This paper proposes a data-aided approach to address the insufficient number of channel state information (CSI) samples in conventional passive WiFi sensing methods using beacon frames to enable high WiFi sensing performance. It selectively uses pilot symbols or all symbols based on error detection to increase the available CSI for WiFi sensing, resembling IEEE 802.11bf's null data packet approach, where all the received symbols are used for CSI measurements. Reusing correctly detected data symbols increases CSI samples, improving signal-to-noise ratio and detection sensitivity. Experimental results using a software-defined radio show enhanced sensing in human presence detection, with potential applications in tasks like respiration rate estimation, fall detection, and people counting.

Multiple antenna techniques, that allow energy beamforming, have been looked upon as a possible candidate for increasing the efficiency of the transfer process between the energy transmitter (ET) and the energy receiver (ER) in wireless energy transfer. This paper introduces a novel scheme that facilitates energy beamforming by utilizing Received Signal Strength Indicator (RSSI) values to estimate the channel. Firstly, in the training stage, the ET will transmit sequentially using each beamforming vector in a codebook, which is pre-defined using a Cramer-Rao lower bound analysis. The RSSI value corresponding to each beamforming vector is fed back to the ET, and these values are used to estimate the channel through a maximum likelihood analysis. The results that are obtained are remarkably simple, requires minimal processing, and can be easily implemented. Also, the results are general and hold for all well known fading models. The paper also validates the analytical results numerically, as well as experimentally, and it is shown that the proposed method achieves impressive results in wireless energy transfer.

Index coding studies multiterminal source-coding problems where a set of receivers are required to decode multiple (possibly different) messages from a common broadcast, and they each know some messages a priori. In this paper, at the receiver end, we consider a special setting where each receiver knows only one message a priori, and each message is known to only one receiver. At the broadcasting end, we consider a generalized setting where there could be multiple senders, and each sender knows a subset of the messages. The senders collaborate to transmit an index code. This paper looks at minimizing the number of total coded bits the senders are required to transmit. When there is only one sender, we propose a pruning algorithm to find a lower bound on the optimal (i.e., the shortest) index codelength, and show that it is achievable by linear index codes. When there are two or more senders, we propose an appending technique to be used in conjunction with the pruning technique to give a lower bound on the optimal index codelength; we also derive an upper bound based on cyclic codes. While the two bounds do not match in general, for the special case where no two distinct senders know any message in common, the bounds match, giving the optimal index codelength. The results are expressed in terms of strongly connected components in directed graphs that represent the index-coding problems.

A promising multi-user access scheme, non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC), is currently under consideration for 5G systems. NOMA allows more than one user to simultaneously access the same frequency-time resource and separates multi-user signals by SIC. These render resource optimization in NOMA different from orthogonal multiple access. We provide theoretical insights and algorithmic solutions to jointly optimize power and channel allocation in NOMA. We mathematically formulate NOMA resource allocation problems, and characterize and analyze the problems' tractability under a range of constraints and utility functions. For tractable cases, we provide polynomial-time solutions for global optimality. For intractable cases, we prove the NP-hardness and propose an algorithmic framework combining Lagrangian duality and dynamic programming to deliver near-optimal solutions. To gauge the performance of the solutions, we also provide optimality bounds on the global optimum. Numerical results demonstrate that the proposed algorithmic solution can significantly improve the system performance in both throughput and fairness over orthogonal multiple access as well as over a previous NOMA resource allocation scheme.

This paper investigates an orthogonal frequency division multiplexing (OFDM)-based wireless powered communication system, where one user harvests energy from an energy access point (EAP) to power its information transmission to a data access point (DAP). The channels from the EAP to the user, i.e., the wireless energy transfer (WET) link, and from the user to the DAP, i.e., the wireless information transfer (WIT) link, vary over both time slots and subchannels (SCs) in general. To avoid interference at DAP, WET and WIT are scheduled over orthogonal SCs at any slot. Our objective is to maximize the achievable rate at the DAP by jointly optimizing the SC allocation over time, and the power allocation over time and SCs, for both WET and WIT links. Assuming availability of full channel state information (CSI), the structural results for the optimal SC/power allocation are obtained and an offline algorithm is proposed to solve the problem. Furthermore, we propose a low-complexity online algorithm when causal CSI is available.

In this paper, we will first motivate the heterogeneous network as an evolutionary path to the fifth generation (5G) communications. We then present an agile software defined heterogeneous network architecture which virtualizes the various radio access networks such as cellular basestations and Wi-Fi access points and the various carrier frequencies from both licensed and unlicensed bands. The software defined heterogeneous network architecture can therefore support much more efficient resource utilization and meet the quality of service requirement. We will also share our work in context-aware Wi-Fi-cellular network traffic steering and mobility management as two use cases in the software defined heterogeneous network.

Non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC), is considered as a candidate multi-user access scheme for 5G cellular systems. In this paper, we provide theoretical insights and solution algorithm for optimizing multi- user power and channel allocation in NOMA systems. We mathematically formulate the NOMA resource allocation problem and prove its NP-hardness. For solving the problem, we propose an algorithm combining Lagrangian duality and dynamic programming to deliver a competitive suboptimal solution. Numerical results demonstrate that the proposed algorithmic solution can significantly improve the system performance over orthogonal frequency division multiple access (OFDMA) as well as over other existing NOMA resource allocation scheme.

Tracking of items on shelves is an important but time-consuming task in inventory control. In particular, books in public libraries are frequently borrowed and returned, even misplaced, and proves a challenge to be tracked on a daily basis. To track the books efficiently, we propose the Autonomous Robotic Shelf Scanning (AuRoSS) system. This paper elaborates on the key enabling robotic technology for the fully autonomous system, namely, a navigation system with surface tracking capability. A Radio Frequency Identification (RFID) reader is carried by the navigation system to identify the RFID tags embedded in each book. Based on the tag information, a tracking report that highlights missing and misplaced books is generated for the end users. To ensure successful identification, the surface tracking requires high accuracy, for which we propose a filtered Hough transform and a macro-mini manipulator structure. Tests of the AuRoSS system in a library show high accuracy in the scanning performance.

This paper addresses the problem of timing synchronization at the receiver of an IEEE 802.11a/n/ac communications system. In such systems, typically with multiple antennas at the transmitter, cyclic delay diversity (CDD) is implemented in the transmitted signal to provide diversity and hence, performance improvement in the error rates. However, the use of CDD creates pseudo-multipaths which degrade the performance of conventional timing synchronization algorithms, due to the increased delay spread of the channel. This paper proposes a method that achieves fine timing synchronization with low latency in two concurrent steps. The proposed scheme first achieves a coarse synchronization point so that important receiver operations can first take place. A fine timing synchronization algorithm operates concurrently to achieve a more accurate synchronization point and, to avoid incurring additional latency, post-processing is performed on the frequency domain samples of the preambles to compensate for the difference in synchronization points. The proposed scheme is shown to achieve excellent packet error rate performance with low latency requirements.

We study RF-enabled wireless energy transfer (WET) via energy beamforming, from a multi-antenna energy transmitter (ET) to multiple energy receivers (ERs) in a backscatter communication system such as RFID. The acquisition of the forward-channel (i.e., ET-to-ER) state information (F-CSI) at the ET (or RFID reader) is challenging, since the ERs (or RFID tags) are typically too energy-and-hardware-constrained to estimate or feedback the F-CSI. The ET leverages its observed backscatter signals to estimate the backscatter-channel (i.e., ET-to-ER-to-ET) state information (BS-CSI) directly. We first analyze the harvested energy obtained using the estimated BS-CSI. Furthermore, we optimize the resource allocation to maximize the total utility of harvested energy. For WET to single ER, we obtain the optimal channel-training energy in a semiclosed form. For WET to multiple ERs, we optimize the channel-training energy and the energy allocation weights for different energy beams. For the straightforward weighted-sum-energy (WSE) maximization, the optimal WET scheme is shown to use only one energy beam, which leads to unfairness among ERs and motivates us to consider the complicated proportional-fair-energy (PFE) maximization. For PFE maximization, we show that it is a biconvex problem, and propose a block-coordinate-descent-based algorithm to find the close-to-optimal solution. Numerical results show that with the optimized solutions, the harvested energy suffers slight reduction of less than 10%, compared to that obtained using the perfect F-CSI.

