Location Fingerprinting (LF) is a promising localization technique that enables many commercial and emergency Location-Based Services (LBS). The idea of this paper is two- folded. First, a Gaussian Process (GP) is used during the training (offline) phase of an indoor positioning algorithm to generate the fingerprint database, reducing the expensive labor of acquiring and maintaining the fingerprint database significantly. Furthermore, during the positioning (online) phase, a Smart algorithm (already proposed in [1]) is used for reducing the computation effort for positioning calculation. We call our idea Smart2since it enhances the advantages of the base Smart approach. Specifically, Smart2reduces the labor during the offline phase by trading it with a small positioning error and, at the same time, it limits the energy consumption in the online phase without incurring in any additional accuracy detriment.

Today's indoor wireless networks employ reactive resource allocation methods to provide fair and efficient usage of the communication system. However, their reactive nature limits the quality of service (QoS) that can be offered to the user locations within the environment. In large crowded areas (airports, conferences), networks can get congested and users may suffer from poor QoS. To mitigate this, we propose and evaluate a location- aware user-centric proactive resource allocation approach (LAPRA), in which the users are proactive and seek good channel quality by moving to locations where the signal quality is good. As a result, the users and their locations are optimized to improve the overall QoS. We demonstrate that the proposed proactive approach enhances the user QoS and improves network throughput of the system.

Location fingerprinting (LF) is an attractive localization technique which relies on existing infrastructures. The major drawback of LF is the requirement of having an updated fingerprint database. Gaussian Process (GP) is a non-parametric modeling technique which can be used to model the received signal strength indicator (RSSI) and create the fingerprint database based on few training data. In this paper we use a parametric pathloss model for the GP mean and a flexible non-parametric covariance function, so we can get reliable estimates with low fingerprinting effort. In our experiment, we show that with 23 fingerprint locations we perform as well as traditional fingerprinting with over 230 fingerprinted locations for an office space of 2500m2.

This paper presents an application of the firefly algorithm (FA) to Gaussian process (GP)-based localization. Partial radio-frequency (RF) signature map is first collected and used to train the GP model. The hyperparameters of the GP prior model are searched by the FA. GP regression is then used to generate an estimation of the RF signature map for the entire area to be localized. This is in contrast to traditional fingerprinting-based localization where a database of RF signature has to be collected for the entire area of interest. Using the estimated signature map, the position of the device is estimated using a combined likelihood function from multiple access points (APs). The proposed scheme relies on only existing infrastructures and can be used both indoor and outdoor. Experiments using indoor WiFi APs show that median error is around 3 m.

The revolutionary success of commercial broadband wireless network has spurred significant interest in employing related technologies such as 3GPP long term evolution (LTE) technologies to build a nationwide mobile broadband network for public safety entities. The successful deployment and operation of a public safety network, however, are more challenging than the traditional commercial wireless networks due to the high reliability and security requirements, unpredictable traffic patterns, and fast time-varying user population density. In this paper, we propose a framework of coordinated dual-layer beamforming schemes to address these challenges. The distinct features of the proposed systems are detailed and the architecture and realization are discussed. While a smart antenna is capable of forming multiple beams, it is unlikely to activate all of them simultaneously. We thus develop efficient beam scheduling algorithms that could adapt to the time-varying channel conditions and traffic loads by adaptively adjusting the beam switching sequence. We also reveal that the coordinated dual-layer beamforming technologie not only has the potential of significantly enhancing the system capacity, but also can turn a NP-complete beam scheduling problem into a problem that can be easily solved if network MIMO technologies are employed. It is seen through simulation studies that the proposed schemes could offer multi-fold capacity improvements over more traditional systems equipped with broader antenna patterns (e.g., omnidirectional). Also, while we focus on public safety network in this paper, the proposed coordinated dual-layer beamforming framework can be equally applied to provide mobile access to any hotspot area with dense mobile users, e.g., a conference room or a stadium.

Distributed space-time block coding with orthogonal frequency division multiplexing (DSTBC-OFDM) and distributed space-frequency block coding with orthogonal frequency division multiplexing (DSFBC-OFDM) are introduced in this paper. Unlike the original DSTBC scheme which was designed for networks with a fixed number of relays operating in frequency-nonselective channels with insignificant Doppler spread, the proposed schemes are designed specifically for networks with an arbitrary number of relays and wireless channels with significant delay spread and Doppler spread. It is shown that geographically distributed antennas employing the new transmission schemes in a frequency-selective and severe Doppler spread environment can achieve the same performance (in terms of diversity gain and coding gain) as the previously proposed scheme.

