge, this is the first published FPGA-based BCJR equalizer implementation suitable for FTN applications, where a binary forward-error-correction decoder is employed in tandem with the equalizer.Through careful tradeoff selection and optimization of the design space, our implementation achieves a maximum per-PE throughput of 602 Mbps, and a total of 15.4 Gbps within the constraints of a Xilinx UltraScale+(xcvu13p) device.

This paper addresses Connectivity-aware Virtual Network Embedding (CoViNE) problem, which consists in embedding a virtual network (VN) on a substrate network while ensuring VN connectivity (without any bandwidth guarantee) against multiple substrate link failures. CoViNE provides a weaker form of survivability incurring less resource overhead than traditional VN survivability models. To optimally solve CoViNE, we present an Integer Linear Program (ILP), namely CoViNE-opt. CoViNE-opt enumerates an exponential number of edge-cuts in a VN severely limiting its scalability. Therefore, we decompose CoViNE into three sub-problems: i) augmenting a VN with virtual links to provide necessary connectivity, ii) identifying the virtual links that should be embedded disjointly, and iii) computing a VN embedding while satisfying the disjointness constraints. We introduce conflicting set abstraction that allows to address sub-problems (i) and (ii) without enumerating all the edge-cuts of a VN. We propose two novel solutions to CoViNE leveraging conflicting set, namely CoViNE-ILP and CoViNE-fast. CoViNE-ILP uses a heuristic algorithm to address sub-problems (i) and (ii), while an ILP is used for sub-problem (iii). In contrast, CoViNE-fast uses heuristics for solving all three sub-problems. Through simulation, we evaluate the optimality and scalability of our solutions and demonstrate a failure restoration use-case enabled by CoViNE.

5G network slicing allows partitioning of network resources to meet stringent end-to-end service requirements across multiple network segments, from access to transport. These requirements are shaping technical evolution in each of these segments. In particular, the transport segment is currently evolving in the direction of elastic optical networks (EONs), a new generation of optical networks supporting a flexible optical-spectrum grid and novel elastic transponder capabilities. In this paper, we focus on the reliability of 5G transport-network slices in EON. Specifically, we consider the problem of slicing 5G transport networks,i.e., establishing virtual networks on 5G transport, while providing dedicated protection. As dedicated protection requires a large amount of backup resources, our proposed solution incorporates two techniques to reduce backup resources: (i) bandwidth squeezing,i.e., providing a reduced protection bandwidth than the original request; and (ii) survivable multi-path provisioning. We leverage the capability of EONs to fine tune spectrum allocation and adapt modulation format and forward error correction for allocating spectrum resources. Our numerical evaluation over realistic network topologies quantifies the spectrum savings achieved by employing EON over traditional fixed-grid optical networks, and provides new insights on the impact of bandwidth squeezing and multi-path provisioning on spectrum utilization. One key takeaway from our evaluation is that multi-path provisioning can guarantee up to 40% of the bandwidth requested by a VN during failures by provisioning only 10% additional spectrum resources. This also caused VN blocking ratio for BSR up to 40% to remain very close to that of the no-backup case.

In 5G networks, slicing allows partitioning of network resources to meet stringent end-to-end service requirements across multiple network segments, from access to transport. These requirements are shaping technical evolution in each of these segments. In particular, the transport segment is currently evolving in the direction of the so-called elastic optical networks (EONs), a new generation of optical networks supporting a flexible optical-spectrum grid and novel elastic transponder capabilities. In this paper, we focus on the reliability of 5G transport-network slices in EON. Specifically, we consider the problem of slicing 5G transport networks, i.e., establishing virtual networks on 5G transport, while providing dedicated protection. As dedicated protection requires a large amount of backup resources, our proposed solution incorporates two techniques to reduce backup resources: (i) bandwidth squeezing, i.e., providing a reduced protection bandwidth with respect to the original request; and (ii) survivable multi-path provisioning. We leverage the capability of EONs to fine tune spectrum allocation and adapt modulation format and Forward Error Correction (FEC) for allocating rightsize spectrum resources to network slices. Our numerical evaluation over realistic case-study network topologies quantifies the spectrum savings achieved by employing EON over traditional fixed-grid optical networks, and provides new insights on the impact of bandwidth squeezing and multi-path provisioning on spectrum utilization.

