Sensor fusion is crucial for an accurate and robust perception system on autonomous vehicles. Most existing datasets and perception solutions focus on fusing cameras and LiDAR. However, the collaboration between camera and radar is significantly under-exploited. Incorporating rich semantic information from the camera and reliable 3D information from the radar can achieve an efficient, cheap, and portable solution for 3D perception tasks. It can also be robust to different lighting or all-weather driving scenarios due to the capability of mmWave radars. In this paper, we introduce the CRUW3D dataset, including 66K synchronized and well-calibrated camera, radar, and LiDAR frames in various driving scenarios. Unlike other large-scale autonomous driving datasets, our radar data is in the format of radio frequency (RF) tensors that contain not only 3D location information but also spatio-temporal semantic information. This kind of radar format can enable machine learning models to generate more reliable object perception results after interacting and fusing the information or features between the camera and radar. We run several camera- and radar-based baseline methods for 3D object detection and multi-object tracking on our dataset. We hope the CRUW3D dataset will foster radar and multi-modal 3D perception research. CRUW3D is available at https://huggingface.co/datasets/uwipl/CRUW3D

The Radar Object Detection 2021 (ROD2021) Challenge, held in the ACM International Conference on Multimedia Retrieval (ICMR) 2021, has been introduced to detect and classify objects purely using an FMCW radar for autonomous driving applications. As a robust sensor to all-weather conditions, radar has rich information hidden in the radio frequencies, which can potentially achieve object detection and classification. This insight will provide a new object perception solution for an autonomous vehicle even in adverse driving scenarios. The ROD2021 Challenge is the first public benchmark focusing on this topic, which attracts great attention and participation. There are more than 260 participants among 37 teams from more than 10 countries with different academic and industrial affiliations, contributing about 300 submissions in the first phase and 400 submissions in the second phase. The final performance is evaluated by average precision (AP). Results add strong value and a better understanding of the radar object detection task for the autonomous vehicle community.

Radar is usually more robust than the camera in severe driving scenarios, e.g., weak/strong lighting and bad weather. However, unlike RGB images captured by a camera, the semantic information from the radar signals is noticeably difficult to extract. In this paper, we propose a deep radar object detection network (RODNet), to effectively detect objects purely from the carefully processed radar frequency data in the format of range-azimuth frequency heatmaps (RAMaps). Three different 3D autoencoder based architectures are introduced to predict object confidence distribution from each snippet of the input RAMaps. The final detection results are then calculated using our post-processing method, called location-based non-maximum suppression (L-NMS). Instead of using burdensome human-labeled ground truth, we train the RODNet using the annotations generated automatically by a novel 3D localization method using a camera-radar fusion (CRF) strategy. To train and evaluate our method, we build a new dataset - CRUW, containing synchronized videos and RAMaps in various driving scenarios. After intensive experiments, our RODNet shows favorable object detection performance without the presence of the camera.

In this paper, we present a novel mobile edge computing (MEC) model where the MEC server has the input and output data of all computation tasks and communicates with multiple caching-and-computing-enabled mobile devices via a shared wireless link. Each task request can be served from local output caching, local computing with input caching, local computing without local caching or MEC downloading, each of which incurs a unique bandwidth requirement of the multicast link. Aiming to minimize the transmission bandwidth, we optimize the joint caching and computing policy at mobile devices subject to latency, caching, power and multicast transmission constraints. The joint policy optimization problem is shown to be NP-hard. To tackle the problem of intractability of priori knowledge of users' request popularity, we approximate the expectation via sampling. When all the output data size is smaller than the input data size, we reformulate the problem as minimization of a monotone submodular function over matroid constraints and obtain the optimal solution via a strongly polynomial algorithm of Schrijver. Otherwise, by leveraging concave convex procedure together with the alternating direction method of multipliers, we propose a low-complexity high-performance algorithm and prove it converges to a local minimum. Furthermore, in homogeneous case, we theoretically reveal how much bandwidth gain can be achieved from computing and caching resources at mobile devices or the multicast transmission. Our results indicate that exploiting the computing and caching resources at mobile devices as well as multicast transmission can provide significant bandwidth savings.

The decentralized coded caching scheme of Maddah-Ali and Niesen for the shared link network achieves an order-optimal memory-load tradeoff when the file size goes to infinity. It is then successively shown by Shanmugam et al. that, in the practical operating regime where the file size is finite, such a scheme yields a much less attractive coded caching gain. In this paper, we focus on designing decentralized coded caching schemes that can achieve low worst case loads of the shared link when the file size is finite and maintain order-optimal memory-load tradeoffs when the file size grows to infinity. First, we propose a decentralized coded caching design framework for designing decentralized coded caching schemes that can achieve significantly lower worst case loads than Maddah-Ali-Niesen's decentralized coded caching scheme in the finite file size regime while maintaining order-optimal memory-load tradeoffs when the file size grows to infinity. Then, within the proposed framework, we propose a decentralized coded caching scheme, which is simple and tractable, and can achieve a low worst case load in both the finite and infinite file size regimes. We analyze the worst case load of the proposed scheme and show that it outperforms Maddah-Ali-Niesen's and Shanmugam et al.'s decentralized schemes in the finite file size regime when the number of users is not too small. We also analyze the asymptotic worst case load of the proposed scheme when the file size goes to infinity and show that the proposed scheme achieves an order-optimal memory-load tradeoff. Finally, we analytically characterize the behavior of the worst case coded caching gain of the proposed scheme as a function of the required file size when the file size is large.

Joint pushing and caching is recognized as an efficient remedy to the problem of spectrum scarcity incurred by tremendous mobile data traffic. In this paper, we design the optimal joint pushing and caching policy to maximize bandwidth utilization, which is of fundamental importance to mobile telecom carriers. In particular, we consider a multiuser wireless network with multicast opportunities where each user is equipped with a cache of limited size. First, we formulate the stochastic optimization problem as an infinite horizon average cost Markov decision process. By the structural analysis, we show that how the optimal policy achieves a balance between the current transmission cost and the future average transmission cost. We also show that the optimal average transmission cost decreases with the cache sizes, revealing a tradeoff between storage and bandwidth. Then, due to the fact that obtaining a numerical optimal solution suffers the curse of dimensionality and implementing it requires a centralized controller and global system information, we develop a low-complexity decentralized policy (LDP) by using a linear approximation of the value function and transforming challenging discrete optimization problems into difference of convex (DC) problems, which can be efficiently solved by using DC algorithms. We also obtain an upper bound on the performance gap between the average cost of LDP and the minimum average cost, which can be easily evaluated. Next, we propose an online decentralized algorithm to implement the proposed LDP, when priori knowledge of user demand processes is not available. Finally, using numerical results, we demonstrate the advantage of the proposed solutions over some existing designs. The results in this paper offer useful guidelines for designing practical cache-enabled multiuser wireless networks.

Virtual reality (VR) over wireless is emerging as an important use case of 5G networks. Fully-immersive VR experience requires the wireless delivery of huge data at ultra-low latency, thus leading to ultra-high transmission rate requirement for wireless communications. This challenge can be largely addressed by the recent network architecture known as mobile edge computing (MEC) network, which enables caching and computing capabilities at the edge of wireless networks. This paper presents a novel MEC-based mobile VR delivery framework that is able to cache parts of the field of views (FOVs) in advance and compute certain post-processing procedures on demand at the mobile VR device. To minimize the average required transmission rate, we formulate the joint caching and computing optimization problem to determine which FOVs to cache, whether to cache them in 2D or 3D as well as which FOVs to compute at the mobile device under cache size, average power consumption as well as latency constraints. When FOVs are homogeneous, we obtain a closed-form expression for the optimal joint policy which reveals interesting communications-caching-computing tradeoffs. When FOVs are heterogeneous, we obtain a local optima of the problem by transforming it into a linearly constrained indefinite quadratic problem and then applying concave convex procedure. Numerical results demonstrate the proposed mobile VR delivery framework can significantly reduce communication bandwidth while meeting low latency requirement.

This paper considers a new mobile edge computing (MEC) model where the MEC server has the input and output data of all computation tasks and communicates with multiple caching-and-computing- enabled mobile devices via a shared wireless link. Each mobile device can pre-store the input or output data of a task and also execute a task locally. We aim to investigate the impact of local caching and computing at mobile devices as well as content-centric multicast transmission on the saving of required bandwidth on the wireless link. To this end, we first formulate a joint caching and computing decision optimization problem to minimize the required transmission bandwidth subject to latency, caching and energy constraints at each mobile device in the general case. The joint policy optimization problem is shown to be NP-hard. Based on equivalent transformation and exact penalization of the problem, a stationary point is obtained via concave convex procedure. In the special case where all the computation tasks are symmetric and user requests are uniform, we obtain the closed- form expressions for the local caching gain, local computing gain, and multicasting gain. Our results indicate that exploiting the computing and caching resources at mobile devices can provide significant bandwidth savings.