This paper studies a wireless-energy-transfer (WET) enabled massive multiple-input-multiple-output (MIMO) system (MM) consisting of a hybrid data-and-energy access point (H-AP) and multiple single-antenna users. In the WET-MM system, the H-AP is equipped with a large number M of antennas and functions like a conventional AP in receiving data from users, but additionally supplies wireless power to the users. We consider frame-based transmissions. Each frame is divided into three phases: the uplink channel estimation (CE) phase, the downlink WET phase, as well as the uplink wireless information transmission (WIT) phase. Firstly, users use a fraction of the previously harvested energy to send pilots, while the H-AP estimates the uplink channels and obtains the downlink channels by exploiting channel reciprocity. Next, the H-AP utilizes the channel estimates just obtained to transfer wireless energy to all users in the downlink via energy beamforming. Finally, the users use a portion of the harvested energy to send data to the H-AP simultaneously in the uplink (reserving some harvested energy for sending pilots in the next frame) . To optimize the throughput and ensure rate fairness, we consider the problem of maximizing the minimum rate among all users. In the large-M regime, we obtain the asymptotically optimal solutions and some interesting insights for the optimal design of WET-MM system.

We consider the problem of minimization of sum transmission energy in cellular networks where coupling occurs between cells due to mutual interference. The coupling relation is characterized by the signal-to-interference-and-noise-ratio (SINR) coupling model. Both cell load and transmission power, where cell load measures the average level of resource usage in the cell, interact via the coupling model. The coupling is implicitly characterized with load and power as the variables of interest using two equivalent equations, namely, non-linear load coupling equation (NLCE) and non-linear power coupling equation (NPCE), respectively. By analyzing the NLCE and NPCE, we prove that operating at full load is optimal in minimizing sum energy, and provide an iterative power adjustment algorithm to obtain the corresponding optimal power solution with guaranteed convergence, where in each iteration a standard bisection search is employed. To obtain the algorithmic result, we use the properties of the so-called standard interference function; the proof is non-standard because the NPCE cannot even be expressed as a closed-form expression with power as the implicit variable of interest. We present numerical results illustrating the theoretical findings for a real-life and large-scale cellular network, showing the advantage of our solution compared to the conventional solution of deploying uniform power for base stations.

In this paper, we consider a wireless communication network with a full-duplex hybrid energy and information access point and a set of wireless users with energy harvesting capabilities. The hybrid access point (HAP) implements full-duplex through two antennas: one for broadcasting wireless energy to users in the downlink and the other for simultaneously receiving information from the users via time division multiple access (TDMA) in the uplink. Each user can continuously harvest wireless power from the HAP until it transmits, i.e., the energy causality constraint is modeled by assuming that energy harvested in the future cannot be used for the current transmission. This leads to the causal dependence of each user's harvesting time on the transmission time of earlier users, e.g., the second user scheduled to transmit can harvest more energy if the first user has longer transmission time. Under this setup, we investigate the sum-throughput maximization (STM) problem and the total-time minimization (TTM) problem for the proposed full-duplex wireless-powered communication network. For the STM problem, the optimal solution is obtained as a closed-form expression, which can be computed with linear complexity. For the TTM problem, by exploiting the properties of the coupled constraints, we propose a two-step algorithm to obtain an optimal solution. Then, low-complexity suboptimal solutions are proposed for each problem by exploiting the characteristics of the optimal solutions. Finally, simulation studies on the effect of user scheduling show that different scheduling strategies should be adopted for STM and TTM.

Optimizing activation and deactivation of base station transmissions provides an instrument for improving energy efficiency in cellular networks. In this paper, we study the problem of performing cell clustering and setting the activation time of each cluster, with the objective of minimizing the sum energy, subject to a time constraint of serving the users' traffic demand. Our optimization framework accounts for inter-cell interference, and, thus, the users' achievable rates depend on cluster formation. We provide mathematical formulations and analysis, and prove the problem's NP hardness. For problem solution, we first apply an optimization method that successively augments the set of variables under consideration, with the capability of approaching global optimum. Then, we derive a second solution algorithm to deal with the trade-off between optimality and the combinatorial nature of cluster formation. Numerical results demonstrate that our solutions achieve more than 40% energy saving over existing schemes, and that the solutions we obtain are within a few percent of deviation from global optimum.

This paper proposes a general model for offloading to an access point (AP) to minimize energy cost in a heterogeneous network with a renewable-energy powered base station (BS) that is connected to a microgrid. We consider the setting where both the BS and the AP can buy energy from the grid to satisfy their requirements, but the BS can sell back any excess renewable energy to the grid. The downlink scenario is considered whereby the BS and the AP cooperate to serve a set of users, and the problem is to determine the user assignment to the BS or the AP. In the specific case of time division multiple access, we show that the optimal user assignment problem is NP Hard. We propose a sub-optimal, low complexity algorithm for this setting and obtain an approximation guarantee to the worst case sub-optimality of our algorithm. Simulations show that our algorithm performs well, with only a small gap to the optimal exhaustive search method over the set of users.

In this paper, we consider a wireless communication network with a hybrid access point (HAP) and a set of wireless users with energy harvesting capabilities. The HAP is assumed to have two antennas: one for transferring wireless energy in the downlink and one for receiving wireless information in the uplink. Without fixed energy sources, users first have to harvest energy from the wireless signals broadcast by the HAP, and then using the harvested energy to transmit their individual collected data to the HAP through dynamic-time-division-multiple-access (D-TDMA). All users can harvest the wireless energy prior to its transmission, and thus latter users can harvest more energy. We investigate the optimal time allocation to minimize the completion time of charging and transmitting all data subject to the constraints that all users have to send back some minimum amount of data. A high-efficient algorithm is then proposed to obtain an optimal time allocation of the formulated problem by exploring the properties of the constraints. It is also shown by simulations that the users with higher SNR should be scheduled to transmit first. Then, a suboptimal algorithm is also proposed based on the optimal time allocation. It is then shown by simulations that the suboptimal time allocation can achieve a close-to-optimal performance.

In this paper, we consider a wireless communication network with a hybrid access point (HAP) and a set of wireless users with energy harvesting capabilities. The HAP is assumed to have two antennas: one for transferring wireless energy in the downlink and one for receiving wireless information in the uplink. Without fixed energy sources, users first have to harvest energy from the wireless signals broadcast by the HAP, and then using the harvested energy to transmit their individual information to the HAP through dynamic-time-division-multiple-access (D-TDMA). We investigate the optimal time allocation to maximize the throughput of the proposed system subject to a total time constant. The formulated throughput maximization problem is proved to be a convex optimization problem. By using convex optimization techniques, the optimal time allocation strategy is obtained in closed-form expression. We show that the optimal time allocation can be obtained with linear complexity. It is then shown by simulations that the total throughput of the network increases with the number of users. It is also shown by simulations that the users with low SNR should be scheduled to transmit first.

We consider the problem of sum transmission energy minimization in a cellular network where base stations interfere with one another. Each base station has to serve a target amount of data to its set of users, by varying its power and load, where the latter refers to the average level of channel resource usage in the cell. We employ the signal-to-interference-and-noise-ratio (SINR) load-coupled model that takes into account the load of each cell. We show analytically that operating at full load is optimal to minimize sum energy. Moreover, we provide an iterative power adjustment algorithm for all base stations to achieve full load. Numerical results are obtained that corroborate the analysis and illustrate the advantage of our solution compared to the conventional solution where uniform power is used for all base stations.

In this paper, we investigate resource allocation strategies for a point-to-point wireless communications system with hybrid energy sources consisting of an energy harvester and a conventional energy source. By assuming that the non-causal information of the energy arrivals and the channel power gains is known, a mixed integer programming problem is formulated to minimize the total energy cost of such a system over N fading slots under the energy harvesting constraints and a proposed outage constraint. The outage constraint requires that a minimum fraction of slots to be reliably decoded. This constraint is useful if, for example, an outer code is used to recover that all data bits. Optimal linear time algorithms are obtained for two extreme cases: when the number of outage slots is 1 or N -1. For the general case, a lower bound based on linear programming relaxation, and two suboptimal algorithms are proposed. Numerical simulations indicate that the proposed suboptimal algorithms exhibit only a small gap from the lower bound for a wide range of given parameters.

