Going toward extreme multiple input multiple output (MIMO) antenna systems, each individual radio frequency (RF) chain will require cost and energy efficient implementations. While low resolution digital-to-analog converters (DACs) present a straightforward path toward achieving this goal, the introduced quantization error will lead to degraded performance. In this paper, we propose the use a spatio-temporal delta sigma modulation (DSM) placed upstream of the converter in a downlink massive MIMO-orthogonal frequency division multiplexing (OFDM) scenario. Its role is to shape quantization noise in frequency and in space, casting it toward unused directions and frequency resources. At the heart lies an optimization based design of the modulator’s noise transfer function (NTF) using available channel state information (CSI). A constraint to shape quantization noise away from adjacent frequency bands is studied as well. Numerical results show that the additional temporal dimension enables better error vector magnitude (EVM) performance than the recently thriving spatial DSM. Additional filtering is however still required to reach spectral masks set by current communication standards.

This paper investigates the network sensing problem in a distributed multiple-input multiple-output (MIMO) radar system. We first formulate the received signal model in distributed MIMO systems as a function of the target's location. Based on the problem formulation, we derive the Cramér-Rao lower bound (CRLB) of the location estimation error for a single target, whose dependence on the layout of the transmitters (TXs) and receivers (RXs) is revealed. Using the tools from stochastic geometry, we then model the locations of TXs and RXs as homogeneous Poisson Point Process (PPP) and investigate the network-level sensing performance. Particularly, we derive the scaling law for the average estimation error, revealing the impact of various system parameters such as the number of antennas, SNR, TX/RX densities, and path loss exponent. More importantly, we unveil that the estimation error scales with the SNR and the number of antennas to the power of -1, and with the TX/RX densities to the power of$-\gamma / 2$, where$\gamma$is the path loss exponent. Our numerical results confirm the accuracy of our theoretical derivations and the correctness of conclusions.

This paper presents a packet scheduler for managing multiple links with varying channel capacities, where each link carries multiple data flows with finite buffers and strict delay constraints. Packet loss can result from buffer overflow or delay violations. We propose a deep reinforcement learning scheduler based on an encoder only transformer architecture, capable of handling a variable number of links without dedicated training. Using deep Q-learning, the scheduler minimizes the packet loss rate. Simulations show that our approach outper-forms a state-of-the-art fully connected scheduler, delivering better performance under diverse configurations of links, packet arrival rates, and channel capacities.

We consider a Non Orthogonal Multiple Access (NOMA)-based wireless network where User Equipments (UEs) are connected to a Base Station (BS) equipped with a Mobile Edge Computing (MEC) server. The UEs can process their buffered data packets with strict delay either locally or by offloading them to the base station's MEC server. In order to minimize the dropped packets due to buffer overflow or delay violation, the scheduling-offloading problem is formulated as a Markov Decision Process (MDP) and solved using various optimal and Reinforcement Learning (RL) algorithms. The output of each policy is, for each user, the number of packets to be processed and the type of processing (locally or remotely). The decisions rely on the channel state information and the buffers states. The numerical results show the great advantage of using NOMA compared to Orthogonal Multiple Access (OMA). We further analyze the scalability capabilities of the used algorithms, which validates the benefits of using Deep Reinforcement Learning (DRL) techniques.

We consider a cooperative X-channel with K transmitters (TXs) and K receivers (Rxs) where Txs and Rxs are gathered into groups of size r respectively. Txs belonging to the same group cooperate to jointly transmit a message to each of the K − r Rxs in all other groups, and each Rx individually decodes all its intended messages. By introducing a new interference alignment (IA) scheme, we prove that when K/r is an integer the Sum Degrees of Freedom (Sum-DoF) of this channel is lower bounded by 2r if K/r ∈ {2, 3} and by $\frac{{K(K - r) - {r^2}}}{{2K - 3r}}$ if K/r ≥ 4. We also prove that the Sum-DoF is upper bounded by $\frac{{{\text{K}}({\text{K}} - {\text{r}})}}{{2{\text{K}} - 3{\text{r}}}}$. The proposed IA scheme finds application in a wireless distributed MapReduce framework, where it improves the normalized data delivery time (NDT) compared to the state of the art.

This paper investigates the decoding of a remarkable set of lattices: We treat in a unified framework the Leech lattice in dimension 24, the Nebe lattice in dimension 72, and the Barnes-Wall lattices. A new interesting lattice, named$L_{3\cdot 24}$, is constructed as a simple application of the single parity check on the Leech lattice. The common aspect of these lattices is that they can be obtained via a single parity check or via the$k$-ing construction. We exploit these constructions to introduce a new efficient paradigm for decoding. This leads to efficient list decoders and quasi-optimal decoders on the Gaussian channel. Both theoretical and practical performance (point error probability and complexity) of the new decoders are provided.

We characterize the complexity of the lattice decoding problem from a neural network perspective. The notion of Voronoi-reduced basis is introduced to restrict the space of solutions to a binary set. On the one hand, this problem is shown to be equivalent to computing a continuous piecewise linear (CPWL) function restricted to the fundamental parallelotope. On the other hand, it is known that any function computed by a ReLU feed-forward neural network is CPWL. As a result, we count the number of affine pieces in the CPWL decoding function to characterize the complexity of the decoding problem. It is exponential in the space dimension$n$, which induces shallow neural networks of exponential size. For structured lattices we show that folding, a technique equivalent to using a deep neural network, enables to reduce this complexity from exponential in$n$to polynomial in$n$. Regarding unstructured MIMO lattices, in contrary to dense lattices many pieces in the CPWL decoding function can be neglected for quasi-optimal decoding on the Gaussian channel. This makes the decoding problem easier and it explains why shallow neural networks of reasonable size are more efficient with this category of lattices (in low to moderate dimensions).