Mobile virtual reality (VR) delivery is gaining increasing attention from both industry and academia due to its ability to provide an immersive experience. However, achieving mobile VR delivery requires ultra-high transmission rate, deemed as a first killer application for 5G wireless networks. In this paper, in order to alleviate the traffic burden over wireless networks, we develop an implementation framework for mobile VR delivery by utilizing caching and computing capabilities of mobile VR device. We then jointly optimize the caching and computation offloading policy for minimizing the required average transmission rate under the latency and local average energy consumption constraints. In a symmetric scenario, we obtain the optimal joint policy and the closed-form expression of the minimum average transmission rate. Accordingly, we analyze the tradeoff among communication, computing and caching, and then reveal analytically the fact that the communication overhead can be traded by the computing and caching capabilities of mobile VR device, and also what conditions must be met for it to happen. Finally, we discuss the optimization problem in a heterogeneous scenario, and propose an efficient suboptimal algorithm with low computation complexity, which is shown to achieve good performance in the numerical results.

Wireless multicast and client caching are two promising approaches to provide scalable video-on-demand (VoD) services. In this paper, we consider VoD services with client pre-caching under asynchronous requests, where a client request watches the desired video from the beginning to the end. A requesting client can retrieve the data of the requested video from its local cache, other clients it meets via device-to-device (D2D) communication, and the BS multicast transmission. Under this model, we optimize the cache placement to minimize the average traffic rate of BS transmission by the optimal exploitation of BS multicast and cooperative caching. Specifically, we first formulate the cache placement problem into a separable concave minimization problem with convex constraints, which can be solved via convex underestimation and branch bound, where the global optimality is achieved at the cost of a very high computational complexity. To reduce the complexity, we further adopt the difference of convex algorithm. Moreover, two special cases are investigated, i.e., without D2D communication and without BS multicast. Simulation results verify the effectiveness of the proposed scheme which combines BS multicast and cooperative caching.

Hierarchical modulation (HM), a promising non-orthogonal multiple access scheme, is able to provide different levels of protection for data streams and achieve a rate region that cannot be realized by traditional orthogonal schemes. In this paper, we propose a novel HM system with non-uniform constellation inputs and analytically derive the bit-interleaved coded modulation (BICM)-capacity for each user by means of the mutual information. Different from the power optimization method for the superimposed uniform constellation, we directly optimize the constellation positions in conjunction with the optimal binary labels design for HM in additive white Gaussian noise channels. For maximizing the BICM-capacity, we propose a fairness balanced criterion for HM system by considering both power constraints and user QoS constraints. Multi-start interior-point algorithm is proposed to deal with the constellation optimization problems. Numerical results show that the proposed HM verify the performance gains of optimized HM compared with the optimized quadrature amplitude modulation (QAM) based HM and other orthogonal transmission methods.

Existing coded caching schemes fail to simultaneously achieve efficient content placement for non-uniform file popularity and efficient content delivery in the presence of common requests, and hence may not achieve desirable average load under a non-uniform, possibly very skewed, popularity distribution. In addition, existing coded caching schemes usually require the splitting of a file into a large number of subfiles, i.e., high subpacketization, and hence may cause huge implementation complexity. To address the above two challenges, we first present a class of centralized coded caching schemes consisting of a general content placement strategy specified by a file partition parameter, enabling efficient and flexible content placement, and a specific content delivery strategy, enabling load reduction by exploiting common requests of different users. Then we consider two cases, namely, the case without considering the subpacketization issue and the case considering the subpacke-tization issue. In the first case, we formulate the coded caching optimization problem over the considered class of schemes with N2Kvariables to minimize the average load under an arbitrary file popularity. Imposing some conditions on the file partition parameter, we transform the original optimization problem into a linear optimization problem with N(K + 1) variables under an arbitrary file popularity and a linear optimization problem with K +1 variables under the uniform file popularity. We also show that Yu et al.'s centralized coded caching scheme corresponds to an optimal solution of our problem. In the second case, taking into account the subpacketization issue, we first formulate the coded caching optimization problem over the considered class of schemes to minimize the average load under an arbitrary file popularity subject to a subpacketization constraint involving the ℓ0-norm. By imposing the same conditions and using an exact DC (difference of two convex functions) reformulation method, we convert the original problem with N2Kvariables into a simplified DC problem with N(K + 1) variables. Then, we use a DC algorithm to solve the simplified DC problem.

Joint pushing and caching is recognized as an efficient remedy to the problem of spectrum scarcity incurred by tremendous mobile data traffic. In this paper, by exploiting storage resources at end-users and predictability of user demand processes, we design the optimal joint pushing and caching to maximize bandwidth utilization, which is one of the most important concerns of network operators. In particular, we formulate the stochastic optimization problem as an infinite horizon average cost Markov Decision Process (MDP). By structural analysis, we show how the optimal policy achieves a balance between the current transmission cost and the future average transmission cost. In addition, we show that the optimal average transmission cost decreases with the cache size, revealing a tradeoff between the cache size and the bandwidth utilization. Due to the fact that obtaining a numerical optimal solution suffers the curse of dimensionality and implementing it requires a centralized controller and global system information, we develop a decentralized policy of polynomial complexity with the numbers of users and files as well as the cache size, by a linear approximation of the value function and optimization relaxation techniques. We also propose an online decentralized algorithm to implement the proposed low-complexity decentralized policy when priori knowledge of user demand processes is not available. Finally, using numerical results, we demonstrate the advantage of the proposed solutions over some existing designs.

Video on-demand (VoD) service is very popular over the mobile Internet. With the demand and quality of video contents become increasingly high, the capacity demand on mobile networks is explosively increasing. In order to sustainably accommodate the future traffic growth, operators such as Verizon start to offload video traffic with broadcasting. However, how to improve VoD users' quality of experience (QoE) under wireless broadcasting is still an open issue, where the major challenge is the time-varying wireless channel capacity. In this paper, we design a wireless VoD scheme with a periodic broadcasting approach. The basic idea is to fragment a video into segments, which are then delivered over different broadcasting channels periodically. We integrate network coding into our scheme so that packet redundancy can counteract the wireless unreliability. Moreover, we reveal the fundamental limits our proposed scheme can achieve in terms of two key QoE metrics: access delay and probability of continuous playout. We then show the intrinsic connections between the two QoE metrics and the choice of design parameters. The tradeoff between QoE improvement and bandwidth overhead is also presented. We implement the scheme in a testbed and demonstrate comprehensive experiment results.

Due to the explosive growth in multimedia traffic demand, scalability and delivery efficiency of video-on-demand (VoD) services have become important issues. In this paper, we develop a joint cache allocation and multicast delivery scheme to minimize the average bandwidth consumption of VoD services with client caching under the zero-delay constraint. Specifically, we first propose a client caching enabled multicast patching (CCE-MP) mechanism which achieves the minimum bandwidth consumption given a certain cache allocation. Then we formulate the cache allocation problem into a convex problem, which can be effectively solved by a water-filling algorithm. In addition to the traditional full access pattern where clients watch the video entirely, we further consider the interval access patterns with uniformly distributed endpoints and fixed-size intervals. The impact of different access patterns on the cache allocation algorithm is also investigated. Simulation results indicate that the proposed scheme significantly outperforms other existing approaches, e.g., more than 50% bandwidth saving under a certain setting.

Recently, a new class of decentralized random coded caching schemes have received increasing interest, as they can achieve order-optimal memory-load tradeoff through decentralized content placement when the file size goes to infinity. However, most of these existing decentralized schemes may not provide enough coded- multicasting opportunities in the practical operating regime where the file size is limited. In this paper, we focus on the finite file size regime and propose a decentralized random coded caching scheme and a partially decentralized sequential coded caching scheme. These two schemes have different requirements on coordination in the content placement phase and can be applied to different scenarios. The content placement of the proposed schemes aims at ensuring abundant coded-multicasting opportunities in the content delivery phase when the file size is finite. We analyze the worst-case (over all possible requests) loads of our schemes and show that the sequential coded caching scheme outperforms the random coded caching scheme in the finite file size regime. Analytical results indicate that, when the file size grows to infinity, the proposed schemes achieve the same memory- load tradeoff as Maddah-Ali-Niesen's decentralized scheme, and hence are also order optimal. Numerical results show that the two proposed schemes outperform Maddah-Ali-Niesen's decentralized scheme when the file size is not very large.

