Compute-Forward Multiple Access (CFMA) is a multiple access transmission scheme based on Compute-and-Forward (CF), which allows the receiver to first decode linear combinations of the transmitted signals and then solve for individual messages. This paper extends the CFMA scheme to a two-user Gaussian multiple-input multiple-output (MIMO) multiple access channel (MAC). We propose the CFMA Serial Coding Scheme (SCS) and the CFMA Parallel Coding Scheme (PCS) with nested lattice codes. We first derive the expression of the achievable rate pair for MIMO MAC with CFMA-SCS. We prove a general condition under which CFMA-SCS can achieve the sum capacity of the channel. Furthermore, this result is specialized to single-input multiple-output (SIMO) and 2-by-2 diagonal MIMO multiple access channels, for which more explicit sum capacity-achieving conditions on power and channel matrices are derived. We then study the achievable rate of CFMA-PCS by using an equivalent SIMO model, and analyze its sum capacity-achieving conditions. Numerical results are provided for the performance of CFMA-SCS and CFMA-PCS in different channel conditions. In general, CFMA-PCS has better sum capacity achievability, although with a higher computational complexity for encoding and decoding.

Motivated by the constant modulus property of frequency shift keying (FSK) based waveforms and the stabilisation of its radar performance with an increase in the number of subpulses, in this paper an FSK-based dynamic subpulse number joint communications and radar waveform design is proposed. From a communications point of view, the system operates based on traditional FSK modulation. From a sensing point of view, although the subpulses are continuously generated and transmitted, radar waveforms are dynamically formed by monitoring the flatness of the spectrum which in return guarantees the accuracy of the delay estimation. Other constraints on the waveform length are used to ensure satisfactory values of the root mean square time duration, ambiguity function sidelobe levels and prevent overly long waveforms. To provide an estimation of the probability of generating extremely long waveforms, the distribution of the number of subpulses is approximated using a Brownian motion process and an existing result on its one-sided exit density. Numerical examples are provided to evaluate the accuracy of the approximate distribution, as well as the ambiguity function sidelobe levels and the delay and Doppler shift estimation performance of the transmitted waveforms.

Motivated by the ideal peak-to-average-power ratio and radar sensing capability of traditional frequency-coded radar waveforms, this paper considers the frequency shift keying (FSK) based waveform for joint communications and radar (JCR). An analysis of the probability distributions of its ambiguity function (AF) sidelobe levels (SLs) and peak sidelobe level (PSL) is conducted to study the radar sensing capability of random FSK. Numerical results show that the independent frequency modulation introduces uncontrollable AF PSLs. In order to address this problem, the initial phases of waveform sub-pulses are designed by solving a min-max optimisation problem. Numerical results indicate that the optimisation-based phase design can effectively reduce the AF PSL to a level close to well-designed radar waveforms while having no impact on the data rate and the receiver complexity. For large numbers of waveform sub-pulses and modulation orders, the impact on the error probability is also insignificant.

Cell-free wireless networks have attracted significant interest for their ability to eliminate cell-edge effects and deliver uniformly high service quality through macro-diversity. In this paper, we develop an algorithm to jointly optimize uplink transmit powers and dynamic user-centric access point (AP) clusters in a centralized cell-free network. This approach aims to efficiently mitigate inter-user interference and achieve higher max-min signal-to-interference-plus-noise ratio (SINR) targets for users. To this end, we re-purpose an iterative power control algorithm based on non-linear Perron-Frobenius theory and prove its convergence for the maximum ratio combiner (MRC) receiver under various AP subset selection schemes. We further provide analytical results by framing the joint optimization as a conditional eigenvalue problem with power and AP association constraints, and leveraging Perron-Frobenius theory on a centrally constructed matrix. The numerical results highlight that optimizing each user’s serving AP cluster is essential to achieving higher max-min SINR targets with the simple MRC receiver.

This research presents a novel framework integrating Flexible-Duplex (FlexD) and Integrated Sensing and Communications (ISAC) technologies to address the challenges of spectrum efficiency and resource optimization in next-generation wireless networks. We develop a unified system model for a dual-functional radar-communication base station with multiple-input multiple-output capabilities, enabling dynamic uplink and downlink channel allocation. The framework maximizes network throughput while maintaining radar sensing performance, subject to signal-to-clutter-plus-noise ratio (SCNR) requirements and power constraints. Given the non-convex and combinatorial nature of the resulting optimization problem, we propose an iterative algorithm that converges to a locally optimal solution. Extensive simulations demonstrate the superiority of the proposed FlexD-ISAC framework compared to conventional half-duplex networks. Additionally, sensitivity analyses reveal the impact of SCNR requirements and power constraints on system performance, providing valuable insights for practical implementation. This work establishes a foundation for future research in dynamic, resource-efficient wireless systems that simultaneously support sensing and communication capabilities.

Compute-forward multiple access (CFMA) is a transmission strategy which allows the receiver in a multiple access channel (MAC) to first decode linear combinations of the transmitted signals and then solve for individual messages. Compared to existing MAC strategies such as joint decoding or successive interference cancellation (SIC), CFMA was shown to achieve the MAC capacity region for fixed channels under certain signal-to-noise (SNR) conditions without time-sharing using only single-user decoders. This paper studies the CFMA scheme for a two-user Gaussian fast fading MAC with channel state information only available at the receiver (CSIR). We investigate appropriate lattice decoding schemes to decode linear combinations with any integer coefficients in the fading MAC and derive the achievable rate pairs. We give a sufficient and necessary condition under which the proposed scheme can achieve the ergodic sum capacity. Furthermore, we investigate the impact of channel statistics on the capacity achievability of the CFMA scheme. In general, the sum capacity is achievable if the channel variance is small compared to the mean value of the channel strengths. Various numerical results are presented to illustrate the theoretical findings.

Cell-free wireless networks have garnered significant interest within the research community due to their potential to eliminate cell-edge effects and exploit macro-diversity. In this paper, we design algorithms to jointly optimize uplink transmit powers and dynamic user-centric clusters within a cell-free network. This strategy aims to effectively mitigate inter-user interference and attain spectral efficiency targets for users in a scalable manner. To serve this goal, we re-purpose a classic iterative algorithm and prove its convergence for both the maximum ratio combiner (MRC) and linear minimum mean square error (LMMSE) receivers. We present several access point (AP) subset selection schemes of varying complexity and demonstrate how clustering requirements differ according to receiver capabilities. In particular, we show that optimizing the serving cluster for each user is crucial when using the simple MRC receiver.

This paper explores Physical-Layer Security (PLS) in Flexible Duplex (FlexD) networks, considering scenarios involving eavesdroppers. Our investigation revolves around the intricacies of the sum secrecy rate maximization problem, particularly when faced with coordinated and distributed eavesdroppers employing a minimum mean square error (MMSE) receiver. Our contributions include an iterative classical optimization solution and a graph neural network (GNNs) based unsupervised learning strategy. To the best of our knowledge, this work marks the initial exploration of GNNs for joint resource allocation for PLS applications. We also extend the GNN approach to address the absence of eavesdroppers' channel knowledge. Extensive numerical simulations highlight FlexD's superiority over half-duplex (HD) communications and the GNN approach's superiority in performance and time complexity over classical methods.

This research introduces Flexible Duplex (FlexD) networks, which offer dynamic time/frequency scheduling for secure uplink and downlink communications. We present novel communication models and analytical tools to evaluate the security aspects of User-User and User-Access Point connections within FlexD networks, thereby bridging the gap between FlexD networks and physical layer security. Our work establishes an analytical framework with closed-form expressions for secrecy outage probability (SOP) in FlexD networks. We also propose a power allocation and user selection strategy to maximize secrecy rates. Additionally, we explore the security performance of FlexD networks when partial channel state information (CSI) and reciprocal CSI channels are available. Extensive simulations validate our theoretical findings and highlight the security benefits compared to traditional half-duplex and full-duplex technologies. Our results suggest that the proposed communication strategies hold promise for securing future tactical wireless networks, including Device-to-Device (D2D), Machine-to-Machine (M2M), and cellular/mobile networks.

Machine learning (ML)-enhanced future communication networks are able to advance human-to-machine (H2M) applications by intelligent bandwidth prediction techniques to achieve bandwidth pre-allocation. Existing methods of H2M bandwidth prediction typically assume the stationary data stream over time. However, in the near future, communication networks are expected to support dynamic and heterogeneous applications. Since different H2M applications will exhibit different traffic distributions and loads across the day, an ML model learned on a specific H2M application at a particular network load will, therefore, be unable to adapt to changing applications and network loads. This will give rise to the phenomenon known as concept drift. This paper addresses concept drift in dynamic and heterogeneous networks supporting H2M applications by proposing a novel framework, the concept drift detection and adaptation (CDDA) framework, to respond and adapt to the concept drift rapidly. CDDA learns the traffic characteristics of H2M applications and combines offline and online learning processes to enhance H2M traffic prediction and improve band-width prediction performance. Results from our investigation using experimental traffic from H2M applications over a 10Gb/s passive optical network simulator show that CDDA can more rapidly respond to concept drift and better predict the bandwidth of changing H2M applications and network load.

This paper investigates the signal detection problem in colored noise with an unknown covariance matrix. In particular, we focus on detecting an unknown non-random signal by capitalizing on the leading eigenvalue of the whitened sample covariance matrix as the test statistic (a.k.a. Roy's largest root test). Since the unknown signal is non-random, the whitened sample covariance matrix turns out to have a non-central F-distribution. This distribution assumes a singular or non-singular form depending on whether the number of observations$p\lessgtr$the system dimensionality$m$. Therefore, we statistically characterize the leading eigenvalue of the singular and non-singular$F$-matrices by deriving their cumulative distribution functions (c.d.f.). Subsequently, they have been utilized in deriving the corresponding receiver operating characteristic (ROC) profiles. We also extend our analysis into the high dimensional domain. It turns out that, when the signal is sufficiently strong, the maximum eigenvalue can reliably detect it in this regime. Nevertheless, weak signals cannot be detected in the high dimensional regime with the leading eigenvalue.

Compute-forward multiple access (CFMA) is a transmission strategy which allows the receiver in a multiple access channel (MAC) to first decode linear combinations of the transmitted signals and then solve for individual messages. Compared to existing MAC strategies such as joint decoding or successive interference cancellation (SIC), CFMA was shown to achieve the MAC capacity region for fixed channels under certain signal-to-noise (SNR) conditions without time-sharing using only single-user decoders. This paper studies the CFMA scheme for a two-user Gaussian fast fading MAC with channel state information only available at the receiver (CSIR). We develop appropriate lattice decoding schemes for the fading MAC and derive the achievable rate pairs for decoding linear combinations of codewords with any integer coefficients. We give a sufficient and necessary condition under which the proposed scheme can achieve the ergodic sum capacity. Furthermore, we investigate the impact of channel statistics on the capacity achievability of the CFMA scheme. In general, the sum capacity is achievable if the channel variance is small compared to the mean value of the channel strengths. Various numerical results are presented to illustrate the theoretical findings.

Orthogonal time frequency space (OTFS) modulation has recently been identified as a suitable waveform for joint radar and communication systems. Focusing on the effect of data modulation on the radar sensing performance, we derive the ambiguity function (AF) of the OTFS waveform and characterize the radar global accuracy. We evaluate the behavior of the AF with respect to the distribution of the modulated data and derive an accurate approximation for the mean and variance of the AF, thus, approximating its distribution by a Rice distribution. Finally, we evaluate the global radar performance of the OTFS waveform with the OFDM waveform.

This paper focuses on waveform design for joint radar and communication systems and presents a new subset selection process to improve the communication error rate performance and global accuracy of radar sensing of the permutation based random stepped frequency radar waveform. An optimal communication receiver based on integer programming is proposed to handle any subset of permutations followed by a more efficient sub-optimal receiver based on the Hungarian algorithm. Considering optimal maximum likelihood detection, the block error rate is analyzed under both additive white Gaussian noise and correlated Rician fading. We propose two methods to select a permutation subset with an improved block error rate and an efficient encoding scheme to map the information symbols to selected permutations under these subsets. From the radar perspective, the ambiguity function is analyzed with regards to the local and the global accuracy of target detection. Furthermore, a subset selection method to reduce peak-to-sidelobe ratio (PSLR) is proposed by extending the properties of Costas arrays. Finally, the process of remapping the frequency tones to the symbol set used to generate permutations is introduced as a method to improve both the communication and radar performances of the selected permutation subset.

This paper investigates the signal detection problem in colored noise with an unknown covariance matrix. To be specific, we consider a scenario in which the number of signal bearing samples$(n)$is strictly smaller than the dimensionality of the signal space$(m)$. Our test statistic is the leading generalized eigenvalue of the whitened sample covariance matrix (a.k.a.$F- \mathbf{matrix}$) which is constructed by whitening the signal bearing sample covariance matrix with noise-only sample covariance matrix. The sample deficiency (i.e.,$m > n)$in turn makes this$F$-matrix rank deficient, thereby singular. Therefore, an exact statistical characterization of the leading generalized eigenvalue (l.g.e.) of a singular$F-\mathbf{matrix}$is of paramount importance to assess the performance of the detector (i.e., the receiver operating characteristics (ROC)). To this end, we employ the powerful orthogonal polynomial approach to derive a new finite dimensional c.d.f. expression for the l.g.e. of a singular F-matrix. It turns out that when the noise only sample covariance matrix is nearly rank deficient and the signal-to-noise ratio is$O(m)$, the ROC profile converges to a limit.

Hardware-limited task-based quantization is a new design paradigm for data acquisition systems equipped with scalar analog-to-digital converters using a small number of bits. By taking into account the system task, task-based quantizers can efficiently recover the desired parameters from the low-bit quantized observation. Current design and analysis frameworks for hardware-limited task-based quantization are only applicable to inputs with bounded support and uniform quantizers with non-subtractive dithering. In this paper, we propose a new framework based on generalized Bussgang decomposition that enables the design and analysis of hardware-limited task-based quantizers equipped with non-uniform scalar quantizers or have inputs with unbounded support. We consider the scenario in which the task is linear. Under this scenario, we derive new pre-quantization and post-quantization mappings for task-based quantizers with mean squared error (MSE) that closely matches the theoretical MSE.

We study the effectiveness of stochastic side information in deterministic online learning scenarios. We propose a forecaster to predict a deterministic sequence where its performance is evaluated against an expert class. We assume that certain stochastic side information is available to the forecaster but not the experts. We define the minimax expected regret for evaluating the forecaster’s performance, for which we obtain both upper and lower bounds. Consequently, our results characterize the improvement in the regret due to the stochastic side information. Compared with the classical online learning problem with regret scales with $O(\sqrt n )$, the regret can be negative when the stochastic side information is more powerful than the experts. To illustrate, we apply the proposed bounds to two concrete examples of different types of side information.

This paper examines the maximum code rate achievable by a data-driven communication system over some unknown discrete memoryless channel in the finite blocklength regime. A class of channel codes, called learning-based channel codes, is first introduced. Learning-based channel codes include a learning algorithm to transform the training data into a pair of encoding and decoding functions that satisfy some statistical reliability constraint. Data-dependent achievability and converse bounds in the non-asymptotic regime are established for this class of channel codes. It is shown analytically that the asymptotic expansion of the bounds for the maximum achievable code rate of the learning-based channel codes are tight for sufficiently large training data.