A compact and closed-form expression of capacity is derived for an uplink multiband satellite system in the presence of nonlinear interference. The nonlinear effect comes from the satellite high-power amplifier modeled by a Volterra series expansion. The derivations reveal that the nonlinear interference can provide a constructive power contribution that could be used to increase the transmission rate. Consequently, decoders designed by viewing this interference as only an additional noise are suboptimal. Numerical results confirm this claim and also show that an appropriate power allocation amongst the subbands may be of interest.

We present new efficient recursive decoders for the Barnes-Wall lattices based on their squaring construction. The analysis of the new decoders reveals a quasi-quadratic complexity in the lattice dimension. The error rate is shown to be close to the universal lower bound in dimensions 64 and 128.

We consider updating strategies for a local cache which downloads time-sensitive files from a remote server through a bandwidth-constrained link. The files are requested randomly from the cache by local users according to a popularity distribution which varies over time according to a Markov chain structure. We measure the freshness of the requested time-sensitive files through their Age of Information (AoI). The goal is then to minimize the average AoI of all requested files by appropriately designing the local cache’s downloading strategy. To achieve this goal, the original problem is relaxed and cast into a Constrained Markov Decision Problem (CMDP), which we solve using a Lagrangian approach and Linear Programming. Inspired by this solution for the relaxed problem, we propose a practical cache updating strategy that meets all the constraints of the original problem. Under certain assumptions, the practical updating strategy is shown to be optimal for the original problem in the asymptotic regime of a large number of files. For a finite number of files, we show the gain of our practical updating strategy over the traditional square-root-law strategy (which is optimal for fixed non time-varying file popularities) through numerical simulations.

This paper proposes a joint packet scheduling and computation offloading policy for an Energy Harvesting (EH) mobile terminal wirelessly connected to a Base Station (BS) when the channel between the mobile and the BS is unavailable at the mobile side. The mobile terminal has to decide if its packet related to one application is computed either locally or remotely by the BS within a strict delay imposed by this application without knowing the channel in advance. Our objective is to guarantee reliable communication by minimizing the packet loss. This packet loss is due to butter overflow, strict delay violation and channel mismatch. We formulate the problem using a Markov Decision Process (MDP) and we propose and implement the optimal deterministic offline policy to solve it. This optimal policy decides: (i) the execution location (locally or remotely), (ii) the number of packets to be executed and (iii) the corresponding transmission power. This policy offers a dramatic increase in the number of executed packets and a significant energy saving.

In order to improve the transmission reliability in current wireless communication systems, the Hybrid Automatic ReQuest (HARQ) protocol is employed to manage the unknown time-varying channel. The acknowledgments are fed back with delay on the return link. To fill up the idle time between a transmission and its acknowledgment, parallel HARQ streams associated with different messages are carried out. In this paper we improve on parallel HARQ by proposing a multi-layer HARQ protocol (also called superposition coding or multi-packet HARQ), where a single transmission may carry information on multiple messages. The multi-layer HARQ protocol works in presence of delay on the return link as parallel HARQ does, and does not require additional feedback such as the channel state information. It aims at improving the accuracy as well as the user's delay distribution, thus achieving throughput increase. Assuming capacity-achieving codes, we show that the proposed protocol outperforms parallel HARQ in throughput, message error rate, and delay distribution. Using practical codes and decoding algorithms the gains are as well significant, at the expense of the receiver's complexity.

Emerging 5G networks will need to efficiently support ultra-reliable, low-latency communication (URLLC), which requires extremely low latency (at msec order) with very high reliability (99.999%). In this work, we consider a URLLC system with incremental redundancy hybrid automatic repeat request (IR-HARQ) and investigate the effect of channel state information (CSI) at the transmitter on throughput and energy consumption optimization. For that, we analyze the feasibility region and the performance in block fading channels for the cases of full and statistical CSI. Our results show that in our setup the full CSI scheme is less robust and we also reveal a desirable balance between the trade-off quantities of energy and throughput.

Emerging 5G networks will need to efficiently support ultra-reliable, low-latency communications (URLLC) services, which require extremely low latency (msec order) with very high reliability (99.999%). We consider a URLLC system with short packets and incremental redundancy hybrid automatic repeat request (IR-HARQ). We aim at maximizing the throughput by optimally tuning the IR-HARQ mechanism subject to URLLC constraints and a fixed energy budget. We propose a dynamic programming algorithm for solving the throughput maximization problem in the finite blocklength regime and assess its performance numerically.

Point lattices and their decoding via neural networks are considered in this paper. Lattice decoding in ℝn, known as the closest vector problem (CVP), becomes a classification problem in the fundamental parallelotope with a piecewise linear function defining the boundary. Theoretical results are obtained by studying root lattices. We show how the number of pieces in the boundary function reduces dramatically with folding, from exponential to linear. This translates into a two-layer ReLU neural network requiring a number of neurons growing exponentially in n to solve the CVP, whereas this complexity becomes polynomial in n for a deep ReLU neural network.

This paper addresses the per-link power and bandwidth allocation problem with the objective of maximizing energy efficiency (EE) related metrics under a per-link minimum goodput constraint when only statistical channel state information is available. We consider a parallel (i.e., without multiuser interference) Rician channel model, which encompasses both Rayleigh and additive white Gaussian noise channels as special cases. We also consider Type-I hybrid automatic repeat request with practical modulation and coding schemes. The addressed problems are the maximization of the sum of the user's EE, the maximization of the EE of the user with the lowest EE, and the maximization of the EE of the network. We derive the optimal solutions of these problems in closed form using fractional programming and a convex optimization framework. We show that substantial gains can be achieved by taking into account the line of sight between the transmitter and the receiver instead of only considering the average channel power.