Caching at the base stations (BSs) has been widely adopted to reduce the delivery delay and alleviate the backhaul traffic between BSs and the core network. In this paper, we consider a collaborative content caching scheme among BSs in cache-enabled multi-cell cooperative networks, where the requested contents can be obtained from the associated BS, the other collaborative BSs or the core network. Novelly, we model the stochastic request traffic and derive a closed form expression for the average delay per request based on multi-class processor sharing queuing theory. We then formulate a cooperative caching optimization problem of minimizing the average delay under the finite cache size constraint at BSs and show it to be at least NP-complete. Furthermore, we prove it equivalent to the maximization of a monotone submodular function subject to matroid constraints, allowing us to adopt the common greedy algorithm with 1/2 performance guarantee. A heuristic greedy caching strategy is also developed, achieving a better performance than the conventional greedy solution. Simulation results verify the accuracy of the analytical results and demonstrate the performance gains obtained by our proposed caching scheme.

By pushing popular contents directly to users through broadcast networks while serving individual requests using cellular networks, a wireless converged network provides a highly effective means to cope with the wireless traffic bottleneck. In this paper, we analyze the network capacity and the pushing and caching schemes for the push-based converged network with limited user storage. Both uniform and non-uniform storage cases are investigated. Specifically, we first derive the converged network capacity under the traditional popularity-based pushing and caching scheme. Furthermore, we establish push-limited and cache-limited regions for the uniform case and an additional transitional region for the non-uniform case, to reveal the constraining factor on the network capacity growth. Moreover, the optimal pushing and caching schemes are investigated from both the network and user perspectives. It is shown that both network and user criterion reduce to the same offloading optimization problem, which can be modeled as the 0-1 knapsack problem and effectively solved by the greedy algorithm. We point out that the optimal scheme depends on not only the traffic load, but also the region where the system operates. Finally, the numerical results are provided to confirm the accuracy of the developed analytical results.

By pushing popular contents directly to users through broadcast networks while serving individual requests using the cellular networks, a “converged” wireless network provides a highly effective means to cope with the wireless traffic bottleneck. In this paper, we contribute to analyze the network capacity of the push-based converged network with limited user storage and then develop the corresponding optimal pushing schemes. Specifically, we first establish two operational regions for the converged network, namely, the push-limited region and the cache-limited region, to reveal the fundamental tradeoff between the broadcast pushing ability and the user caching capacity. The network capacity is then derived under the traditional popularity-based pushing scheme. Moreover, we investigate the optimal pushing scheme under two different criteria, i.e., to maximize either the offloading probability or the offloading data percent. The pushing optimization problem is modeled as the 0-1 knapsack problem, and can be easily solved by the greedy algorithm. Interestingly, the optimal scheme depends on not only the optimization objective, but also the region where the system operates. Finally, numerical results are provided to confirm the accuracy of the developed analytical results.

In this paper, the joint design of power allocation and mapping strategy is developed to maximize the achievable uplink rate for MIMO two-way relay channels (TWRCs) with finite-alphabet inputs. In contrast with the traditional water-filing scheme based on Gaussian inputs, the optimal power allocation for constellation constrained MIMO TWRCs does not only depend on the channel gain, but also on the mapping strategy. The proposed scheme creates multiple non-interference parallel channels in the uplink phase, and multiple independent physical-layer network coding (PNC) streams are implemented over those channels. We then derive the achievable uplink rate in terms of power allocation and mapping strategy. To maximize the achievable rate, the optimal power allocation is obtained for a given mapping strategy, which reduces the joint design problem to one-dimension search of the mapping strategy. Numerical results demonstrate that for the finite-alphabet inputs, the proposed scheme significantly outperforms the traditional water-filling scheme, e.g., 3 dB gain over 8-PSK 4 × 4 MIMO TWRCs.

In this paper, we investigate the shaping loss in a two-user Gaussian multiple-access channel (MAC) with network coding. We conjecture that the shaping loss in this system model is much larger than 1.53 dB, verified by simulation results. To recover such large shaping loss, we consider the combination of physical-layer network coding with constellation shaping, and propose a physical-layer shaped network coding (PLSNC) scheme. Exploiting the constellation shaping, the proposed PLSNC scheme enables the network codeword to be shaped and the relay recovers the network codeword from the shaped network codeword instead of decoding individual node's shaping bits. We thus investigate the design criteria for constructing a non-linear shaping code to avoid the ambiguous detection. The corresponding decoding algorithm of shaped network codeword is then presented to calculate the likelihood of the information about the transmitted shaped network codeword based on the bit-interleaved coded modulation (BICM) scheme. Furthermore, we derive the achievable rate of the proposed scheme. The simulation results show that the proposed scheme with 16-PAM can achieve a maximum shaping gain of 2.61 dB.

Energy harvesting is an efficient method for extending the lifetime of energy-constrained networks. In this paper, we develop a wireless energy harvesting and information transfer protocol in cognitive two-way relay networks, in which a secondary network scavenges energy from ambient signals of primary network while shares the spectrum by assisting the primary transmission. In particular, two primary users exchange information through an energy harvesting secondary user which firstly harvests energy from the received primary signals and then uses the harvested energy to forward the remaining primary signals along with the secondary signals. The exact expressions of the outage probabilities for the primary network are analytically formulated. Besides, we derive the lower and upper bounds of the outage probability for the secondary network. Following aforementioned deduction, we analyze the energy efficiency of the whole system. Simulation results show that we can achieve maximum energy efficiency if we set up proper parameters. Finally, numerical results verify our theoretical derivation and demonstrate that the proposed protocol enables the high-quality transmission for both the primary and secondary network without extra relay energy consumed.

We investigate a wireless energy harvesting and information transfer protocol in cognitive relay networks, where an energy harvesting secondary network shares the spectrum as well as harvests energy by assisting the primary transmission. In particular, the secondary transmitter scavenges energy from the received primary signal and then forwards the resulting signals along with the secondary signal. The secondary receiver can also harvest the ambient energy, and use the remaining signal to remove the primary interference. We analytically derive the exact expressions of the outage probabilities for both primary and secondary networks. Based on the proposed protocol, we analyze the rate-energy trade-off between the maximum ergodic capacity and the maximum harvested energy in the secondary network. Our results demonstrate that the proposed protocol not only achieves both the primary and secondary transmissions but also harvests the ambient energy without the performance loss.

Storing the popular contents in the caches to enable frequency reuse, wireless cooperative caching approach offers an exciting new way to unleash the ultimate potential of wireless networks. In this paper, we formulate the optimal caching problem to minimize the energy consumption in the wireless cooperative caching network by considering some important constraints, including the limited storage capacity, the content popularity and the content access protocol. A sub-optimal caching strategy is then proposed to handle the optimal content placement in the caches, reducing the energy consumption. Simulation results show that the proposed sub-optimal caching scheme has significant energy saving compared with the random caching scheme. Furthermore, the impact of the network resource and parameters on the system performance is also investigated in terms of the energy saving and the cache hit rate.

In this paper, we present and analyze spectrum and energy efficient coded modulation schemes for two-way relay channels. Depending on how the side information is utilized, two relay-to-destination decode-and-forward approaches are investigated. The first one, termed joint modulation relaying (JMR), jointly modulates two source messages by applying side information at the modulator and the demodulator. The second one, termed bit-cooperative coded modulation relaying (BCCMR), is a new joint coded modulation scheme for coded relay systems. In particular, a new constellation mapping for the JMR is proposed. The proposed constellation mapping is a many-to-one function and can be considered as a new form of network coding operation, in which the network coding is performed on the redundancy labeling of each constellation point. We establish the corresponding asymptotic optimal constellation labeling criterion by formulating the constellation constrained capacity with side information. For the BCCMR, a novel transceiver structure based on systematic low-density parity-check (LDPC) codes is developed. The joint coding-modulation design introduces the benefits of increased minimum Euclidean distance and the Hamming distance by jointly exploiting side information in the demapping and the decoding. The advantages of the LDPC-coded BCCMR and the JMR are analyzed using the density evolution method.

In this paper, we propose a multi-level physical-layer network coding (MPLNC) scheme that optimizes the relay performance for both symmetric and asymmetric traffic in a Gaussian two-way relay channel. The proposed MPLNC scheme enables each source to employ multiple linear binary codes for encoding, one per modulation level, and the relay node to decode superimposed network codewords at each modulation level. We first derive the achievable rate for the transmission of arbitrary constellations and then prove that MPLNC with multistage decoding (MPLNC/MSD) can achieve the achievable rate if binary code rates are properly chosen for both sources. Furthermore, the design criteria for the proposed MPLNC scheme is investigated, which includes the rate design rule and the labeling strategy. Moreover, we derive the error exponent and an upper bound of the overall error probability for MPLNC. Our analysis and simulation results show that MPLNC/MSD has a significant performance advantage in comparison to the existing bit-interleaved coded modulation (BICM)-based PLNC scheme.