This paper derives the optimum detection rule for communication systems with n-bit phase quantization when data is differentially encoded at the transmitter. The proposed approach avoids the channel estimation problem at the receiver. First, a maximum likelihood detection rule for block-2 detectors utilizing only two consecutive quantized observations at the channel output is obtained. Second, it is shown that the derived maximum likelihood detection rule continues to be optimum for the class of block-L detectors for L ≥ 3 when n = log2M, where M is the input alphabet size. Finally, utilizing the structure of the derived optimum detector, a message error probability expression is obtained for Rayleigh fading wireless channels. A simulation study is performed to illustrate the performance of the optimum detectors as well as the performance loss due to the lack of channel knowledge at the receiver. The proposed approach and the solutions presented in this paper provide an initial step to communicate with low-resolution ADCs without requiring receiver-side channel knowledge.

Compute-forward multiple access (CFMA) is a multiple access transmission scheme based on Compute-and-Forward (CF) which allows the receiver to first decode linear combinations of the transmitted signals and then solve for individual messages. This paper extends the CFMA scheme to a two-user Gaussian multiple-input multiple-output (MIMO) multiple access channel (MAC). We first derive the expression of the achievable rate pair for MIMO MAC with CFMA. We prove a general condition under which CFMA can achieve the sum capacity of the channel. Furthermore, this result is specialized to SIMO and 2-by-2 diagonal MIMO multiple access channels, for which more explicit sum capacity-achieving conditions on power and channel matrices are derived. Numerical results are also provided for the performance of CFMA on general MIMO multiple access channels.

Cell-free wireless networks have gained the interest of the research community in recent years due to their potential to provide good quality of service (QoS) to all users. However, this comes at the cost of complex signal processing and high computational demands from the network to actively suppress inter-user interference. In this paper, we undertake joint optimization of the uplink power and the dynamic clusters in a cell-free wireless network to achieve spectral efficiency targets for users in a scalable and efficient manner. To this end, we re-purpose a classic iterative algorithm and theoretically prove its convergence to our objectives. We introduce various base station (BS) subset selection schemes and study their performance, establishing a trade-off between the performance and computational cost of the algorithm while facilitating a distributed operation. The numerical results convey the favorable impact of allowing variable cluster size in a system that employs maximal ratio combining (MRC).

Flexible duplex networks allow users to dynamically employ uplink and downlink channels without static time scheduling, thereby utilizing the network resources efficiently. This work investigates the sum-rate maximization of flexible duplex networks. In particular, we consider a network with pairwise-fixed communication links. Corresponding combinatorial optimization is a non-deterministic polynomial (NP)-hard without a closed-form solution. In this respect, the existing heuristics entail high computational complexity, raising a scalability issue in large networks. Motivated by the recent success of Graph Neural Networks (GNNs) in solving NP-hard wireless resource management problems, we propose a novel GNN architecture, named Flex-Net, to jointly optimize the communication direction and transmission power. The proposed GNN produces near-optimal performance meanwhile maintaining a low computational complexity compared to the most commonly used techniques. Furthermore, our numerical results shed light on the advantages of using GNNs in terms of sample complexity, scalability, and generalization capability.

In this paper, we consider an underlay cognitive radio network assisted by dual-hop decode-and-forward (DF) relaying. For a general multi-user network, we adopt a max-min fairness relay selection scheme and analyse the outage probability when the channels are subject to independent and non-identical Nakagami-m fading. The relay network operates within the constraint imposed on the peak interference power tolerable by the primary receiver. We then analyse the asymptotic outage probability performance and illustrate the existence of i) the full-diversity order when the interference level at the primary user increases proportionally with the relay transmit power; and ii) an outage floor when the transmit powers of the relays are restricted by the primary receiver. We also analyse the outage probability with imperfect channel state information (CSI) and the average throughput over Rayleigh fading channels. Illustrative analytical results are accurately validated by numerical simulations.

In this paper, we focus on the sum-rate optimization problem in a general dual-hop relay network by considering the joint relay selection and power control in the presence of interference. First, we propose a new relay selection algorithm which has better sum-rate performance than the existing relay selection techniques. Then we combine relay selection and power control to propose a novel iterative algorithm based on the tight lower bound approximation which maximizes the achievable sum-rate. We also prove that for the special case of two-user networks, binary power allocation is optimum for at least two transmitting nodes. Extensive numerical examples are used to compare the performance of the proposed algorithm and to illustrate the accuracy of the analysis.

The polar receiver architecture is a receiver design that captures the envelope and phase information of the signal rather than its in-phase and quadrature components. Several studies have demonstrated the robustness of polar receivers to phase noise and other nonlinearities. Yet, the information-theoretic limits of polar receivers with finite-precision quantizers have not been investigated in the literature. The main contribution of this work is to identify the optimal signaling strategy for the additive white Gaussian noise (AWGN) channel with polar quantization at the output. More precisely, we show that the capacity-achieving modulation scheme has an amplitude phase shift keying (APSK) structure. Using this result, the capacity of the AWGN channel with polar quantization at the output is established by numerically optimizing the probability mass function of the amplitude. The capacity of the polar-quantized AWGN channel with$b_{1}$-bit phase quantizer and optimized single-bit magnitude quantizer is also presented. Our numerical findings suggest the existence of signal-to-noise ratio (SNR) thresholds, above which the number of amplitude levels of the optimal APSK scheme and their respective probabilities change abruptly. Moreover, the manner in which the capacity-achieving input evolves with increasing SNR depends on the number of phase quantization bits.

The use of 1-bit analog-to-digital converters (ADCs) is seen as a promising approach to significantly reduce the power consumption and hardware cost of multiple-input multiple-output (MIMO) receivers. However, the nonlinear distortion due to 1-bit quantization fundamentally changes the optimal communication strategy and also imposes a capacity penalty to the system. In this paper, the capacity of a Gaussian MIMO channel in which the antenna outputs are processed by an analog linear combiner and then quantized by a set of zero threshold ADCs is studied. A new capacity upper bound for the zero threshold case is established that is tighter than the bounds available in the literature. In addition, we propose an achievability scheme which configures the analog combiner to create parallel Gaussian channels with phase quantization at the output. Under this class of analog combiners, an algorithm is presented that identifies the analog combiner and input distribution that maximize the achievable rate. Numerical results are provided showing that the rate of the achievability scheme is tight in the low signal-to-noise ratio (SNR) regime. Finally, a new 1-bit MIMO receiver architecture which employs analog temporal and spatial processing is proposed. The proposed receiver attains the capacity in the high SNR regime.

The reconfigurable intelligent surface (RIS) is a promising technology that is anticipated to enable high spectrum and energy efficiencies in future wireless communication networks. This paper investigates optimum location-based RIS selection policies in RIS-aided wireless networks to maximize the end-to-end signal-to-noise ratio for product-scaling and sum-scaling path-loss models where the received power scales with theproductandsumof the transmitter-to-RIS and RIS-to-receiver distances, respectively. These scaling laws cover the important cases of end-to-end path-loss models in RIS-aided wireless systems. The random locations of all available RISs are modeled as a Poisson point process. To quantify the network performance, the outage probabilities and average rates attained by the proposed RIS selection policies are evaluated by deriving the distance distribution of the chosen RIS node as per the selection policies for both product-scaling and sum-scaling path-loss models. We also propose a limited-feedback RIS selection framework to achieve distributed network operation. The outage probabilities and average rates obtained by the limited-feedback RIS selection policies are derived for both path-loss models as well. The numerical results show notable performance gains obtained by the proposed RIS selection policies.

In this paper, we propose an adaptive opportunistic spatial modulation scheme (AOSM), in which, our unique opportunistic group-antenna-selection scheme is amalgamated with a simple phase alignment (PA) transmit pre-scaling to boost the transmit-diversity of SM. To characterize and analytically evaluate the AOSM, a comprehensive error performance analysis is presented with single antenna reception. We theoretically derive a tight upper-bound for the symbol error probability (SEP) in closed-form based on an improved union-bound. The exact explicit asymptotic formula of the SEP at high SNR is provided as well. These results demonstrate that the transmit-diversity of proposed AOSM-(multiple-input single-output) MISO scheme is not only successfully achieved, but also elevated to be equal to the number of antennas in each antenna group. Moreover, it also reveals an important performance trend: at high SNR, with a fixed number of transmit antennas and a given data-rate, the SEP performance of the AOSM-MISO scheme can be successively enhanced by utilizing less antennas groups and bigger size of the signal constellation, because of the increase in transmit-diversity. Furthermore, the PA-aided SM-MISO is a special case of our AOSM-MISO. We are the first to provide the exact transmit-diversity gain for this conventional system and show that the PA pre-scaling scheme only can not improve the transmit-diversity of SM. This result sharpens the empirical diversity approximation results proposed in the existing literature. Simulation results well validate the analytical derivations, and indicate that our AOSM system can effectively improve system performance with high transmit-diversity at low-complexity.

Several information-theoretic studies on channels with output quantization have identified the capacity-achieving input distributions for different fading channels with 1-bit in-phase and quadrature (I/Q) output quantization. However, an exact characterization of the capacity-achieving input distribution for channels with multi-bit phase quantization has not been provided. In this paper, we consider four different channel models with multi-bit phase quantization at the output and identify the optimal input distribution for each channel model. We first consider a complex Gaussian channel with$b$-bit phase-quantized output and prove that the capacity-achieving distribution is a rotated$2^{b}$-phase shift keying (PSK). The analysis is then extended to multiple fading scenarios. We show that the optimality of rotated$2^{b}$-PSK continues to hold under noncoherent fast fading Rician channels with$b$-bit phase quantization when line-of-sight (LoS) is present. When channel state information (CSI) is available at the receiver, we identify$\frac {2\pi }{2^{b}}$-symmetry and constant amplitude as the necessary and sufficient conditions for the ergodic capacity-achieving input distribution; which a$2^{b}$-PSK satisfies. Finally, an optimum power control scheme is presented which achieves ergodic capacity when CSI is also available at the transmitter.

In this paper, we focus on the problem of transmitter and receiver scheduling to maximize the achievable sum-rate of a flexible half-duplex network where nodes have the flexibility to either transmit, receive or be silent in a given time slot. We consider a network with multiple transmitters and receivers where each transmitter has specific information it needs to send to a set of receiving nodes. First, we conduct some structural analysis and show that the achievable sum-rate is maximized when each transmitter only transmits to a single receiver at a given time. Next, we consider one instance of the flexible network and by reducing the symmetric multiple receiver network to a single receiver network, we also show that the achievable sum-rate is maximized when either one transmitter or all the transmitters transmit. In fact, there exists a unique received signal-to-noise ratio at which the optimality changes from all-to-one. Finally, we design a novel low-cost algorithm that gives a sub-optimal solution to the achievable sum-rate maximization problem in a flexible half-duplex network. We also provide a comprehensive comparison of the proposed algorithm with respect to existing resource allocation techniques, and observe that our proposed algorithm provides significant sum-rate gains.

This paper presents a new joint radar and communication technique based on the classical stepped frequency radar waveform. The randomization in the waveform, which is achieved by using permutations of the sequence of frequency tones, is utilized for data transmission. A new signaling scheme is proposed in which the mapping between incoming data and waveforms is performed based on an efficient combinatorial transform called the Lehmer code. Considering the optimum maximum likelihood detection, the union bound and the nearest neighbour approximation on the communication block error probability is derived for communication in an additive white Gaussian noise channel. The results are further extended to incorporate the Rician fading channel model, of which the Rayleigh fading channel is presented as a special case. Furthermore, an efficient communication receiver implementation is discussed based on the Hungarian algorithm which achieves optimum performance with much less operational complexity when compared to an exhaustive search. From the radar perspective, two key analytical tools, namely, the ambiguity function and the Fisher information matrix are derived. Furthermore, accurate approximations to the Cramer-Rao lower bounds on the delay and Doppler estimation errors are derived based on which the range and velocity estimation accuracy of the waveform is analysed.

This paper establishes the capacity of additive white Gaussian noise (AWGN) channels with phase-quantized output. We show that a rotated$2^{b}$-phase shift keying scheme is the capacity-achieving input distribution for a complex AWGN channel with b-bit phase quantization. The result is then used to establish the expression for the channel capacity as a function of average power constraint$P$and quantization bits$b$. The outage performance of phase-quantized system is also investigated for the case of Rayleigh fading when the channel state information (CSI) is only known at the receiver. Our findings suggest the existence of a threshold in the rate$R$, above which the outage exponent of the outage probability changes abruptly. In fact, this threshold effect in the outage exponent causes$2^{b}$-PSK to have suboptimal outage performance at high SNR.

In this paper, we investigate antenna selection strategies for multi-antenna wireless communication systems with low-resolution quantizers. We propose three sub-optimum but low-complexity antenna selection strategies and characterize their symbol error probability performance. We show that the strategy that selects the diversity branch with channel rotated constellation points being furthest away from the decision boundary is the best strategy in terms of symbol error probability. Using numerical analysis, we provide evidence to suggest that this selection strategy achieves the same diversity order as the optimum maximum likelihood (ML) detector under the same operating conditions. An extensive simulation study is performed to illustrate the accuracy of the derived results.

This paper presents a new waveform that is suitable for simultaneous data transmission and radar sensing. The approach considers a classical random stepped frequency radar waveform that is suitable for the emerging automotive radar application. The randomization in the waveform, which is achieved by using permutations of the sequence of frequency tones, is utilized for data transmission. More specifically, we propose a new Lehmer code based signaling model that modulates data based on the selection of the permutation. Considering maximum likelihood detection, the union bound on the communication block error probability is derived for baseband communication both in an additive white Gaussian noise (AWGN) channel and Rayleigh fading channel. Using the Hungarian Algorithm, an efficient implementation method for the communications receiver is also presented. From the radar perspective, we derive the ambiguity function, which is a key analytical tool in radar waveform design, and characterize the behavior of the Lehmer code based random stepped frequency radar waveform. Numerical examples are used to illustrate the performance of the proposed waveform.

We analyze the symbol error probability (SEP) of$M$-ary pulse amplitude modulation ($M$-PAM) receivers equipped with optimal low-resolution quantizers. We first show that the optimum detector can be reduced to a simple decision rule. Using this simplification, an exact SEP expression for quantized$M$-PAM receivers is obtained when Nakagami-$m$fading channel is considered. The derived expression enables the optimization of the quantizer and/or constellation under the minimum SEP criterion. Our analysis of optimal quantization for equidistant$M$-PAM receiver reveals the existence of error floor which decays at a double exponential rate with increasing quantization bits,$b$. Moreover, by also allowing the transmitter to optimize the constellation based on the statistics of the fading channel, we prove that the error floor can be eliminated but at a lower decay exponent than the unquantized case. Characterization of this decay exponent is provided in this paper. We also expose the outage performance limitations of SEP-optimal uniform quantizers. To be more precise, its decay exponent does not improve with$b$. Lastly, we demonstrate that the decay exponent of a quantized receiver can be complemented by receive antenna diversity techniques.

This paper studies the performance and key structural properties of the optimum location-based relay selection policy for wireless networks consisting of homogeneous Poisson distributed relays. The distribution of the channel quality indicator at the optimum relay location is obtained. A threshold-based distributed selective feedback policy is proposed for the discovery of the optimum relay location with finite average feedback load. It is established that the total number of relays feeding back obeys a Poisson distribution and an analytical expression for the average feedback load is derived. The analytical expressions for the average rate and outage probability with and without selective feedback are obtained for general path-loss models. It is shown that the optimum location-based relay selection policy outperforms other common relay selection strategies notably. It is also shown that utilizing location information from five relays on average is enough to achieve almost the same performance with the infinite feedback load case. As generalizations, isotropic Poisson point processes and heterogeneous source-to-relay and relay-to-destination links are also studied.