We consider an ultra-reliable low-latency communication (URLLC) system with short packets employing hybrid automatic repeat request (HARQ). Depending on the delay of HARQ feedback and retransmissions, the latency constraint can be either violated or fulfilled at the expense of power consumption. We focus on the energy-latency tradeoff and explore whether it is better to do one-shot transmission or use HARQ. We analyze the energy consumption for incremental redundancy (IR) HARQ and compare it with the no HARQ case. The analysis relies on closed-form expressions for the outage probability of IR-HARQ with variables both the blocklength and the power. Our results show that for a wide range of blocklength, when the feedback delay is more than half the latency constraint, it is beneficial in terms of energy to use one-shot transmission (i.e., no HARQ).

This paper investigates the resource scheduling minimizing the packet loss when the wireless communication system operates with Energy Harvesting (EH) based devices. The packet loss occurs when the buffer is overflowed and when the queued packet is older than a certain pre-defined threshold. We so consider a strict delay constraint rather than an average delay constraint. The associated optimization problem can be modeled as Markov Decision Problem (MDP) where the actions are the number of packets sent on the known channel at each slot. The optimal deterministic offline policy is exhibited through dynamic programming techniques, i.e. Value Iteration (VI) algorithm. We show the gain in the number of transmitted packets and the consumed energy is substantial compared to a naive policy which forces the system to send the maximum number of packets using the available energy in the battery.

In current wireless communication systems, the feedback required by the Hybrid Automatic ReQuest (HARQ) mechanism is received with some delay at the transmitter side. To alleviate this issue, parallal Stop-and-Wait HARQ is usually employed. In this paper, we propose a multi-packet HARQ protocol (also called superposition coding or multi-layer HARQ) to improve the user's delay distribution and increase the throughput, without any additional feedback such as Channel State Information. The performance analysis, provided from an information-theoretic point-of-view, shows that the proposed protocol offers better delay distribution, higher throughput and lower message error rate compared to the conventional parallel Stop-and-Wait HARQ, at the expense of increased decoding complexity.

High-fidelity, real-time interactive applications are envisioned with the emergence of the Internet of Things and tactile Internet by means of ultra-reliable low-latency communications (URLLC). Exploiting time diversity for fulfilling the URLLC requirements in an energy efficient manner is a challenging task due to the nontrivial interplay among packet size, retransmission rounds and delay, and transmit power. In this paper, we study the fundamental energy-latency tradeoff in URLLC systems employing incremental redundancy (IR) hybrid automatic repeat request (HARQ). We cast the average energy minimization problem with a finite blocklength (latency) constraint and feedback delay, which is non-convex. We propose a dynamic programming algorithm for energy efficient IR-HARQ optimization in terms of number of retransmissions, blocklength, and power per round. Numerical results show that our IR-HARQ approach could provide around 25% energy saving compared with one-shot transmission (no HARQ).

We address the problem of energy efficient power and bandwidth allocation for hybrid automatic repeat request (HARQ) in multiuser ad hoc networks with statistical channel state information (CSI) and practical modulation and coding schemes (MCS) under quality of service (QoS) constraints. Using an upper-bound of the packet error rate, we propose an algorithm to maximize the sum of the energy efficiency (SEE) of the different users. We also propose two suboptimal less-complex algorithms, one based on alternating optimization and the other on a high signal-to-noise ratio (SNR) approximation. It is shown that the proposed algorithms allow a substantial gain in term of SEE compared with conventional algorithms.

We consider a system where a local cache maintains a collection of N dynamic content items that are randomly requested by local users. A capacity-constrained link to a remote network server limits the ability of the cache to hold the latest version of each item at all times, making it necessary to design an update policy. Using an age of information metric, we show under a relaxed problem formulation that an asymptotically optimal policy updates a cached item in proportion to the square root of the item's popularity. We then show experimentally that a physically realizable policy closely approximates the asymptotic optimal policy.

In the context of wireless mobile ad hoc networks, node clustering is a well-known solution for handling the scalability issue. While existing work focused on unstructured (i.e., flat) networks, this paper investigates a clustering algorithm to handle stable size-restricted clusters for structured (i.e., group-based) networks. In addition, we have identified that the ad hoc network clustering literature lacks a theoretical framework. This paper fills this gap by proposing to use coalition game theory, identifying coalitions to clusters and players to nodes. This theoretical framework allows us to derive a novel generic distributed node clustering algorithm. The algorithm is proved to converge to Nash-stable partitions. It is based on the concept of switch operations, where nodes take decision whether to leave or not their current coalition based on the coalition values. These decisions are made independently on any node individual payoff, meaning that the coalition formation game has a transferable utility. This generic algorithm is then tailored to both structured and unstructured networks, by defining judiciously the value functions and the heuristics dedicated to selecting suitable switch operations. Based on extensive simulations, we show that our proposed solutions outperform the existing ones especially in terms of cluster size and stability.

Primary User Emulation Attack (PUEA), in which attackers emulate primary user signals causing restriction of secondary access on the attacked channels, is a serious security problem in Cognitive Radio Networks (CRNs). A user performing a PUEA for selfishly occupying more channels is called a selfish PUEA attacker. Network managers could adopt a surveillance process on disallowed channels for identifying illegal channel occupation of selfish PUEA attackers and hence mitigating selfish PUEA. Determining surveillance strategies, particularly in multichannel context, is necessary for ensuring network operation fairness. In this paper, we formulate a game, called multi-channel surveillance game, between the selfish attack and the surveillance process in multi-channel CRNs. The sequence-form representation method is adopted to determine the Nash Equilibrium (NE) of the game. We show that performing the obtained NE surveillance strategy significantly mitigates selfish PUEA.