The fast growing wireless data traffics have brought a significant burden on the mobile cellular network and would soon cause severe congestion in the near future. In this paper, we contribute to the design and theoretical understanding of push-based content delivery in a converged broadcasting and cellular network to relieve the burden. Specifically, we are interested in analyzing the scheme in which the most popular contents are pushed through broadcasting to alleviate the cellular data bottleneck. This strategy not only offloads the multimedia traffics from the cellular network, but also improves the user experience by eliminating download waiting time. To evaluate the performance gains of the converged network, we first develop a mathematic framework to model the converged network, the multimedia content characteristics and the mobile user behaviors. The improvement of the network capacity is then derived and quantified theoretically. Furthermore, we obtain valuable insights on the impact of the network resource and parameters on the system performance. Numerical results are provided to confirm the accuracy of the developed analytical results and to show the significant performance advantages of the converged network over the cellular-only network in terms of the system power consumption and users' quality of service (QoS).

Bit-cooperative coded modulation (BCCM), known as an improved bit-interleaved coded modulation (BICM) scheme [1], introduces known bits to jointly design the channel coding and modulation. In this paper, we contribute to the theoretical analysis of the turbo-coded BCCM scheme, and specifically, the impact of the known bits on the turbo decoding process. The characteristic of the iteration process is also analyzed for BCCM by the EXIT chart. Furthermore, to describe the fundamental tradeoff between the communication reliability and the data rate, we derive the error exponent for BCCM. Numerical results show that BCCM has a larger exponent than BICM and achieves a considerable gain on AWGN channels.

In this paper, we present and analyze high efficiency joint coded modulation schemes for two-way relay channels. Depending on how the side information is utilized, two relay-to-destination decode-and-forward approaches are investigated. The first one, termed joint modulation relaying (JMR), jointly modulates two source messages by applying the side information at the modulator and the demodulator. The second one, termed bit-cooperative coded modulation relaying (BCCMR), is a new joint coded modulation scheme for coded relay systems. In particular, we propose new constellation mapping for the JMR and establish the corresponding optimal constellation labeling criterion. For the BCCMR, the joint coding-modulation design introduces the benefits of increased minimum Euclidean distance and the Hamming distance by exploiting the side information in the demapping and decoding. The advantages of the low-density parity-check (LDPC)-coded BCCMR and JMR are analyzed using the density evolution approach. Numerical results are provided to illustrate the significant gains for the symmetric and asymmetric relaying on Rayleigh fading channels.

In this paper, we consider a random wireless sensor network where the sensor nodes are distributed randomly and form clusters to cooperatively transmit data packets using cooperative multi-input-multi-output (CMIMO) technique. We focus on analyzing the overall system performance in terms of packet error rate (PER). And then propose a new node sleep strategy to minimize the overall energy consumption. We take the random distribution of sensor nodes into consideration and get a closed form of overall PER expression that simplifies the overall energy consumption optimization. We analyze the tradeoff between the overall energy consumption and sensor nodes sleep rate. The simulation results corroborate our analysis and show that the proposed sensor node sleep strategy has significant energy saving compared with other transmission schemes.

In this paper, we propose the multi-level physical-layer network coding (MPLNC) for two-way relay channels (TWRC) to optimize the relay performance. In the proposed MPLNC scheme, each source node involves multiple linear binary codes for encoding, one per modulation level. The relay node receives these streams of signals and attempts to decode superimposed network codewords at each modulation level. We first derive the constellation constrained capacity for TWRC, and then prove that MPLNC can approach the channel capacity if and only if binary code rates are properly chosen for both source nodes. Furthermore, to facilitate practical implementation MPLNC, the design criteria for the proposed MPLNC scheme is investigated, which includes the rate design rule, the decoding order design rule and the labeling strategy design rule. Also discussed is the relation between MPLNC and bit-interleaved coded modulation (BICM)-based PLNC. Our analysis and simulation results indicate that MPLNC has a significant performance advantage in comparison to the BICM-based PLNC.

A new coded modulation technique, termed bit-cooperative coded modulation (BCCM), is proposed in this paper. By introducing a pseudo-random sequence at the transmitter to systematic low-density parity-check (LDPC) codes, the receiver enjoys the benefits of increased minimum Euclidean distance and the Hamming distance when applying this side-information at the receiver. In particular, we propose a new constellation mapping principle to take advantage of the side information. Our analysis indicates that the side information makes the regular LDPC code irregular, with irregularity specified by the check node degree distribution, which allows the density evolution approach to be used to design and optimize the regular LDPC code for the BCCM. We then analytically derive the constellation constrained capacity for the BCCM. The performance of the LDPC-coded BCCM on AWGN channels and Rayleigh fading channels is analyzed using the density evolution and extensive simulations.

Network coding (NC), in principle, is a Layer-3 innovation that improves network throughput in wired networks for multicast/broadcast scenarios. Due to the fundamental differences between wired and wireless networks, extending NC to wireless networks generates several new and significant practical challenges. Two-way information exchange (both symmetric and asymmetric) between a pair of 802.11 sources/sinks using an intermediate relay node is a canonical scenario for evaluating the effectiveness of Wireless Network Coding (WNC) in a practical setting. Our primary objective in this work is to suggest pragmatic and novel modifications at the MAC and PHY layers of the 802.11 protocol stack on a Software Radio (SORA) platform to support WNC and obtain achievable throughput estimates via lab-scale experiments. Our results show that network coding (at the MAC or PHY layer) increases system throughput-typically by 20-30%.

This paper considers random wireless sensor networks where nodes are distributed randomly and form clusters to transmit the packets to relay clusters using cooperative multi-input-multi-output (CMIMO) technique. First, we study CMIMO energy consumption and divide it into intra-cluster energy consumption and inter-cluster energy consumption. Then we focus on analyzing the system performance in terms of overall packet error rate (PER) and energy consumption. Based on system performance metrics, we propose a new node sleep strategy, where overall energy consumption is minimized by coordinating inter-cluster transmitting energy consumption and the number of sensor nodes in activation. Compared to existing works, our contributions are (1) we consider random distribution of nodes and get a closed-form expression of overall PER that simplifies the energy minimization problem; (2) we analyze the tradeoff between overall energy consumption and nodes active rate. In simulation, we investigate the relationships among nodes active rate, inter-cluster transmitting energy consumption and the overall energy consumption. Furthermore, we discuss how these parameters are affected by the nodes density, the inter-cluster transmitting distance, the cluster radius and the intra-cluster energy consumption. Finally, we validate that the proposed nodes sleep strategy has significant energy savings compared with non-sleep cooperative transmission and direct transmission.

In this paper, we present a structured LDPC decoding algorithm for hierarchical QAM modulation systems. Unlike the traditional hierarchical demodulation approaches which suffer from serious inter-layer interference (ILI), the proposed method exploits structure information of the secondary layer to mitigate the ILI impairment, thereby offers significant gains in reception performance. An achievable rate analysis is performed to calculate the theoretical limit of the basic layer in HM reception, which in certain cases is 8 dB better than the required SINR threshold for QAM detection with all Gaussian interference. The new algorithm has been implemented on a broadband in-band on-channel (IBOC) digital radio broadcasting system. Numerical results from both simulations and experiments are provided to quantify the performance advantages of the new algorithm against the DVB-T benchmark.

In this paper, we present an in-band on-channel (IBOC) broadcasting system suitable for digital audio and data broadcasting in FM and AM channels. The new system utilizes a combination of frequency hopping, LDPC coding, hierarchical modulation and band aggregation techniques to increase the data rate, improve spectrum efficiency, and at the same time, provide spectrum flexibility to meet the requirements of high quality audio and multimedia services. A prototype has been developed on a personal computer (PC) based software defined radio (SDR) platform. Numerical results and initial laboratory tests demonstrate significant performance advantages of the new design over existing systems such as the Digital Radio Mondiale (DRM) and Hybrid Digital (HD) radio.

In this paper, we investigate a novel slotted ALOHA-based distributed cognitive network in which a secondary user (SU) selects a random subset of channels for sensing, detects an idle (unused by licensed users) subset therein, and transmits in any one of those detected idle channels. First, we derive a range for the number of channels to be sensed per SU. Based on that, the analytical average system throughput is derived in both saturation and non-saturation networks. Second, the relationship between the average system throughput and the number of sensing channels is attained. We show that the optimal number of sensed channel in a given number of SUs is dependent on the number of licensed channels, the number of idle channels, and the transmission probability of each SU. Finally, the analytical results are validated by substantial simulations.