The novel reconfigurable intelligent surface (RIS) is an emerging technology which facilitates high spectrum and energy efficiencies in Beyond 5G and 6G wireless communication applications. Against this backdrop, this paper investigates two-way communications via reconfigurable intelligent surfaces (RISs) where two users communicate through a common RIS. We assume that uplink and downlink communication channels between two users and the RIS can be reciprocal. We first obtain the optimal phase adjustment at the RIS. We then derive the exact outage probability and the average throughput in closed-forms for single-element RIS. To evaluate multiple-element RIS, we first introduce a gamma approximation to model a product of Rayleigh random variables, and then derive approximations for the outage probability and the average throughput. For large average signal-to-interference-plus-noise ratio (SINR) $\rho$, asymptotic analXsis also shows that the outage decreases at the rate $(\log(\rho)/\rho)$ where L is the number of elements, whereas the throughput increases with the rate $\log(\rho)$.

This paper investigates a three-layer IoT-fog-cloud computing system to determine the optimum workload and power allocation at each layer. The objective is to minimize maximum per-layer latency (including both data processing and transmission delays) with individual power constraints. The resulting optimum resource allocation problem is a mixed-integer optimization problem with exponential complexity. Hence, the problem is first relaxed under appropriate modeling assumptions, and then an efficient iterative method is proposed to solve the relaxed but still non-convex problem. The proposed algorithm is based on an alternating optimization approach, which yields close-to-optimum results with significantly reduced complexity. Numerical results are provided to illustrate the performance of the proposed algorithm compared to the exhaustive search method. The latency gain of three-layer distributed IoT-fog-cloud computing is quantified with respect to fog-only and cloud-only computing systems.

In molecular communications, the direct detection of signaling molecules may be challenging due to the lack of suitable sensors and interference from co-existing substances in the environment. Motivated by examples in nature, we investigate an indirect detection mechanism using chemical reactions between the signaling molecules and a molecular probe to produce an easy-to-measure product at the receiver. The underlying reaction-diffusion equations that describe the concentrations of the reactant and product molecules in the system are non-linear and coupled, and cannot be solved in closed-form. To analyze these molecule concentrations, we develop an efficient iterative algorithm by discretizing the time variable and solving for the space variables in each time step. We also derive insightful closed-form solutions for a special case. The accuracy of the proposed algorithm is verified by particle-based simulations. Our results show that the concentration of the product molecules has a similar characteristic over time as the concentration of the signaling molecules. We analyze the bit error rate (BER) for a threshold detector and highlight that significant improvements in the BER can be achieved by carefully choosing the molecular probe and optimizing the detection threshold.

In this paper, we focus on maximization of the instantaneous sum-rate in flexible half-duplex networks, where nodes have the flexibility to choose to either transmit, receive or be silent in a given time slot. Since the corresponding optimization problem is NP-hard, we design low-cost algorithms that give sub-optimal solutions with good performance. We first consider two existing approximation techniques to simplify the sum-rate optimization problem: arithmetic-geometric means inequality and another utilising the tight lower bound approximation. We then propose a novel pattern search algorithm that performs close to exhaustive search but with significantly lower complexity. Comparing the performance of the proposed algorithm with respect to existing resource allocation techniques, we observe that our proposed algorithm provides significant sum-rate gains.

In this paper, we analyse the optimality of binary power allocation in a network that includes full-duplex communication links. Considering a network with four communicating nodes, two of them operating in half-duplex mode and the other two in full-duplex mode, we prove that binary power allocation is optimum for the full-duplex nodes when maximizing the sum rate. We also prove that, for half-duplex nodes binary power allocation is not optimum in general. However, for the two special cases, 1) the low signal-to-noise-plus-interference (SINR) regime and, 2) the approximation by the arithmetic mean-geometric mean inequality, binary power allocation is optimum for the approximated sum rate even for the half-duplex nodes. We further analyse a third special case using a symmetric network for which the optimum power allocation is binary, under a sufficient condition. Numerical examples are included to illustrate the accuracy of the results.

In this paper, we investigate the two-way communication between two users assisted by a reconfigurable intelligent surface (RIS). The scheme that two users communicate simultaneously over Rayleigh fading channels is considered. The channels between the two users and RIS can either be reciprocal or non-reciprocal. For reciprocal channels, we determine the optimal phases at the RIS to maximize the signal-to-interference-plus-noise ratio (SINR). We then derive exact closed-form expressions for the outage probability and spectral efficiency for single-element RIS. By capitalizing the insights obtained from the single-element analysis, we introduce a gamma approximation to model the product of Rayleigh random variables which is useful for the evaluation of the performance metrics in multiple-element RIS. Asymptotic analysis shows that the outage decreases at (log(ρ)/ρ)Lrate where L is the number of elements, whereas the spectral efficiency increases at log(ρ) rate at large average SINR p. For non-reciprocal channels, the minimum user SINR is targeted to be maximized. For single-element RIS, closed-form solution is derived whereas for multiple-element RIS the problem turns out to be non-convex. The latter one is solved through semidefinite programming relaxation and a proposed greedy-iterative method, which can achieve higher performance and lower computational complexity, respectively.

The energy detector and the sphericity test are two widely used spectrum sensing techniques that utilize different properties of the signal received at the secondary user terminal. In this paper we use meta analysis to combine these two techniques and derive two novel mixture detectors that outperform both techniques. Since the spectrum sensing capability of the energy detector is limited by the uncertain knowledge of the noise power, first, we analyze the performance of the energy detector with estimated noise power. We derive analytical expressions for the false alarm and the detection probabilities when the secondary user terminal is equipped with multiple antennas. Next, we apply meta analysis to combine the outputs of the energy detector and the sphericity test to derive two mixture detectors, namely, Fisher's method and the weighted z-transform method. Furthermore, we extend our analysis to consider cooperative spectrum sensing where multiple secondary user terminals cooperatively detect the presence of primary users. Based on the mixture detectors, we propose two new cooperative spectrum sensing techniques and derive simple analytical expressions for false alarm probabilities. Extensive numerical examples are used to illustrate the accuracy of our analysis and to highlight the performance gains obtained by the mixture detectors.

This paper considers a location-based optimal relay selection scheme for a relay-assisted wireless network where available decode-and- forward relays are distributed as a homogeneous Poisson point process. To solve an optimum relay selection problem, a central entity or the source requires information pertaining to all relay locations. Since the task of feeding this information back is impractical, we investigate a threshold-based limited feedback distributed relay selection policy. We show that the total number of relays feeding back is a Poisson distributed random variable. For a given threshold-based limited feedback distributed relay selection policy, we obtain analytical expressions for the average rate and the outage probability over the fading and no-fading communication scenarios. The derived analytical expressions are verified and the performance achieved by the proposed relay selection policy is illustrated through extensive simulations. It is observed that the limited feedback distributed relay selection policy can achieve almost the same performance with the optimum relay selection policy by only utilizing location information from a few number of relays.

This paper considers simultaneous wireless information and power transfer (SWIPT) over a dual-hop dynamic decode-and-forward (DF) relay network with the power-splitting (PS) energy harvesting protocol at the relay. The circuit power consumption (CPC), which includes power requirements for both decoding and encoding circuits, is considered at the relay. For a rate- dependent linear CPC model, we formulate an optimization problem to decide the optimal throughput, PS ratio, relay transmit power and time ratio for the source to relay transmission. Although the resultant optimization problem is nonconvex, we derive an efficient optimization algorithm, requiring significantly less floating point operations than an interior point method. Finally, we present numerical results which lead to some interesting insights for system design.

This paper investigates the location-based relay selection problem, where the source node chooses its relay from a set of spatially deployed decode-and-forward relays. The advantages of location-based relay selection are the elimination of excessive relay switching rate and the feedback reduction avoiding the requirement of having full channel state information at the source node. For a homogeneous Poisson point process of candidate relays, we first derive the distribution for the distance of the relay (relative to the source and destination nodes) selected by the optimum location-based relay selection policy. This result is independent of the functional form of the path-loss function as long as it is a non-increasing function of the transmitter-receiver separation. By utilizing the derived optimum relay distance distribution, we then obtain analytical expressions for the average rate and outage probability by considering the power-law decaying path-loss function for the no-fading and Rayleigh fading communication scenarios. It is observed that the optimum relay selection policy outperforms the other common selection strategies notably, including the ones choosing the relay closest to the source, the relay closest to the destination and the relay closest to the mid-point between source and destination.

Nanoparticle drug carriers play an important role in facilitating efficient targeted drug delivery, i.e., improving treatment success and reducing drug costs and side effects. However, the mobility of nanoparticle drug carriers poses a challenge in designing drug delivery systems. Moreover, healing results critically depend on the rate and time duration of drug absorption. Therefore, in this paper, we aim to design a controlled-release drug delivery system with a mobile drug carrier that minimizes the total amount of released drugs while ensuring a desired rate of drug absorption during a prescribed time period. We model the mobile drug carrier as a mobile transmitter, the targeted diseased cells as an absorbing receiver, and the channel between the transceivers as a time-variant channel since the carrier mobility results in a time-variant absorption rate of the drug molecules. Based on this, we develop a molecular communication (MC) framework to design the controlled-release drug delivery system. In particular, we develop new analytical expressions for the mean, variance, probability density function, and cumulative distribution function of the channel impulse response (CIR). Equipped with the statistical analysis of the CIR, we design and evaluate the performance of the controlled-release drug delivery system. Numerical results show significant savings in the amount of released drugs compared to a constant-release rate design and reveal the necessity of accounting for drug carrier mobility for reliable drug delivery.

This paper considers a low-resolution wireless communication system in which transmitted signals are corrupted by fading and additive noise. First, a universal lower bound on the average symbol error probability (SEP), correct for all M-ary modulation schemes, is obtained when the number of quantization bits is not enough to resolve M signal points. Second, in the special case of M-ary phase shift keying (M-PSK), the optimum maximum likelihood detector for equi-probable signal points is derived. Third, utilizing the structure of the derived optimum receiver, a general average SEP expression for the M-PSK modulation with n-bit quantization is obtained when the wireless channel is subject to fading with a circularly-symmetric distribution. Finally, an extensive simulation study of the derived analytical results is presented for general Nakagami-m fading channels. It is observed that a transceiver architecture with n-bit quantization is asymptotically optimum in terms of communication reliability if n ≥ log2M + 1. That is, the decay exponent for the average SEP is the same and equal to m with infinite-bit and n-bit quantizers for n ≥ log2M + 1. On the other hand, it is only equal to 1/2 and 0 for n = log2M and n ≥ log2M, respectively. Hence, for fading environments with a large value of m, using an extra quantization bit improves communication reliability significantly.

The problem of characterising the zero-error capacity region for multiple access channels even in the noiseless case has remained an open problem for over three decades. Motivated by this challenging question, a recently developed theory of nonstochastic information is applied to characterise the zero-error capacity region for the case of two correlated transmitters. Unlike previous contributions, this analysis does not assume that the blocklength is asymptotically large. Finally, a new notion of nonstochastic information is proposed for a non-cooperative problem involving three agents. These results are preliminary steps towards understanding information flows in worst-case distributed estimation and control problems.

Wireless energy transferring technology offers a constant and instantaneous power for low-power applications such as Internet of Things (IoT) to become an affordable reality. This paper considers simultaneous wireless information and power transfer (SWIPT) over a dual-hop decode-and-forward (DF) relay network with the power-splitting (PS) energy harvesting protocol at the relay. The relay is equipped with a finite capacity battery. The system performance, which is characterized by the average success probability of source to destination transmission, is a function of the resource allocation policy that selects the PS ratio and the transmit energy of the relay. We develop a mathematical framework to find an upper bound for the maximum the average success probability. The upper bound is formulated by a discrete state space Markov decision problem (MDP) and make use of a policy iteration algorithm to calculate it.

In this paper we consider wireless energy transfer for a point-to-point link. The energy transmitter sees a finite number of independent channel realizations, and, armed with (causal) knowledge of the channels, must decide how much energy to transmit in each time slot. The objective is to maximize the expected energy transferred to the receiver at the end of the time period. We show that the optimal energy allocation policy is binary: the transmitter sends no energy or all energy in a slot with this decision based on a simple threshold on the channel. As intuition demands, this threshold for transmission decreases as we move closer to the last available time slot. The performance of the optimal scheme is studied both analytically and numerically.

This paper investigates a user-fairness relay selection (RS) problem for decode-and-forward (DF) full-duplex (FD) relay networks, where multiple users cooperate with multiple relays in each coherence time. We consider two residual self-interference (RSI) models with or without direct links. We propose a sub-optimal relay selection (SRS) scheme which requires only the instantaneous channel state information (CSI) of source-to-relay and relay-to-destination links. To evaluate the performance, the outage probability of SRS is derived for different scenarios depending on RSI models and the availability of direct links. To further investigate, asymptotic expressions are derived for the high-transmit power regime. For comparison purposes, 1) the average throughputs of the FD and half-duplex (HD) modes are derived; 2) non-orthogonal transmission is considered and its performance is discussed with approximations; and 3) the impact of imperfect CSI is investigated with the aid of analysis. While simulation results are provided to verify the analytical results, they reveal interesting fundamental trends. It turns out that a significant throughput degradation occurs with FD mode over HD mode when self-interference is fully proportional to the transmit power. Since all users can communicate in the same coherence time with the FD mode, these joint RS schemes are useful for user-fairness low-latency applications.

We consider spectrum sensing in a cognitive radio network with arbitrary numbers of primary and secondary users. Based on the sphericity test, we first analyze the centralized spectrum sensing where all the data available at the secondary users are combined for the signal detection of primary users. We derive accurate approximations for the false alarm and detection probabilities that are also compared against the approximations already available in the literature. Next, we analyze the distributed spectrum sensing where only partial data from each secondary user are used in the signal detection of primary users. Two novel techniques, namely, the multisample sphericity test and metaanalysis, are proposed and analyzed. Instead of sending all the raw data received at the secondary user terminals, in the multisample sphericity test and metaanalysis, only one or two real numbers are required to be sent to a central processor to make a decision about the presence of primary users. Accurate analytical expressions on the false alarm and detection probabilities are derived, and numerical examples are provided to verify their accuracy. Receiver operating characteristic curves are also presented to compare the performance of the proposed methods.

Directional wireless power transfer (WPT) technology provides a promising energy solution to remotely recharge the Internet of things sensors using directional antennas. Under a harvest-then-transmit protocol, the access point can adaptively allocate the transmit power among multiple energy directions to maximize the social welfare of the sensors, i.e., downlink sum received energy or uplink sum rate, based on full or quantized channel gains reported from the sensors. However, such power allocation can be challenged if each sensor belongs to a different agent and works in a competitive way. In order to maximize their own utilities, the sensors have the incentives to falsely report their channel gains, which unfortunately reduces the social welfare. To tackle this problem, we design the strategy-proof mechanisms to ensure that each sensor’s dominant strategy is to truthfully reveal its channel gain regardless of other sensors’ strategies. Under the benchmark full channel gain reporting (CGR) scheme, we adopt the Vickrey-Clarke-Groves (VCG) mechanism to derive the price functions for both downlink and uplink, where the truthfulness is guaranteed by asking each sensor to pay the social welfare loss of all other sensors attributable to its presence. For the 1-bit CGR scheme, the problem is more challenging due to the severe information asymmetry, where each sensor has true valuation of full channel gain but may report the false information of quantized channel gain. We prove that the classic VCG mechanism is no longer truthful and then propose two threshold-based price functions for both downlink and uplink, where the truthfulness is ensured by letting each sensor pay its own achievable utility improvement due to its participation. The numerical results validate the truthfulness of the proposed mechanism designs.