We investigate the primary user emulation (PUE) attack, which is a serious security problem in cognitive radio (CR) networks. There exist three types of PUE attackers: 1) a selfish one, which aims at maximizing its selfish usage of channel resource; 2) a malicious one, which points for obstructing the operation of CR network; and 3) a mixed one, which is between a selfish and malicious PUE attacker. For combating a selfish PUE attacker, a channel surveillance process has to be implemented in order to determine active user's identification and so selfish PUE attacker. An extra-sensing process has to be implemented for observing new opportunities to access the channel and so for mitigating the malicious PUE attacker's effect. Relevant strategies for deploying the above processes are obtained through a game theory-based analysis and the exhibition of Nash equilibrium (NE). We show the NE strongly depends on the network demand, the availability of the spectrum resource, and the type of the attacker.

We address multiuser power and bandwidth allocation for Orthogonal Frequency Division Multiple Access (OFDMA) networks employing a Type-II Hybrid Automatic Repeat reQuest (HARQ) mechanism, practical Coding and Modulation Schemes (MCSs) and Bit Interleaved Coded Modulation (BICM). The problem is formulated as minimizing the sum power required to satisfy a goodput constraint for each link while its transmit power does not exceed a certain level. Assuming statistical Channel State Information (CSI), we propose an algorithm to compute the corresponding optimal resource allocation and a practical selection of the MCSs that significantly boosts the proposed algorithm.

Many wireless public safety and military networks are ad hoc networks, for which clustering is a well-known strategy to improve scalability. In addition these networks are structured according to a hierarchical organization, i.e. nodes belong to specific operational groups which implies that the traffic is mainly intra-group. So far, clustering algorithms are built using metrics such as node identifiers, node mobility, etc., and thus do not take into account the network hierarchical structure. The goal of this paper is therefore twofold: i) specify a way to benchmark clustering solutions from a system point of view, and ii) thanks to this benchmark determine the importance of using operational group information to build clusters. Therefore we define a novel network cost function based on additive metrics (e.g. delay) incorporating the traffic structure and the inter-clusters communications costs. Thanks to this function we show that the clustering solutions providing the best QoS to the end user depend on the group structure.

We address the problem of multiuser power and bandwidth allocation for Orthogonal Frequency Division Multiple Access (OFDMA) networks employing a Type-II Hybrid Automatic Repeat reQuest (HARQ) mechanism, practical Coding and Modulation Schemes (MCSs) and Bit Interleaved Coded Modulation (BICM). The problem is formulated as minimizing the sum power required to satisfy a goodput constraint for each link while its post-HARQ Packet Error Rate (PER) does not exceed a certain threshold. Assuming statistical Channel State Information (CSI), we propose a computationally-efficient algorithm to compute the corresponding optimal resource allocation. We finally provide a practical selection of the MCSs that significantly boosts the performance of the proposed resource allocation algorithm.

We address the problem of multiuser power and bandwidth allocation for a general class of OFDMA-based wireless networks that employ a Type-II hybrid automatic repeat request (HARQ) mechanism along with practical modulation and coding schemes (MCSs). This problem is formulated as minimizing the sum transmit power required to satisfy individual goodput constraints without exceeding maximum allowable per-link or per-node transmit power levels. We assume that the resource manager has only statistical knowledge of the channel state information (CSI) of the Rayleigh-distributed fast-fading links of the network. Using a tight approximation of the goodput, we propose an algorithm allowing computation of the corresponding optimal resource allocation. We finally provide an efficient selection of the different MCSs that can be coupled with the proposed resource allocation algorithm to significantly boost its performance.

The distributed estimation of the average value of the sensors initial measures is one of the most popular issues in the Wireless Sensor Networks (WSN) area. In WSNs, broadcasting data seems natural to exchange information quickly because of the broadcast nature of the Wireless channel. Nevertheless, although broadcast-based algorithms converge faster than pairwise algorithms, the obtained consensus is not necessarily the true average. By the means of additional side-information exchange, we propose a broadcast-based algorithm converging rapidly to the true average. The convergence of this new algorithm is established and its convergence speed is exhibited. We remark that the proposed algorithm outperforms the existing ones.

We investigate several types of Stop and Wait HARQ schemes under imperfect feedback conditions. Performance are measured through the usual metrics, i.e., packet error rate, delay, and efficiency. The main novelties of this paper are twofold: i) the considered feedback undergoes the random arrival time of the acknowledgment. ii) the analysis is done at the network level (IP) which enlarges the vision of practical systems performance. In particular, our analysis enables us to consider a recent cross-layer MAC-IP allocation strategy. Finally our results provide a way to design the feedback link.

In this work, a static relaying protocol, called Decode or Quantize and Forward (DoQF), is introduced for half duplex single-relay networks, and its performance is studied in the context of communications over slow fading wireless channels. The proposed protocol is inspired by the so-called Compress-and-Forward (CF) but only needs statistical Channel State Information at the Transmitter (CSIT). First, we analyze the behavior of the outage probability Poof the proposed protocol as the SNR p tends to infinity. In this case, we prove that ρ2Poconverges to a constant ξ. We refer to this constant as the outage probability gain and we derive its closed-form expression for a general class of wireless channels that includes Rayleigh and Rice. We furthermore prove that the DoQF protocol has the best achievable outage gain in the wide class of half-duplex static relaying protocols and we minimize ξ w.r.t the power allocation to the source and the relay and the durations of the slots. Next, we focus on Rayleigh channels to derive the Diversity-Multiplexing Tradeoff (DMT) of the DoQF. Our results show that the DoQF achieves the 2 by 1 MISO DMT upper-bound for multiplexing gains r <; 0.25.

In this paper we derive performance metrics closed-form expressions of retransmission schemes such as Automatic Repeat reQuest (ARQ) and Hybrid ARQ (HARQ) in the case of memoryless block fading channels. As performance metrics, we consider the Packet Error Rate (PER), the efficiency, and the packet delay. The novelty of the paper is twofold: i) the metrics are considered at the Network level; ii) we introduce a new general framework which enables us to derive analytically the considered performance metrics for most retransmission schemes, including recent cross-layer strategies with the Network layer. The metrics at the Medium Access Control (MAC) level are obtained as a byproduct. Among the considered retransmission schemes, we especially consider the Incremental Redundancy HARQ scheme with different lengths redundancy packets.