In this paper, we investigate a novel slotted ALOHA-based distributed access cognitive network in which a secondary user (SU) selects a random subset of channels for sensing, detects an idle (unused by licensed users) subset therein, and transmits in any one of those detected idle channels. First, we derive a range for the number of channels to be sensed per SU access. Then, the analytical average system throughput is attained for cases where the number of idle channels is a random variable. Based on that, a relationship between the average system throughput and the number of sensing channels is attained. Subsequently, a joint optimization problem is formulated in order to maximize average system throughput. The analytical results are validated by substantial simulations.

In this paper, we consider a two-way asymmetric relaying channel with unmatched traffics from two sources. Aiming at maximizing the sum rate under a minimum rate constraint, we address the optimization problem (in terms of time and power allocation) for two decode-and-forward (DF) strategies, namely, the decode-and-forward with joint modulation (DF-JM) and the decode-and-forward with network-superposition coding (DF-NSC). Closed-form solutions under different relay settings are presented. Numerical results are provided to illustrate the performance gains due to the optimal resource allocation, as well as the achievable rate gaps between two different DF strategies.

We study the capacity of ad hoc network with heterogenous traffic patterns, specifically on the fields of multicast and unicast traffic. We apply hierarchical cooperation scheme, MIMO technology and multihop transmission mechanisms in both traffic patterns. The major contribution of this paper is a new capacity analysis method and procedure for the network with heterogenous traffic patterns. We use the index Unicast capacity/Multicast capacity to decide the capacity-dominant traffic pattern, analyze the traffic parameters and derive the network capacity seating in case of both hierarchical layers hM→ ∞ and hU→ ∞. Less-than-half multicast nodes would dominate the whole network capacity. Two indices 1-logn2 and logn(n - 1) of the multicast scale m are very important in deciding the dominant traffic pattern for the network capacity, because when 0 ≤ m ≤ 1-logn2 or logn(n-1) ≤ m <; 1, the aggregate network capacity is irrelated to the multicast destination scale d.

We consider a two-way relay system where two sources exchange information with the assistance of multiple, non-cooperative, relay nodes. Specifically, we propose a capacity upper bound for the two-way relay channel and derive the achievable regions for different relay strategies, such as the amplify-and-forward (AF), the decode-and-forward (DF) and the compress-and-forward (CF). Furthermore, the upper bound and the achievable regions for the additive white Gaussian noise channel are presented. Numerical results are provided to illustrate how the capacity varies with respect to the number of relay nodes, the distance between the relays and the sources, and the transmission powers.

The impact of inaccurate channel knowledge on the system performance of a cooperative multi-cell system is studied. Using optimization theory, we investigate the performance degradation due to optimizing the collaborative downlink transmit signals of multiple base stations (BSs) based on inaccurate channel knowledge. Two important sources of channel knowledge inaccuracy are considered: quantization and delay. Based on mathematical analysis, multi-cell systems with different levels of cooperation are numerically evaluated and compared.

In this paper, the performance of orthogonal frequency division multiple access orthogonal frequency division multiple access (OFDMA) is studied in the context of the general multiuser multicarrier multiple-input multiple-output (MIMO) (M3) system for both uplink (UP) and downlink (DL) channels. The relationship between OFDMA and the optimal M3solution is investigated from an information theoretical point of view. Specifically, the necessary and sufficient conditions of OFDMA being the capacity-maximizing M3transmission scheme is derived. In addition, OFDMA optimality is explicitly analyzed in some special cases.

Wireless infrastructures can be conveniently categorized into two types: those that provide point-to-point dasiaunicastpsila connectivity (e.g., cellular) and others that are dasiabroadcastpsila in nature (such as TV and radio) and are inherently point-to-multipoint. This paper introduces a transmission scheme for multicarrier hybrid system that supports both broadcasting and unicasting on a single-frequency platform. In particular, we present a dirty-paper coding (DPC) based overlay architecture to enable interference-free reception of unicast signals. The fundamental limits of hybrid communications are investigated, along with power allocation algorithms that approach the hybrid capacity. Our results show that OFDMA for unicast offers the best hybrid performance and considerable gains over orthogonal multiplexing (e.g., TDM/FDM) can be achieved through interference-free overlay. In addition, we describe approaches to further improve the spectral efficiency and the broadcast coverage through multicell collaboration.

This paper looks at the fairness achievable in an OFDMA system. Based on the results, an online resource allocation algorithm is defined for a reservation-based OFDMA system where the frame duration is much shorter than the duration of a call. Simulations show that the algorithm leads to a considerable reduction in the probability of outage. A protocol is introduced for fair resource allocation between two nodes in an ad hoc system. The minimum required overhead is shown to depend on the level of fairness required, and the bounds that are derived.

Motivated by the increasing popularity of OFDMA, this paper studies the sum-rate optimality of OFDMA as an uplink multicarrier multiple-access scheme. We address the relationship between OFDMA and the optimal multicarrier multiple- access schemes. More specifically, we are interested in answering the following three questions: (i) what are the conditions under which OFDMA is sum-rate optimal? (ii) what is the probability of OFDMA being sum-rate optimal? and (iii) in the case OFDMA is suboptimal, what is the performance gap between OFDMA and the optimal multicarrier multiple access solution? Within a generic discrete multicarrier uplink framework, we present the necessary and sufficient conditions for OFDMA optimality and derive the probabilities of these conditions for both low and high SNR regions. Our results show that the number of shared subchannels under the optimal solution should be less than the number of total users, thus the performance gap between the OFDMA scheme and the optimal solution is negligible when the number of subchannels is large.

This paper studies the optimality of OFDMA in MIMO enabled multi-user multicarrier uplink network. We present the necessary and sufficient conditions for OFDMA being optimal on any boundary point of the capacity region. In the low SNR case, we further derive the probabilities of these conditions. For the general MIMO case, we provide an example with which we quantify OFDMA optimality and show the probability of OFDMA being optimal is non-travial regardless of the SNR regions. To reach the the MISO sum capacity limit, our results show that the number of shared subchannels under the optimal solution should be less than the number of total users, thus the performance gap between the OFDMA scheme and the optimal solution is negligible when the number of subchannels is sufficiently large.

Motivated by the increasing popularity of OFDMA, this paper studies the sum rate optimality of OFDMA as an uplink multicarrier multiple access scheme. More specifically, we are interested in answering the following three questions: (i) what are the conditions under which OFDMA is sum-rate optimal? (ii) what is the probability of OFDMA being sum-rate optimal? and (iii) in the case OFDMA is suboptimal, what is the performance gap between OFDMA and the optimal multicarrier solution? Within a generic discrete multicarrier uplink framework, we present the necessary and sufficient conditions for OFDMA optimality and derive the probabilities of these conditions for both low and high SNR regions. Our results show that the number of shared subchannels under the optimal solution should be less than the number of total users, thus the performance gap between the OFDMA scheme and the optimal solution is negligible when the number of subchannels is sufficiently large.

This paper studies the resource allocation for orthogonal frequency-division multiple-access relay network with multiple source nodes, multiple relay nodes, and a single destination node. The optimal source/relay/subcarrier allocation problem with fairness constraint on relay nodes is formulated as a binary integer programming problem. The problem is then tackled using a graph theoretical approach by transforming it into a linear optimal distribution problem in a directed graph. Upper bound of the solution is discussed and simulation results are studied to evaluate the algorithm performance

This paper presents a radio resource control (RRC) scheme for OFDMA systems where dynamic resource allocation is realized at both a radio network controller (RNC) and base stations (BSs). The scheme is semi-distributed in the sense that the RRC decision is split between RNC and BSs. RNC makes decision on which channel is used by which BS at super-frame level and BSs then make decision on which user is assigned to which channel at frame-level. Two optimization problems for RNC and BSs are formulated and computationally efficient algorithms that perform the function of interference avoidance and traffic/channel adaptation are developed. Numerical analysis is performed under several cell configurations to show tradeoffs between sector interference suppression and dynamic interference avoidance. The results indicate that with reasonable signaling overhead, the protocol and the associated algorithms yield excellent performance for both real-time and non real-time services, even under fast fading

With superior error correction capability, low-density parity-check (LDPC) has initiated wide scale interests in wireless telecommunication fields. In the past, various structures of single code rate LDPC decoders have been implemented for different applications. However, in order to cover a wide range of service requirements and diverse interference conditions in wireless applications, LDPC decoders that can operate in both high and low code rates are desired. In this paper, a new multi-rate LDPC decoder architecture is presented and implemented in a Xilinx FPGA device. Through selection pins, three operating modes with the irregular 1/2 rate, regular 5/8 rate and regular 7/8 rate are supported. The measurement results show LDPC decoder can achieve BER below 10-5 at SNR of 1.4dB in the most critical case with the irregular 1/2 mode.