Elastic Optical Network (EON) virtualization has recently emerged as an enabling technology for 5G network slicing. A fundamental problem in EON slicing (known as Virtual Network Embedding (VNE)) is how to efficiently map a virtual network (VN) on a substrate EON characterized by elastic transponders and flexible grid. Since a number of 5G services will have strict latency requirements, the VNE problem in EONs must be solved while guaranteeing latency targets. In existing literature, latency has always been modeled as a constraint applied on the virtual links of the VN. In contrast, we argue in favor of an alternate modeling that constrains the latency of virtual paths. Constraining latency over virtual paths (vs. over virtual links) poses additional modeling and algorithmic challenges to the VNE problem, but allows us to capture end-to-end service requirements. In this paper, we first model latency in an EON by identifying the different factors that contribute to it. We formulate the VNE problem with latency guarantees as an Integer Linear Program (ILP) and propose a heuristic solution that can scale to large problem instances. We evaluated our proposed solutions using real network topologies and realistic transmission configurations under different scenarios and observed that, for a given VN request, latency constraints can be guaranteed by accepting a modest increase in network resource utilization. Latency constraints instead showed a higher impact on VN blocking ratio in dynamic scenarios.

Network operators must continuously scale the capacity of their optical backbone networks to keep apace with the proliferation of bandwidth-intensive applications. Today’s optical networks are designed to carry large traffic aggregates with coarse-grained resource allocation, and are not adequate for maximizing utilization of the expensive optical substrate. Elastic Optical Network (EON) is an emerging technology that facilitates flexible allocation of fiber spectrum by leveraging finer-grained channel spacing, tunable modulation formats and Forward Error Correction (FEC) overheads, and baud-rate assignment, to right size spectrum allocation to customer needs. Virtual Network Embedding (VNE) over EON has been a recent topic of interest due to its importance for 5G network slicing. However, the problem has not yet been addressed while simultaneously considering the full flexibility offered by an EON. In this paper, we present an optimization model that solves the VNE problem over EON when lightpath configurations can be chosen among a large (and practical) set of combinations of paths, modulation formats, FEC overheads and baud rates. The VNE over EON problem is solved in its splittable version, which significantly increases problem complexity, but is much more likely to return a feasible solution. Given the intractability of the optimal solution, we propose a heuristic to solve larger problem instances. Key results from extensive simulations are: (i) a fully-flexible VNE can save up to 60% spectrum resources compared to that where no flexibility is exploited, and (ii) solutions of our heuristic fall in more than 90% of the cases, within 5% of the optimal solution, while executing several orders of magnitude faster.

A key challenge in network virtualization is to efficiently map a virtual network (VN) on a substrate network (SN), while accounting for possible substrate failures. This is known as the survivable VN embedding (SVNE) problem. The state-of-the-art literature has studied the SVNE problem from infrastructure providers' (InPs') perspective, i.e., provisioning backup resources in the SN. A rather unexplored solution spectrum is to augment the VN with sufficient spare backup capacity to survive substrate failures and embed the resulting VN accordingly. Such augmentation enables InPs to offload failure recovery decisions to the VN operator, thus providing more flexible VN management. In this paper, we study the problem of jointly optimizing spare capacity allocation in a VN and embedding the VN to guarantee full bandwidth in the presence of multiple substrate link failures. We formulate the optimal solution to this problem as a quadratic integer program that we transform into an integer linear program. We also propose a heuristic algorithm to solve larger instances of the problem. Based on analytical study and simulation, our key findings are: 1) provisioning shared backup resources in the VN can yield ~33% more resource efficient embedding compared to doing the same at the SN level and 2) our heuristic allocates ~21% extra resources compared to the optimal, while executing several orders of magnitude faster.

Faster-than-Nyquist (FTN) transmission employing antenna polarization multiplexing and higher order modulation (HoM) schemes can significantly increase the spectral efficiency (SE) of the existing wireless backhaul links. However, the benefits of each of these SE enhancement techniques come with a price. While FTN introduces inter-symbol interference, a dual-polarized (DP) transmission suffers from cross-polarization interference (XPI), and HoM makes a communication system vulnerable to phase-noise (PN) distortions. In this paper, we investigate, for the first time, a DP-FTN HoM transmission system. We propose a XPI cancellation and PN mitigation structure, coupled with adaptive decision-feedback equalization or linear precoding, to jointly mitigate interference and accomplish carrier-phase tracking. The DP systems combined with the FTN signaling presented in this paper offer more than 150% increase in SE compared with a single-polarized Nyquist transmission. The effectiveness of the proposed algorithms is demonstrated through computer simulations of a coded DP-FTN microwave communication system in the presence of PN. Numerical results suggest that with the proposed interference cancellation methods, a DP-FTN transmission can yield a 3-5.5-dB performance improvement over an equivalent DP-Nyquist system that employs a higher modulation order to achieve the same data rate.