This paper investigates the relay selection (RS) problem for multi-hop full-duplex relay networks where multiple source-destination (SD) pairs compete for the same pool of relays, under the attack of multiple eavesdroppers. To enhance the physical-layer security, within a given coherence time, our objective is to jointly assign the available relays at each hop to different SD pairs to maximize the minimum secrecy rate among all pairs. Two RS schemes, optimal RS and suboptimal RS (SRS), are proposed for two-hop networks based on global channel state information (CSI) and only SD pairs CSI, respectively. Since all users can communicate within the same coherence time, our joint RS schemes are important for the user-fairness and ultra-reliable low-latency communications. To evaluate the performance, the exact secrecy outage probability of the SRS scheme is derived under two residual self-interference models. The asymptotic analysis shows that the SRS scheme achieves full diversity. A relay-based jamming scheme is also proposed by using unassigned relays for user communications. Finally, the two-hop RS schemes and the analysis are extended to the general multi-hop network with multiple eavesdroppers. The numerical results reveal interesting fundamental trends where the proposed schemes can significantly enhance the secrecy performance.

This paper studies the relay selection (RS) problem for full-duplex (FD) relay networks with multiple source-destination (SD) pairs under the attack of colluding eavesdroppers. Based on available channel state information (CSI), both optimal relay selection (ORS) and suboptimal relay selection (SRS) schemes are considered to maximize the minimum secrecy rate among all pairs in order to enhance the physical-layer security. The secrecy performance of the more practical SRS scheme is then evaluated in terms of intercept probability and diversity order. The SRS achieves full diversity when the gains of the main-to- eavesdropper and the main-to-interference channels increase asymptotically.

We consider a molecular communication system comprised of a transmitter, an absorbing receiver, and an interference source. Assuming amplitude modulation, we analyze the dependence of the bit error rate (BER) on the duration of the detection interval, which is the time within one transmission symbol interval during which the receiver is active to absorb and count molecules. We then propose algorithms to obtain the optimal detection interval that minimizes the BER of the considered molecular communication system. Simulation and numerical evaluations are provided to highlight further insights into the optimal results. For example, we demonstrate that the optimal detection interval can be very small compared to the transmission symbol interval. Moreover, our numerical results show that significant BER improvements are achieved by using the optimal detection interval.

A novel opportunistic antenna selection aided spatial modulation, called opportunistic spatial modulation (OSM), is proposed, which exhibits an attractive system reliability enhancement with low complexity. Its unique features enable a comprehensive analytical framework, which is challenging to acquire with existing transmit-antenna-selection-aided spatial modulation (TASS-SM) schemes. Closed-form expression of improved union bound for the average symbol error probability (ASEP) of proposed OSM-MISO system is derived. Furthermore, we compare the proposed OSM with a prevalent existing TASS-SM scheme to confirm the feasibility and effectiveness of our scheme. Simulation results are provided to corroborate the analytical results.

A common approach to obtain channel state information for massive MIMO networks is to use the same orthogonal training sequences in each cell. We call this the full-pilot reuse (FPR) scheme. In this paper, we study an alternative approach where each cell uses different sets of orthogonal pilot (DOP) sequences. Considering uplink communications with matched filter (MF) receivers, we first derive the SINR in the large system regime where the number of antennas at the base station, the number of users in each cell, and training duration grow large with fixed ratios. For tractability in the analysis, the orthogonal pilots are drawn from Haar distributed random unitary matrices. The resulting expression is simple and easy to compute. As shown by the numerical simulations, the asymptotic SINR approximates the finite-size systems accurately. Secondly, we derive the user capacity of the DOP scheme under a simple power control and show that it is generally better than that of the FPR scheme.

In this paper, we present a new theoretical analysis of the performance of a distributed antenna system (DAS) with a constraint on the number of RF chains. We consider a cooperating network model with receiver sites distributed across the network. Each receive site is equipped with a limited number of RF chains that picks the best set of antennas based on the channel between the transmitter and the receiver. Considering a hybrid generalized selection/maximal ratio combining (GS/MRC) scheme, we derive new expressions for three important performance measures, namely, the achievable rate, symbol error probability (SEP) and outage probability. Our expressions are based on closed-form expressions we derive for the moment generating function (MGF) of the received signal-to-noise-ratio (SNR). We further analyse the high SNR error performance to accurately characterize the diversity order and the array gain of the system. Numerical examples demonstrate that our analytical results accurately reflect the simulations for a wide range of network scenarios. We highlight that the DAS with GS/MRC requires fewer RF chains and introduces a lower hardware complexity when compared to conventional MRC.

We consider distributed spectrum sensing in cognitive radio networks with multiple primary and secondary user terminals. Two novel techniques based on the sphericity test, namely, the multisample sphericity test and meta analysis, are analysed in such a scenario. Instead of sending all the raw data received at the secondary user terminals, as in the case with centralized spectrum sensing, in the multisample sphericity and meta analysis tests only one or two real numbers are required to be sent to the central processor to make a decision about the presence of primary users. Accurate analytical expressions on the false alarm probability are derived for both techniques and numerical examples are provided to verify their accuracy. Receiver operating characteristic (ROC) curves are also presented to compare the performance of the proposed methods and other simple fusion techniques.

We consider a distributed detection system for a wireless sensor network over slow-fading channels. Each sensor only has knowledge of quantized channel state information (CSI) which is received from the fusion center via a limited feedback channel. We then consider transmit power allocation at each sensor in order to maximize a J-divergence based detection metric subject to a total and individual transmit power constraints. Our aim is to jointly design the quantization regions of all sensors CSI and the corresponding power allocations. A locally optimum solution is obtained by applying the generalized Lloyd algorithm (GLA). To overcome the high computational complexity of the GLA, we then propose a low-complexity near-optimal scheme which performs very close to its GLA based counterpart. This enables us to explicitly formulate the problem and to find the unique solution despite the non-convexity of the optimization problem. An asymptotic analysis is also provided when the number of feedback bits becomes large. Numerical results illustrate that only a small amount of feedback is needed to achieve a detection performance close to the full CSI case.

This paper proposes an opportunistic spatial modulation (OSM) scheme where the transmit antennas are divided into K ≥ 1 equal groups, and the best antenna from each group is selected to form a K transmit antenna subset for implementing spatial modulation (SM). Thus, the activation of one antenna from the subset to transmit one of the M-ary modulation symbols achieves a data rate of log2(K) + log2(M) bits per channel use. Notably, special cases of OSM include conventional SM and pure single transmit antenna selection. To characterize and comparatively evaluate OSM, we first consider phase-shift keying modulation and derive a closed-form, improved union-bound of symbol error probability (SEP) with a single-antenna receiver. Explicit expressions for the SEP in the high signal-to-noise ratio regime are also presented. Extensions to quadrature amplitude modulation analysis and simulations of multiple-antenna reception case are also provided. Simulation results corroborate the analytical results and reveal the interesting interplay between the signal and spatial constellation diagrams. For a given number of transmit antennas and targeted data rate, asymptotically, the SEP can be minimized by using only one antenna group and the largest size of signal constellation, as this configuration achieves the full-diversity order.

This paper studies the stability and dynamic control of underlay mobile edge networks. First, the stability region for a multiuser edge network is obtained under the assumption of full channel state information. This result provides a benchmark figure for comparing performance of the proposed algorithms. Second, a centralized joint flow control and scheduling algorithm is proposed to stabilize the queues of edge devices while respecting the average and instantaneous interference power constraints at the core access point. This algorithm is proven to converge to a utility point arbitrarily close to the maximum achievable utility within the stability region. Finally, more practical implementation issues such as distributed scheduling are examined by designing efficient scheduling algorithms taking advantage of communication diversity. The proposed distributed solutions utilize mini-slots for contention resolution and achieve a certain fraction of the utility optimal point. The performance lower bounds for distributed algorithms are determined analytically. The detailed simulation study is performed to pinpoint the cost of distributed control for mobile edge networks with respect to centralized control.

This paper analyzes the performance of a multi-user multihop relay network using a low complexity decentralized relay selection (DRS) scheme for decode-and-forward cooperative networks. We carry out a rigorous diversity order analysis, with Nakagami-m fading and pathloss and show that the DRS scheme achieves full diversity while maintaining a complexity that is quadratic in the number of users, quadratic in the number of relays and independent of the number of hops. For a special case of two-user networks we derive exact closed-form expressions for the outage probability by considering the order statistics. Furthermore, we extend our analysis to consider interfering relay networks and derive an accurate lower bound on the outage of an arbitrary network user. Based on the lower bound we also show how the outage probability saturates in the high signal-to-interference-plus-noise ratio regime. Extensive numerical examples are used to illustrate the accuracy of the analysis and to highlight the use of the DRS scheme in multi-user multihop relay networks.

This paper investigates the relay selection (RS) problem in full-duplex (FD) wireless networks with multiple users and multiple common relays. We consider three self-interference models at FD relays. For amplify-and-forward (AF) and decode- and-forward (DF) relaying, the exact and asymptotic expressions for the outage probability are derived over Rayleigh fading channels with distance-dependent path loss for three RS schemes: i) optimal RS (ORS); ii) naive RS; and iii) random RS. Simulation results are provided to verify the analytical results.

This paper considers an energy harvesting (EH) relay network in which the relay node harvests energy using the hybrid EH protocol, which is a composite of power-splitting (PS) and time- switching (TS) protocols. As an energy storage may help to reduce the randomness of the energy availability at the relay, we consider two cases; i) the relay does not have a long-term energy storage, and ii) the relay has an infinite capacity long-term energy storage. By maximizing the average success probability, the optimal PS ratio is analytically derived, and the optimal TS ratio is numerically calculated based on the optimal PS ratio. Numerical results of the success probability are validated with simulations.

In this paper, we analyze the outage and diversity performance of a low-complexity relay selection routing algorithm which applies to large-scale distributed decode-and-forward relay networks with two source-destination user pairs. We analyze a suboptimal decentralized relay selection (DRS) strategy that only utilizes local channel state information of the relays within a given hop, to select distinct multihop paths for each user pair. Specifically, we derive exact closed-form expressions for the outage probability and diversity order of the DRS algorithm which prove that the full diversity order is achieved with complexity that is quadratic with the number of relays in each hop. Illustrative analytical results are accurately validated by numerical simulations.

Reconfigurable antenna arrays provide a means for efficient use of the spatial domain in wireless communication systems. Despite its potential, the topic is only briefly explored in the literature. In this paper, we present a comprehensive theoretical analysis of the performance of reconfigurable systems. We consider a receiver equipped with multiple reconfigurable antennas that pick the best state based on the channel between the transmitter and the receiver. For such a system, we derive a new expression for the moment generating function (MGF) of the received signal-to-noise ratio by employing maximal ratio combining. Based on the MGF, we analyze three important performance measures, specifically, achievable rate, error probability, and outage probability. Furthermore, we conduct an asymptotic analysis incorporating the correlation between reconfigurable states and show that a reconfigurable system can achieve a diversity order of the number of antennas times the number of reconfigurable states. Finally, we discuss the applicability of reconfigurable antennas in novel wireless networks with large antenna arrays and distributed antenna systems, highlighting the performance gains and requirement for fewer RF chains.

Cooperative relays have been used in many wireless applications to reduce transmit power as well as adding receiver diversity. However, the energy efficiency trade-offs of relay networks have not been thoroughly investigated using a more complete energy model of the system. In this paper we present an energy model for a cooperative dual-relay system using decode-and-forward (DF) protocol with receiver diversity. Energy consumption is based on models of both analog and digital components for transmission and reception. A quartic transmit power amplifier (PA) energy equation is derived that is a function of the following parameters: relay location, relative transmit power allocation amongst the relays, spectral efficiency, transmission distance, pathloss, and target bit-error rate (BER). The energies of the remaining circuit components are added to form a multi-relay DF system energy equation, which is then numerically evaluated to minimize total energy by finding the optimal relay locations, relay transmit PA power allocations, and spectral efficiency. Results are compared to our previous work that considered the energy of direct transmission and a single-relay system, and the minimum-energy system as a function of source-destination distance is considered.

This paper analyzes the symbol error probability (SEP) of cluster-based cooperative networks where a finite cluster of base stations jointly detect multiple in-cluster users in the presence of out- of-cluster interference. For such a network, we derive new accurate upper and lower bounds on the SEP of the in-cluster users with Rayleigh fading, arbitrary path loss, and M-ary phase-shift keying modulation. We further derive new asymptotic expressions to accurately characterize the SEP saturation limit arising from out-of-cluster interference. To obtain deeper insights, we extend our analysis to consider Rician fading with line- of-sight paths from the in-cluster users to their nearest base stations. Numerical examples illustrate the accuracy of our results and highlight novel aspects of fading channels, path loss, cluster configurations, and user locations on the SEP of cluster-based cooperative cellular networks.

Under the conditions where performance in a massive MIMO network is limited by pilot contamination, the uplink signal-to-interference ratio (SIR) exhibits different distributions when using different pilot allocation schemes. By utilising different sets of orthogonal pilot sequences, as opposed to reused sequences amongst adjacent cells, the resulting SIR distribution is more favourable with respect to maximising the number of users on the network while maintaining a given quality of service (QoS) for all users. This paper provides a simple expression for uplink user capacity on such networks and presents uplink user capacity figures for both pilot allocation schemes for a selection of quality of service targets.

In this paper, we study a three-dimensional (3D) massive multiple-input-multiple-output (MIMO) system where a horizontal planar antenna array hovers in the sky, serving multiple single-antenna users in a cell on the ground. We consider the uplink where the users transmit to the antenna array utilising perfect uplink power control. Assuming pure line-of-sight (LoS) propagation conditions, the planar array performs receiver maximum-ratio-combining (MRC). We adopt a 3D freespace propagation channel model, which enables us to exploit both the azimuth and elevation dimensions of the space. Under such a setting, we analyse the intra-cell uplink interference. The effective single-user interference and total interference can then be modelled by Beta and Beta-mixture distributions, respectively. In particular, we point out that the height of the hovering antenna can be adjusted to minimise the interference in the system, and thus optimise all the signal-to-interference ratio (SIR) related performance metrics such as coverage and average throughput.

Wireless energy harvesting is one of the promising alternative techniques to power wireless networks. To improve the energy harvesting capability, energy beamforming has recently drawn significant attention. In this paper, we consider energy harvesting in a multi-user multi-input single-output (MU-MISO) network, and investigate two energy beamforming schemes which ensure fairness among the energy levels harvested by all users in the network. In particular, we investigate how to maximize the minimum harvested energy in the network by using i) a single beamforming vector; and ii) multiple beamforming vectors. We solve these problems by using semidefinite programming. The performance of different beamforming techniques is discussed with the aid of numerical simulations.