In order to improve the reliability of any HARQ technique at the IP level, a cross-layer strategy between the MAC and the IP levels has been recently developed in. This strategy consists in replacing a retransmisssion credit per MAC packet with a retransmission credit per IP packet. In other words, the transmission credit is shared by the MAC packets belonging to the same IP packet. Packet Error Rate, throughput, delay and jitter for this new approach have been extensively analyzed in. In this paper, we remark and theoretically prove that the first MAC packets are better protected than the last ones. This leads to a natural "unequal packet loss protection" that can be useful in many applications, such as video streaming, compressed image transmission, audio and protocols (TCP/IP). Exploiting this novel manner of protecting packets unequally may not exclude the use of the standard manners, such as different feedforward error correcting codes (FEC), modulation size, or hybrid ARQ scheme per required quality of protection. Finally, our claims are supported by numerical simulations.

We aim to study a specific cross-layer optimized Hybrid Automatic Retransmission reQuest (HARQ) introduced in [1] and called early-drop HARQ. We remind that in [1], instead of giving a transmission credit per MAC packet (as usually done), it is proposed to share the transmission credit among all the MAC packets belonging to the same IP packet. The early-drop version stops the retransmission as soon as the number of remaining MAC packets is higher than the number of possible transmission attempts. As the early-drop has never been analyzed neither through simulations nor analytical derivations, the purpose of this paper is to fill this gap. As the packet error rate and the delay are not modified, we only focus on the efficiency which is expressed in closed-form.

In this paper, we propose a resource allocation algorithm for the downlink of sectorized two-dimensional (2-D) OFDMA cellular networks assuming statistical Channel State Information (CSI) and fractional frequency reuse. The proposed algorithm can be implemented in a distributed fashion without the need to any central controlling units. Its performance is analyzed assuming fast fading Rayleigh channels and Gaussian distributed multicell interference. We show that the transmit power of this simple algorithm tends, as the number of users grows to infinity, to the same limit as the minimal power required to satisfy all users' rate requirements i.e., the proposed resource allocation algorithm is asymptotically optimal. As a byproduct of this asymptotic analysis, we characterize a relevant value of the reuse factor that only depends on an average state of the network.

In this paper, we investigate the timing and carrier frequency offset (CFO) synchronization problem in decode and forward cooperative systems operating over frequency selective channels. A training sequence which consists of one OFDM block having a tile structure in the frequency domain is proposed to perform synchronization. Timing offsets are estimated using correlation-type algorithms. And since some subcarriers are nulled in the proposed tile structure, CFOs are readily estimated using subspace-based methods. By judiciously designing the size of the tile, these algorithms are shown to have better performance, in terms of synchronization errors and bit error rate, than the computationally demanding SAGE algorithm.

New ARQ or HARQ schemes taking into account the link between the MAC and IP layers have been recently introduced. In this paper, we analyze these new schemes in terms of delay and jitter by deriving these metrics in closed-form expressions at the IP level. As the framework developed for obtaining such terms in closed-form is generic, we show that the proposed expressions hold for any standard ARQ or HARQ scheme (ARQ, Incremental Redundancy HARQ, Chase Combining HARQ without cross-layer optimization) at any level (MAC or IP).

In the context of cognitive radio, this paper addresses the challenge of OFDM system identification. We show that it is possible to take advantage of pilot tone structures to perform identification. Pilot subcarrier usage is of great interest since it enables to discriminate OFDM systems that have the same modulation parameters (subcarrier spacing, cyclic prefix duration, etc.). The proposed method relies on the periodic redundancy often induced between pilot symbols. We demonstrate that most OFDM systems (Wifi, WiMAX, DVB-T, etc.) can be identified thanks to this redundancy by conducting a hypothesis test based on second order statistics. Detailed numerical examples demonstrate the efficiency of the proposed identification criterion in various kinds of environments.

We address the optimization issue of the outage probability in Rician block fading channels with N transmit antennas and 1 receive antenna. It is already known that the eigenvectors of the covariance matrix of the transmit signal that minimizes the outage probability are the normalized mean vector of the Rician channel and N-1 orthonormal vectors belonging to the space orthogonal to the range of the mean vector. Our contribution is to optimize the eigenvalues. At high SNR, we show that the part of the power allocated to the orthogonal space of the range of the mean vector should be uniformly distributed amongst its N-1 eigenvectors. In addition, thanks to an original approximation of the outage probability, we characterize the part of power associated with the mean vector. We numerically observe improvement achieved by the proposed allocation scheme compared to uniform power allocation.

In Impulse Radio Ultra Wide Band (IR-UWB) based systems, we show that the Multi-User Interference (MUI) assuming fixed spreading codes can be well approximated by a Generalized-Gaussian Distribution (GGD). Then, we derive an accurate closed-form expression of the approximation for the Average Error Probability (AEP) in both Direct-Sequence (DS) and time-hopping (TH) multiple access context. From this approximation, we are able to characterize and to select the set of codes minimizing the AEP for both multiple access techniques. The merit of each multiple access technique is then analyzed: we especially prove that the probability to find an optimal pair of codes goes to one when increasing the number of chips per symbol with TH technique whereas this probability goes to zero with DS technique. Numerical illustrations confirm our claims.

An opportunistic radio is a radio able to detect the spectrum unused bands, and to adapt its transmission parameters in order to transmit within these free bands. An opportunistic terminal has also to be able to detect opportunistic access points and to recognize their used standards. As most standards are now based on OFDM modulation with distinct intercarrier spacing, this parameter can be estimated to build standard recognition algorithm. We hence propose in this paper an algorithm for blind estimation of the intercarrier spacing of an OFDM modulation based on the second order statistics of the received signal. The algorithm construction is explained in detail. Some theoretical results are derived and numerical simulations show the gain in regard to the state of art methods.