With the superior error correction capability, low-density parity-check (LDPC) codes have initiated wide scale interests in wireless communication and storage fields. In the past, various structures of single code-rate LDPC decoders have been reported. However, to cover a wide range of service requirements and diverse interference conditions in wireless applications, LDPC decoders that can operate at both high and low code rates are desirable. In this paper, a 9k code length multi-rate LDPC decoder architecture is presented and implemented on a Xilinx FPGA device. Using pin selection, three operating modes, namely, the irregular 1/2 code, the regular 5/8 code and the regular 7/8 code, are supported. Furthermore, to suppress the error floor level, a characterization on the conditions for short cycles in a LDPC code matrix expanded from a small base matrix is presented, and a cycle elimination algorithm is developed to detect and break such short cycles. The effectiveness of the cycle elimination algorithm has been verified by both simulation and hardware measurements, which show that the error floor is suppressed to a much lower level without incurring any performance penalty. The implemented decoder is tested in an experimental LDPC-OFDM system and achieves the superior measured performance of block error rate below 10/sup -7/ at SNR 1.8 dB.

This letter provides a comparative study of joint channel estimation and low-density parity-check decoding algorithms for flat Rayleigh fading channels based on the receiver factor graphs. Two approaches for joint channel estimation and decoding are proposed. Intensive simulation studies are carried out to evaluate the receiver sensitivity to the choice of the factor graph. Results show that when channel statistical information is not available at the receiver, a low-pass filtering approach gives a more robust and simpler solution, and it is only 0.5 dB worse than the optimum Wiener filtering. All methods have about 2 dB improvements over the noniterative receivers.

Two types of wireless system imperfections, namely, frequency offset and in-phase/quadrature (I/Q) imbalance, are addressed in this paper. The I/Q imbalance in radio frequency (RF) direct-conversion not only introduces an unwanted in-band image interference but also degrades the accuracy of carrier estimation. Toward this end, we propose a pilot-based scheme for both frequency offset and I/Q imbalance compensation at the baseband. A low-cost nonlinear least squares (NLS) frequency estimator robust to the I/Q imbalance is developed. Also derived is an I/Q imbalance compensation structure that consists of two stages: a finite impulse response (FIR) filter that compensates the frequency dependent imbalance; and an asymmetric phase compensator that corrects the frequency independent error. The compensation coefficients are optimized by exploiting the phase rotation embedded in the pilot symbols. Both computer simulations and experiment results verify the effectiveness of the proposed scheme in various I/Q imbalance scenarios.

This paper studies uplink traffic channel design tradeoffs for broadband OFDMA systems. For fixed and portable users, where the propagation channels are semi-static, we show that the uplink capacity can be maximized when user traffic channels are configured with the maximum frequency selectivity. For mobile services on the other hand, we show that the optimality depends on the outage threshold associated with the "outage capacity." In the low outage probability region, the outage capacity is maximized when the traffic channels are configured with the maximum frequency diversity. The opposite is true for the high outage probability region. Using the results presented, designers of OFDMA system can determine the optimum traffic channel configuration based on the types of services supported by the network.

The paper describes a multicarrier terrestrial multimedia broadcasting (TMB) system that offers system performance and service versatility suitable for future digital broadcasting networks. The new system utilizes variable rate, variable size LDPC (low-density parity-check) codes to allow simultaneous transmission of different programs with diverse requirements. A prototype system is implemented based on the proposed protocol. Results from both lab tests and field trials demonstrate significant performance advantages of the new design over existing systems such as DVB-T.

Field-programmable gate arrays (FPGAs) have become an extremely popular implementation technology for custom hardware because they offer a combination of low cost and very fast turnaround. Because of their in-system reconfigurability, FPGAs have also been suggested as an efficient replacement for application-specific integrated circuits (ASICs) and digital signal processors (DSPs) for applications that require a combination of high performance, low cost, and flexibility. Unfortunately, the use of FPGAs in mobile embedded systems platforms is hampered by the very large overhead of FPGA-based architectures. Coarse-grained configurable architectures can reduce this overhead substantially by taking advantage of the application domain to specialize the reconfigurable architecture via coarse-grained components and interconnects. This paper presents the design and implementation of an OFDM receiver in the RaPiD reconfigurable architecture as a case study for comparing the relative cost and performance of ASIC, DSP, FPGA, and coarse-grained reconfigurable architectures. RaPiD is a coarse-grained reconfigurable architecture specialized to the domain of signal and image processing. The RaPiD architecture provides a reconfigurable pipelined datapath controlled by efficient reconfigurable control logic: We have implemented the computationally intensive parts of an OFDM receiver on the RaPiD architecture and have developed careful estimates of corresponding implementations in representative ASIC, DSP and FPGA technology. Our results show that, for this application, RaPiD fills the cost/performance gap between programmable DSP and ASIC architectures, achieving a factor of 6 better than a DSP implementation but a factor of 6 less than an ASIC implementation.

Fueled by the explosive growth of the Internet, applications are demanding higher data rates and better services. Given the scarcity of radio resources, higher network capacities need to be achieved through more efficient use of the available bandwidth. Current cellular networks utilize frequency planning schemes that are optimized for circuit-switched applications, and thus is inherently problematic for future wireless packet networks with bursty, high peak-rate traffics. Random access schemes such as the ALOHA are seen as better solutions for packet networks. However, co-channel interference may significantly reduce the network throughput when the multicell load is heavy. In this paper, we propose a distributed rate adaptive packet access (DRAPA) scheme to combine the advantages of rate adaptation (in circuit-switched networks) and random access (in packet-switched networks). In particular, DRAPA allows terminal stations to transmit packets in random access fashion in the presence of brusty interference from neighboring cells. The packet code rate is adjusted according to interference level so that the retransmisson is controlled at an acceptable level. The DRAPA scheme subsumes two traditional schemes as the extreme cases, and has superior performance over the traditional schemes in terms of throughput and stability.

The paper presents a radio resource allocation scheme for OFDMA-based wireless broadband networks. The problem of maximizing the base-station packet throughput subject to individual users' outage probability constraints is formulated. The proposed algorithm assumes a finite buffer for the arrival packets and allocates the radio resource based on users' channel characteristics and traffic patterns. By performing the radio resource allocation in two steps, namely bandwidth allocation and channel assignment, admission control is realized with low complexity. Simulations show that the algorithm yields significant lower outage probability and higher throughput compared to existing multiple access methods.

In this paper, we develop an analytical framework to quantify the effects of the channel estimation error on ultra-wideband systems operating in dense multipath environments in terms of the receiver performance, the receiver complexity and the channel estimation measurement. The focus of the paper is to characterize the SEP performance using the saddle-point approximation. We find that the performance of P-RAKE with a limited number of fingers degrades much less than the A-RAKE with imperfect channel estimation. And channel estimation by averaging two channel measurements greatly improves the performance with a small rate loss. Furthermore, the performance of the P-RAKE receiver with greater than four fingers is similar to that achievable by using a S-RAKE with the same number of fingers in the presence of channel estimation error.

Two types of RF front-end imperfections in direct-conversion receivers, namely frequency offset and I/Q imbalance, are addressed. The I/Q imbalance not only introduces an unwanted image interference, but also degrades the accuracy of carrier estimation. We propose a pilot-based scheme for both frequency offset and I/Q imbalance compensation. A nonlinear least squares (NLS) frequency estimator, robust to the I/Q imbalance, is developed. Also derived is an I/Q compensation structure that consists of two stages: the frequency dependent imbalance is compensated with an FIR filter whereas the frequency independent part is corrected with an asymmetric phase compensator. The compensation coefficients are optimized by restoring the phase rotation embedded in the pilot symbols.

This paper presents a dynamic resource allocation scheme for OFDMA-based wireless broadband networks. The problem of maximizing the total packet throughput subject to individual user's outage probability constraint is formulated. The proposed algorithm assumes a finite buffer for the arrival packets and dynamically allocates the radio resource based on users' channel characteristics, traffic patterns and QoS requirements. By performing the radio resource allocation into two steps, namely bandwidth allocation and channel assignment, efficient admission control is realized with low complexity. Specifically, the number of channels to be assigned to each user is first determined based on its traffic requirement and the average SNR. The second stage of the algorithm finds the best channel allocation for the users. Simulations show that the algorithm yields significant lower outage probability and higher throughput than existing multiple access methods.