In this paper, we propose jointly-optimized beamforming algorithms for cognitive networks to maximize the achievable rates, where primary and cognitive users share the same spectrum and are equipped with multiple antennas. We consider the transmission of a single information stream in both primary and secondary links. No coordination is required between the primary and cognitive users and the interference cancellation is done at the cognitive user. Specifically, the beamforming vectors of the cognitive link are designed to maximize the achievable rate under the condition that the interference both at the primary and cognitive receivers is completely nullified. Furthermore, it is proved that the achievable rate of a general N_t^{C}x 2 (N_t^{C} transmit antennas at the cognitive transmitter and 2 receive antennas at the cognitive receiver) cognitive multiple-input multiple-output (MIMO) link employing the optimal proposed beamformers (which completely nullifies the interference to and from the primary link while maximizing its own achievable rate) is the same as the rate of an interference-free (N_t^{C}-1)x 1 multiple-input single-output (MISO) link employing an optimal maximal ratio transmission beamforming vector. The sum rate performance of the proposed algorithms is evaluated by Monte Carlo simulations. The impact of the number of transmit and receive antennas on the proposed algorithm is also discussed.

The capacity of a wireless network could be considerably improved by employing directional antennas that are capable of illuminating multiple beams toward different directions. However, more beams from the same BS may lead to stronger inter-cell interference. In this paper, we consider coordinated beam scheduling schemes to mitigate the inter-cell interference. We first formulate this problem as a combinatorial optimization problem. We then reveal that the complexity of this problem hinges upon a single scalar termed as degree of constraint (DoC), which is related to the degree of conflict a beam is subject to. If the DoC is at least three, the general beam scheduling problem is NP-hard. If DoC is smaller than three, which corresponds to a relevant subclass of the beam scheduling problem arising in practice, this problem can be solved in polynomial time. We propose an optimal beam scheduling algorithm based on the auction method to this particular subclass of problem. This algorithm is of low complexity and is well suited for distributed implementations. We then extend the auction algorithm to solve the general multi-cell beam scheduling problem. The performance of the proposed algorithms is finally assessed through extensive simulation studies.

We consider beamforming in a cognitive network with multiple primary users and a secondary user sharing the same spectrum. Each primary and secondary user consists of a transmitter and a receiver. In particular, we assume that the secondary transmitter hasNtantennas and transmits data to its single-antenna receiver using beamforming. The beamformer is designed to maximize the cognitive signal-to-interference ratio (CSIR). Using mathematical tools from random matrix theory, we derive both lower and upper bounds on the average interference created by the cognitive transmitter at the primary receivers and the average CSIR of the cognitive user. We further analyze and prove the convergence of these two performance measures asymptotically as the number of antennasNtor primary usersNpincreases. Specifically, we show that the average interference per primary receiver converges toE[d-α], the expected value of the path loss in the network, whereas the average CSIR decays as1/cwhenc = Np/Nt→∞. In the special case ofNt≥ Npthe lower bound of the average total interference approaches 0 and the upper bound of the average CSIR approachesNtE[d->-α]/σC2whereσC2is the noise variance at the cognitive receiver.

In this letter, we derive a multiple-symbol differential detection (MSDD) and a novel MSDD-based decision-feedback differential detection (MS-DFDD) receiver for differential space-time modulation transmitted over spatially correlated multiple-input multiple-output fading channels. We show that MS-DFDD outperforms previously proposed DFDD schemes that are based on scalar and vector prediction (SP-DFDD and VP-DFDD). In addition, we prove that at high signal-to-noise ratio (SNR) VP-DFDD is equivalent to SP-DFDD and thus fails to properly exploit the spatial fading correlations.

We consider beamforming in a cognitive network with multiple primary users and secondary users sharing the same spectrum. In particular, we assume that each secondary transmitter has Nt antennas and transmits data to its single- antenna receiver using beamforming. The beamformer is designed to maximize the cognitive user's signal-to-interference ratio (SIR), defined as the ratio of the received signal power at the desired cognitive receiver to the total interference created at all the primary receivers. Using mathematical tools from random matrix theory, we derive both lower and upper bounds on the average interference at the primary receivers and the average SIR of the cognitive user. We further analyze and prove the convergence of these two performance measures asymptotically as the number of antennas Ntor primary users Ntincreases. Specifically, the average interference per primary receiver converges to the expected value of the path loss in the network whereas the average SIR of the secondary user decays as 1/c when c = Np/Ntrarr infin. In the special case of Nt= Np, the average total interference approaches 0 and the average SIR approaches infin.