We consider a multi-input-single-output (MISO) system where a wireless power transmitter sends wireless power to a receiver via beamforming, taking into account the effects of channel estimation. In this paper, we address the problem of dynamically balancing the amount of time resource used for training, as well as the energy resource used for wireless power transfer, so as to maximize the amount of energy harvested at the receiver. First, the optimal length of preamble is obtained under the case when the length is optimized offline, or dynamically optimized online, depending on the channel estimate. For the latter case, the solution is obtained by solving a dynamic programming (DP) problem, which is shown to be a threshold-type policy that depends only on the channel estimate power. We further derive the optimal dynamic power allocation scheme for DP-based wireless power delivery. The analysis results are validated by numerical simulations. The scheme based on dynamic-length preamble with power allocation is shown to significantly outperform the fixed-length preamble scheme.

In this paper, we provide an overview of the recent advances in microwave-enabled wireless energy transfer (WET) technologies and their applications in wireless communications. Specifically, we divide our discussions into three parts. First, we introduce the state-of-the-art WET technologies and the signal processing techniques to maximize the energy transfer efficiency. Then, we discuss an interesting paradigm named simultaneous wireless information and power transfer (SWIPT), where energy and information are jointly transmitted using the same radio waveform. At last, we review the recent progress in wireless powered communication networks (WPCN), where wireless devices communicate using the power harvested by means of WET. Extensions and future directions are also discussed in each of these areas.

In wireless communication systems, the nonlinear effect and inefficiency of power amplifier (PA) have posed practical challenges for system designs to achieve high spectral efficiency (SE) and energy efficiency (EE). In this paper, we analyze the impact of PA on the SE-EE tradeoff of orthogonal frequency division multiplex (OFDM) systems. An ideal PA that is always linear and incurs no additional power consumption can be shown to yield a decreasing convex function in the SE-EE tradeoff. In contrast, we show that a practical PA has an SE-EE tradeoff that has a turning point and decreases sharply after its maximum EE point. In other words, the Pareto-optimal tradeoff boundary of the SE-EE curve is very narrow. A wide range of SE-EE tradeoff, however, is desired for future wireless communications that have dynamic demand depending on the traffic loads, channel conditions, and system applications, e.g., high-SE-with-low-EE for rate-limited systems and high-EE-with-low-SE for energy-limited systems. For the SE-EE tradeoff improvement, we propose a PA switching (PAS) technique. In a PAS transmitter, one or more PAs are switched on intermittently to maximize the EE and deliver an overall required SE. As a consequence, a high EE over a wide range SE can be achieved, which is verified by numerical evaluations: with 15% SE reduction for low SE demand, the PAS between a low power PA and a high power PA can improve EE by 323%, while a single high power PA transmitter improves EE by only 68%.

We provide a general framework for the problem of data offloading in a heterogeneous wireless network, where some demand of cellular users is served by a complementary network. The complementary network is either a small-cell network that shares the same resources as the cellular network, or a WiFi network that uses orthogonal resources. For a given demand served in a cellular network, the load, or the level of resource usage, of each cell depends in a non-linear manner on the load of other cells due to the mutual coupling of interference seen by one another. With load coupling, we optimize the demand to be served in the cellular or the complementary networks, so as to maximize a utility function. We consider three representative utility functions that balance, to varying degrees, the revenue from serving the users vs the user fairness. We establish conditions for which the optimization problem has a feasible solution and is convex, and hence tractable to numerical computations. Finally, we propose a strategy with theoretical justification to constrain the load to some maximum value, as required for practical implementation. Numerical studies are conducted for both under-loaded and over-loaded networks.

In this paper, we investigate resource allocation strategies for a point-to-point wireless communications system with hybrid energy sources consisting of an energy harvester and a conventional energy source. In particular, as an incentive to promote the use of renewable energy, we assume that the renewable energy has a lower cost than the conventional energy. Then, by assuming that the non-causal information of the energy arrivals and the channel power gains are available, we minimize the total energy cost of such a system over N fading slots under a proposed outage constraint together with the energy harvesting constraints. The outage constraint requires a minimum fixed number of slots to be reliably decoded, and thus leads to a mixed-integer programming formulation for the optimization problem. This constraint is useful, for example, if an outer code is used to recover all the data bits. Optimal linear time algorithms are obtained for two extreme cases, i.e., the number of outage slot is 1 or N - 1. For the general case, a lower bound based on the linear programming relaxation, and two suboptimal algorithms are proposed. It is shown that the proposed suboptimal algorithms exhibit only a small gap from the lower bound. We then extend the proposed algorithms to the multi-cycle scenario in which the outage constraint is imposed for each cycle separately. Finally, we investigate the resource allocation strategies when only causal information on the energy arrivals and only channel statistics is available. It is shown that the greedy energy allocation is optimal for this scenario.

In this paper, we study the optimal design for simultaneous wireless information and power transfer (SWIPT) in downlink multiuser orthogonal frequency division multiplexing (OFDM) systems, where the users harvest energy and decode information using the same signals received from a fixed access point (AP). For information transmission, we consider two types of multiple access schemes, namely, time division multiple access (TDMA) and orthogonal frequency division multiple access (OFDMA). At the receiver side, due to the practical limitation that circuits for harvesting energy from radio signals are not yet able to decode the carried information directly, each user applies either time switching (TS) or power splitting (PS) to coordinate the energy harvesting (EH) and information decoding (ID) processes. For the TDMA-based information transmission, we employ TS at the receivers; for the OFDMA-based information transmission, we employ PS at the receivers. Under the above two scenarios, we address the problem of maximizing the weighted sum-rate over all users by varying the time/frequency power allocation and either TS or PS ratio, subject to a minimum harvested energy constraint on each user as well as a peak and/or total transmission power constraint. For the TS scheme, by an appropriate variable transformation the problem is reformulated as a convex problem, for which the optimal power allocation and TS ratio are obtained by the Lagrange duality method. For the PS scheme, we propose an iterative algorithm to optimize the power allocation, subcarrier (SC) allocation and the PS ratio for each user. The performances of the two schemes are compared numerically as well as analytically for the special case of single-user setup. It is revealed that the peak power constraint imposed on each OFDM SC as well as the number of users in the system play key roles in the rate-energy performance comparison by the two proposed schemes.

In this paper, we study the optimal design for simultaneous wireless information and power transfer (SWIPT) in downlink multiuser orthogonal frequency division multiplexing (OFDM) systems, where the users harvest energy and decode information using the same signals received from a fixed access point (AP). For information transmission, we consider two types of multiple access schemes, namely, time division multiple access (TDMA) and orthogonal frequency division multiple access (OFDMA). At the receiver side, due to the practical limitation that circuits for harvesting energy from radio signals are not yet able to decode the carried information directly, each user applies either time switching (TS) or power splitting (PS) to coordinate the energy harvesting (EH) and information decoding (ID) processes. For the TDMA-based information transmission, we employ TS at the receivers; for the OFDMA-based information transmission, we employ PS at the receivers. Under the above two scenarios, we address the problem of maximizing the weighted sum-rate over all users by varying the time/frequency power allocation and either TS or PS ratio, subject to a minimum harvested energy constraint on each user as well as a peak and/or total transmission power constraint. For the TS scheme, by an appropriate variable transformation the problem is reformulated as a convex problem, for which the optimal power allocation and TS ratio are obtained by the Lagrange duality method. For the PS scheme, we propose an iterative algorithm to optimize the power allocation, subcarrier allocation and the PS ratio for each user. Numerical results show that the peak power constraint imposed on each OFDM subcarrier as well as the number of users in the system play a key role in the rate-energy performance comparison by the two proposed schemes.

We study the optimality of separate network-channel coding (SJNC) for three independent sources on the orthogonal access scheme. By SJNC, we mean that the network coded messages are formed from the binary XOR of the sources' messages, followed by channel encoding which is independent of the network coding. Decoding is performed jointly across the network and channel codes. Using standard random coding and joint typical set decoding, we obtain an achievable rate region for SJNC. By allowing time-sharing of codes, the rate region coincides with the capacity region of joint network-channel coding, where network and channel coding are performed jointly. This proves, surprising, that there is no loss of optimality for SJNC.