Network virtualization (NV), considered as a key enabler for overcoming the ossification of the Internet allows multiple heterogeneous virtual networks to co-exist over the same substrate network. Resource allocation problems in NV have been extensively studied for single layer substrates such as IP or Optical networks. However, little effort has been put to address the same problem for multi-layer IP-over-optical networks. The increasing popularity of multi-layer networks for deploying backbones combined with their unique characteristics ( e.g., topological flexibility of the IP layer) calls for the need to carefully investigate the resource provisioning problems arising from their virtualization. In this paper, we address the problem of multi-layer virtual network embedding (MULE; similar to multi-layer networks, this hybrid species brings the best of two species together.) on IP-over-optical networks. We propose two solutions to MULE: 1) an integer linear program formulation for the optimal solution (OPT-MULE) and 2) a heuristic to address the computational complexity of the optimal solution (FAST-MULE). We demonstrate through extensive simulations that on average our heuristic performs within $\boldsymbol \approx 1.47\boldsymbol \times $ of optimal solution while executing several orders of magnitude faster. Simulation results also show that FAST-MULE incurs ≈66% less cost on average than the state-of-the-art heuristic while accepting ≈60% more virtual network requests on average.

Network Virtualization (NV), considered as a key enabler for overcoming the ossification of the Internet allows multiple heterogeneous virtual networks to co-exist over the same substrate network. Resource allocation problems in NV have been extensively studied for single layer substrates such as IP or Optical networks. However, little effort has been put to address the same problem for multi-layer IP-over-Optical networks. The increasing popularity of multi-layer networks for deploying backbones combined with their unique characteristics (e.g., topological flexibility of the IP layer) calls for the need to carefully investigate the resource provisioning problems arising from their virtualization. In this paper, we address the problem of MUlti-Layer virtual network Embedding (MULE) on IP-overOptical networks. We propose two solutions to MULE: an Integer Linear Program (ILP) formulation for the optimal solution and a heuristic to address the computational complexity of the optimal solution. We demonstrate through extensive simulations that on average our heuristic performs within ≈1.47 × of optimal solution and incurs ≈66% less cost than the state-of-the-art heuristic.

Perceived as a key enabling technology for the future Internet, Network Virtualization (NV) allows an Infrastructure Provider (InP) to better utilize their Substrate Network (SN) by provisioning multiple Virtual Networks (VNs) from different Service Providers (SPs). A key challenge in NV is to efficiently map the VN requests from SPs on an SN, known as the Virtual Network Embedding (VNE) problem. VNE algorithms are typically online in nature. A VN embedding can become suboptimal over time due to the arrival and departure of other VNs as well as due to changes in SN such as failures. One way to mitigate the impact of such dynamism is to periodically reallocate resources for the existing VNs. VNE reallocation can increase an InP's revenue by decreasing bandwidth consumption and by increasing the possibility of accepting future VNs. In this paper, we study Reallocation of Virtual Network Embedding (ReViNE) problem to minimize the number of over utilized substrate links and total bandwidth cost on the SN. We propose an Integer Linear Programming formulation for the optimal solution (ReViNE-OPT) and a simulated annealing based heuristic (ReViNE-FAST) to solve larger problem instances. Simulation results show that on average our proposed heuristic performs within ∼19% of the optimal solution. Moreover, ReViNE-FAST generates more than 2.5× better solutions compared to the state-of-the-art simulated annealing based heuristic for VNE reallocation.

Faster-than-Nyquist (FTN) transmission applies non-orthogonal linear modulation to increase spectral efficiency compared with the well-known orthogonal transmission at Nyquist rate. This comes at a price of inter-symbol interference (ISI), which usually is equalized through receiver processing. In this paper, we investigate the alternative approach of pre-equalization at the transmitter. First, we consider Tomlinson-Harashima precoding (THP) for FTN and propose two novel soft demapping algorithms to generate the soft-input for the error-correction decoder. The developed demappers effectively compensate the modulo-loss associated with conventional THP transmission. Second, we propose a linear pre-filtering strategy to pre-equalize the ISI induced by FTN. We show that the linear pre-equalization approach is equivalent to an orthogonal transmission with a modified pulse shape. It thus yields the optimal error-rate performance while affording higher spectral efficiency. We validate our proposed precoding algorithms through computer simulations of a coded coherent optical communication system as a practical application example for FTN.