In this paper, we propose a novel signaling scheme and corresponding noncoherent fusion rules for wireless sensor networks. In the proposed scheme, sensors transmit censored decisions to the fusion center using signature vectors. To improve bandwidth efficiency the signature vector length can be chosen smaller than the number of sensors in the network resulting in non-orthogonal sensor channels. We derive the optimum noncoherent likelihood-ratio (LR) based fusion rule as well as a low-complexity energy-based fusion rule for the considered signaling scheme and Rayleigh fading. Furthermore, the performance of the energy-based fusion rule is analyzed. Numerical and simulation results show that with the proposed scheme significant improvements in bandwidth efficiency are possible at the expense of a small loss in power efficiency.

In this paper, we consider the application of distributed space-time coding in wireless sensor networks (WSNs). In particular, sensors use a common noncoherent distributed space-time block code (DSTBC) to forward their local decisions to the fusion center (FC) which makes the final decision. We show that the performance of distributed space-time coding is negatively affected by erroneous sensor decisions. To overcome this problem, we introduce censored distributed space-time coding where only reliable decisions are forwarded to the FC. The optimum noncoherent maximum-likelihood and a low-complexity, suboptimum generalized likelihood ratio test (GLRT) FC decision rules are derived and the performance of the GLRT decision rule is analyzed. Based on this performance analysis we derive a gradient algorithm for optimization of the decision/censoring threshold. Numerical and simulation results show the effectiveness of the proposed censoring scheme making distributed space-time coding a prime candidate for signaling in WSNs.

Distributed space-time filtering (DSTF) facilitates node cooperation and achieves a diversity gain while not requiring node coordination, i.e., cooperating nodes do not have to be aware of their partners. This makes DSTF attractive for application in future sensor, ad hoc, and wireless networks. In this paper, we derive a novel cost function and related practical algorithms for optimization of the signature filter vectors (SFVs) used in DSTF. We extend DSTF to frequency-selective fading channels and compare its performance with that of delay diversity transmission with co-located antennas. For the special case of frequency-nonselective channels we show that SFV optimization is closely related to the signature sequence design for code-division multiple access (CDMA) systems and the design of Grassmannian frames. Numerical and simulation results show that the novel SFV designs perform significantly better than previously proposed designs even if non-ideal effects such as suboptimum equalization, imperfect channel estimation, and imperfect timing synchronization are taken into account

In this paper, we introduce a new class of distributed space-time trellis codes (DSTTCs) for wireless networks with a large set of decode-and-forward relay nodes N. We consider the general case where each relay node is equipped with NTantennas and the destination node is equipped with NRantennas. It is assumed that at any given time only a small, a priori unknown subset of nodes S ∈ N is active and neither the relay nodes nor the destination node know which relay nodes are active. In the proposed scheme, each node is assigned a signature matrix and the signal transmitted by an active node is the product of the signature matrix and a space-time trellis code (STTC) matrix originally designed for Ncco-located antennas. It is shown that existing full-rank STTCs designed for Nc⩾ 2 co-located antennas are a favorable choice for the code matrix. Two practical designs for good deterministic signature matrix sets are provided and compared with random signature matrices. If the signature matrices are properly optimized, the proposed DSTTCs achieve a diversity order of d = min{NcNR, NTNSNR} if NSnodes are active. Simulation results confirm that DSTTCs yield significant performance gains over distributed space-time block codes and distributed space-time filtering. Furthermore, equipping relay nodes with a second antenna can be highly beneficial especially if only few nodes can be active at any given time.

In this paper, we introduce a new class of distributed space-time trellis codes (DSTTCs) for wireless networks with a large set of decode-and-forward relay nodes N. It is assumed that at any given time only a small, a priori unknown subset of nodes S - N is active. We consider the general case where each relay node is equipped with NT antennas and the destination node has NR antennas. In the novel distributed space-time trellis coding scheme each relay node is assigned a unique signature matrix but all active nodes use the same trellis for encoding. Efficient methods for the optimization of the set of signature matrices are provided and it is shown that existing full-rank STTCs designed for Nc - 2 co-located antennas are a favorable choice for the trelis encoding. If properly designed, the proposed DSTTCs achieve a diversity order of d = min{NcNR, NTNSNR} if NS nodes are active. Simulation results show the superior performance of the novel DSTTCs compared to distributed space-time filtering and distributed space-time block coding.