We consider cellular networks with co-operative clusters of neighboring base stations detecting multiple in-cluster users subject to interference from out-of-cluster users. We assume that the base stations, equipped with multiple antennas, are connected to a central processor in each cluster. For such a network, we first consider centralized processing where all the in-cluster user signals are sent to the central processor for linear minimum mean squared error (LMMSE) estimation. Next, we consider partially decentralized processing where the in-cluster user signals are locally estimated at each base station, and the local estimates are combined at the central processor. For both processing architectures, we derive new expressions for the achievable rate of an in-cluster user when the channels between the users and base stations are subject to independent Rayleigh fading and distance-dependent path loss. The solutions are based on accurate approximations we derive for the characteristic function (CF) and the probability density function (PDF) of each user's signal-to-interference-plus-noise ratios (SINRs). Numerical examples highlight the accuracy of the analysis and compare the performance of centralized and partially decentralized processing under different cluster scenarios.

In this paper, we consider a relay network over a flat-fading channel, where the relay has no fixed power supply and thus needs to replenish energy via wireless energy harvesting (EH) from the signals transmitted by the source. We propose a novel hybrid protocol, which is a combination of existing EH protocols, such as power splitting (PS) and time switching (TS). We formulate the optimization problems and derive some explicit results. In particular, we derive the optimal PS and TS ratios at the relay for all three EH protocols to achieve the maximum throughput for information transfer from the source to the destination for both decode-and-forward and amplify-and-forward relaying schemes. We show that the proposed hybrid protocol outperforms both PS and TS protocols.

This paper introduces a model to study both single tier and multitier wireless communication systems consisting of a multitude of wireless access points (AP), and operating according to the classical opportunistic beamforming framework. The AP locations in the proposed network model are determined by using planar Poisson point processes. The extreme value distribution of signal-to-interference-plus-noise-ratio (SINR) on a beam is of fundamental importance for obtaining performance bounds for such an opportunistic communication system. Two tight distribution approximation results are provided for the distribution of maximum SINR on a beam, which is hard to obtain due to correlation structure of the underlying inter-AP interference field, using key tools from stochastic geometry. These approximations hold for general path loss models that satisfy some mild conditions. Simulations and numerical evaluations are presented to validate the results, to provide further insights into the derived approximate maximum beam SINR distributions, and to illustrate the utility of these approximations in obtaining performance bounds for opportunistic communication systems having multiple interfering APs. In particular, key performance measures such as beam outage probability and ergodic aggregate data rate of an AP are derived by utilizing the approximated distributions.

We consider a wireless energy harvesting (EH) relay network. Relays without embedded energy supply harvest energy from the source node. Considering a time switching protocol, performance measures such as average signal-to-noise ratio (SNR), outage and throughput are analyzed. Subsequently, optimal EH time is selected in order to maximize the throughput. Then, a multiple-relay network is considered with relay selection, which can achieve full diversity at any EH time. All theoretical results are validated by numerical simulations.

We examine the sum capacity of a cluster-based cooperative cellular network where a linear minimum-mean squared error (LMMSE) estimator is deployed across a cluster of base stations to estimate multiple in-cluster users. Different from previous works, we examine the impact of interference from out-of-cluster users whose transmit power scales with that of the in-cluster users. For such a network, we derive the sum capacity of the in-cluster users with independent Rayleigh fading and arbitrary path loss. The sum capacity expression is based on accurate approximations we derive for the characteristic function and the probability density function of the in-cluster users' signal-to-interference-plus-noise ratios (SINRs). Numerical examples demonstrate that our new analytical expressions accurately characterize the impact of out-of-cluster interference and cluster size on the sum capacity. We observe that out-ofcluster interference results in a sum capacity saturation regime when the transmit power is large. We also illustrate that the saturation threshold increases with the cluster size. Furthermore, we examine the sum capacity under different path loss exponents which highlights the significance of out-of-cluster interference.

We consider a cooperative cellular network with multiple users transmitting within a cooperative cluster of multiple antenna base stations. Different from global cooperation where all the signal processing is performed at a central processor, we analyze a distributed processing architecture that performs the estimation of user symbols in two steps, namely, 1) Local LMMSE estimation of the user symbols at each base station, and 2) Central combining of all the estimates from the base stations. For such a network, we derive new expressions for the capacity of a given user with independent Rayleigh fading and arbitrary path loss between the users and all the antennas at the base stations. Our capacity expression is based on accurate approximations we derive for the characteristic function and the probability density function of the users' signal-to-interference-plus-noise ratios (SINRs). Numerical examples demonstrate that our analytical solutions accurately approximate the exact capacity. Furthermore, we highlight that the distributed approach introduces less overhead to the network compared with global cooperation. We note that the performance gap is small when the network is lightly loaded.

We consider a wireless energy harvesting (EH) relay network, in which the relay harvests energy from the source node. Considering a power splitting (PS) protocol, performance measures such as average signal-to-noise ratio (SNR), outage probability and throughput are analyzed. Subsequently, optimal PS ratios are analytically derived in order to maximize the instantaneous SNR and throughput. All theoretical results are validated by numerical simulations.

In this paper, we focus on the ergodic downlink sum-rate performance of a system consisting of a set of heterogeneous users. We study three user selection schemes to group near-orthogonal users for simultaneous transmission. The first scheme is a random selection policy that achieves fairness, but does not exploit multi-user diversity. The second scheme is a greedy selection policy that fully exploits multi-user diversity, but does not achieve fairness, and the third scheme achieves fairness while partially exploiting multi-user diversity. We also consider two beamforming methods for data transmission, namely, maximum-ratio transmission and zero-forcing beamforming. In all scheduling schemes studied in the paper, there is a key parameter that controls the degrees of orthogonality of channel directions between co-scheduled users. We focus on optimally setting this parameter for each scheduling scheme such that the ergodic downlink sum-rate is maximized. To this end, we derive analytical expressions for the ergodic downlink sum-rate considering each scheduling scheme. Numerical results are also presented to provide further insights.

We consider physical-layer security in a novel MISO cooperative overlay cognitive radio network (CRN) with a single eavesdropper. We aim to design an artificial noise (AN) aided secondary transmit strategy to maximize the joint achievable secrecy rate of both primary and secondary links, subject to a global secondary transmit power constraint and guaranteeing any transmission of secondary should at least not degrade the receive quality of primary network, under the assumption that global CSI is available. The resulting optimization problem is challenging to solve due to its non-convexity in general. A computationally efficient approximation methodology is proposed based on the semidefinite relaxation (SDR) technique and followed by a two-step alternating optimization algorithm for obtaining a local optimum for the corresponding SDR problem. This optimization algorithm consists of a one-dimensional line search and a non-convex optimization problem, which, however, through a novel reformulation, can be approximated as a convex semidefinite program (SDP). Analysis on the extension to multiple eavesdroppers scenario is also provided. Simulation results show that the proposed AN-aided joint secrecy rate maximization design (JSRMD) can significantly boost the secrecy performance over JSRMD without AN.

In this paper, we study an uplink Poisson cellular network model, where the BSs and mobiles are located according to two independent Poisson processes with different densities. A Voronoi tessellation is formed based on the locations of BSs, dividing the network plane into cells. Each mobile communicates with the closest BS. Assuming an orthogonal channelised multiple access technique and a random channel reuse scheme, a BS randomly assigns its available channels to the users within its cell in a one-to-one manner. Under this setting, uplink coverage probability in a channel is studied. In addition, we introduce the concept of user spatial blocking, which occurs when a user has no access to any channel due to limited number of channels available in each cell. Approximate expressions for user spatial blocking probability and uplink coverage probability are derived in this paper.

We consider a multi-cell cooperative network where a cluster of base stations jointly detect the signals from multiple users transmitting within the cluster. Different from previous works, we examine the impact of interference from out-of-cluster users whose transmit power scales with that of the in-cluster users. For such a network, we derive new upper and lower bounds on the uncoded bit error probability (BEP) of the in-cluster users with independent Rayleigh fading and arbitrary path loss. We observe that our lower bound accurately approximates the BEP at low signal-to-noise ratios (SNRs), whereas the upper bound is accurate at high SNRs. Our analytical bounds accurately characterize the impact of out-of-cluster interference and cluster size on the BEP. Specifically, we highlight that out-of-cluster interference results in a BEP saturation regime when the transmit power is large. We also show that the saturation threshold increases with the cluster size.

In this paper, we consider a multi-cell multi-user MISO broadcast channel. The system operates according to the opportunistic beamforming framework in a multi-cell environment with variable number of transmit beams (may alternatively be referred as the transmission rank) at each base station. The maximum number of co-scheduled users in a cell is equal to its transmission rank, thus increasing it will have the effect of increasing the multiplexing gain. However, this will simultaneously increase the amount of interference in the network, which will decrease the rate of communication. This paper focuses on optimally setting the transmission rank at each base station such that a set of Quality of Service (QoS) constraints, that will ensure a guaranteed minimum rate per beam at each base station, is not violated. Expressions representing the achievable region of transmission ranks are obtained considering different network settings. The achievable transmission rank region consists of all achievable transmission rank tuples that satisfy the QoS constraints. Numerical results are also presented to provide further insights on the feasibility problem.

This paper studies downlink coverage maximization for cellular networks in which base station (BS) locations are modeled using a spatial Poisson point process, considering three different coverage models, and under constraints on transmit power, BS density and transmit power density. Firstly, the coverage optimization problem is solved analytically for the first coverage model that focuses on noise-limited communication by ignoring interference and random fading effects. This model provides useful insights into the significance of bounded path loss models to obtain meaningful solutions for this problem. The other two coverage models are based on the users' received signal-to-interference-plus-noise-ratio (\sinr) from their associated BSs. For these models, it is shown that the coverage optimization problem can be reduced to a constrained single dimensional optimization problem without any loss of optimality. The related solutions can be obtained with limited computational complexity by resorting to a numerical search over a compact subset of candidate values. Bounds on the optimum BS density are also provided to further truncate the search space. All results are derived for general bounded path loss models. Specific applications are also illustrated to provide further design insights and to highlight the importance of using bounded path loss models for coverage analysis.

This paper studies the outage capacity of a network consisting of a multitude of heterogeneous mobile users and operating according to the classical opportunistic beamforming framework. The base station is located at the center of the cell, which is modeled as a disk of finite radius. The random user locations are modeled using a homogeneous spatial Poisson point process. The received signals are impaired by both fading and location dependent path loss. For this system, we first derive an expression for the beam outage probability. This expression holds for all path loss models that satisfy some mild conditions. Then, we focus on two specific path loss models (i.e., an unbounded model and a more realistic bounded one) to illustrate the applications of our results. In the large system limit, where the cell radius tends to infinity, the beam outage capacity and its scaling behavior are derived for the selected specific path loss models. This paper also studies opportunistic schemes that achieve fairness among the heterogeneous users. Numerical evaluations are performed to give further insights and to illustrate the applicability of the outage capacity results even to a cell having a small finite radius.

In this paper, we study feedback optimization problems that maximize the users' signal to interference plus noise ratio (SINR) in a two-cell multiple-input multiple-output broadcast channel. Assuming the users learn their direct and interfering channels perfectly, they can feed back this information to the base stations (BSs) over the uplink channels. The BSs then use the channel information to design their transmission scheme. Two types of feedback are considered: 1) analog and 2) digital. In the analog feedback case, the users send their unquantized and uncoded channel state information (CSI) over the uplink channels. In this context, given a user's fixed transmit power, we investigate how he/she should optimally allocate it to feed back the direct and interfering (or cross) CSI for two types of BS cooperation schemes, namely, multicell processing (MCP) and coordinated beamforming. In the digital feedback case, the direct and cross link channel vectors of each user are quantized separately, each using the random vector quantization scheme, with different size codebooks. The users then send the index of the quantization vector in the corresponding codebook to the BSs. Similar to the feedback optimization problem for the analog feedback, we investigate the optimal bit partitioning for the direct and interfering link for both types of cooperation. We focus on regularized channel inversion precoding structures and perform our analysis in the large system limit in which the number of users per cell (K) and the number of antennas per BS (N) tend to infinity with their ratio β = (K/N) held fixed. We show that for both types of cooperation, for some values of interfering channel gain, usually at low values, no cooperation between the BSs is preferred. This is because, for these values of cross channel gain, the channel estimates for the cross link are not accurate enough for their knowledge to contribute to improving the SINR and there is no benefit in doing BS cooperation under that condition. We also show that for the MCP scheme, unlike in the perfect CSI case, the SINR improves only when the interfering channel gain is above a certain threshold.

The Wyner interference network is a popular model used in research on cellular networks due to its simplicity and analytical tractability. In this paper, the optimal power allocation strategies in symmetric one- and two-sided Wyner models are investigated. We determine a sufficient condition for binary power control (BPC) to be optimal that can be applied to the one-sided symmetric model. We consider binary power schemes for the symmetric two-sided Wyner network. Using a method of grouping links and performing a piecewise comparison of the group rates, we are able to determine the optimal power policy that maximizes the network sum rate. The result of the optimization can be expressed as follows for both types of networks: When the interfering channel gain √ϵ is small, it is optimal (in the class of binary schemes) to have all links on; otherwise, alternate links are switched off to remove interference. We characterize the critical values of ϵ where the transitions occur.

In this paper, we consider a heterogeneous network where the statistics of fading channels between the Base Station (BS) and various users are different. The network can operate either in a single-user mode (i.e. the BS transmits to one user at a time) or in a multi-user mode via opportunistic beamforming. Given the profile of users' fading channel statistics, we are able to derive the exact downlink capacities associated with different modes assuming a 2-Tx BS, as well as determine the optimal mode that maximizes the throughput. The switching point between transmission modes depends on the signal-to-noise ratio (SNR), the distribution of users and the path loss of the fading channel. Simulations are provided to verify the derived expressions and to illustrate the movement of the switching point. In addition, a method is proposed to address the fairness issue in user scheduling, which allows for a reasonable trade-off between performance and fairness.

This paper studies the outage capacity of a network consisting of a multitude of heterogenous mobile users, and operating according to the classical opportunistic beamforming framework. The base station is located at the center of the cell, which is modeled as a disk of finite radius. The random user locations are modeled using a homogenous spatial Poisson point process. The received signals are impaired by both fading and location dependent path loss. For this system, we first derive an expression for the beam outage probability. This expression holds for all path loss models that satisfy some mild conditions. Then, we focus on two specific path loss models (i.e., an unbounded model and a more realistic bounded one) to illustrate the applications of our results. In the large system limit where the cell radius tends to infinity, the beam outage capacity and its scaling behavior are derived for the selected specific path loss models. It is shown that the beam outage capacity scales logarithmically for the unbounded model. On the other hand, this scaling behavior becomes double logarithmic for the bounded model. Intuitive explanations are provided as to why we observe different scaling behavior for different path loss models. Numerical evaluations are performed to give further insights, and to illustrate the applicability of the outage capacity results even to a cell having a small finite radius.

This paper studies coverage maximization for cellular networks in which base station (BS) locations are modeled using a homogenous spatial Poisson point process, and user locations are arbitrary. A user is covered for communication if its received signal-to-interference-plus-noise-ratio (SINR) is above a given threshold value. Two coverage models are considered. In the first model, the coverage of a user is determined based on the received SINR only from the nearest BS. The nearest BS happens to be the BS maximizing the received SINR without fading. In the second model, on the other hand, the coverage of a user is determined based on the maximum SINR from all BSs in the network. The objective is to maximize the coverage probability under the constraints on transmit power density (per unit area). Using stochastic geometry, coverage probability expressions for both coverage models are obtained. Using these expressions, bounds on the coverage maximizing power per BS and BS density are obtained. These bounds truncate the search space of the optimization problem, and thereby simplify the numerical evaluation of optimum BS power and density values considerably. All results are derived for general bounded path loss models satisfying some mild conditions. Specific applications are also illustrated to provide further insights into the optimization problem of interest.