Network virtualization has evolved as a key enabling technology for offering the next generation network services. Recently, it is being rolled out in data center networks as a means to provide bandwidth guarantees to cloud applications. With increasing deployments of virtual networks (VNs) in commercial-grade networks with commodity hardware, VNs need to tackle failures in the underlying substrate network. In this paper, we study the problem of recovering a batch of VNs affected by a substrate node failure. The combinatorial possibilities of alternate embeddings of the failed virtual nodes and links of the VNs make the task of finding the most efficient recovery both non-trivial and intractable. Furthermore, any recovery approach ideally should not cause any service disruption for the unaffected parts of the VNs. We take into account these issues to design a generalized recovery approach that can achieve customized objectives such as fair treatment on the failed VNs, partial treatment based on priority, and so on. We provide integer linear programming (ILP) formulations for two variants of our recovery scheme, namely, fair recovery model and priority-based recovery model. We also propose a fast and scalable heuristic algorithm to tackle the computational complexity of the ILP solution. Evaluation results demonstrate that our heuristic performs close to the optimal solution and outperforms the state-of-the-art algorithm.

Network visualization (NV) has evolved as a key enabling technology for offering the next generation network services. Recently, it is being rolled out in data center networks as a means to provide bandwidth guarantees to cloud applications. With increasing deployments of virtual networks (VNs) in commercial-grade networks with commodity hardware, VNs need to tackle failures in the underlying substrate network. In this paper, we study the problem of recovering a batch of VNs affected by a substrate node failure. The combinatorial possibilities of alternate embeddings of the failed virtual nodes and links of the VNs makes the task of finding the most efficient recovery both non-trivial and intractable. Furthermore, any recovery approach ideally should not cause any service disruption for the unaffected parts of the VNs. We take into account these issues to design a recovery approach for maximizing recovery and minimizing the cost of recovery and network disruption. We provide an Integer Linear Programming (ILP) formulation of our recovery scheme. We also propose a fast and scalable heuristic algorithm to tackle the computational complexity of the ILP solution. Evaluation results demonstrate that our heuristic performs close to the optimal solution and outperforms the state-of-the-art algorithm.

Network virtualization is enabling infrastructure providers (InPs) to offer new services to higher level service providers (SPs). InPs are usually bound by Service Level Agreements (SLAs) to ensure various levels of resource availability for different SPs' virtual networks (VNs). They provision redundant backup resources while embedding an SP's VN request to conform to the SLAs during physical failures in the infrastructure. An extreme of this backup resource provisioning is to reserve a dedicated backup of each element in an SP's VN request. Such dedicated protection scheme can enable an InP to ensure fast VN recovery, thus, providing high uptime guarantee to the SPs. In this paper, we study the 1 + 1-Protected Virtual Network Embedding (1 + 1-ProViNE) problem. We propose Dedicated Protection for Virtual Network Embedding (DRONE), a suite of solutions to the 1 + 1-ProViNE. DRONE includes an Integer Linear Programming (ILP) formulation for optimal solution (OPT-DRONE) and a heuristic (FAST-DRONE) to tackle the computational complexity in computing the optimal solution. Trace driven simulations show that FAST-DRONE allocates only 14.3% extra backup resources on average compared to the optimal solution, while executing 200-12000x faster.

Network virtualization is enabling infrastructure providers (InPs) to offer new services to service providers (SPs). InPs are usually bound by service level agreements to ensure various levels of resource availability for different SPs' virtual networks (VNs). They provision redundant backup resources while embedding an SP's VN request to conform to the SLAs during physical failures in the infrastructure. An extreme backup resource provisioning is to reserve a mutually exclusive backup of each element in an SP's VN request. Such dedicated protection scheme can enable an InP to ensure fast VN recovery, thus, providing high uptime guarantee to the SPs. In this paper, we study the 1 + 1-Protected Virtual Network Embedding (1 + 1-ProViNE) problem. We propose Dedicated Protection for Virtual Network Embedding (DRONE), a suite of solutions to the 1 + 1-ProViNE problem. DRONE includes an integer linear programming formulation for optimal solution (OPT-DRONE) and a heuristic (FAST-DRONE) to tackle the computational complexity of the optimal solution. Trace driven simulations show that FAST-DRONE allocates only 14.3% extra backup resources on average compared to the optimal solution, while executing 200-1200 times faster. Simulation results also show that FAST-DRONE can accept four times more VN requests on average compared to the state-of-the-art solution for providing dedicated protection to VNs.