We consider the scenario in which multiple sensors send spatially correlated data to the fusion center (FC) via independent Rayleigh-fading channels with additive noise. Assuming that the sensor data is sparse in some basis, we show that the recovery of the signal can be formulated as a compressive sensing (CS) problem. To model the scenario where sensors operate with intermittently available energy that is harvested from the environment, we propose that each sensor transmits independently with some probability, and adapts the transmit power to its harvested energy. Due to the probabilistic transmissions, the elements of the equivalent sensing matrix are not Gaussian. Besides, since the sensors have different energy harvesting rates and different sensor-to-FC distances, the FC has different receive signal-to-noise ratios (SNRs) for each sensor, referred to as the inhomogeneity of SNRs. Thus, the elements of the sensing matrix are also not identically distributed. We provide theoretical guarantees on the number of measurements for reliable reconstruction, by showing that the sensing matrix satisfies the restricted isometry property (RIP), under some mild conditions. We then compute an achievable system delay under an allowable mean-squared-error (MSE). Furthermore, using techniques from large deviations theory, we analyze the impact of inhomogeneity of the SNRs on the so-called k-restricted eigenvalues, which governs the number of measurements required for the RIP to hold. Our analysis is corroborated by numerical results.

We focus on the following instance of an index coding problem, where a set of receivers are required to decode multiple messages, whilst each knows one of the messages a priori. In particular, here we consider a generalized setting where they are multiple senders, each sender only knows a subset of messages, and all senders are required to collectively transmit the index code. For a single sender, Ong and Ho (ICC, 2012) have established the optimal index codelength, where the lower bound was obtained using a pruning algorithm. In this paper, the pruning algorithm is simplified, and used in conjunction with an appending technique to give a lower bound to the multi-sender case. An upper bound is derived based on network coding. While the two bounds do not match in general, for the special case where no two senders know any message bit in common, the bounds match, giving the optimal index codelength. The results are derived based on graph theory, and are expressed in terms of strongly connected components.

In many communication scenarios, such as in cellular systems, the energy cost is substantial and should be conserved, yet there is a growing need to support many real-time applications that require timely data delivery. To model such a scenario, in this paper we consider the problem of minimizing the expected sum energy of delivering a message of a given size from a source to a destination subject to a deadline constraint. A relay is present and can assist after it has decoded the message. Causal channel state information (CSI), in the form of present and past SNRs of all links, is available for determining the optimal power allocation for the source and relay. We obtain the optimal power allocation policy by dynamic programming and explore its structure. We also obtain conditions for which the minimum expected sum energy is bounded given a general channel distribution. In particular, we show that for Rayleigh and Rician fading channels, relaying is necessary for the minimum expected sum energy to be bounded. This illustrates the fundamental advantage of relaying from the perspective of energy efficient communications when only causal CSI is available. Numerical results are obtained which show the reduction in the expected sum energy under different communication scenarios.

Simultaneous information and power transfer over the wireless channels potentially offers great convenience to mobile users. Yet practical receiver designs impose technical constraints on its hardware realization, as practical circuits for harvesting energy from radio signals are not yet able to decode the carried information directly. To make theoretical progress, we propose a general receiver operation, namely, dynamic power splitting (DPS), which splits the received signal with adjustable power ratio for energy harvesting and information decoding, separately. Three special cases of DPS, namely, time switching (TS), static power splitting (SPS) and on-off power splitting (OPS) are investigated. The TS and SPS schemes can be treated as special cases of OPS. Moreover, we propose two types of practical receiver architectures, namely, separated versus integrated information and energy receivers. The integrated receiver integrates the front-end components of the separated receiver, thus achieving a smaller form factor. The rate-energy tradeoff for the two architectures are characterized by a so-called rate-energy (R-E) region. The optimal transmission strategy is derived to achieve different rate-energy tradeoffs. With receiver circuit power consumption taken into account, it is shown that the OPS scheme is optimal for both receivers. For the ideal case when the receiver circuit does not consume power, the SPS scheme is optimal for both receivers. In addition, we study the performance for the two types of receivers under a realistic system setup that employs practical modulation. Our results provide useful insights to the optimal practical receiver design for simultaneous wireless information and power transfer (SWIPT).

Wireless power transfer (WPT) is a promising new solution to provide convenient and perpetual energy supplies to wireless networks. In practice, WPT is implementable by various technologies such as inductive coupling, magnetic resonate coupling, and electromagnetic (EM) radiation, for short-/mid-/long-range applications, respectively. In this paper, we consider the EM or radio signal enabled WPT in particular. Since radio signals can carry energy as well as information at the same time, a unified study on simultaneous wireless information and power transfer (SWIPT) is pursued. Specifically, this paper studies a multiple-input multiple-output (MIMO) wireless broadcast system consisting of three nodes, where one receiver harvests energy and another receiver decodes information separately from the signals sent by a common transmitter, and all the transmitter and receivers may be equipped with multiple antennas. Two scenarios are examined, in which the information receiver and energy receiver are separated and see different MIMO channels from the transmitter, or co-located and see the identical MIMO channel from the transmitter. For the case of separated receivers, we derive the optimal transmission strategy to achieve different tradeoffs for maximal information rate versus energy transfer, which are characterized by the boundary of a so-called rate-energy (R-E) region. For the case of co-located receivers, we show an outer bound for the achievable R-E region due to the potential limitation that practical energy harvesting receivers are not yet able to decode information directly. Under this constraint, we investigate two practical designs for the co-located receiver case, namely time switching and power splitting, and characterize their achievable R-E regions in comparison to the outer bound.

We consider the problem of carrier frequency offset (CFO) estimation for a two-way relaying system based on the amplify-and-forward (AF) protocol. Our contributions are in designing an optimal preamble, and the corresponding estimator, to closely achieve the minimum Cramer-Rao bound (CRB) for the CFO. This optimality is asserted with respect to a novel class of preambles, referred to as block-rotated preambles (BRPs). This class includes the periodic preamble that is used widely in practice, yet this class provides an additional degree of design freedom via a block rotation angle. We first identify the catastrophic scenario of an arbitrarily large CRB when a conventional periodic preamble is used. We next resolve this problem by using a BRP with a non-zero block rotation angle. This angle creates, in effect, an artificial frequency offset that separates the desired relayed signal from the self-interference that is introduced in the AF protocol. With appropriate optimization, the CRB incurs only marginal loss from one-way relaying under practical channel conditions. To facilitate implementation, a specific low-complexity class of estimators is examined, and conditions for the estimators to achieve the optimized CRB are established. Numerical results corroborate with theoretical findings.

Optimal channel allocation is a key performance engineering aspect in single-carrier frequency-division multiple access (SC-FDMA). In SC-FDMA with localized channel assignment, the channels of each user must form a consecutive block. Subject to this constraint, various performance objectives, such as maximum utility, minimum power, and minimum number of channels, have been studied. We present a unified graph labeling algorithm for these problems, based on the structural insight that SC-FDMA channel allocation can be modeled as finding an optimal path in an acyclic graph. By this insight, our algorithm applies the concept of labeling and label domination that represent non-trivial extensions of finding a shortest or longest path. The key parameter in trading performance versus computation is the number of labels kept per node. Increasing the number ultimately enables global optimality. The algorithm's approach is further justified by its global optimality guarantee with strong polynomial-time complexity for two specific scenarios, where the input is user-invariant and channel-invariant, respectively. For the general case, we provide numerical results demonstrating the algorithm's ability of attaining near-optimal solutions.

Spectral efficiency (SE) and energy efficiency (EE) are key measures for wireless communication systems. In OFDM systems, the non-linear effects and inefficiencies of power amplifiers (PAs) have posed practical challenges for system design. In this paper, we analyze the impact of the PA on the SE and EE tradeoff of OFDM systems. We also propose a PA switching technique which achieves a better SE-EE tradeoff, e.g., the EE can be improved by 323% with 15% SE reduction.

Simultaneous information and power transfer over the wireless channels potentially offers great convenience to mobile users. Yet practical receiver designs impose technical constraints on its hardware realization, as practical circuits for harvesting energy from radio signals are not yet able to decode the carried information directly. To make theoretical progress, we propose a general receiver operation, namely, dynamic power splitting (DPS), which splits the received signal with adjustable power for energy harvesting and for information decoding. Moreover, we propose two types of practical receiver architectures, namely, separated versus integrated information and energy receivers. The integrated receiver integrates the front-end components of the separated receiver, thus achieving a smaller form factor. The rate-energy tradeoff for these two architectures are characterized by a so-called rate-energy (R-E) region. Numerical results show that the R-E region of the integrated receiver is superior to that of the separated receiver when more harvested power is desired.