The paper studies the problem of finding an optimal subcarrier and power allocation strategy for downlink communication to multiple users in an orthogonal-frequency-division multiplexing-based wireless system. The problem of minimizing total power consumption with constraints on bit-error rate and transmission rate for users requiring different classes of service is formulated and simple algorithms with good performance are derived. The problem of joint allocation is divided into two steps. In the first step, the number of subcarriers that each user gets is determined based on the users' average signal-to-noise ratio. The algorithm is shown to find the distribution of subcarriers that minimizes the total power required when every user experiences a flat-fading channel. In the second stage of the algorithm, it finds the best assignment of subcarriers to users. Two different approaches are presented, the rate-craving greedy algorithm and the amplitude-craving greedy algorithm. A single cell with one base station and many mobile stations is considered. Numerical results demonstrate that the proposed low complexity algorithms offer comparable performance with an existing iterative algorithm.

Efficient exploitation of spatial diversity is fundamentally important to resource critical wireless applications (Tsoulos 1999). In this paper, we first study the performance of intelligent scheduling for space-division multiple-access (SDMA) wireless networks (Suard 1998, Farsakh 1998). Based on the existing scheme, we propose a new medium access protocol (MAC) for multimedia SDMA/time-division multiple-access (TDMA) packet networks (Xu 1994, Ward 1993). The improved protocol performs scheduling based on users' spatial characteristics and quality-of-service parameters to achieve throughput multiplication and packet delay reduction. Performance of SDMA with scheduling is evaluated under mixed audio and data traffic patterns and results show that significant improvement in network performance can be achieved under the new protocol.

This paper introduces a reservation subframe protocol for multimedia services in a 3rd generation mobile telecommunication system. The packet reservation multiple access (PRMA) protocol is modified to achieve more efficient bandwidth management. Simulation results show that the resulting multiple access multimedia system can serve a large number of users with low probability of packet loss, enabling the use of high compression rates for both voice and video services.

Recently multicarrier communications (MC), eg, OFDM systems, have received a lot of interest. Multicarrier schemes increase the efficiency and capacity of wireless networks, but are very sensitive to carrier frequency synchronization errors. We explore a framework to exploit temporal and spatial diversities to improve the performance of such schemes in the downlink or uplink by enhancing synchronization. The estimator developed here does not depend on the presence of repeated known symbols or pilot tones. Diversity modes are accessed via a common notation and this allows us to conveniently generalize to temporal or spatial diversity reception schemes. The acquisition limit of the proposed algorithm is not limited to half a subcarrier spacing as is the case with many other blind algorithms. Performance of the algorithm is carried out with simulation studies and a summary of our findings for temporal and spatial diversity are reported.

Multicarrier direct-sequence code-division multiple access (MC-DS-CDMA) has emerged recently as a promising candidate for the next generation broad-band mobile networks. We consider the design of multiuser receivers for MC-DS-CDMA communications over fading channels. We present a class of spreading codes that enables the simple despreading-combining receiver to achieve the performance of the optimum multiuser linear receiver. These codes are shown to be optimum for independent fading channels under a code design criterion derived. Also derived are analytic solutions of optimum spreading codes for any given channel fading statistics. Simulation results are provided to demonstrate the significant gains in performance and simplicity due to the proposed techniques.

In this paper we present a novel loading algorithm for OFDM-based multiuser communication system to maximize the total system throughput while satisfying the total power and users' rate constraints. The new scheme determines the subcarrier, bit, and power allocation by decoupling an NP-hard combinatorial problem into two steps: (1) resource allocation (how much power and how many subcarriers for each user) based on users' average channel gains and their rate requirements; and (2) subcarrier assignment and bit loading based on users' channel profiles across all subcarriers. Compared to existing iterative methods, the two-step approach offers comparable capacity gain with much lower complexity.

Multicarrier CDMA (MC-CDMA) is a flexible and adaptable radio interface to provide dynamically changing bit rates and a variety of services on radio channels. An accurate channel estimate allows the use of multiuser detection and power loading to improve system performance. A blind linear multiuser channel estimator for MC-CDMA is proposed. Unlike the time varying spreading code or information bearing sequence, the channel is stationary over long time intervals. This fact is exploited in a new method for simultaneous estimation of the channel impulse response vector of multiple users. The complexity of the derived channel estimator can be reduced at the expense of accuracy. It is shown by computer simulation that the proposed method performs well under different scenarios.

Efficient exploitation of spatial diversity is fundamentally important to resource critical wireless applications. A medium access protocol (MAC) for space division multiple access (SDMA) packet networks is proposed. The new protocol performs scheduling based on users' spatial characteristics and quality-of-service (QoS) requirements to achieve throughput multiplication and reduction of packet delays. The system's performance is evaluated under mixed audio and data traffic patterns and the results show that the new protocol can lead to a significant improvement in network performance.

In orthogonal frequency-division multiplex (OFDM) communications, the loss of orthogonality due to the carrier-frequency offset must be compensated before discrete Fourier transform-based demodulation can be performed. This paper proposes a new carrier offset estimation technique for OFDM communications over a frequency-selective fading channel. We exploit the intrinsic structure information of OFDM signals to derive a carrier offset estimator that offers the accuracy of a super resolution subspace method, ESPRIT.

This work considers the problem of jointly estimating symbol frame boundaries and carrier frequency offsets for orthogonal frequency division multiplexed (OFDM) communications in frequency selective fading environments. Orthogonality between the modulated and virtual carriers over an interference free window of the received signal is used to develop an algorithm for estimating the carrier frequency offset and detecting the beginning of a symbol frame. By using a cyclic prefix to remove interference from neighboring frames, the the method is applicable in the presence of dispersive channels. The main contribution of this work is the joint estimation of the frequency offset and the frame boundary.

For high performance CDMA communications, multiuser detection is often required to suppress the multiple access interference (MAI). Most multiuser detectors rely on accurate channel information to recover the multiuser digital signals. This paper studies the blind channel estimation problem for DS-CDMA systems using aperiodic spreading codes. The maximum likelihood (ML) estimator is formulated for channel estimation. We first convert the multiuser parameter estimation problem into a set of single user optimization problems via alternating optimization, and then determine the channel parameters for each user using an iterative algorithm derived. It is shown by computer simulation that this iterative algorithm can reach global maxima almost always under medium SNR values.

We consider the design of multiuser receivers for improving the performance of MC DS CDMA communications over fading channels. We present a class of spreading codes that reduces the dimension of MMSE receiver and ML receiver without any performance loss. These codes are shown to be optimum for independent fading channels under a code design criterion derived. Simulation results are provided to demonstrate the significant gains in performance and simplicity due to the proposed techniques.

Previous studies showed that multiuser detection in code-division multiple-access (CDMA) communications can be performed without explicit knowledge of users' channel characteristics in a frequency-selective fading environment. However, the computations of these blind approaches are an order of magnitude higher than existing adaptive minimum output energy (MOE) receivers which require at least knowledge of the desired user's channel response. Although the high-complexity problem can be alleviated by constrained adaptive filtering, the tradeoff is a significant drop in receiver performance, especially when the multipath pattern is time varying. In this paper, we present an adaptive receiver for CDMA communications over frequency-selective, and possibly time-varying, wireless channels. A salient feature of the new receiver is that it has complexity and performance comparable to that of the well-known MOE receivers, and yet requires no knowledge of the desired user's channel characteristics.

Differential coding allows signal demodulation without carrier phase estimation, and thus is commonly used to cope with phase ambiguity and residual carriers. In a wireless scenario where the system transfer function is FIR due to multipath reflections, channel estimation and equalization is usually required. Inspired by the work by Tong (1995) on blind sequence estimation, we propose a vector differential coding scheme that allows instantaneous signal detection at the receiver without knowledge of the channel. The new technique can be regarded as a generalization of the standard differential coding method for removing convolutional ambiguities.

In OFDM communications, the loss of orthogonality due to carrier offset must be compensated before DFT-based demodulation can be performed. We present a high accuracy blind carrier offset estimation algorithm and a blind channel equalizer which exploit the intrinsic structure information of OFDM signals. The latter method allows the receiver to perform coherent demodulation in changing environments without the overhead required for additional pilots.

The problem of joint carrier offset and channel estimation in code division multiple access (CDMA) communications is studied. An analytic algorithm that provides closed-form channel and carrier offset estimates is proposed. The algorithm first converts the multiuser estimation problem into single-user problems, and then analytically solves the resulting nonlinear multivariate optimization problems utilizing polynomial matrix manipulation. Computer simulations are provided to verify the efficacy of the proposed algorithm.