It has been widely acknowledged that the aggregate interference at the receiver for various practical communication channels can often deviate markedly from the classical additive white Gaussian noise (AWGN) assumption due to various ambient phenomena. Moreover, the physical nature of the underlying interference generating process in such cases can lead to a bursty behaviour of the interfering signal, implying that it is highly likely that consecutive symbols are affected by similar noise levels. In this paper, we devise and analyze detection techniques, in conjunction with a convolution code, for such interference channels that possess non-negligible memory by considering optimum and sub-optimum decoding metrics. In particular the inherent memory in the noise process is modeled as a first-order Markov chain, whose state selects the variance of the instantaneous Gaussian noise, leading to a Markov-Gaussian channel model. Analytical expressions are obtained for the cut-off rate, which is an ensemble code parameter, and the bit error rate for a convolutionally coded system, that are subsequently employed for an extensive evaluation of the various metrics considered. Furthermore, the interleaving depth is considered as a design parameter and its effect on performance is analyzed over a range of noise scenarios.

In this letter, we design and analyze the performance of single-user-type non-linear detectors that are able to cope with the impulsive nature of multiuser interference (MUI) in timehopping impulse-radio ultra-wideband (TH IR-UWB) systems. We collectively refer to these detectors as "robust" detectors. We first propose two novel detectors and then derive semi-analytical expressions for the bit-error rate (BER) of TH IR-UWB with general robust detection. The evaluation of these expressions greatly facilitates the optimization of detector parameters and provides insight into the effects of MUI. A performance comparison shows that (1) robust detection significantly improves performance over conventional detection in the presence of MUI, (2) the parameters for various parametric robust detectors can be chosen to be constant over many transmission scenarios with only little performance degradation compared to using the optimal parameter value, and (3) the proposed two-term detector, which requires a modest amount of parameter estimation, achieves consistently the best performance.

In this paper, binary linear block codes with very low encoding and decoding complexity (therefore, "simple" codes) are introduced as outer codes in a serially concatenated coding scheme employing multilevel differential phase-shift keying, i.e., differential modulations, as inner codes. Such a concatenated coding scheme is particularly suitable for power- and bandwidth-efficient transmission over channels with phase ambiguities or completely unknown phase at the receiver. Using extrinsic information transfer chart based analysis and optimization, the performance of the new concatenated codes is found to be within less than 1 dB of the pertinent capacity limit for the additive white Gaussian noise channel. This compares favorably with more complex benchmark schemes using e.g. outer low-density parity-check codes proposed recently in the literature. Simulation results confirm the predicted performance advantages for the proposed simple codes.

In this paper, binary linear block codes with very low encoding and decoding complexity are introduced for the use as outer codes in a serial concatenated coding scheme employing differential modulations as inner codes. Using extrinsic information transfer chart based analysis and optimization the performance of the new concatenated codes is found to be within less than 1 dB of the pertinent capacity limit for the additive white Gaussian noise channel. This compares favorably with more complex benchmark schemes using e.g. outer low density parity check codes proposed recently. For decoding without channel phase estimation a low-complexity noncoherent decoder is presented, which achieves performance close to that of a coherent decoder in channels with moderate phase noise.

Data transmission over power lines is an attractive alternative to well-established wireline and wireless communication technologies. One of the main challenges in accomplishing reliable power-line communication (PLC) is channel impairment through electromagnetic interferences, or so-called impulse noise. In this paper, we consider transmission over impulse-noise channels for a typical narrowband system architecture employing convolutional codes and Viterbi decoding. We present different decoding metrics, including new designs adopted from the multiuser detection literature, and we derive expressions for cutoff rate and bit-error rate (BER) performances of the corresponding decoders. These expressions are amenable for quick numerical evaluation and thus, constitute a valuable tool for decoder optimization and performance comparison. Our numerical and BER simulation results show that one of the proposed metrics enables robust decoding without knowledge of the statistic of the impulse noise with a performance close to that of optimum decoding, which relies on the noise statistic. It is further highlighted that, different from transmission over the Gaussian-noise channel, quadrature detection is beneficial in case of real-valued modulation and passband transmission over impulse-noise channels.