In this paper, we employ a relay to assist in transmission from a source to a destination over a fixed number of time slots. We seek to minimize the expected sum transmission energy used by the source and relay, by choosing the transmission power of each node dynamically slot by slot. We assume that both receivers, namely the relay and the destination, can accumulate mutual information across previous and present slots by using earlier received packets for joint decoding. Assuming availability of causal channel state information (CSI) at all transmitters, we obtain the structure of the optimal solutions by dynamic programming. A key insight is that relaying is necessary for the expected sum energy to be bounded in Rayleigh fading channels. Numerical results show that the reduction in the expected sum energy is significant.

Medical implants are devices manufactured to replace damaged biological organs or structures. Once implanted, these devices communicate with other devices outside the body wirelessly. In this paper, we present our work on a particular type of WMI (wireless medical implants): artificial pancreas. We investigate challenges and issues in the context of artificial pancreas designs. In particular, we focus on how to achieve a low power design for WMI and propose a Virtual Patient system to evaluate control algorithms and communication protocols without an actual implantation of the device. Simulation and experiment results presented in this paper serves as design guidelines for wireless medical implant development.

This paper studies a special class of multicast index coding problems where a sender transmits messages to multiple receivers, each with some side information. Here, each receiver knows a unique message a priori, and there is no restriction on how many messages each receiver requests from the sender. For this class of multicast index coding problems, we obtain the optimal index code, which has the shortest codelength for which the sender needs to send in order for all receivers to obtain their (respective) requested messages. This is the first class of index coding problems where the optimal index codes are found. In addition, linear index codes are shown to be optimal for this class of index coding problems.

We consider a time-slotted source-relay-destination network where data is to be delivered by a deadline. Our goal is to minimize the sum transmission energy by power allocation for each node and time slot, with knowledge of full channel state information (CSI) in the form of the SNRs of all slots before the deadline. We assume that both receivers, namely the relay and the destination, can accumulate mutual information across previous and present slots by using earlier received packets for joint decoding. We obtain the structure of the optimal solution in a semi-analytical closed-form. The structural result shows that the source and relay jointly perform a generalized form of water-filling over slots. Finally, we use the structural result to develop a heuristic scheme that uses only causal CSI, in the form of the SNRs of only past and present slots.

We consider an energy efficient resource allocation method for orthogonal frequency division multiple access (OFDMA) systems consisting of M users and N subchannels. Transmit power assignment follows the optimal strategy, i.e., a water-filling strategy, for the given subchannels. For the subchannel allocation, we introduce a recently proposed algorithm called a sequential linear assignment algorithm (SLAA). The SLAA determines how many subchannels are allocated to each user and which subchannels are allocated to each user by solving the outer and inner problems, respectively, in an iterative manner; as a result, it requires O(MN2(N-M)2) complexity. To reduce the computational complexity of SLAA, we propose a simplified sequential linear assignment algorithm (SSLAA). Based on the observation that a user requiring the higher power would take the more subchannels, SSLAA uses each user's power consumption as a metric to determine who will take an additional subchannel in each iteration. Consequently, contrast to SLAA using network power consumption as a metric, the SSLAA incurs significant reduction of computational complexity, by O(N2(N-M)2). Computer simulations in compared with other existing heuristic algorithms verify that the proposed SSLAA results in high energy efficiency and low computational complexity.

We introduce a generalized two-way relay channel where two sources exchange information (not necessarily of the same rate) with help from a relay, and each source additionally sends private information to the relay. We consider the Gaussian setting where all point-to-point links are Gaussian channels. For this channel, we consider a two-phase protocol consisting of a multiple access channel (MAC) phase and a broadcast channel (BC) phase. We propose a general decode-and-forward (DF) scheme where the MAC phase is related to computation over MAC, while the BC phase is related to BC with receiver side information. In the MAC phase, we time share a capacity-achieving code for the MAC and a superposition code with a lattice code as its component code. We show that the proposed DF scheme is near optimal for any channel conditions, in that it achieves rates within half bit of the capacity region of the two-phase protocol.

Power minimization of a cooperative decode-and-forward (DF) half-duplex relaying, where two users communicate with one destination node (D), is studied in this paper. Recognizing the difference in link qualities, one user not only transmits its own message to D but also acts as a relay node (R) for the other user. To achieve total network power minimization, we consider two strategies: (i) a rate splitting strategy (RSS) which splits the whole message into two sub-messages with one being transmitted via R and the other directly to D, (ii) a transmission power boosting strategy (TPBS) in the broadcast phase that allocates more power than needed to ensure correct decoding of the message at R. We first consider joint optimization of rate splitting, a time sharing factor (between the broadcast phase and cooperation phase), and transmission powers. We then consider the second scenario where the time sharing factor is fixed. We prove that TPBS is not required for the first scenario, but is necessary for the second scenario to minimize the total network power. We also obtain channel conditions for which the optimal solution is to employ a multiple access channel (MAC) strategy, i.e., without relaying. Finally we show through numerical results that significant power saving is achieved by our proposed strategies.

In this paper, we consider the half-duplex cooperative multiple-access channel (CMAC) with frequency-selective block-fading. Each link employs an orthogonal frequency division multiplexing (OFDM) system, where modulated symbols are drawn from a finite constellation set. We first obtain the diversity order of the CMAC, as a function of the time sharing variables of the users and the rates of the codes. To achieve this rate-diversity tradeoff, we use the principle of network coding where messages of the two sources are jointly encoded. Both separate and joint network-channel coding approaches are considered. Specifically, we design multiple turbo codes that minimize the outage probabilities of these approaches. We also give a code structure for the multiple turbo codes to achieve full diversity of the system. The codes are optimized using the extrinsic information transfer (EXIT) chart analysis with iterative decoding tailored for OFDM modulated CMAC. Numerical examples show that with our proposed design technique, the achieved frame error rate is within 0.5dB from the information outage. Without network-channel coding, the outage probability of distributed coding cannot achieve the diversity order given in the rate-diversity tradeoff.

This paper studies the performance limits of multi-antenna wireless broadcasting systems for simultaneous information and power (energy) transfer. For the purpose of exposition, a three-node network is investigated, in which one receiver harvests energy and another receiver decodes information separately from the signals broadcast by a common transmitter. Two scenarios are examined, where the information receiver and energy receiver are separated and see different channels from the transmitter, or co-located and see the same channel from the transmitter. For the case of separated receivers, we derive the optimal transmission strategies to achieve different tradeoffs for maximal information rate versus energy transfer, which are characterized by the boundary of a so-called rate-energy (R-E) region. For the case of co-located receivers, we show an outer bound for the achievable R-E region, due to the potential limitation that practical circuits for harvesting energy from radio signals are not yet able to decode the carried information at the same time. Under this constraint, we propose two practical receiver designs for the co-located receivers, namely, time switching and power splitting, and characterize their achievable R-E regions in comparison with the outer bound.

In this paper, we will analyze how different amount of receiver side information (RSI) available at the receivers of a broadcast channel will affect the overall network throughput. Two transmission schemes, namely random linear network coding (RLNC) and round robin scheduling (RRS), are considered. Taking into account the differing amount of RSI available at each receiver, closed-form expressions of network throughput and asymptotic throughput at high SNR are obtained for a broadcast channel. Our results show that the asymptotic throughput of RLNC is always no worse than RRS no matter how much RSI is available at each receiver. With a sum constraint for the overall arrival rate of receiver side information in the broadcast channel, we show that the the maximum throughput is achieved for RLNC if each receiver accumulates the same amount of RSI.

The encoding and decoding of network coding at network layer and channel coding at the physical layer can be performed either jointly or separately. The largest achievable rate region is obtained when both are done jointly. Our interest in this paper lies in investigating the optimality of separate encoding and joint decoding of network-channel (NC) code (SJNC) assuming an orthogonal access scheme. Our key result is that SJNC is optimal as it achieves the capacity region of JJNC. However, SJNC is suboptimal if time sharing of codes is not allowed, or if separate decoding is used. We then apply these results to the cooperative multiple-access channel (CMAC) where time-sharing of codes is practically infeasible. Optimality of SJNC is compared to JJNC in terms of outage region. The conditions for full diversity order of orthogonal frequency division multiplexing (OFDM) modulated CMAC with JJNC and JSNC are then derived. Using numerical examples, multiple turbo codes are designed to show that the achieved frame error rate (FER) is within 0.6dB from the information outage.