In the context of wireless relay networks operating on slow fading channels, the outage probability optimization is of central importance. It is often hard to give a closed form expression of the outage probability Pofor all possible values of the Signal to Noise Ratio (SNR). On the other hand, it is possible to analyze the behaviour of Poin the asymptotic regime where the SNR rho converges to infinity. In this regime, rhoN+1Pousually converges to a constant xi where N is the number of relays. This paper presents a general method for deriving and minimizing xi with respect to the power distribution between the source and the relays, and with respect to the durations of the slots specified by the relaying protocol. Convexity of xi with respect to the design parameters is shown. The method applies to a general class of radio channels that includes the Rayleigh and the Rice channels as particular cases. Decode-and-forward as well as amplify-and-forward protocols are considered in the half duplex mode. While the proposed approach is designed for the high SNR regime, simulations show that outage probability is reduced in a similar proportion at moderate SNR.

One of the main task to be done by a cognitive receiver is to sensing its spectral environment in order to distinguish surrounding systems from each others. Actually most systems (such as WiMAX, WiFi, DVBT) are based on OFDM modulations but differ from their intercarrier spacing used in OFDM modulation. Therefore carrying out accurate intercarrier spacing estimator is a crucial step in cognitive radio. In this paper, we propose a new efficient algorithm to estimate the intercarrier spacing based on maximum-likelihood principle. Its performance is analysed through numerical simulations and compared to standard existing approaches.

An approximation for the Average Error Probability (AEP) of the Pulse Amplitude Modulation (PAM) Impulse Radio Ultra Wide Band (IR-UWB) systems in the presence of Multi-User Interference (MUI) is derived assuming that the spreading codes are fixed in time. The comparison of the proposed theoretical expression and the empirical result shows the accuracy of our approximation for both Direct-Sequence (DS) and Time-Hopping (TH) multiple access techniques. From this approximation, we deduce criterion that enable us to select the set of codes optimizing the performance in terms of the AEP.

In the context of cognitive radio, system identification is a crucial step towards radio environment awareness. In this paper, we present a new OFDM system identification method based on m-sequence (MS) specific characteristics. Thanks to their good random properties, m-sequences are commonly used in existing standards (such as Wifi or WiMAX) to modulate pilot tones for channel estimation and/or for synchronization purposes. We demonstrate that such sequences show extra-properties relevant to distinguish systems from each other and therefore advocate to generalize their use in a cognitive context. MS signatures are indeed of interest since they are able to discriminate OFDM based systems that have the same modulation parameters (intercarrier spacing, cyclic prefix duration, etc.). In order to detect these signatures, we conduct a hypothesis test based on the MS high order statistics. Detailed numerical examples demonstrate the efficiency of the proposed identification criterion and especially show its benefits compared to classical correlation based methods.

In the cognitive radio context, sensing process is a crucial task. The cognitive devise has to be able to detect and to identify several radio systems. As more standards are now based on OFDM modulation the paper aims with the parameters estimation of such a modulation. All actual OFDM-based standards differ from their subcarrier spacing therefore it will be ingenious to focus on this parameter to identify these systems in a non data aided context. We propose a new efficient algorithm based on the matched filter principle. The behavior of the proposed approach will be studied in the context of practical impairments like frequency and/or time offsets and a multi-paths fading channel. Finally, the performance of the proposed algorithm will be evaluated in contrast with the state of art methods by means of computer simulations.

Carrier frequency offset (CFO) estimation is a key challenge in multicarrier systems such as OFDM. Often, this task is carried out using a preamble made of a number, say J, of repetitive-slots (RS). Here, we address the issue of optimal RS preamble design using the Cramer -Rao bound. We show that the optimal value of J is a trade-off between the multipath diversity gain and the number of unknowns to be estimated. In the case of correlated channel taps, we show that uniform power loading of the active subcarriers is not optimal (in contrast with the uncorrelated case) and a better power loading scheme is proposed. The theoretical results are supported by computer simulations.

In the context of PAM time-hopping/direct sequence impulse radio ultra wide band (IR-UWB), a residual interference occurs at the output of the Rake receiver when realistic multipath propagation channel is considered even if a large guard-time interval is used. As a consequence, the performance is limited and exhibits BER floor. In this paper, we explicitly explains what causes such a residual interference. Secondly, we propose a simple way to modify the Rake receiver in order to totally remove this residual interference. The results are illustrated by simulations that validate the proposed method.

The paper deals with design and performance analysis of orthogonal frequency-division multiple-access (OFDMA)-based downlink cellular wireless communications. Due to a high degree of user mobility, the base station is assumed to have only a statistical knowledge of the users' channels. Relying on the ergodic capacities connected to the user rates, a subcarrier and power allocation that minimizes the total transmitted power is proposed. The allocation strategy requires only the knowledge of the channel statistics and the rate requirements for all users. An extension and a performance analysis of this allocation algorithm in a multicell environment working with a frequency reuse factor equal to one is also conducted. A condition for the multicell network to be able to satisfy all rate requirements is derived

We address the problem of blind carrier frequency-offset (CFO) estimation in quadrature amplitude modulation, phase-shift keying, and pulse amplitude modulation communications systems. We study the performance of a standard CFO estimate, which consists of first raising the received signal to the Mth power, where M is an integer depending on the type and size of the symbol constellation, and then applying the nonlinear least squares (NLLS) estimation approach. At low signal-to noise ratio (SNR), the NLLS method fails to provide an accurate CFO estimate because of the presence of outliers. In this letter, we derive an approximate closed-form expression for the outlier probability. This enables us to predict the mean-square error (MSE) on CFO estimation for all SNR values. For a given SNR, the new results also give insight into the minimum number of samples required in the CFO estimation procedure, in order to ensure that the MSE on estimation is not significantly affected by the outliers