In this paper, we consider transmission in relatively wide-stretched power line communication (PLC) networks, where repeaters are required to bridge the source-to-destination distance. In particular, it is assumed that each network node is a potential repeater and that multihop transmission is accomplished in an ad hoc fashion without the need for complex routing protocols. In such a scenario, due to the broadcasting nature of the power line channel, multiple repeater nodes may receive and retransmit the source message simultaneously. It is shown that, if no further signal processing is applied at the transmitter, simultaneous retransmission often deteriorates performance compared with single-node retransmission. We therefore advocate the application of distributed space-time block codes (DSTBCs) to the problem at hand. More specifically, we propose that each network node is assigned a unique signature sequence, which allows efficient combining at the receiver. Most notably, DSTBC-based retransmission does not require explicit collaboration among network nodes for multihop transmission and detection complexity is not increased compared with single-node retransmission. Numerical results for multihop transmission over PLC networks show that DSTBC-based retransmission achieves a considerably improved performance in terms of required transmit power and multihop delay compared with alternative retransmission strategies.

In this paper, a new class of distributed space-time block codes (DSTBCs) is introduced. These DSTBCs are designed for wireless networks which have a large set of single-antenna relay nodes Nscr, but at any given time only a small, a priori unknown subset of nodes SsubeNscr can be active. In the proposed scheme, the signal transmitted by an active relay node is the product of an information-carrying code matrix and a unique node signature vector of length Nc. It is shown that existing STBCs designed for Nc2 co-located antennas are favorable choices for the code matrix, guaranteeing a diversity order of d=min{NS,Nc} if NSnodes are active. For the most interesting case, NSgesNc, the performance loss entailed by the distributed implementation is analytically characterized. Furthermore, efficient methods for the optimization of the set of signature vectors are provided. Depending on the chosen design, the proposed DSTBCs allow for low-complexity coherent, differential, and noncoherent detection, respectively. Possible applications include ad hoc and sensor networks employing decode-and-forward relaying

Distributed space-time filtering (DSTF) has been recently proposed as a means for node cooperation in wireless networks. In this paper, we derive a new optimization criterion for the node signature filter vectors (SFVs) used in DSTF and we provide two practical design methods for sets of SFVs. We show that for the special case of a frequency-nonselective channel and two active nodes the SFV optimization problem is related to the design of Grassmannian frames. This relation allows us to derive a lower bound for the performance penalty incurred by the distributed implementation. We show that SFV sets designed with the proposed methods perform close to this lower bound and achieve a significant performance gain over previously proposed designs. Simulation results confirm the excellent performance of the proposed SFV sets in frequency-nonselective and frequency-selective channels

In this paper, a new class of distributed space-time block codes (DSTBCs) is introduced. These DSTBCs are designed for wireless networks which have a large set of nodes N but at any given time only a small, a priori unknown subset of nodes S /spl sub/ N can be active. In the proposed scheme, the signal transmitted by an active node is the product of an information-carrying code matrix and a unique node signature vector. It is shown that existing STBCs designed for N/sub c/ /spl ges/ 2 co-located antennas are favorable choices for the code matrix guaranteeing a diversity order of d = min{N/sub S/, N/sub c/} if n/sub S/ nodes are active. For the most interesting case N/sub S/ /spl ges/ N/sub c/ the performance loss entailed by the distributed implementation is analytically characterized. Furthermore, efficient methods for the optimization of the set of signature vectors are provided. Possible applications of the proposed DSTBCs include ad hoc and sensor networks employing decode-and-forward relaying.

We optimize delay diversity (DD) for the case when simple linear equalization (LE) is used at the receiver. We consider the general case of transmission over a correlated multiple-input multiple-output (MIMO) frequency-selective fading channel. The proposed optimization requires the knowledge of the statistical properties of the wireless channel at the transmitter, but channel state information is only required at the receiver side. Based on an approximation of the bit error rate for LE, we derive a stochastic gradient algorithm that allows us to optimize the DD transmit filters. Simulation results for the GSM/EDGE system show significant performance gains for the proposed optimized DD scheme over DD schemes reported previously (Gore, D. et al., Proc. IEEE Int. Conf. Commun., ICC, p.1949-53, 2002; Schober, R. et al., IEEE Trans. Wireless Commun., 2004), if LE is used at the receiver.

We optimize delay diversity (DD) for the case when computationally efficient decision-feedback equalization (DFE) is used at the receiver. We consider the general case of transmission over a correlated multiple-input multiple-output (MIMO) frequency-selective fading channel. The proposed optimization requires knowledge of the statistical properties of the wireless channel at the transmitter, but channel state information is only required at the receiver side. Based on an approximation of the pairwise error probability for DFE, we derive a stochastic gradient algorithm that allows us to optimize the DD transmit filters. Simulation results for the GSM/EDGE system show significant performance gains of the proposed optimized DD scheme over the DD schemes reported by D. Gore et al. (see Proc. IEEE Int. Conf. Commun., p.1949-53, 2002) and R. Schober et al. (see Proc. IEEE ICC, 2004), respectively, if DFE is used at the receiver.