This paper addresses the problem of timing synchronization in an IEEE 802.11n-based multiple-input single-output (MISO) communications system. In IEEE 802.11n systems, cyclic delay diversity (CDD) is implemented to avoid the problems associated with the unintentional beamforming effect which occurs when scalar multiples of one signal combine destructively after being transmitted through different antennas. However, the use of CDD results in the creation of pseudo-multipaths which will degrade the performance of correlation-based timing synchronization algorithms commonly used in such systems, due to the increased delay spread of the channel. Thus, a frequency domain timing synchronization algorithm, which is robust to the increase in delay spread due to the presence of pseudo-multipaths, is proposed. The proposed algorithm achieves a higher probability of timing synchronization than correlation-based timing synchronization algorithms typically implemented.

Power minimization of a cooperative decode-and-forward (DF) half-duplex relaying is considered in this paper. In the system considered, two source nodes (S) try to communicate with one destination (D). Recognizing the difference in link qualities, one of source nodes not only transmits its own information to D but also acts as a relay for the other S. An optimization problem is formulated to minimize total network power by adjusting transmission powers and a time sharing variable. Since the objective function is not convex, an optimal solution cannot be obtained with low complexity. Instead, we approximate the objective function by a convex function to obtain a suboptimum time sharing factor among transmission phases. Then, transmission power is calculated based on the obtained suboptimum time sharing factor. Power saving of the system considered is compared to multiple access channel (MAC) with optimum power allocation.

Initial ranging (IR) involves detecting the binary phase shift keying (BPSK) code transmitted by the new subscriber station (SS). In this paper, we formulate a simple detector using the Neyman-Pearson theorem (NPT) for single-code detection in IEEE 802.16-compliant time division duplex (TDD) orthogonal frequency division multiple access (OFDMA) systems. Exploiting the BPSK structure in the ranging code together with the time-invariant channel, a novel method for separating multiple ranging codes is presented. The NPT-based detector is then used to detect the separated codes. The proposed detector is computationally simple and has favorable detection performance for practical implementation.

In this paper, we consider a wireless multicast network with multiple sources, relays, and destinations. We adopt a multi-hop decode-and-forward relay protocol such that two canonical subnetworks are relevant, namely broadcast channel with receiver side information (BC-RSI) and orthogonal multiple access channel with correlated sources and receiver side information (MAC-CS-RSI). A joint network and channel coding (JNCC) strategy is proposed by exploiting ARQ, RSI, and correlated sources. The proposed JNCC strategy does not require the knowledge of RSI nor any transmit channel state information, such that each transmitter simply performs retransmissions until the intended receivers have accumulated enough mutual information to successfully decode all desired messages. This successful decoding is then conveyed from the receivers to the respective transmitters via an acknowledgement message. To measure the performance of the proposed JNCC strategy with ARQ, we derive closed-form expressions of network throughput by applying the renewal-reward theorem. Analytical results show that the proposed JNCC strategy outperforms, in terms of network throughput, the conventional separate network and channel coding strategy with random linear network coding.

Cooperative communication using decode and forward (DF)-based distributed relays is one important solution to achieve better connectivity and higher data rates in wireless fading channels. In distributed relay networks, each relay has an independent local oscillator which when unsynchronized results in the presence of multiple carrier frequency offsets (CFOs) at the destination. The maximum likelihood estimator (MLE) for estimating the multiple CFOs requires a two dimensional grid search involving matrix inversion for each search point. Thus, the multiple CFO and channel estimation is computationally prohibitive and challenging in distributed relay networks. In this paper, we propose a simple estimator for multiple CFO and channel estimation at the destination by exploiting the fact that the relays receive information from a common source. Computer simulations show that the mean square error (MSE) performance of the proposed estimator is close to the Cramer-Rao lower bound for small carrier frequency synchronization errors at the relays.

In two-way relaying, two sources exchange information via a relay. In the generalized two-way relay, private information is also sent by each source to the relay, which may be used for overhead data, such as for channel state information. We consider the generalized two-way relay in a multi-carrier system over the wireless channel. Our problem is to maximize the rate that the sources exchange information, subject to some minimum rates of private information. We employ a coding scheme based on time-sharing and superposition of lattice codes and multiple access codes for each subcarrier, and show that the problem of optimizing the time-sharing variables is solved as a linear program. To simplify implementation, we propose a heuristic scheme where the time-sharing variables arequasi-extremal, i.e., all time-sharing variables, except for at most one, are assigned the largest or smallest possible values. Interestingly, the proposed scheme is provably optimal under certain channel scenarios. Numerical results show that the proposed scheme performs close to the optimal scheme in most practical channel scenarios.

In this paper, we employ a relay for scheduling with deadline, i.e., a relay assists to deliver a given number of bits from a source to a destination over a fixed number of slots. Prior to every transmission, the channel of the present slot is made known to all nodes, but not of future slots. We seek to minimize the sum energy used, by choosing the transmission power and transmission duration of each node dynamically slot by slot. Assuming a decode-and-forward scheme for every slot, we obtain the optimal policy via dynamic programming. We also propose and analyze an asymptotically optimal policy that can be solved as a series of convex optimization problems. Numerical results show that the proposed policy gives a tight upper bound on the minimum sum energy, and that a significant fraction of energy can be saved with relaying.

In this paper, we consider the half-duplex relay channel. We seek to design multiple turbo codes to minimize the information outage in the block fading channel. An analysis on the diversity order of the relay channel, which depends on the time sharing variable and the rate of the code, is given for practical modulations. We also give a code structure for the multiple turbo codes to achieve the full diversity when it is achievable. The codes are optimized using the extrinsic information transfer (EXIT) chart analysis, based on the convergence thresholds of the iterative decoding tailored for relay channel. Numerical examples shows that with our design technique, the achieved frame error rate is within 0.7dB of the information outage. To reduce complexity, a suboptimum code search approach is proposed, resulting codes which perform 1dB away from the information outage.

We consider the use of energy harvesters, in place of conventional batteries with fixed energy storage, for point-to-point wireless communications. In addition to the challenge of transmitting in a channel with time selective fading, energy harvesters provide a perpetual but unreliable energy source. In this paper, we consider the problem of energy allocation over a finite horizon, taking into account a time varying channel and energy source, so as to maximize the throughput. Two types of side information are assumed to be available: causal side information of the immediate past channel condition and harvested energy, and full side information. We obtain structural results for the optimal energy allocation, via the use of dynamic programming and convex optimization techniques. In particular, if unlimited energy can be stored in the battery with harvested energy, we prove the optimality of a water-filling energy allocation solution with multiple, non-decreasing water levels.

In the conventional two-way relay channel, two sources exchange information with help from a relay. We introduce a generalized two-way relay channel where each source additionally sends private information to the relay. For this channel, we consider a protocol that consists of a multiple- access (MA) phase and a broadcast (BC) phase and obtain achievable rate regions for both phases. The MA phase is related to computation over the multiple-access channel (MAC). We show that our scheme achieves rates that always lie within half bit of the capacity region for computation over MAC, and is capacity achieving in the BC phase. This near optimality in the MA phase can be achieved by time sharing a capacity-achieving code for the MAC and a superposition code that uses a lattice code as its component code.

In a typical network coding scenario, a node has to broadcast a set of messages to a group of receiver nodes who have already received a subset of the messages through other links. In this paper, we will first present the achievable rate region for this broadcasting scenario, and then extend the result to the case with retransmissions. With the proposed nonlinear network coding strategy, the achievable rate region can be achieved without requiring the exact knowledge of a priori information available at the receiver nodes. The broadcasting node simply performs retransmissions with independent codebooks until every receiver node has received the whole set of messages. Through the analysis of network throughput, we will show that each receiver node is able to effectively accumulate mutual information over the multiple transmissions thus achieving higher overall throughput than the conventional random linear network coding strategy.