In this work we tackle the problem of mitigating the multi-user interference by optimizing the time-hopping codes, in an asynchronous impulse radio multiple access scheme. We derive the expression of the multi-user interference variance at the output of a Rake receiver assuming that the codes are deterministic, for both pulse position modulation and pulse amplitude modulation formats, when propagating through multipath channels. The result shows that the code contribution is independent of the other parameters. We derive from this expression a practical criterion that enables us to find a set of optimal codes that ensures minimal multi-user interference variance at the receiver output. We check through simulations, that the set of optimal codes found using the criterion, leads to bit error rate improvement

A new decision-directed (DD) synchronization scheme is proposed for joint estimation of carrier frequency offset (CFO) and sampling clock frequency offset (SFO) in orthogonal frequency-division multiplexing (OFDM) systems. By exploiting the hard decisions, we report accurate estimators of residual CFO and small SFO. The performance analysis and simulation results indicate that the proposed novel DD scheme achieves much better performance than the conventional pilot-based schemes in both additive white Gaussian noise and frequency-selective channels.

We address the problem of harmonic retrieval in the presence of multiplicative and additive noise. We derive the finite-sample Cramer Rao bound (CRB) as well as the asymptotic (large sample) CRB when the multiplicative noise is complex-valued and noncircular. These bounds are then analyzed with respect to the signal parameters. Finally, we prove that the square-power based frequency estimate, which is equivalent to the so-called nonlinear least square estimate, is asymptotically efficient when the multiplicative noise is white.

We derive the Cramer-Rao bound (CRB) on the joint estimates of the sampling clock offset and the channel impulse response when a training sequence is available. Simple closed form expressions are obtained for the CRB in the case where the observation window is large, and furthermore in the case where the channel degree is large. Our derivations are suited for single-carrier as well as for multi-carrier orthogonal frequency division multiplexing (OFDM) schemes. Data-aided maximum-likelihood (ML) estimates are also carried out.

A new decision-directed (DD) synchronization scheme is proposed for joint estimation of carrier frequency offset (CFO) and sampling clock frequency offset (SFO) in coded orthogonal frequency division multiplexing (OFDM) systems. By exploiting the decisions provided by a Viterbi decoder and the information available on all the modulated subcarriers, we report accurate estimators of residual CFO and small SFO without relying on pilots. The performance analysis and simulation results indicate that the proposed novel DD scheme achieves much better performance than the conventional pilot-based schemes in both AWGN and frequency-selective channels.

By exploiting a general cyclostationary (CS) statistics-based framework, this letter develops a rigorous and unified asymptotic (large sample) performance analysis setup for a class of blind feedforward timing epoch estimators for linear modulations transmitted through time nonselective flat-fading channels. Within the proposed CS framework, it is shown that several estimators proposed in the literature can be asymptotically interpreted as maximum likelihood (ML) estimators applied on a (sub)set of the second- (and/or higher) order statistics of the received signal. The asymptotic variance of these ML estimators is established in closed-form expression and compared with the modified Crame/spl acute/r-Rao bound. It is shown that the timing estimator proposed by Oerder and Meyr achieves asymptotically the best performance in the class of estimators which exploit all the second-order statistics of the received signal, and its performance is insensitive to oversampling rates P as long as P/spl ges/3. Further, an asymptotically best consistent estimator, which achieves the lowest asymptotic variance among all the possible estimators that can be derived by exploiting jointly the second- and fourth-order statistics of the received signal, is also proposed.

This letter deals with the problem of nondata-aided carrier frequency offset estimation of noncircular modulations transmitted through unknown frequency-selective channels. By exploiting the unconjugated cyclostationary statistics induced by oversampling in the received waveform, an optimized carrier frequency offset estimator is proposed and its asymptotic (large sample) performance analyzed. In order for the proposed frequency estimator to achieve the minimum mean-square estimation error, it is shown that the oversampling rate need not be larger than twice the symbol rate. It is also shown that the proposed synchronizer is asymptotically jitter-free, admits a feedforward structure that may be implemented in digital form, and is suitable for burst transmissions.

Recently, S.J. Lee proposed a blind feedforward symbol timing estimator that exhibits low computational complexity and requires only two samples per symbol (see IEEE Commun. Lett., vol.6, p.205-7, 2002). We analyze Lee's estimator rigorously by exploiting efficiently the cyclostationary statistics present in the received oversampled signal; its asymptotic (large sample) bias and mean-square error (MSE) are derived in closed-form expression. A new blind feedforward timing estimator that requires only two samples per symbol and presents the same computational complexity as Lee's estimator is proposed. It is shown that the proposed new estimator is asymptotically unbiased and exhibits smaller MSE than Lee's estimator. Computer simulations are presented to illustrate the performance of the proposed new estimator with respect to Lee's estimator and existing conventional estimators.

The paper introduces and analyzes the asymptotic (large sample) performance of a family of blind feedforward nonlinear least-squares (NLS) estimators for joint estimation of carrier phase, frequency offset, and Doppler rate for burst-mode phase-shift keying transmissions. An optimal or "matched" nonlinear estimator that exhibits the smallest asymptotic variance within the family of envisaged blind NLS estimators is developed. The asymptotic variance of these estimators is established in closed-form expression and shown to approach the Cramer-Rao lower bound of an unmodulated carrier at medium and high signal-to-noise ratios (SNR). Monomial nonlinear estimators that do not depend on the SNR are also introduced and shown to perform similarly to the SNR-dependent matched nonlinear estimator. Computer simulations are presented to corroborate the theoretical performance analysis.