We present new analytical expressions for optimal multiuser detection in the uplink of a cellular network with base station cooperation. We consider a cooperative multicell scenario where multiple base stations jointly detect the signals from multiple users distributed throughout the network. For such a network, we derive new upper and lower bounds on the uncoded bit error probability (BEP) with independent Rayleigh fading and arbitrary path loss between the users and the base stations. Our analytical results are further simplified to produce closed-form bounds on the BEP when the path loss from a given user to each base station is distinct. We demonstrate that the lower bound is accurate at low signal-to-noise ratios (SNRs) while the upper bound is accurate at medium to high SNRs.

In this paper, we propose a dynamic multi-user MIMO beamforming and scheduling scheme, where co- scheduled users are served by mutually orthogonal beams. Our scheme differs from the traditional opportunistic beamforming and scheduling scheme in two ways. Firstly, multiple sets of orthogonal beams are employed as candidate beamforming directions (instead of one as in the traditional opportunistic beamforming case) and only users whose preferred beams are within the same set of orthogonal beams can be co-scheduled. This can effectively improve co-scheduled users' SINR. Secondly, the number of co-scheduled users is variable, which will change as the SNR and/or the total number of users in the system change in order to achieve a higher throughput. In addition, some analytical results are provided for the interference limited regime (i.e. at high SNR). In particular, for a reasonably large number of users, the sum-rate of our scheme grows logarithmically as the total number of users increases and by introducing multiple sets of orthogonal beams (or what we call precoder diversity), a gain in sum rate can be achieved.

This paper focuses on a cognitive radio network consisting of a secondary user (SU) equipped with orthogonal frequency-division multiplexing (OFDM) technology able to access N randomly fading frequency bands for transmitting delay-insensitive (e.g. data) as well as delay-sensitive (e.g. voice or video) data. Each band is licensed to a distinct delay-sensitive primary user (PU) interested in meeting a minimum rate guarantee for delay-sensitive services with a maximum allowable primary outage probability or a primary outage constraint (POC) . Typically, a PU is oblivious to the SU's existence and has its own power policy based on the channel side information (CSI) of its direct gain between the PU transmitter and the PU receiver only. Under the assumption that the SU knows PUs' power policies and CSI of the entire network, we solve the SU's ergodic capacity maximization problem subject to SU's average transmit power and outage probability constraints (SOC) and all POCs or the so-called service-outage based capacity maximization for SU with POCs. We use a rigorous probabilistic power allocation technique that allows us to derive optimal power policies applicable to both continuous and discrete fading channels. Also, a suboptimal power control policy is proposed in order to avoid the high computational complexity of the optimal policy when N is large. Numerical results are presented to illustrate the performance of the power allocation algorithms.

We consider a multiuser system where a single transmitter equipped with multiple antennas (the base station) communicates with multiple users each with a single antenna. Regularized channel inversion is employed as the precoding strategy at the base station. Within this scenario we are interested in the problems of power allocation and user admission control so as to maximize the system throughput, i.e., which users should we communicate with and what power should we use for each of the admitted users so as to get the highest sum rate. This is in general a very difficult problem but we do two things to allow some progress to be made. Firstly we consider the large system regime where the number of antennas at the base station is large along with the number of users. Secondly we cluster the downlink path gains of users into a finite number of groups. By doing this we are able to show that the optimal power allocation under an average transmit power constraint follows the well-known water filling scheme. We also investigate the user admission problem which reduces in the large system regime to optimization of the user loading in the system.

This paper studies the structure of downlink sum-rate maximizing selective decentralized feedback policies for opportunistic beamforming under finite feedback constraints on the average number of mobile users feeding back. First, it is shown that any sum-rate maximizing selective decentralized feedback policy must be a threshold feedback policy. This result holds for all fading channel models with continuous distribution functions. Second, the resulting optimum threshold selection problem is analyzed in detail. This is a nonconvex optimization problem over finite-dimensional Euclidean spaces. By utilizing the theory of majorization, an underlying Schur-concave structure in the sum-rate function is identified, and the sufficient conditions for the optimality of homogenous threshold feedback policies are obtained. Applications of these results are illustrated for well-known fading channel models such as Rayleigh, Nakagami, and Rician fading channels. Rather surprisingly, it is shown that using the same threshold value at all mobile users is not always a rate-wise optimal feedback strategy, even for a network in which mobile users experience statistically the same channel conditions. For the Rayleigh fading channel model, on the other hand, homogenous threshold feedback policies are proven to be rate-wise optimal if multiple orthonormal data carrying beams are used to communicate with multiple mobile users simultaneously.

Channel state information (CSI) at the base stations (BSs) can significantly increase the spectral efficiency in single and multi-cell broadcast channels. Assuming the users learn their direct and interfering channels, they can feed back this information to the BSs over the uplink. The BSs then form channel estimates which they use to design their transmission scheme. Clearly, the quality of these estimates affects system performance. In this paper, we study limited feedback in a two-cell MIMO broadcast channel. For a fast transfer of CSI, we consider the analog feedback scheme where the users send their unquantized and uncoded CSI over the uplink channels. In this context, given a fixed user's transmit power, we investigate how a user should optimally allocate this power to feed back the direct and interfering CSI for two types of base station cooperation schemes, namely, network MIMO and coordinated beamforming. We focus on regularized channel inversion precoding structures and perform our analysis in the large systems limit in which the number of users per cell (K) and the number of antennas per BS (N) tend to infinity with their ratio β = K/N held fixed.

In a multicell network, the quality of the channel state knowledge at the base stations (BSs) affects system performance. When this knowledge is acquired through a quantized feedback scheme, its quality is roughly determined by the number of feedback bits. In this paper we investigate feedback optimization problems for the quantized feedback scheme via random vector quantization (RVQ) in a two-cell MIMO broadcast channel. Assuming each user knows its direct and interfering channel states perfectly, we address the following question: given a total number of feedback bits, how should a user allocate those bits to quantize its direct and cross channel information? We pose that question for different levels of BS cooperation, namely Network MIMO and Coordinated Beamforming (CBf). We focus on regularized zero-forcing (RZF) precoding structures and derive our results in the large system limit where the number of users and antennas per BS tend to infinity with their ratio being fixed. We show that for both cooperation schemes, each user should allocate more bits to quantize the cross channel as the latter's gain (ϵ) increases. We also show that, contrary to the full channel knowledge case, under this limited feedback scheme, for some values of (ϵ) no cooperation between the BSs is preferred.

Opportunistic beamforming (OBF) is a well-known communication technique that utilizes partial channel state information (CSI) to obtain multiuser diversity gains in the downlink. We focus on the structure of the optimal homogenous threshold feedback policy that maximizes the ergodic downlink sum-rate for OBF under a peak feedback load constraint, which we model by using a multi-packet reception model for the uplink. We solve the resulting quasi-convex optimization problem by obtaining a formula for the sum-rate maximizing feedback probability. While providing insights on the implications of our results in practical systems, we also illustrate the tradeoff between feedback and rate by obtaining the Pareto optimal boundary between feasible and infeasible feedback-rate pairs.

Multi-User MIMO (MU-MIMO) systems have gained numerous attention from researchers in the past decade due to its substantial gains in the system throughput. Most of the initial research assumes the knowledge of perfect channel state information at the transmitter (CSIT). However, this is considered impractical and research over the past few years has been focused on receivers feeding back limited information to the transmitter. In this work, we consider a transparent MU-MIMO system model with limited feedback. We propose a general framework for dynamic MU-MIMO scheduling with the capability to switch between Single-User MIMO mode and (Multi-Rank) Multi-User MIMO mode without users feeding back additional multi-user information. Specifically, this is done by each user carefully estimating its CQI under the hypothesis of Multi-Rank MultiUser MIMO transmission and taking advantage of the codebook structure. We consider the sum rate of the system assuming each user uses ML and LMMSE receiver and show that our proposed scheduler, with significantly reduced feedback load, outperforms the best-companion user pairing.

This paper looks at the problem of theoretically characterizing the nonlinear biasing and clipping (BAC) effects on an optical Orthogonal Frequency-Division Multiplexing (OFDM) signal in intensity-modulated, direct-detected (IM/DD) optical systems. Due to the unipolarity of the IM/DD optical channel, a large DC bias and associated nonlinear clipping distortion (NLCD) is inevitable, resulting in a significant performance penalty. This NLCD can be well modelled as a linear deterministic attenuation plus an uncorrelated random additive clipping noise in the time domain. In the frequency domain, the NLCD results in an additive or impulsive noise on the received OFDM constellation. A total effective signal-to-noise ratio (SNR) formula is then presented which is a function of biasing power, modulation constellation and receiver SNR figure. This suggests that rather than eliminating all clippings, the system performance is indeed optimized with some deliberately introduced NLCD as a result of higher power efficiency. Analytical results are in agreement with simulations for various cases which help us to accurately and efficiently evaluate the performance of such systems.

In spectrum sharing based cognitive radio networks, unlicensed users (secondary users) are allowed to communicate over the same frequency band as the licensed users (primary users) as long as the required quality-of-service (QoS) of the licensed users is guaranteed. This paper focuses on a cognitive radio network, where a secondary user (SU) sharing the same frequency band with a primary user (PU) wishes to transmit delay-insensitive as well as delay-sensitive data while the PU is interested in meeting a minimum rate guarantee for delay-sensitive services. Typically, PU's are oblivious to the SU's existence and has its own power policy based on channel side information (CSI) of its direct gain between PU transmitter and PU receiver. Under the assumption that SU knows PU's power policy and CSI of the entire network, we solve the optimal power allocation problem of maximizing SU's ergodic capacity subject to PU's outage probability constraint (POC), SU's outage probability constraint (SOC), and SU's average power constraint. We generalize earlier results which considered either ergodic capacity maximization or outage probability minimization for SU with POC, to the so-called service-outage based capacity optimization for SU with POC. We use a rigorous probabilistic power allocation technique that allows us to derive optimal power policies that are applicable to both continuous and discrete fading channels.

In this paper, we consider a large system analysis of regularized channel inversion (RCI) beamforming for MISO broadcast channels (BC) with transmit-side channel correlation. In the analysis, we assume that both the number of users and transmit antennas grow unbounded with a constant ratio. We also assume that the channel correlation model is separable. Under this channel condition, we are particularly interested to find the optimal regularization parameter of RCI that maximizes the signal to interference plus noise ratio (SINR). First, we derive the large system limit of the SINR of each user by applying some results on large random matrices. Then, we determine the cor- responding optimal regularization parameter and show that this optimal regularization parameter is not affected by the transmit correlation. We verify this result through simulations where the channel has the exponential transmit-correlation model.

Optimum capacity scaling in the downlink of a single-cell multiple-input multiple-output communication system can be achieved by a communication strategy called opportunistic beamforming in which information carrying beams are randomly formed and users are opportunistically scheduled based on their partial channel state information. Even though opportunistic beamforming reduces the amount of feedback required to achieve optimum capacity scaling laws, the number of users feeding back in its plain implementations still grows linearly with the total number of users in the system, which is an onerous requirement on the feedback channel. In this paper, we focus on a more stringent but realistic O(1) feedback constraint on the feedback channel, and obtain the tradeoff curve tracing the Pareto optimal boundary between feasible and infeasible feedback-rate pairs. We show that any point on this tradeoff curve can be obtained by means of homogenous decentralized thresholding policies, in which a user feeds back only if the received signal quality at her best link is good enough, and derive the form of these optimum policies. We further show that if the O(1) feedback constraint is relaxed, we can achieve the optimum capacity scaling by a feedback amount growing like O((log n)ϵ) for any e ∈ (0, 1), where n is the number of users in the system.

Beamforming techniques utilizing only partial channel state information (CSI) has gained popularity over other communication strategies requiring perfect CSI thanks to their lower feedback requirements. The amount of feedback in beamforming based communication systems can be further reduced through selective feedback techniques in which only the users with channels good enough are allowed to feed back by means of a decentralized feedback policy. In this paper, we prove that thresholding at the receiver is the rate-wise optimal decentralized feedback policy for feedback limited systems with prescribed feedback constraints. This result is highly adaptable due to its distribution independent nature, provides an analytical justification for the use of threshold feedback policies in practical systems, and reinforces previous work analyzing threshold feedback policies as a selective feedback technique without proving its optimality. It is robust to selfish unilateral deviations. Finally, it reduces the search for rate-wise optimal feedback policies subject to feedback constraints from function spaces to a finite dimensional Euclidean space.

Threshold feedback policies are well known and provably rate-wise optimal selective feedback techniques for communication systems requiring partial channel state information (CSI). However, optimal selection of thresholds at mobile users to maximize information theoretic data rates subject to feedback constraints is an open problem. In this paper, we focus on the optimal threshold selection problem, and provide a solution for this problem for finite feedback systems. Rather surprisingly, we show that using the same threshold values at all mobile users is not always a rate-wise optimal feedback strategy, even for a system with identical users experiencing statistically the same channel conditions. By utilizing the theory of majorization, we identify an underlying Schur-concave structure in the rate function and obtain sufficient conditions for a homogenous threshold feedback policy to be optimal. Our results hold for most fading channel models, and we illustrate an application of our results to familiar Rayleigh fading channels.

This paper introduces and solves the sum rate maximization problem at the multiple-input multiple-output (MIMO) downlink as a function optimization problem subject to feedback constraints at the uplink. It is first shown that this optimization problem can be reduced to a finite dimensional non-convex optimization problem. Then, the resulting problem can be solved by investigating Schur-concavity of the aggregate communication rate across multiple traffic flows. Necessary and sufficient conditions for the rate optimality of homogenous threshold feedback policies are established. With some surprise, it is shown that homogenous thresholding is not always rate-wise optimal even if mobile users experience the same channel conditions statistically. Applications of these results are illustrated for Rayleigh fading channels.

Binary power control has been shown to be optimal in a variety of network conditions. In the context of sumrate maximisation in interference networks, our main result states that the optimal power policy for a link is binary if it only interferes with one other link in the network. Examples of networks which have at least one link with this attribute include the two-link interference channel (IC), the one-sided Wyner network and the many-to-one interference channel. Previous works have fully characterised the optimal power scheme for the two-link IC and they agree with our results. For the one-sided Wyner network, our main result leads to a problem which is tractable and can be solved using dynamic programming methods.

In a Wyner model each link only interferes with links adjacent to it. We consider the problem of finding the optimal power allocation which maximizes the sum-rate of such a network. Each link has a maximum power constraint and the power allocation is assumed to be time and frequency flat. In the case of 3- and 4-link Wyner models, we show that the optimal power schemes are in fact binary, i.e. a link is either switched off or turned on at full power. The problem is then extended to larger-sized Wyner models by limiting to optimal binary power schemes. Interesting phase transitions are observed as the interference cross-gain, ε, traverses various thresholds.

This paper investigates Orthogonal Frequency- Division Multiplexing (OFDM) transmission in intensity modulated, direct-detected (IM/DD) optical systems. Due to the unipolarity of the IM/DD optical channel, DC biasing and associated nonlinear clipping distortion (NLCD) is inevitable, unless an anti-periodic signal is created by limiting bit loading to only half of the available bandwidth. To generalize this idea, we first provide a class of such signals generated with different subchannel combinations. Using the theoretical characterization of NLCD on these signals, we show that by parallel transmission and successive decoding, a set of anti-periodic OFDM signals can be transmitted simultaneously and recovered without a distortion penalty. The proposed method requires minimum change on the existing optical plant, and has the potential to achieve both high power and high spectral efficiencies.