Using relays to extend the coverage of base-stations in cellular systems has been of interest in recent years as it can reduce the cost of installing more base-stations and yet cater to mobile terminals within a wider coverage area. There have been recent advances where two or more half duplex relay stations are used to recover the loss of bandwidth due to the use of half duplex relays. However, such schemes would not be beneficial in cases where the inter-relay channel is strong, and the relays start interfering each other. In this paper, we study the use of beamforming techniques to resolve the problem of inter-relay interference.

In a previous work by Wu et ah, it is shown that the performance of the pre-transformed space-time block coded orthogonal frequency division multiplexing (PT-STBC-OFDM) system has much superior performance compared to the normal STBC-OFDM system. In this paper, we present an analytical study on the bit error rate (BER) of the PT-STBC-OFDM system. We derive the noise distribution of PT-STBC-OFDM system and hence, obtain the BER numerically. We also derived two closed- form BER approximations for the PT-STBC-OFDM system at two different SNR regions. We show that by adding the PT operation, the system has better diversity compared to STBC-OFDM system at the medium SNR region. Using linear detection, the diversity of both systems at the high SNR regions are the same. Moreover, we demonstrate that at high SNR, a PT-STBC- OFDM system of transform size N requires 5log10(N) dB less SNR to achieve the same BER as without PT.

In this paper, the average sum rate of two-way amplify-and-forward (AF) half-duplex relaying system is analyzed. To this end, we first derive the harmonic mean of two independent gamma distributed random variables which have the same shape parameter but different scale parameters. By deriving tight upper and lower bounds for the average sum rate of two-way relaying, we verify that two-way relaying can significantly recover the spectrum efficiency loss of one-way relaying. We also extend the two-way AF half-duplex relaying to the case where source and destination terminals both transmit Alamouti's orthogonal space time block code (OSTBC) utilizing two antennas and the relay has only one antenna. By deriving both upper and lower bounds for the average sum rate as well as an upper bound for the pairwise error probability (PEP) for the proposed two-way OSTBC scheme, we show that the average sum rate is further improved compared to the single antenna case and a diversity order of two is also achieved. Furthermore, optimal power allocations under a global power constraint for two-way relaying with single antenna and the proposed two-way OSTBC scheme are derived analytically.

We investigate packet-by-packet rate adaptation so as to maximize the throughput. We consider a finite-state Markov channel (FSMC) with collisions, which models channel fading as well as collisions due to multi-user interference. To limit the amount of feedback data, we only use past packet acknowledgements (ACKs) and past rates as channel state information. The maximum achievable throughput is computationally prohibitive to determine, thus we employ a two-pronged approach. Firstly, we derive new upper bounds on the maximum achievable throughput, which are tighter than previously known ones. Secondly, we propose the particle-filter-based rate adaptation (PRA), which employs a particle filter to estimate the a posteriori channel distribution. The PRA can easily be implemented even when the number of available rates is large. Numerical studies show that the PRA performs within one dB of SNR to the proposed upper bounds for a slowly time-varying channel, even in the presence of multi-user interference.

In this paper, we consider a network consisting of one source, one destination and two relays, under a half duplex constraint. We analyze an amplify-and-forward (AF) two-path relaying scheme where one of the relays additionally performs inter-relay interference cancellation. With this inter-relay self interference cancellation, we show that degradation in performance can be significantly reduced when inter-relay channel gain increases. By treating the AF two-path relaying as an equivalent MIMO space time code, we derive an upper bound for the pairwise error probability (PEP) of this system. Previous works indicate that only a diversity order of two can be achieved even though physically there are three possibly independent spatial paths to the destination. From our analysis of the PEP, we analytically prove and identify the reason to the limited diversity order of two. We then propose a simple but novel precoding scheme which allows the system to achieve a full diversity order of three while maintaining the spectral efficiency gain of the two-path relaying protocol. A robust precoding design to achieve full diversity order with high coding gain is also proposed.

We consider packet-by-packet rate adaptation to maximize the throughput over a finite-state Markov channel. To limit the amount of feedback data, we use past packet acknowledgements (ACKs) and past rates as channel state information. It is known that the maximum achievable throughput is computationally prohibitive to determine. Thus, in this paper we derive two upper bounds on the maximum achievable throughput, which are tighter than previously known ones. We compare the upper bounds with a known myopic rate-adaptation policy. Numerical studies over a wide range of SNR suggest that the myopic rate-adaptation policy is close to the upper bounds and may be adequate in slowly time-varying channels.

We consider an amplify-and-forward scheme for two-way relaying over OFDM, in which two nodes wish to exchange information via a relay. Assuming full channel knowledge, we perform power allocation for the relay and both information-exchanging nodes, as well as tone permutation at the relay, so as to maximize the sum capacity. A dual decomposition technique is employed for power allocation, while a greedy approach is proposed for tone permutation. In particular, we explore an interesting water-filling behavior displayed by this power allocation solution. Numerical results demonstrate that substantial capacity gains are achieved by implementing the two proposed solutions, either individually or successively.

In this paper, we derive tight upper and lower bounds for the average sum rate of two-way amplify-and-forward (AF) half-duplex relaying, which show that two-way AF half-duplex relaying can significantly mitigate the spectral efficiency loss of conventional one-way AF half-duplex relaying. We then extend the AF half-duplex two-way relaying to the case where source and destination terminals both transmit Alamouti's orthogonal space time block code (OSTBC) utilizing two antennas and relay has only one antenna. We derive both upper and lower bounds for the average sum rate as well as an upper bound for the pair-wise error probability (PEP) for the proposed OSTBC scheme. We also find the optimal power allocation for both two- way relaying schemes analytically. Our theoretical analysis and numerical simulation results show that higher average sum rate compared to the single antenna case and diversity order of two are achieved by the proposed OSTBC scheme.

We consider relay-assisted communications from a source to a destination based on OFDM. The relay performs sub-carrier permutation by exploiting full channel state information (CSI), in addition to conventional amplify-and-forwarding. That is, the relay permutes the received signals over the subcarriers in the frequency domain, then amplifies and forwards the data to the destination. In this paper, we obtain optimal subcarrier permutations that minimize the uncoded BER for several relaying scenarios. From these insights, we develop a low-complexity ordering-based subcarrier permutation for the general relaying scenario. Simulations indicate that the proposed subcarrier permutation outperforms a random subcarrier permutation (where CSI is not exploited), and performs close to an optimal subcarrier permutation (which incurs a high complexity).

In this paper, we analyze a full rate cooperative communication system using amplify-and-forward (AF) two-path relaying scheme with inter-relay interference cancellation at the relay. By treating AF two-path relaying as an equivalent MIMO system, we derive the pairwise error probability (PEP) of this system. Given one source node, two half duplex relay nodes, and one destination node, all equipped with single antenna, our theoretical analysis and simulation results show that diversity order of 2 can be achieved by this system. Furthermore, with inter-relay interference cancellation, degradation in performance can also be significantly reduced when inter-relay channel gain increases. Our analysis also shows that diversity order of the system will not be affected by the magnitude of this inter-relay channel gain.

We consider a cognitive wireless relay system that operates on secondary spectrum sharing basis using spectrum pooling. A spectrum pool comprises of a set of available narrowband subchannels over which secondary users may communicate without causing harmful interference to primary users. In the proposed relay system, a source node transmits data symbols on an available spectrum pool to the relay node. At the relay, the received data symbols are amplified, permuted and forwarded to the destination node on an available spectrum pool. The problem of spectrum pool reassignment constitutes the determination of the permutation mapping at the relay node. Our goal is to maximize the ergodic capacity of the relay system, given knowledge of the average signal to noise ratios pertaining to the subchannels in the spectrum pools, by determining the optimum spectrum pool reassignment.

We consider a wireless ad-hoc network with random ALOHA transmissions between nodes. However nodes can decide to use the Request-to-Send (RTS) / Clear-to-Send (CTS) protocol for occasional long packets. Via the RTS/CTS protocol, nodes proactively make a temporal and spatial reservation of the channel before sending the actual data payload (DATA). The aim of this paper is to explore the effectiveness of the RTS/CTS protocol in such a wireless network, in particular by alleviating the commonly made assumption that an entire RTS/CTS/DATA cycle is always successful if only the RTS packet is recovered. Our results show that this assumption loses its accuracy if the data rate is optimized for the link throughput or if one aims for allowing dense frequency reuse. We quantify the spatial and temporal impact of the RTS/CTS reservation on the network traffic, as well as the link throughput achieved. Numerical results demonstrate that for sufficiently long packets, a significant throughput gain can be achieved by employing the RTS/CTS reservation.