This paper introduces a family of blind feedforward nonlinear least-squares (NLS) estimators for joint estimation of the carrier phase and frequency offset of general quadrature amplitude modulated (QAM) transmissions. As an extension of the Viterbi and Viterbi (1983) estimator, a constellation-dependent optimal matched nonlinear estimator is derived such that its asymptotic (large sample) variance is minimized. A class of conventional monomial estimators is also proposed. The asymptotic performance of these estimators is established in closed-form expression and compared with the Cramer-Rao lower bound. A practical implementation of the optimal matched estimator, which is a computationally efficient approximation of the latter and exhibits negligible performance loss, is also derived. Finally, computer simulations are presented to corroborate the theoretical performance analysis and indicate that the proposed optimal matched nonlinear estimator improves significantly the performance of the classic fourth-power estimator.

This paper introduces a family of blind feedforward nonlinear estimators for joint estimation of carrier phase, frequency offset and Doppler rate for burst-mode phase shift keying (PSK) transmissions. An optimal or “matched” nonlinear estimator that exhibits the smallest asymptotic variance within the family of envisaged estimators is developed. The asymptotic performance of these estimators is established in closed-form expression and compared with the Cramèr-Rao lower bound for an unmodulated carrier. Finally, computer simulations are presented to corroborate the theoretical performance analysis.

This paper examines the asymptotic (large sample) performance of a family of non-data aided feedforward (NDA FF) nonlinear least-squares (NLS) type carrier frequency estimators for burst-mode phase shift keying (PSK) modulations transmitted through AWGN and flat Rician-fading channels. The asymptotic performance of these estimators is established in a closed-form expression and compared with the modified Cramer-Rao bound (MCRB). A best linear unbiased estimator (BLUE), which exhibits the lowest asymptotic variance within the family of NDA FF NLS-type estimators, is also proposed.

This paper considers the problem of blind symbol rate estimation of signals linearly modulated by a sequence of unknown symbols. Oversampling the received signal generates cyclostationary statistics that are exploited to devise symbol-rate estimators by maximizing in the cyclic domain a (possibly weighted) sum of modulus squares of cyclic correlation estimates. Although quite natural, the asymptotic (large sample) performance of this estimator has not been studied rigorously. The consistency and asymptotic normality of this symbol-rate estimator is established when the number of samples N converges to infinity. It is shown that this estimator exhibits a fast convergence rate (proportional to N/sup -3/2/), and it admits a simple closed-form expression for its asymptotic variance. This asymptotic expression enables performance analysis of the rate estimator as a function of the number of estimated cyclic correlation coefficients and the weighting matrix. A justification for the high performance of the unweighted estimator in high signal-to-noise scenarios is also provided.

This paper proposes to analyze the performance of a family of non-data aided open-loop carrier frequency offset (FO) estimators for a linearly modulated signal transmitted through an unknown flat-fading (possibly frequency-selective) channel. The exact asymptotic (large sample) performance of these estimators is established and analyzed as a function of the received signal sampling frequency, signal-to-noise ratio (SNR), timing delay, and number of samples (N). It is shown that in the presence of timing errors, the performance of the estimators can be improved by oversampling (fractionally sampling) the received signal and by taking into account the entire cyclostationary information that is present in the received sequence.

By exploiting the received signal's second-order cyclostationary statistics, blind algorithms have been previously proposed for joint estimation of the frequency offset and the symbol timing delay of a linearly modulated waveform transmitted through a flat-fading channel. The goal of this paper is to establish and analyze the asymptotic (large sample) performance of the Gini-Giannakis (1998) and Ghogho-Swami-Durrani (1999) estimators as a function of the pulse shape bandwidth and the oversampling factor. It is shown that the performance of these estimators improves as the pulse shape bandwidth increases, and the best performance is obtained by selecting small values for the oversampling factor.

We address the problem of non-data aided frequency offset estimation for non-circular transmissions over frequency-selective channels in a downlink DS-CDMA system or an OFDM communications context. We observe that twice the frequency offset is a cyclic frequency of the received signal. We thus introduce an estimator relying on the maximisation of the empirical cyclocorrelations. We analyse its asymptotic behaviour and obtain a closed-form expression for the asymptotic covariance. This enables us to design relevant system parameters. Simulations are provided and confirm our assertions.

We consider blind estimation of the carrier frequency offset of a linearly modulated non-circular transmission through an unknown frequency selective channel. A frequency estimator is developed based on the unique conjugate cyclic frequency of the received signal that equals twice the frequency offset. We establish consistency and asymptotic normality of the frequency estimator, and calculate its asymptotic variance in closed form. This expression enables performance analysis of the proposed frequency offset estimator as a function of the number of estimated cyclic correlation coefficients used. Numerical simulations show that estimation and compensation of the carrier frequency offset in the presence of an unknown frequency selective channel can be performed with no loss in performance relative to methods where the channel is pre-equalized first and the frequency offset is compensated afterwards.

Most of the second order based fractionally sampled blind equalizers are known to perform poorly in the context of band-limited signals. In this paper, we analyse the behaviour of the subspace method in the particular context of band-limited signals. As it is well known, the subspace channel estimate is obtained as the eigenvector associated to the eigenvalue 0 of a certain positive quadratic form Q. We show that apart from 0, Q has quite small eigenvalues, and that this induces poor statistical performance. More importantly, we characterize the numerical kernel of Q, and show that it contains vectors constructed from certain spheroidal wave sequences. From this, we deduce that the subspace method does not allow to estimate accurately the transfer function of the channel on a certain frequency interval.

A simple interpolation method from low frequencies to high frequencies in a two-band filter bank is shown to be a very efficient way for enhancing signal zooming or for erasing blocking artifacts. This paper introduces a new and simple way for generating that spatial adaptability of the filters. It shows that a linear interpolation between the two subbands can be reduced to a modification of the filters. From this result, a local adaptation of the filters is envisaged for two applications: signal zooming and reduction of blocking artifacts in multigrid decomposition of images.