We consider the power optimization problem of maximizing the sum rate of a symmetric network of interfering links in Gaussian noise. All transmitters have an average transmit power constraint, the same for all transmitters. This problem has application to DSL, as well as wireless networks. We solve this nonconvex problem by indentifying some underlying convex structure. In particular, we characterize the maximum sum rate of the network, and show that there are essentially two possible states at the optimal solution depending on the cross-gain (√ε) between the links, and/or the average power constraint: the first is a wideband (WB) state, in which all links interfere with each other, and the second is a frequency division multiplexing (FDM) state, in which all links operate in orthogonal frequency bands. The FDM state is optimal if the cross-gain between the links is above 1/√2. If √ε <; 1/√2, then FDM is still optimal provided the SNR of the links is sufficiently high. With √ε <; 1/√2, the WB state occurs when the SNR is low, but as we increase the SNR from low to high, there is a smooth transition from the WB state to the FDM state: For intermediate SNR values, the optimal configuration is a mixture, with some fraction of the bandwidth in the WB state, and the other fraction in the FDM state. We also consider an alternative formulation in which the power is mandated to be frequency flat. In this formulation, the optimal configuration is either all links at full power, or just one link at full power. In this setting, there is an abrupt phase transition between these two states.

This paper looks at the problem of performance enhancement for orthogonal frequency-division multiplexing (OFDM) transmission in intensity modulated, direct detected (IM/DD) optical systems. Due to the unipolarity of the IM/DD channel, asymmetric clipping and associated biasing is inevitable, which results in a significant signal degradation. ACO-OFDM has been previously proposed to avoid this detrimental distortion with the cost of a lower spectral efficiency. In this paper, with the help of a detailed analysis of such distortion, a simple combining algorithm is proposed. This algorithm extracts the instructive information inside the clipping distortion, and requires no changes to the existing optical plant and no extra transmitting power. By exploiting this spectral diversity gain, the receiver SNR can be significantly improved.

We consider the power optimization problem of maximizing the sum rate of a symmetric network of interfering links in Gaussian noise. All transmitters have an average transmit power constraint, the same for all transmitters. We solve this nonconvex problem by indentifying some underlying convex structure. In particular, we characterize the maximum sum rate of the network, and show that there are essentially two possible states at the optimal solution depending on the cross-gain (radicisin) between the links, and/or the average power constraint: the first is a wideband (WB) state , in which all links interfere with each other, and the second is a frequency division multiplexing (FDM) state, in which all links operate in orthogonal frequency bands. The FDM state is optimal if the cross-gain between the links is above 1/radic2. If isin < 1/2, then FDM is still optimal provided the average power of the links is sufficiently high. With e < 1/2, the WB state occurs when the average power level is low (relative to the noise and the cross-gain factor), but as we increase the average power level from low to high, there is a smooth transition from the WB state to the FDM state: For intermediate average power levels, the optimal configuration is a mixture, with some fraction of the bandwidth in the WB state, and the other fraction in the FDM state. This work has applications to DSL, as well as to wireless networks.

This paper investigates the effects of nonlinear biasing and clipping (BAC) on a random optical orthogonal frequency-division multiplexing (OFDM) signal with direct detection. We look at the problem of theoretical performance evaluation of such a system. Specifically, we derive an approximation for the symbol error rate (SER). The nonlinear BAC process is modeled as a linear deterministic gain plus a random additive clipping noise. The total effective SNR is presented as a function of biasing power, modulation constellation and receiver SNR. Analytical results are in good agreement with simulations in various cases.

For (n, k, m) systematic polynomial convolutional encoders, there exists an upperbound on the length of a correctable burst of erasures in terms of code parameters. In this paper, we restrict ourselves to the case k = n - 1 and provide a necessary and sufficient condition to achieve the upperbound in terms of the encoder coefficients. In addition, for selected values of m, we present explicit (n, n - 1, m) systematic polynomial convolutional encoders that achieve the upperbound.

Dynamic spectrum management of digital subscriber lines (DSLs) has the potential to dramatically increase the capacity of the aging last-mile copper access network. This paper takes an important step toward fulfilling this potential through power spectrum balancing. We derive a novel algorithm called SCALE, that provides a significant performance improvement over the existing iterative water-filling (IWF) algorithm in multiuser DSL networks, doing so with comparable low complexity. The algorithm is easily distributed through measurement and limited message passing with the use of a spectrum management center. We outline how overhead can be managed, and show that in the limit of zero message-passing, performance reduces to IWF.

In this paper, we consider a situation where a number of remote sensors is linked to a decision fusion center by a noncoherent fading multiaccess channel. Each sensor makes a binary local decision and communicates it to the fusion center. The signaling is designed such that a local sensor transmits a pulse when its decision is positive and remains silent when its decision is negative. The optimal fusion rule is derived which takes into account the fading channel statistics. And the asymptotic detection performance as the number of sensors goes large is studied. Compared with the conventional parallel access scheme, the multiaccess scheme will improve the detection performance when the noise level at the fusion center is high.

In this paper, we analyze the performance of multiuser transmit beamforming for the broadcast channel. We focus on transmit beamforming via regularized channel inversion and our analysis is in the large system limit where both the number of users and the number of antennas approach infinity with their ratio held constant. We derive an expression for the signal-to-interference-plus-noise ratio in this large system limit. We then use this result to obtain a very simple expression for a locally optimal regularization parameter, that which maximizes the asymptotic signal-to-interference-plus-noise ratio.

The solutions to an optimal power allocation problem in dual-hop amplify-and-forward (non-regenerative) OFDM relay systems over given channel gains are provided with a joint transmit power constraint. We analytically derive the allocated power to each hop/subcarrier that satisfies the Karush-Kuhn-Tucker (KKT) conditions by solving simultaneous equations. The solutions imply that at most 3N- 1 possible power allocation candidates could exist on both source and relay nodes satisfying KKT conditions when N is the number of subcarriers. Based on the solution, a sub-optimal power allocation method that selects only significant allocated power in strong subcarriers is proposed and achieves higher capacity (achievable rate) than conventional sub-optimal methods. As for the feasibility, the number of candidates to be evaluated is N in the proposed method.

In this paper, we consider the problem of distributed detection with binary sensors over a noisy multiaccess channel. Under the assumption of conditionally independent sensor observations, we investigate the optimal decision regions at the fusion centre. For a special case, it is shown that the optimal fusion rule can be reduced to a simple threshold test on the signal received by the fusion centre. We will demonstrate that the traditional amplitude-scaling approach to satisfy the power constraint is suboptimal. With a given power budget, a solution featuring in the joint optimization of the local mapping rule and the fusion rule is proposed and is shown to lead to a gain in performance.

In multihop OFDM relay systems the end-to-end average capacity can be increased by incorporating subcarrier mapping (SCM) at the relay nodes. In this paper, we propose an exact analytical technique of evaluating the average capacity of a dual-hop OFDM relay system with SCM in a Rayleigh fading channel. Closed-form expressions are derived for the probability density function of the end-to-end SNR of mapped subcarrier pairs. Comparison with simulation results confirms the accuracy of the proposed analytical technique. Also, the results show that an average capacity increase of the order of 10%-30% can be achieved using SCM in the low SNR regime. Moreover, the achievable percentage capacity increase with SCM is more when the average transmit power of the relay is less than that of the source.

In this paper, we analyze the capacity of orthogonal frequency division multiplexing (OFDM) systems with carrier frequency offset (CFO) in frequency-selective Nakagami-m fading channels. Previous work on this topic has not taken into account the frequency selectivity of the channel. In this work, we have explicitly attributed the effect of channel frequency selectivity, i.e. frequency domain correlations, in evaluating the OFDM system performance in the presence of CFO. A closed-form expression is derived of the probability density function (PDF) of the signal-to-interference-and-noise ratio (SINR) in terms of CFO and channel correlation vector. Capacity is evaluated using numerical integration. The frequency-flat fading scenario and the perfectly frequency-selective fading (uncorrelated subcarriers) scenario form the two extremes, i.e. bounds, of the achievable OFDM capacity in the presence of CFO in Nakagami-m fading channels.

In this paper, we consider multicell processing on the downlink of a cellular network to accomplish ldquomacrodiversityrdquo transmit beamforming. The particular downlink beamformer structure we consider allows a recasting of the downlink beamforming problem as a virtual linear mean square error (LMMSE) estimation problem. We exploit the structure of the channel and develop distributed beamforming algorithms using local message passing between neighboring base stations. For 1-D networks, we use the Kalman smoothing framework to obtain a forward-backward beamforming algorithm. We also propose a limited extent version of this algorithm that shows that the delay need not grow with the size of the network in practice. For 2-D cellular networks, we remodel the network as a factor graph and present a distributed beamforming algorithm based on the sum-product algorithm. Despite the presence of loops in the factor graph, the algorithm produces optimal results if convergence occurs.

This paper considers the problem of joint detection in the uplink of cellular multiaccess networks with base-station cooperation. Distributed multiuser detection algorithms with local message passing among neighbor base stations are proposed and compared in terms of computational complexity required in the base stations, the amount of serial communications among them, error rate performance, and convergence speed. The algorithms based on the belief propagation algorithm result in complexity and delay per base station which do not grow as the network size increases. In addition, it is observed that these algorithms have near single-user error rate performance for the fading channels considered. Thus it is illustrated that using the belief propagation algorithm, it is possible to have full frequency re-use and achieve near-optimal performance with moderate computational complexity and a limited amount of message passing between base stations of adjacent cells.

Band preference (BP) attempts to improve the performance of iterative water-filling (IWF) when applied to the multiuser interference channel. With important applications relating to digital subscriber line (DSL) access networks such as those based on current-day ADSL2 and VDSL standards, we develop novel techniques for determining suitable band levels that lead to significant performance gains in those circumstances where IWF would otherwise perform far from the optimum. Our techniques are based on a novel optimization framework that avoids inherent difficulties in analysis that have, until now, obscured an elegant design path. Our developments are easily customized to any clustering band-pattern, particularizing to the spectrum balancing result in the special-case of unit band-sizes. A practically palatable implementation is induced by a two-level primal decomposition whereby a master problem is responsible for band management, based on the result from independent slaves each undertaking a relaxed water-filling (WF) operation. Numerical results show that 2-3 bands are sufficient for a downstream near-far scenario comprising two user-groups.

We address a lifetime maximization problem for a single-hop wireless sensor network where multiple sensors encode and communicate their measurements of a Gaussian random source to a fusion centre (FC). The FC is required to reconstruct the source within a prescribed distortion threshold. The lifetime optimization problem is formulated as a joint power, rate and timeslot (for TDMA) allocation problem under the constraints of the well known rate distortion constraints for the Gaussian CEO problem, the capacity constraints of the wireless links, the energy constraints of the sensor nodes and the strict delay constraint within which the encoded sensor data must arrive at the FC. We study the performances of TDMA and an interference limited non-orthogonal multiple access (NOMA) (with single user decoding) based protocols and compare them against the upper bound provided by the optimal lifetime performance where the capacity constraints are given by the Gaussian multiaccess capacity region. While the constrained non-linear optimization problems for the TDMA and the Gaussian multiaccess cases are convex, the NOMA case results in a non-linear nonconvex D.C. (difference of convex functions) programming problem. We provide a simple successive convex approximation based algorithm for the NOMA case that converges fast to a suboptimal lifetime performance that compares favourably against the upper bound provided by the Gaussian multiaccess case. Extensive numerical studies are presented for both static and slow fading wireless environments with full channel state information at the fusion centre.

Frequency planning is a common intercell interference (ICI) management strategy in narrowband cellular networks. In this paper, we consider an alternative approach that allows full frequency reuse in every cell and deploys a network-wide linear minimum mean square error (LMMSE) receiver as the front-end processor to suppress ICI. Assuming equal transmit power for all users, we compare the achievable rate of the LMMSE receiver in the information-theoretic sense against the rates achieved by two different frequency reuse schemes, namely the conventional reuse scheme with single-cell processing and a reuse scheme that allows interference-free processing of signals from adjacent cells. We first compare the performance under a fixed path-gain model and then extend to a random fading model.

We consider a cellular network where base stations can cooperate to determine the signals to be transmitted on the downlink. Using a particular downlink beamformer structure, we recast our downlink beamforming problem as a virtual linear minimum mean square error (LMMSE) estimation problem. Based on this virtual set up, we remodel the network as a factor graph with loops and present a simple distributed cooperative scheme for base stations based on the sum-product algorithm. We study the condition for convergence for the distributed algorithm and demonstrate its performance via simulations.

In this paper, we address the optimal power allocation problem for minimizing capacity outage probability in multiple time-scale parallel fading channels. Extending ideas from the work of Dey and Evans (2005), we derive the optimal power allocation scheme for parallel fading channels with fast Rayleigh fading, as a function of the slow fading gains. Numerical results are presented to demonstrate the outage performance of this scheme for lognormal slow fading on two parallel channels.

This paper considers the problem of joint detection in the uplink of cellular multiaccess networks with base-station cooperation. Distributed multiuser detection algorithms with local passing among neighbor base stations are proposed and compared in terms of computational complexity required in the base stations, the amount of serial communications among them, error rate performance, and convergence speed. The algorithms based on the belief propagation algorithm result in complexity and delay per base station which do not grow as the network size increases. In addition, it is observed that these algorithms have near single user error rate performance for the fading channels considered. Thus it is illustrated that using the belief propagation algorithm, it is possible to use non-orthogonal signaling and still achieve near single user performance with moderate computational complexity and a limited amount of message passing between base stations of adjacent cells.

We provide scaling results for the sum capacity of the multi-access, uplink channel in a flat fading environment, when there is interference from other cells. We consider a scaling regime where the number of antennas per user remains fixed but the number of antennas at the base station and the number of users in each cell grow large together. We characterize the asymptotic behaviour of the spectral efficiencies in each cell, in three scenarios: 1) single cell processing with full frequency reuse 2) single cell processing, with frequency re-use partitioning of adjacent cells and 3) base station cooperative decoding (macro-diversity). It is shown that base station cooperation provides very significant gains in spectral efficiency over single cell processing.

Cross-layer design can significantly improve the performance of mobile ad-hoc networks (MANETs), as indicated by the flurry of recent results in the literature. Much of this work stems from the Kelly network utility maximization (NUM) framework, where convexity is crucial for developing algorithms that reach the global optimum. Unfortunately many problems are nonconvex in nature, so convex approximations are abundant. In this paper, we consider the joint optimization of source data-rates and link transmitter powers in a MANET, specifically dealing with the statistical variations of the wireless channel. In this paradigm we show that the commonly applied high-SIR convex approximation is unrealistic, so we seek to find solutions of the unmodified NUM problem. Our first result shows that the canonical formulation (previously thought to be nonconvex) is indeed a convex problem for logarithmic TCP-Vegas utilities; we then derive an algorithm reaching the global optimum. Our main result caters for the general case of strictly concave utilities, where we derive an algorithm that provably converges to the global solution of the underlying nonconvex NUM problem.