A computationally efficient optimal double correlation (ODC) filter, which works on the fourth order statistics of received samples, is proposed for carrier frequency offset estimation in MIMO OFDM systems using repetitive pilot symbols. The integral weights applied to the double correlation coefficients together with the requirement of a single phase operation allow particularly simple hardware implementation. Theoretical analysis shows that the mean square error in the estimate reaches the Cramer-Rao bound at high signal-to-noise ratios over a wide frequency range. Simulation results further indicate that the performance of the new algorithm is comparable to that of the maximum likelihood estimation solution even for large carrier frequency offsets.

The Request-to-Send (RTS) / Clear-to-Send (CTS) scheme uses a four-way handshake to ensure proper spatial and temporal reservation of the multiple-access wireless LAN channel. By considering the (interdependence of) successive capture probabilities for a cycle of an RTS, CTS, data packet and acknowledgement, we analyze the throughput of the RTS/CTS scheme in a new mathematical framework. We also propose closed-form bounds which are useful for rate optimization.

Throughput maximization in a packet switched wireless communication system is considered in this paper. The channel variation is accounted for by modeling the channel as a finite state Markov channel. To maximize the throughput, rate adaptation is performed based on a single bit acknowledgement feedback. In general, when the window of packets considered is large, obtaining a global optimal rate adaptation solution is computationally difficult. A successive rate adaptation is derived which we show is necessarily sub-optimal by relating it to the optimal solution. This is complemented by a particle filter that is used to estimate the a posterior distribution of the channel. Numerical results illustrate that significant throughput can be recovered for slowly varying channels.

This paper studies the problem of signal detection for generic MIMO channels with large signal dimensions. We propose a block-iterative generalized decision feedback equalization (BI-GDFE) receiver to recover the transmitted symbols in a block-iterative manner. By exploiting the input-decision correlation (IDC), a measure for the reliability of the earlier-made decisions, we design the feed-forward equalizers (FFEs) and feedback equalizers (FBEs) in such a way that maximized signal-to-interference-plus-noise ratio (SINR) is achieved for each iteration. We propose an IDC determination method to achieve fast and guaranteed convergence for the proposed receiver. Computer simulations are presented to illustrate the capability of the proposed receiver to achieve single user matched-filter bound for large MIMO channels with high SNR.

In this paper, we present a pre-transformed (PT) Multiple Input Multiple Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) system, in short PT-MIMO-OFDM system. We propose two receiver structures that combine the iterative subcarrier reconstruction and iterative interference cancellation to fully exploit both the spatial and the frequency diversity of MIMO-OFDM systems. Signi cant performance gain can be achieved. Based on the performance and receiver complexity tradeoff, we also present a set of guidelines for choosing either one of the two receivers in practical systems.

Recently, an iterative detection method is proposed for a pre-transformed OFDM system, where significant performance gain is shown to be achieved through linear processing (Z.Lei et al., Apr. 2003). An analytical treatment of the subject is given. The optimum transform design and reconstruction criteria are given to maximize the minimum SNR. In the case when no iteration is applied (i = 0), we compare the effect of the transform on the performance in a channel with and without diversity. Under the assumption that the previous detection is error free, it is shown analytically that the iterative method achieves a diversity advantage of i + 1 in the i/sup th/ iteration, thus providing an explanation on its superior performance. Simulations are conducted to corroborate the analysis. Due to the generality of the transform design, the analysis conducted is applicable for other common systems (such as MC-CDMA and SC-FDE systems) as well.

Robust minimum mean-square error (MMSE) channel estimation for orthogonal frequency-division multiplexing (OFDM) systems with practical timing synchronization is considered. The correlation between the channel coefficients at the different subcarriers depends upon the channel power delay profile (PDP) and the probability density function (pdf) of the timing synchronization offset. Since both of these are not known at design time, there is a need for a robust estimator that results in mean-square error performance independent of the actual channel power delay profile (PDP) and the actual timing synchronization offset pdf. We develop the notion of effective PDP, given by the convolution of the channel PDP and the pdf of the timing synchronization offset. We show that designing with a uniform PDP of the same length as the effective PDP leads to a robust solution. This follows from the robustness of the uniform PDP for MMSK estimation, a known result for which we provide an alternative derivation. We illustrate the performance of the robust estimator using a numerical example.

A general approach that allows the extension of an CRB-achieving unbiased frequency estimator developed for the AWGN channel to be used for the multipath fading channel by using a periodic transmit sequence is given. The optimality of its performance is guaranteed in a multipath fading channel under certain conditions. The proposed method has low complexity but a reduced frequency operating range. The exploitation of the multipath diversity for fading channel is also investigated. It is illustrated through a correlation based frequency estimator how the general approach can be used, leading to a known estimator being used in a novel way.

This paper describes how to perform low complexity frequency offset estimation in the presence of direct current (DC) offset based on a received periodic preamble. By designing a suitable preamble, we perform frequency and DC offsets estimation concurrently, followed by a simple compensator which reduces the frequency offset error induced by the presence of either a residual or actual DC offset. Its performance is illustrated based on the preamble of a wireless LAN system defined in IEEE 802.11a, as well as another preamble design that offers additional performance gain. Compared to the case when no compensator is used, the proposed solution gives superior performance over a wide range of DC offset power in both AWGN and multipath quasi-static channels.

The iterative detector and decoder (IDD) in the serial concatenated coded and pre-transformed system is studied in this paper. The performance of the IDD in the single-carrier system over flat fading channel and the orthogonal frequency division multiplexing (OFDM) system over frequency-selective fading channel are thoroughly evaluated. Numerical results show that in the single-carrier system, IDD with 2 to 3 iterations over flat fading channel achieves performance only about 1dB away from that over the additive white Gaussian noise (AWGN) channel. Our study also finds that for OFDM system, the IDD provides considerable performance improvement compared to the coded or pre-coded systems, even when the multipath diversity is low. IN the meanwhile, the excellent performance of IDD does not rely on the optimal design of pre-transformation matrix.

In this paper we present several results of our study on training sequence assisted channel estimation for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. After developing a linear matrix algebraic model for the cyclic prefix based MIMO OFDM systems, we define a generalised preamble structure which is a simple extension of the preambles used in single-input single-output (SISO) OFDM so that its good properties, such as low peak to average power ratio, can be maintained. We then derive the least squares (LS) and linear minimum mean squared error (LMMSE) channel estimation algorithms based on the proposed preamble design. In order to reduce the preamble length, we further propose a switched subcarrier preambles scheme in which the transmit antennas are divided into groups, and preambles are transmitted in alternative subset of subcarriers in each group. A LMMSE filter-based interpolation scheme and a DFT-based LS interpolation scheme are then used to obtain the channel estimates for all the subcarriers of interest. In all the proposed schemes in this paper, the filter parameters can be fixed and robust performance is obtained even when mismatched SNR and channel statistics are used in the filter parameter calculations.

We investigate the effects of performing channel estimation on an OFDM system in AWGN and Rayleigh channels using non-ideal interpolating and decimating filters. Pilots are used to estimate the combined response of the channel and the filters. By making use of the standard Gaussian approximation to the interference for the AWGN channel, we illustrate how to estimate the symbol error rate of an M-QAM system. By using an improved Gaussian approximation for the Rayleigh fading channel for error rate estimation, we show that this is equivalent to making the assumption that the interference is Gaussian noise experienced in a Rayleigh fading channel. It is shown that the predicted symbol error rate is close to that produced by simulations for a 64QAM modulated OFDM system based on the wireless LAN system described in IEEE 802.11a.

An added pilot semi-blind (APSB) scheme used for channel identification of OFDM systems, which have been adopted for DAB and DVB-T, is proposed. This scheme uses pilot symbols that are added directly to the data symbols and hence does not require any additional bandwidth for its implementation. To remove the pilots, a two dimensional Wiener filter is used for the purpose of channel interpolation by using successive iterations. Although its complexity increases with additional iterations, the APSB scheme is found to compare favorably to existing blind schemes proposed for the OFDM system in terms of bit error rate and convergence.