The signal-to-noise ratio (SNR) and signal bandwidth have been viewed as the dominant factors determining the channel capacity. In wireless communications, the channel capacity can be increased for a given SNR and a given spectral region, by exploiting the spatial diversity provided by the use of multiple antennas and transceivers at a base station. We calculate the channel capacity enhancement of a so-called space-division-multiple-access (SDMA) system and investigate its dependence with respect to different decoding schemes, terminal positions, and receiver numbers. Inner and outer capacity boundaries for joint decoding and independent decoding are presented, along with physical explanations as to how these boundaries can be achieved. We show that exploitation of the spatial diversity not only increases the overall achievable rates of both joint and independent decoding, but also closes the gap between their corresponding capacity regions, thus bringing the performance of the low-cost independent decoding scheme close to that of the optimal joint decoding. Practical issues of optimum projection and power control are also addressed.

In mobile communications, often time-varying multipath is too rapid for a conventional adaptive algorithm to track. This motivates expansion of the time varying channel impulse response over a basis. Rather than estimating the time-invariant coefficients of this basis expansion, it is possible to equalize the channel output directly even when the input is not known. This requires multichannel data as well as a minimal persistence of the excitation condition on the input and a coprimeness condition on the multiple channels obtained via fractional sampling and/or multiple antennas. Since the coefficients of the basis expansion will not be constant due to noise and unmodelled dynamics, the equalizer coefficients may also change slowly with time. Adaptive algorithms to track this change are proposed and basis mismatch problems are also investigated.

In a multi-transmitter broadcast system, the weight vector for each message signal can provide an additional degree of freedom for signal enhancement and interference suppression by taking advantage of the spatial diversity among the users. The design of optimal weight vectors that maximize the overall channel capacity is an open problem. Under certain power constraints, the channel capacity R is a highly nonlinear function of the M-dimensional weight vectors {w/sub i/}, where M is the number of transmitters. Hence, a closed-form algebraic solution that maximizes R over {w/sub i/} does not seem to be tractable. We decouple the weight vectors in R to simplify the optimization problem to a search for the maxima of a smooth multidimensional function. Based on this decoupling, we derive and evaluate two algorithms for computing weight vectors for the two-user and three-user cases: orthogonal and optimal. We also propose a near-optimum algorithm for the two-user case. The optimal algorithm requires an iterative search.

An algorithm is presented for estimating the quantities needed by a space-time RAKE receiver for DS-CDMA to achieve maximum SINR for the desired user. The proposed algorithm, which is applicable in the case of either periodic or aperiodic spreading codes, asymptotically provides the exact time of arrival of each dominant multipath within a bit period, and the optimum beamformer for extracting each multipath. This is achieved by using cyclostationarity to exploit the difference between the respective spectra of the desired user and each multiuser access interferer at the output of the correlator based on the desired user's code. Estimates of the relative time delay and optimal beamformer for each RAKE finger are used by the space-time RAKE receiver to optimally combine the desired user's multipath while simultaneously canceling strong multiuser access interference. Simulations of a near-far scenario are presented demonstrating the efficacy of the proposed algorithm.

Space-division multiple-access (SDMA) schemes have been proposed to increase the capacity of wireless communication systems by simultaneously transmitting and receiving multiple co-channel signals through different spatial channels. We address the uplink (remote users to the base station antenna array) blind channel and sequence identification problem for an SDMA system. We show that multiuser blind identification can be accomplished by exploiting the spatial and temporal diversities of an antenna array system. In particular, a recursive estimation algorithm is developed to recover multiple signals from intersymbol interference (ISI) and co-channel interference (CCI) by taking advantage of a special structure of the array output and the finite-alphabet property of digital communication signals. The implementation of the proposed approach in practical applications is discussed, and field experiments have been conducted to demonstrate the effectiveness of the proposed algorithm.

Multipath induced interchip-interference (ICI) alters waveforms of transmitted signals and presents a major obstacle to direct-sequence (DS) code-division-multiple-access (CDMA) communications. Since practical systems often employ aperiodic pseudorandom (PN) spreading sequences, source separation/equalization based on users signature waveforms may be prohibitive. We consider antenna array CDMA systems with aperiodic PN sequences and present a blind method to estimate the filter coefficients in a standard 2D RAKE receiver. A simple cost function is formulated based on which we derive a least-squares equalizer to effectively suppress both ICI and multiuser interference (MUI) by exploiting the channel diversities.

By exploiting the spatial diversity among different signals, anti-jamming communications can be accomplishing using antenna arrays. In this paper, we discuss the self-recovery problem for antenna array wireless systems after the communication link is broken due to abrupt environmental changes. In particular, we derive a novel approach that can extract the desired signal from heavy interference without using a priori spatial information of the desired signal or jamming sources. We show how this new method can be modified to handle intersymbol interference and demonstrate its effectiveness using computer simulations. Results from field experiments show that the new approach can reliably reject interference or jamming sources and recover the desired signal under 0 dB C/I.

Synchronous code-division multiple-access (CDMA) techniques possess intrinsic protection against co-channel interference due to orthogonal codes employed and thus, offers higher capacity than existing frequency-division multiple-access (FDMA) or time-division multiple-access (TDMA) systems. In the presence of multipath, however, each signal is subject to frequency-selective fading and the orthogonality condition does not necessarily hold leading to increased cross correlation. In these scenarios, multiuser detection need to be performed to suppress interference and recover the message symbols. To implement such a technique, explicit knowledge of the (nonorthogonal) signature waveforms of all users is required. We propose a blind estimation scheme that provides closed-form estimates of the signature waveforms by exploiting the structure information of the data output. In particular, we show that the subspace of the data matrix contains sufficient information for unique determination of the signature waveforms. Based on this observation, a multiple signal classification (MUSIC)-like algorithm is derived. Performance analysis of the new approach is also presented.

In this paper, we study two of the fundamental operations of a TDD smart antenna system, namely, the uplink channel and sequence identification and downlink selective transmission. For uplink, our focus is on the development of a blind estimation algorithm which is capable of resolving a multiuser system without the use of training sequence or any input statistics. For downlink, we propose a spatial channel pre-equalization scheme which simultaneously eliminates the intersymbol interference (ISI) and the co-channel interference (CCI) for all users using FIR filters. Both algorithms were validated by RF experiments using the smart antenna testbed developed in the University of Texas at Austin.

Most wireless communication systems use different carriers for uplinks and downlinks, hence the downlink beamforming can only be performed based on the directions-of-arrival (DOA) information of the uplink signals. In this paper, we propose an effective downlink transmission scheme for TDMA mobile communication systems via the integration of direction finding and blind signal estimation techniques. With an M-element antenna array, our new scheme can estimate up to 2M/sup 2//3 DOAs of direct path and multipath signals, while a conventional DOA estimation algorithm can resolve no more than M DOAs. RF experiments show that by incorporating these additional DOA estimates, much improved interference suppression was obtained.

Synchronous code-division multiple-access (CDMA) techniques possess intrinsic protection against co-channel interference due to orthogonal codes employed and thus, offers higher capacity than existing frequency-division multiple-access (FDMA) or time-division multiple-access (TDMA) systems. In the presence of multipath, however, each signal is subject to frequency-selective fading and the orthogonality condition does not necessarily hold leading to increased cross correlation. In these scenarios, multiuser detection need to be performed to suppress interference and recover the message symbols. To implement such a technique, explicit knowledge of the (nonorthogonal) signature waveforms of all users is required. We propose a blind estimation scheme that provides closed-form estimates of the signature waveforms by exploiting the structure information of the data output. In particular, we show that the subspace of the data matrix contains sufficient information for unique determination of the signature waveforms. Based on this observation, a multiple signal classification (MUSIC)-like algorithm is derived. Performance analysis of the new approach is also presented.

Conventional blind channel identification algorithms are based on channel outputs and the knowledge of the probabilistic model of a channel input. In some practical applications, however, the input statistical model may not be known, or there may not be sufficient data to obtain accurate enough estimates of certain statistics. We consider the system input to be an unknown deterministic signal and study the problem of blind identification of a channel which can be decomposed into multichannel FIR systems driven by a input signal. A new deterministic blind identification algorithm based solely on the system outputs are proposed. Necessary and sufficient identifiability conditions concerning the channel and the deterministic input signal are also presented.>

Tong et al. (see IEEE Trans. on Information Theory, March 1994) has proposed a parametric approach for blind equalization based solely on the second-order statistics. It first demonstrates the possibility of achieving satisfactory blind equalization by using a few hundred data samples in some typical wireless communication scenarios. Nevertheless, the algorithm is still far from optimal and is quite computationally intensive. In this paper, we use a different approach and derive a new algorithm which is less complicated as the original one. Simulation studies also show that some improvement of the new approach over its alternative in some scenarios.>

A blind symbol estimation technique for digital communication is developed by exploiting a special data structure of the oversampled channel output. The proposed method achieves direct symbol estimation without determining the channel characteristics. Moreover, if the transmitting symbols belong to a finite set of alphabets, the new approach can be extended to handle multiple sources.>