Dynamic Spectrum Management of Digital Subscriber Lines (DSL) has the potential to dramatically increase the capacity of the aging last-mile copper access network. This paper takes an important step toward fulfilling this potential through power spectrum balancing. We derive a novel algorithm called SCALE, that provides a significant performance improvement over the existing iterative water-filling (IWF) algorithm in multi-user DSL networks, doing so with comparable low complexity. The algorithm is easily distributed through measurement and limited message-passing with the use of a Spectrum Management Center. We outline how overhead can be managed, and show that in the limit of zero message-passing, performance reduces to IWF. Numerical convergence of SCALE was found to be extremely fast when applied to VDSL, with performance exceeding that of iterative water-filling in just a few iterations, and to over 90% of the final rate in under 5 iterations. Lastly, we return to the problem of iterative water-filling and derive a new algorithm named SCAWF that is shown to be a very simple way to waterfill, particularly suited to the multi-user context.

We address the problem of achieving outage probability constraints on the uplink of a code-division multiple-access (CDMA) system employing power control and linear multiuser detection, where we aim to minimize the total expended power. We propose a generalized framework for solving such problems under modest assumptions on the underlying channel fading distribution. Unlike previous work, which dealt with a Rayleigh fast-fading model, we allow each user to have a different fading distribution. We show how this problem can be formed as an optimization over user transmit powers and linear receivers, and, where the problem is feasible, we provide conceptually simple iterative algorithms that find the minimum power solution while achieving outage specifications with equality. We further generalize a mapping from outage probability specifications to average signal-to-interference-ratio constraints that was previously applicable only to Rayleigh-faded channels. This mapping allows us to develop suboptimal, computationally efficient algorithms to solve the original problem. Numerical results are provided that validate the iterative schemes, showing the closeness of the optimal and mapped solutions, even under circumstances where the map does not guarantee that constraints will be achieved.

Scaling results for the sum capacity of the multiple access, uplink channel are provided for a flat-fading environment, with multiple-input-multiple-output (MIMO) links, when there is interference from other cells. The classical MIMO scaling regime is considered in which the number of antennas per user and per base station grow large together. Utilizing the known characterizations of the limiting eigenvalue distributions of large random matrices, the asymptotic behavior of the sum capacity of the system is characterized for an architecture in which the base stations cooperate in the joint decoding process of all users (macrodiversity). This asymptotic sum capacity is compared with that of the conventional scenario in which the base stations only decode the users in their cells. For the case of base station cooperation, an interesting "resource pooling" phenomenon is observed: in some cases, the limiting performance of a macrodiversity multiuser network has the same asymptotic behavior as that of a single-user MIMO link with an equivalent amount of pooled received power. This resource pooling phenomenon allows us to derive an elegant closed-form expression for the sum capacity of a new version of Wyner's classical model of a cellular network, in which MIMO links are incorporated into the model.

We consider a cellular network where base stations can cooperate to determine the signals to be transmitted on the downlink. In such a scenario, it would be possible to use "macroscopic" transmit beamforming to improve system performance. The downlink beamformer of interest is generalised from some transmit beamformers that have been shown to meet various optimality criteria in the literature. The particular downlink beamformer structure enables us to recast our downlink beamforming problem as a virtual LMMSE estimation problem. Based on this virtual set up, we exploit the structure of the channel and develop distributed beamforming algorithms using local message passing between neighbouring base stations. Two algorithms are outlined, both of which are based on the Kalman smoothing framework. The first algorithm is a forward-backward algorithm that produces optimal performance, but it has the disadvantage of a delay that grows linearly with array size. The second algorithm, which is a limited extent algorithm, solves the delay problem by using only local information

We derive the optimal power control strategy to maximize the sum rate of a multiple access channel with two time-scale fading, where transmitters have access to each of the other users' 'slow' fading information and the statistics of the 'fast' fading, but no knowledge of the instantaneous fast fading states. Assuming identical fast fading distributions for all users, it is found that the optimal strategy is to let at most one user transmit, with the user transmitting the one with the 'best' slow fading condition. An example with users undergoing lognormal shadowing and Rayleigh fast fading is considered, and capacity comparisons are made. Simple sub-optimal power control schemes which provide close to optimal performance in certain favorable channel conditions are also proposed and analysed.

This paper considers the power-control problem for a fading channel in an information-theoretic framework. We derive power-control schemes to optimize ergodic capacity, outage capacity, and capacity with a service outage constraint. The novelty in the paper lies in the use of a two-time-scale fading process and its implications for the channel-state information available at the transmitter.

How can we achieve the conflicting goals of reduced transmission power and increased capacity in a wireless network, without attempting to follow the instantaneous state of a fading channel? In this paper, we address this problem by jointly considering power control and multiuser detection (MUD) with outage-probability constraints in a Rayleigh fast-fading environment. The resulting power-control algorithms (PCAs) utilize the statistics of the channel and operate on a much slower timescale than traditional schemes. We propose an optimal iterative solution that is conceptually simple and finds the minimum sum power of all users while meeting their outage targets. Using a derived bound on outage probability, we introduce a mapping from outage to average signal-to-interference ratio (SIR) constraints. This allows us to propose a suboptimal iterative scheme that is a variation of an existing solution to a joint power control and MUD problem involving SIR constraints. We further use a recent result that transforms complex SIR expressions into a compact and decoupled form, to develop a noniterative and computationally inexpensive PCA for large systems of users. Simulation results are presented showing the closeness of the optimal and mapped schemes, speed of convergence, and performance comparisons.

In this paper, we analyze a linear multiuser receiver for code-division multiple-access systems that is based on a matrix polynomial expansion. We focus on the receiver where the polynomial coefficients are chosen to minimize the mean squared error at the output and observe that the resultant coefficients are also signal-to-interference ratio maximizing. We present a simple derivation for the (known) large system coefficients and signal-to-interference ratio of this optimal multistage receiver and make a significant step toward a direct derivation of Honig and Xiao's recursive expression for this large system signal-to-interference ratio. Finally, we extend these results to take into account arbitrary power distributions.

We consider the problem of multiuser detection in cellular networks. In particular, we present a distributed forward-backward algorithm with local message passing for efficient implementation of the linear minimum mean square error (LMMSE) receiver, for a simple model of a 1D cellular system. The distributed algorithm is based on the well-known interpretation of Kalman smoothing as a linear combination of the forward and backward filtered estimates. We also show that near-optimal performance can be achieved by only relying on information from a local linear segment of the entire array. This results in a limited extent distributed algorithm that greatly reduces processing delay, especially for large networks, yet with little loss in performance.

We derive the Linear Minimum Mean Square Error (LMMSE) receiver for Wyner's model of a linear cellular array. By exploiting the special structure of the channel model, we obtain explicit Mean Square Error (MSE) expressions and information capacity formulae for both finite-sized as well as infinite-sized linear cellular arrays. We show that the performance of an individual user becomes insensitive to the array size for large array sizes and that the proportion of users achieving almost the same performance converges to one, as the array size tends to infinity. The effects of intercell interference and noise on the performance of each user are also investigated.

We consider an uplink power control problem with constraints on outage probability, for cellular CDMA systems where allocation decisions are made on a slow time-scale. A generalized framework to solve such problems for a wide range of fading distributions is proposed, including an extension that couples power control with a minimum outage probability multiuser receiver. The resulting algorithms are simple and iterative in nature that yield the optimal minimum sum-power solution. Deriving a general upper bound on outage probability, we map these problems to equivalent, sub-optimal and computationally efficient iterative algorithms. We give numerical results to validate the methods developed for a variety of Nakagami-m fading figures.

Distributed decoding in the uplink of a rectangular planar cellular array with local message passing is considered. Two algorithms are proposed and compared: a BCJR-type algorithm applied to linear subgraphs, and belief propagation applied to the 2D graph of the cellular array.

This paper proposes a new scheme coupling power control with a minimum outage probability multiuser detector. The resultant iterative algorithm is conceptually simple and finds the minimum sum transmission power of all users with a set of outage probability constraints. Bound on the outage probability expression are found that extend a previous result that did not include receiver noise. These bounds are used to create a suboptimal scheme coupling power control and a MMSE multiuser detector. This new problem becomes a variant of an existing problem where outage probability constraints are first mapped to average SIR threshold constraints. Simulation results are presented showing the closeness of the two schemes and speed of convergence.

We analyze the performance of a second-order linear multistage multiuser code-division multiple-access receiver. The receiver's filtered output is designed to converge to that of the linear minimum mean-squared error solution as the number of stages increase. Our analysis is based on a related second-order stationary iterative solution method. We derive the large system output signal to interference-plus-noise ratio for each stage. We use this result to perform a numerical optimization with respect to the two second-order parameters of our receiver. Within this iterative framework, we can achieve performance extremely close to the optimal linear multistage multiuser receiver.

We consider a synchronous code-division multiple-access system where each user undergoes independent, frequency-flat Rayleigh fading and where pilot symbols are periodically inserted into the data stream of each user in order to assist in the coherent detection of the data symbols. In this paper, we derive, and analyze the performance of, the linear minimum mean-squared-error channel estimator based on the entire sequence of pilot symbols. Employing classical linear filtering theory and previous results for large systems, we are able to obtain a compact expression for the minimum mean-squared-error in terms of the key system parameters: pilot insertion period, channel fading rate, signal-to-noise ratio, and the ratio of the number of users to the spreading gain.

We analyse the optimal linear multistage receiver based on a weighted matrix polynomial expansion. We prove that the optimal linear multistage receiver which minimises the mean squared error for a particular stage also maximises the signal-to-interference-plus-noise ratio for a particular stage. We present an alternative derivation of the large system signal-to-interference-plus-noise ratio. We observe an important link between the optimal linear multistage receiver and the multistage reduced rank Wiener filter receiver.

We derive upper bounds on the symbol error probability for a communication system that sends quadrature amplitude modulated data over a frequency-flat Rayleigh fading channel. We first derive simple error bounds in terms of a key parameter, namely, the channel estimation error variance. We move on to derive expressions for the this parameter for a pilot symbol assisted channel estimation scheme. The estimation error variance, and thus the symbol error probability, are expressed succinctly in terms of the statistics of the channel fading process, the frequency of insertion of pilot symbols, and the average signal-to-noise ratio.

We consider a synchronous code-division multiple-access system where each user undergoes independent frequency-flat Rayleigh fading, and where pilot symbols are periodically inserted into the data stream of each user in order to assist in the coherent demodulation of the data symbols. The motivating question for this work is: for any given set of system parameters, how often should we insert pilot symbols? Along the way to answering this question, we: (1) derive and analyze the performance of the linear minimum mean-squared-error channel estimator and (2) study the performance of a linear minimum mean-squared-error data estimator which is coupled to the channel estimator. We are able to obtain a very compact expression for the average signal-to-interference ratio in terms of the key system parameters: pilot insertion period, channel fading rate, signal-to-noise ratio, and the ratio of the number of users to the spreading gain. The average signal-to-interference ratio is numerically optimized and results are presented to illustrate the optimal rate of inserting pilot symbols for a range of system parameters.

In this paper, we derive an expression for the signal to interference-plus-noise ratio of a linear multistage parallel interference cancellation receiver. We focus on a linear multistage receiver which converges to the linear minimum mean-squared error receiver as the number of stages increases. The signal to interference-plus-noise ratio is given in terms of the system loading, the partial cancellation factor, the number of stages, and the signal-to. noise ratio. Our expression also allows a simple approximation for the bit error rate at each stage. Finally, we perform a numerical optimization to maximize the signal to interference-plus-noise ratio expression with respect to the partial cancellation factor of the resulting linear multistage receiver.

We derive an expression for the signal-to-interference-plus-noise ratio (SINR) of a second order iterative receiver. We focus on a second order linear iterative receiver which converges to the linear minimum mean squared error receiver as the number of stages increases. The SINR is given in terms of the system loading, the two receiver parameters, the number of stages and the signal-to-noise ratio. We can now estimate the bit error rate performance of iterative receivers. Finally, we perform a numerical optimisation of the SINR with respect to the parameters of the resulting second order linear iterative receiver. Within this iterative framework we can achieve performance close to the optimal linear multistage receiver.

We present a new approach to blind adaptive detection for CDMA systems based on regularized independent component analysis (ICA). Classical ICA algorithms are effective in separating linearly weighted signal mixtures consisting of subGaussian and superGaussian signals. However, they do not incorporate any information of the weighting matrix, in this case, the user's signature sequence into the formulation. This results in underutilization of the information available. To address this difficulty, we propose a new ICA algorithm that combines a contrast function and a regularization functional to integrate the information of the user's signature. A blind adaptive detector based on stochastic gradient optimization of the new cost function is derived. Simulation results show that the new technique provides good interference suppression, fast convergence and low BER performance when compared with other blind detectors.

Recently a number of modified MMSE receivers have been efficiently applied to code-division multiple-access (CDMA) communications with dynamic fading channels. These receivers can successfully cope with multiple access interference (MAI) but are limited to BPSK signals. This paper presents new adaptive implementations of MMSE receivers for higher order signals in multi-user environments. High-order signal constellations, e.g. MQAM, have been extensively investigated in single-user fading channels due to their high spectral efficiency. This paper provides performance evaluations and analysis for the proposed adaptive multiuser receiver. It also presents a new computationally efficient adaptive algorithm for these high-order signal constellations.

We derive an expression for the signal to interference-plus-noise ratio of a multistage parallel interference cancellation receiver. We focus on a linear multistage receiver employing partial parallel interference cancellation which converges to the linear minimum mean squared error receiver as the number of stages increase. The signal to interference-plus-noise ratio is given in terms of the system loading, the partial cancellation factor, the number of stages and the background noise variance. The expression allows a simple approximation for the bit error rate at each stage. Finally, we verify and apply our results to optimise the signal to interference-plus-noise ratio of the resulting linear multistage receiver.

A linear multiuser receiver for a particular user in a code-division multiple-access (CDMA) network gains potential benefits from knowledge of the channels of all users in the system. In fast multipath fading environments we cannot assume that the channel estimates are perfect and the inevitable channel estimation errors will limit this potential gain. We study the impact of channel estimation errors on the performance of linear multiuser receivers, as well as the channel estimation problem itself. Of particular interest are the scalability properties of the channel and data estimation algorithms: what happens to the performance as the system bandwidth and the number of users (and hence channels to estimate) grows? Our main results involve asymptotic expressions for the signal-to-interference ratio of linear multiuser receivers in the limit of large processing gain, with the number of users divided by the processing gain held constant. We employ a random model for the spreading sequences and the limiting signal-to-interference ratio expressions are independent of the actual signature sequences, depending only on the system loading and the channel statistics: background noise power, energy profile of resolvable multipaths, and channel coherence time. The effect of channel uncertainty on the performance of multiuser receivers is succinctly captured by the notion of effective interference.

Consider the Hidden Markov model where the realization of a single Markov chain is observed by a number of noisy sensors. The sensor scheduling problem for the resulting Hidden Markov model is as follows: design an optimal algorithm for selecting at each time instant, one of the many sensors to provide the next measurement. Each measurement has an associated measurement cost. The problem is to select an optimal measurement scheduling policy, so as to minimize a cost function of the estimation errors and measurement costs. The problem of determining the optimal measurement policy is solved via stochastic dynamic programming. Numerical results are presented.

This paper considers state estimation for a discrete-time hidden Markov model (HMM) when the observations are delayed by a random time. The delay process is itself modelled as a finite state Markov chain which allows an augmented state HMM to model the overall system. State estimation algorithms for the resultant HMM are then presented. The motivation for the model stems from the situation when distributed sensors transmit measurement over a connectionless packet switched communications network.